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Internet of Things Enabling the Circular Economy: An Expert Study of Digitalisation Practices in B2B Firms



The circular economy and the Internet of Things (IoT) are two major developments reshaping the business environment. Firms may embrace the opportunities provided by the circular economy approach to innovate their business models and offer higher value to their customers and society. In order to address circularity, firms can adopt a range of activities known as circular strategies (maintenance, repair, reuse, remanufacturing, and recycling, among others). However, there are barriers that hinder the operationalisation of circular strategies, such as the lack of knowledge by the producer firm (or service provider) about the condition, location, and status of the product. Producer firms may not know what customers actually do with their products during the use phase, which may prevent the planning and execution of take-back, reuse, or remanufacturing activities. In this sense, digital technologies within the framework of the IoT could enable the operationalisation of circular strategies. Smart products and their amplified capabilities could deliver valuable data to create transparency about their life cycle, facilitating decision-making, and streamlining the extension of their service life. Against this background, the question arises of how firms deploy digital technologies for developing circular strategies. To answer this question, and due to the emergent nature of this field, we conducted a qualitative study of business practices of pioneering firms in the business-to-business (B2B) domain. Qualitative research is well suited to understand new phenomena due to its logic of discovery and openness. We conducted 35 expert interviews at 27 different organisations to gain viewpoints from various industries. We focused on experts from German-speaking countries (mainly from Austria), which were selected considering their experience in either developing IoT-enabled circular strategies or in diffusing their implementation. The results of the study suggest that the implementation of IoT-enabled circular strategies in the B2B market is still at an early stage of development, with pioneering firms driving their respective industries forward. We consider these developments as “embryonic patterns”1. These patterns forecast further developments in the broader industry context in the years to come. Our main insights are as follows: 1. The main driver of the implementation of digital technologies is the need to better understand the use phase and improve customer experience. Firms may use product and customer data to enable smart processes and services during the use phase (what we aggregate under the umbrella concept of “smart use”). These smart processes and services may include remote control, location tracking, or condition monitoring, and aim at improving the experience of the customer. Firms may create added value for their customers by extending their service portfolio with smart use services. 2. Companies are increasingly going beyond smart use and are adopting “smart circular strategies”. Smart maintenance and repair activities enable product uptime and an overall extension of the product lifetime; smart reuse, remanufacturing, and recycling enable additional use cycles of products, components, and materials. 3. Firms may also develop “smart cross-strategy capabilities” such as product passports and product life cycle management (PLM) applications. These capabilities enable and support the operationalisation of smart use and smart circular processes. For instance, firms may require a well-designed life cycle management system to manage the flow of product lifetime data between their IT systems and the product. 4. Moreover, firms may use product data and insights gained along the product’s life cycle as feedback for research and development (R&D) to improve future product design. 5. Finally, our results suggest higher servitisation levels among the firms under study. Pioneering firms are going through an intensification of their service offering to enable smart circular strategies and satisfy diverse customer needs. Such higher degrees of servitisation of the firm’s underlying business model allow for easier implementation of smart circularity due to higher proximity to the customer, better access to product data, and easier execution of closed product loops. However, not all service business models are the same. In comparison with business models based on product sales, the extended scope of rental, leasing, or sharing contracts enable an easier implementation of smart circularity.
An Expert Study of Digitalisation Practices in B2B Firms
Andres Alcayaga
Erik G. Hansen
Institute for Integrated Quality Design (IQD), Johannes Kepler University Linz (JKU), Austria
March 2022
IQD Research 2022, No. 1
Institute for Integrated Quality Design (IQD)
Johannes Kepler University Linz (JKU)
Altenberger Straße 69
A-4040 Linz, Austria
Tel. +43 732 2468-5521
DOI No: 10.35011/iqd.2022-02
© Andres Alcayaga, Erik G. Hansen, 2022. All rights reserved.
Suggested citation:
Alcayaga, A. & Hansen, E. G. (2022): Internet of Things Enabling the Circular Economy: An Expert
Study of Digitalisation Practices in B2B Firms (IQD Research 2022, No. 1). Institute for Integrated
Quality Design (IQD), Johannes Kepler University Linz (JKU), Austria.
The circular economy and the Internet of Things (IoT) are two major developments reshaping
the business environment. Firms may embrace the opportunities provided by the circular
economy approach to innovate their business models and offer higher value to their customers
and society. In order to address circularity, firms can adopt a range of activities known as
circular strategies (maintenance, repair, reuse, remanufacturing, and recycling, among
others). However, there are barriers that hinder the operationalisation of circular strategies,
such as the lack of knowledge by the producer firm (or service provider) about the condition,
location, and status of the product. Producer firms may not know what customers actually do
with their products during the use phase, which may prevent the planning and execution of
take-back, reuse, or remanufacturing activities. In this sense, digital technologies within the
framework of the IoT could enable the operationalisation of circular strategies. Smart products
and their amplified capabilities could deliver valuable data to create transparency about their
life cycle, facilitating decision-making, and streamlining the extension of their service life.
Against this background, the question arises of how firms deploy digital technologies for
developing circular strategies. To answer this question, and due to the emergent nature of this
field, we conducted a qualitative study of business practices of pioneering firms in the
business-to-business (B2B) domain. Qualitative research is well suited to understand new
phenomena due to its logic of discovery and openness. We conducted 35 expert interviews at
27 different organisations to gain viewpoints from various industries. We focused on experts
from German-speaking countries (mainly from Austria), which were selected considering their
experience in either developing IoT-enabled circular strategies or in diffusing their
The results of the study suggest that the implementation of IoT-enabled circular strategies in
the B2B market is still at an early stage of development, with pioneering firms driving their
respective industries forward. We consider these developments as “embryonic patterns1.
These patterns forecast further developments in the broader industry context in the years to
come. Our main insights are as follows:
1. The main driver of the implementation of digital technologies is the need to better
understand the use phase and improve customer experience. Firms may use product
and customer data to enable smart processes and services during the use phase (what
we aggregate under the umbrella concept of “smart use”). These smart processes and
services may include remote control, location tracking, or condition monitoring, and aim
at improving the experience of the customer. Firms may create added value for their
customers by extending their service portfolio with smart use services.
1 Noci and Verganti (1999)
2. Companies are increasingly going beyond smart use and are adopting “smart circular
strategies”. Smart maintenance and repair activities enable product uptime and an
overall extension of the product lifetime; smart reuse, remanufacturing, and recycling
enable additional use cycles of products, components, and materials.
3. Firms may also develop “smart cross-strategy capabilitiessuch as product passports
and product life cycle management (PLM) applications. These capabilities enable and
support the operationalisation of smart use and smart circular processes. For instance,
firms may require a well-designed life cycle management system to manage the flow
of product lifetime data between their IT systems and the product.
4. Moreover, firms may use product data and insights gained along the product’s life cycle
as feedback for research and development (R&D) to improve future product design.
5. Finally, our results suggest higher servitisation levels among the firms under study.
Pioneering firms are going through an intensification of their service offering to enable
smart circular strategies and satisfy diverse customer needs. Such higher degrees of
servitisation of the firm’s underlying business model allow for easier implementation of
smart circularity due to higher proximity to the customer, better access to product data,
and easier execution of closed product loops. However, not all service business models
are the same. In comparison with business models based on product sales, the
extended scope of rental, leasing, or sharing contracts enable an easier
implementation of smart circularity.
Internet of Things (IoT), smart products, digital technologies, digitalisation, business-to-
business (B2B), circular economy, circular strategies, best practices, service business model,
product-service systems (PSS), servitisation, sustainability-oriented innovation.
We thank Quality Austria Trainings, Zertifizierungs und Begutachtungs GmbH for funding the
contract research project “I4L - Business Models for Extending Industry 4.0 towards the entire
Product Life Cycle” (2016-2021). We thank Dr Anni Koubek as responsible lead at Quality
Austria and DI Axel Dick (Lead Business Development, Environmental Management, Energy,
and CSR, Quality Austria) for their support and intense feedback. We thank all interviewees of
our qualitative expert study for their time and expertise.
We also thank the State of Upper Austria for their contribution to the Endowed Institute for
Integrated Quality Design (IQD), without which this project would not have been possible.
EXECUTIVE SUMMARY .......................................................................................................... III
ACKNOWLEDGEMENTS .......................................................................................................... V
CONTENTS ........................................................................................................................... VI
LIST OF FIGURES ............................................................................................................... VIII
LIST OF TABLES ................................................................................................................... IX
LIST OF ABBREVIATIONS ....................................................................................................... X
1 INTRODUCTION .............................................................................................................. 1
1.1 Long-term developments reshaping business innovation .................................... 1
1.2 Sustainability from a circular economy perspective ............................................. 2
1.3 Smart products and strategies for the circular economy ..................................... 2
1.4 Business-to-business focus ................................................................................. 4
2 CONCEPTUAL BACKGROUND ......................................................................................... 5
2.1 Smart products and digital infrastructure ............................................................. 5
2.2 Smart circular products: frequency of circulation ................................................. 6
3 ABOUT THE STUDY ........................................................................................................ 8
3.1 Sample ................................................................................................................ 8
3.2 Data collection ................................................................................................... 10
3.3 Data analysis ..................................................................................................... 11
4 OVERVIEW OF RESULTS AND FRAMEWORK ................................................................... 12
5 SMART CROSS-STRATEGY CAPABILITIES .................................................................... 15
5.1 Goals and characterisation ................................................................................ 15
5.2 Smart processes and infrastructure ................................................................... 15
5.2.1 Product life cycle management system overview .................................................... 15
5.2.2 Product passports and life cycle management applications .................................... 16
5.2.3 Sensing and identification technologies ................................................................... 18
5.2.4 Product data collection ............................................................................................. 20
5.2.5 Backend architecture ................................................................................................ 21
5.2.6 Product data analysis ............................................................................................... 22
5.2.7 Managing data security ............................................................................................ 23
5.2.8 Archiving rules .......................................................................................................... 24
6 SMART USE ................................................................................................................ 25
6.1 Goals and characterisation ................................................................................ 25
6.2 Smart processes ................................................................................................ 26
6.2.1 Condition monitoring ................................................................................................ 26
6.2.2 End-user support applications .................................................................................. 27
6.2.3 Product and process optimisation ............................................................................ 29
7 SMART MAINTENANCE AND REPAIR .............................................................................. 35
7.1 Goals and characterisation ................................................................................ 35
7.2 Smart processes ................................................................................................ 35
7.2.1 Preventative maintenance and repair ...................................................................... 35
7.2.2 Inventory management for spare parts .................................................................... 37
7.2.3 Customer integration in maintenance and repair ..................................................... 38
7.2.4 Warranty extension and rejection ............................................................................. 40
7.2.5 Smart devices assisting technicians’ smart maintenance and repair ....................... 41
8 SMART REUSE ............................................................................................................. 43
8.1 Goals and characterisation ................................................................................ 43
8.2 Smart processes ................................................................................................ 45
8.2.1 Increasing transparency and optimising the reuse system ...................................... 45
8.2.2 Data-based feedback to help improve customers’ care for the product ................... 45
8.2.3 Smart product customisation by the producer or service provider ........................... 46
9 SMART REMANUFACTURING ......................................................................................... 47
9.1 Goals and characterisation ................................................................................ 47
9.2 Smart processes ................................................................................................ 48
9.2.1 Monitoring-based take-back decisions ..................................................................... 48
9.2.2 Product history-based inspection and remanufacturing decision ............................. 48
9.2.3 Data-based technological upgrading ........................................................................ 49
10 SMART RECYCLING ..................................................................................................... 50
10.1 Goals and characterisation ................................................................................ 50
10.2 Smart processes ................................................................................................ 50
10.2.1 IoT-enabled closed-loop product cycles as a basis for recycling ............................. 50
10.2.2 Use of product passports to improve high-quality recycling ..................................... 51
11.1 Direct feedback to improve circular product design ........................................... 52
11.2 Indirect feedback to improve circular product design ........................................ 53
12.1 Role of the service business model for integrating circular strategies ............... 54
12.1.1 Product-oriented business model ............................................................................. 55
12.1.2 Use-oriented business model ................................................................................... 56
12.1.3 Result-oriented business model ............................................................................... 56
13 CHALLENGES AND FUTURE OUTLOOK .......................................................................... 58
13.1 Challenges related to smart use and circular strategies .................................... 58
13.2 Challenges related to service business models ................................................. 60
13.3 A shift in organisational resources and skills ..................................................... 61
13.4 Future outlook .................................................................................................... 62
14 CONCLUSIONS ............................................................................................................ 65
REFERENCES ...................................................................................................................... 66
Figure 1: Architecture for smart circularity. ............................................................................... 5
Figure 2: Smart circular products by frequency of circulation and internal smartness. ............ 6
Figure 3: Industry allocation of interviewed organisations. ....................................................... 9
Figure 4: Managerial role of the interviewees. ....................................................................... 10
Figure 5: Framework of best practices for a smart circular economy. .................................... 12
Figure 6: Architecture of a life cycle management system. .................................................... 16
Figure 7: The use phase black-box ........................................................................................ 25
Figure 8: Condition monitoring across product types. ............................................................ 26
Figure 9: Service degree of business models and smart circularity. ...................................... 55
Table 1: Data sources. ........................................................................................................... 11
Table 2: Summary of results .................................................................................................. 13
Business to Business
Business to Consumer
Chief Executive Officer
Customer Relationship Management
Enterprise Resource Planning
Global System for Mobile Communication
International Electrotechnical Commission
Internet of Things
Intergovernmental Panel on Climate Change
International Organisation for Standardisation
Information Technology
Near Field Communication
Original Equipment Manufacturer
Product Life cycle Management
Quick Response
Research and Development
Radio-Frequency Identification
Small and Medium-sized Enterprise
Short Message Service
Ultra High Frequency
Wireless Fidelity
1.1 Long-term developments reshaping business innovation
Sustainability and digitalisation are two long-term developments reshaping current business
innovation. Regarding sustainability, recent extreme weather events around the world and the
latest report of the Intergovernmental Panel on Climate Change (IPCC) have sparked an
intense public debate whether the climate crisis is unfolding at an accelerated pace and what
meaningful solutions are available to ensure climate protection. The report, released in August
2021, is the most comprehensive assessment of the current state of the earth’s climate. The
report stresses the unequivocal influence of human activity on changes across the global
climate system and concludes that human-induced climate change is affecting the frequency
and severity of extreme weather events.2
Regarding digitalisation, related technologies represent an opportunity for firms to gain
competitive advantage and could be a key enabler of a transition towards sustainable
solutions. As indicated by a recent survey by the European Investment Bank, the digital
transformation is still a priority for European firms and may be crucial to boosting productivity.
In addition, firms that invest more in digitalisation are more innovative, have better
management practices, and are more likely to address climate change. Digital firms tend to
have more business monitoring systems and targets for environmental strategies. Although
European firms have embraced the digital revolution, many are still lagging behind the United
States in adoption, and effective policymaking is needed to foster investments in digital
technologies.3 Independent of these regional differences, the global Coronavirus disease 2019
(COVID-19) pandemic has strongly accelerated the digitalisation process in the public and
private realms.
When linking sustainability and digitalisation to business activity beyond policymaking, the
discussion may focus on solutions that integrate environmental and technological objectives
into the core business of a firm. A strategy focused on gaining competitive advantage through
sustainability-oriented products, services, and processes may become a key source of
innovation.4 In this report, we focus on the business model as a key lever for implementing
such strategy. We understand the business model as the way firms develop their products and
service offerings, how they create and deliver value for their customers, and how they capture
economic benefits from these activities.5
2 IPCC (2021)
3 European Investment Bank (2021, July)
4 Schaltegger et al. (2012)
5 Richardson (2008); Teece (2010)
1.2 Sustainability from a circular economy perspective
The circular economy is a framework to address sustainability challenges at the core of the
business model of a firm.6 It has attracted significant attention by practitioners, researchers,
and policymakers as it proposes an alternative to our current “take-make-use-dispose” linear
economic system. The circular economy is based on the core principles of restoration and
regeneration, where products are kept at their highest value during their entire life cycle, and
materials are recovered for further use at the end of the product lifetime.7 Transitioning towards
a circular economy could bring benefits to business and society. Circular flows can reduce the
risks from disruptions in the supply chain and add job opportunities to the market, fostering
local and regional economies.8 They can also contribute to increased material independence
(by displacing imports) and energy efficiency.9 When it comes to business scenarios, the
circular economy can be operationalised by a set of circular strategies. Circular strategies
are activities designed to achieve product lifetime extension and material circulation. This study
focuses on the so-called technical (or industrial) cycles, where products, components, and
finite materials are kept in circulation through maintenance, repair, reuse, remanufacturing,
and recycling.10 From the perspective of the firm, circular strategies do not only require
changes in internal processes, but also the creation of circular service operations as the basis
for new offerings to their customers.11 These services usually require the collaborative
participation of multiple stakeholders for their completion.12
1.3 Smart products and strategies for the circular economy
With respect to the utilisation of digital technologies, we refer to them as enablers of an
extended value proposition, and thus, the circular economy.13 First of all, technologies within
the frameworks of Industry 4.0 and IoT could close the information gap among different
stakeholders, offer increased transparency about product usage, and allow for new service
offerings. For instance, producers could obtain data about the condition, location, and
performance of their products. This data may allow them to offer services to their customers
such as location tracking, performance analysis, or process optimisation while the product is
being used.14 In addition, offering IoT-enabled services such as performance analysis may
6 Circular Economy Initiative Deutschland (Ed.) (2021)
7 Ellen MacArthur Foundation (2013)
8 Ghisellini et al. (2016); Stahel (2010)
9 Cooper et al. (2020)
10 McDonough and Braungart (2002)
11 Hansen and Revellio (2020)
12 Hansen and Schmitt (2019); Hansen and Schmitt (2021)
13 Alcayaga et al. (2019); Chiappetta Jabbour et al. (2019); Ellen MacArthur Foundation (2016); Ingemarsdotter et
al. (2020); Kristoffersen et al. (2020); Ranta et al. (2021); Rosa et al. (2020)
14 Ellen MacArthur Foundation (2016)
imply a broader understanding of the quality management approach. The availability of product
data allows the extension of quality assurance towards the product and the customer.15
Furthermore, adding these services to the portfolio could increase customer satisfaction and
expand the revenue base of the firm. In this report, we define these digitally-enabled services
(which are not explicit circular strategies) under the umbrella concept of “smart use”.16
Second, digital technologies can facilitate a cost-effective implementation of circular
strategies across all actors of the value cycle. Firms can use product data transparency to
improve customer relationships and proactively manage their suppliers.17 Firms can collect
data from products amplified with digital technologies (henceforth simply referred to as “smart
products)18 and deploy predictive maintenance solutions to proactively prevent failure.
Remote data collection and analysis may facilitate decision-making on product reuse. Sensor
data in remanufacturing settings can allow for early detection of damages and product
replacement at the most opportune time. Smart products can also provide data about their
materials and repair history to enable high-quality recycling. Thus, when firms put in place
digital technologies to improve circular product and material flows, they can develop a set of
“smart circular strategies”.19 For this report, we consider the following strategies:
smart maintenance and repair,
smart reuse,
smart remanufacturing, and
smart recycling.
At this point, and after presenting the overall direction of this report, it is paramount to clearly
elucidate the specific aim of this research endeavour. The primary aim of this report is to
explore business practices related to digitalisation and the circular economy. As the circular
economy is an emerging field of research and practice, we take an exploratory approach to
find the ways in which firms are using digital technologies to enable circularity. As guidance
for action, business practices are reflected in activities, methods, and procedures that are
regularly executed and shall achieve business objectives. In particular, we look for ways that
benefit from digital technologies to develop services that extend the value proposition and
operationalise circularity, that is, activities that extend the product lifetime and close material
15 Kristoffersen et al. (2020)
16 Alcayaga et al. (2019)
17 Alcayaga et al. (2021)
18 Porter and Heppelmann (2014)
19 Alcayaga et al. (2019)
1.4 Business-to-business focus
While we explore different industries, we still aim at some form of comparability of business
practices by focusing on the B2B domain. In this regard, a relevant distinction can be drawn
between business and consumer markets. First, solutions that work in the B2B domain usually
do not work in business-to-consumer (B2C) settings, and vice versa. For instance, B2B firms
are embedded in complex supply chains with multiple decision points that may make the sales
process difficult. Multiple actors participate in the value creation chain of final products.20
Second, the B2B market is more significant in value than the B2C market. For instance, the
B2B e-commerce market value of 2019 was estimated at US$12.2 trillion, while the B2C
market was only at US$2.0 trillion.21 The higher complexity and size of the B2B market may
encourage firms to innovate for a smart and circular economy. Third, initiatives related to
Industry 4.0 that aim at the digitalisation of the value chain have been primarily driven by
manufacturing industries. In the last decade, significant investments in sensors, connectivity,
and other technologies have been made within production lines to automate production
processes and create smart factories.22 Finally, extending a business model based on product
sales with circular services (e.g., predictive maintenance as a service) or servitising the offering
through a full-service approach could bring benefits and efficiencies in the B2B domain; longer
lifetimes in industrial markets may favour take-back systems for reuse and remanufacturing
under a service business model, reducing cost-related barriers that hinder the adoption of
these practices. Moreover, keeping the ownership of a product seems to be less relevant for
customers in B2B than in B2C markets due to the reduced significance of comfort,
convenience, or emotional attachment, which may facilitate servitisation in industrial markets.23
20 Geyer and Niessing (2020)
21 Mehta and Hamke (2019, August)
22 Industrie 4.0 Working Group (2013); PwC (2016)
23 Tukker (2015)
2.1 Smart products and digital infrastructure
Before diving into the core of this report, we shall briefly define the architecture that allows the
value-generating function of products, components, and business models (Figure 1). As
mentioned in the previous chapter, we focus on products that circulate in technical cycles, also
known as durable products or products of service24. In particular, we centre our attention on
smart products, that is, traditional products amplified with distinctive digital enablers. Digital
enablers are, in general, the hardware and software that enable data collection, storage,
integration, and analysis. They act as building blocks that, combined with the product,
generate a particular service configuration for the customer.25 Relevant digital enablers are
sensing and identification technologies, digital twins, digital product passports, online
platforms, blockchain and distributed-ledger technology, big data analytics, and artificial
intelligence, among others.26
Figure 1: Architecture for smart circularity.27
24 Braungart et al. (2007)
25 Alcayaga et al. (2019); Noll et al. (2016)
26 Circular Economy Initiative Deutschland (Ed.) (2021)
27 Based on Alcayaga et al. (2019); and Circular Economy Initiative Deutschland (Ed.) (2021)
Traditional product components
(e.g. textile, bearing)
Firm-centric hardware
(e.g., servers, IT and communication infrastructure, other hardware)
Firm-centric software
(e.g., remote monitoring, digital twin, life cycle management, product
passport, blockchain, big data, artificial intelligence)
Data storage,
integration &
Producer /
(and ecosystem
Integrated hardware
(e.g., barcode, RFID tag)
Product and customer-centric peripheral devices
(e.g., RFID reader, smart box, smart glasses, tablet, smartphone)
“Simple Products”
(narrow internal smartness)
Integrated hardware and
(e.g., control software,
temperature sensor)
Traditional product components
(e.g., construction machine)
“Complex Products”
(wide internal smartness)
As can be seen in Figure 1, smart products can be classified according to their internal
smartness, or the degree to which digital technologies are integrated in the physical product.
Simple products, such as textiles, may have very narrow internal smartness because they
may only include a single barcode or radio-frequency identification (RFID) tag, while complex
products, such as construction vehicles, may have multiple hardware devices and software
When thinking about software applications, the idea of building blocks is reflected in the
generation of a tailor-made solution for the customer. Firms may have a portfolio of
applications, including dashboards, control tools, monitoring software, store management,
analytics, etc. These building blocks are flexible and could be combined in different
arrangements according to the customer’s needs and the particularities of each product
category. In an extreme scenario, the firm may customise these building blocks and use
specific modules for one customer while using totally different ones for another.
2.2 Smart circular products: frequency of circulation
In this report, we classify products according to the frequency of circulation (Figure 2).
Figure 2: Smart circular products by frequency of circulation and internal smartness.
The frequency of circulation refers to how often any circular strategy is executed on the
product. A key concept to understand this classification are use cycles. The first use cycle of
a product starts after manufacturing, when the first customer uses the product. Then, once the
firm has executed a maintenance or repair activity, the second cycle begins. The same
Machines and heavy vehicles
Tools and components
Professional textiles
+ Frequency
+ Internal
happens with the rest of the circular strategies, such as reuse or remanufacturing. From this
perspective, circular strategies allow the start of a new use cycle, either by the same or a new
user. For example, professional textiles may need a repair a few days after the customer has
worn them for the first time, while industrial machines may need maintenance only a few times
per year. The same assessment can be used when thinking about remanufacturing. Tools can
be in use for a few months or years before they enter the remanufacturing loop, while heavy
vehicles can be in use for 10, 20, or even 30 years before they need to be remanufactured. In
other words, some products (e.g., professional textiles) circulate more frequently or have
shorter use cycles than others (e.g., industrial machines) throughout their entire lifetime.
As can be seen in Figure 2, we cover three product types according to the frequency of
circulation: fast, medium, and slow-cycling products. Use cycles of fast-cycling products (e.g.,
professional textiles) may last several days or even weeks, while use cycles of slow-cycling
products (e.g., industrial machines and heavy vehicles) may last several weeks or even
months. The internal smartness is usually inversely proportional to the frequency of circulation.
This study is a qualitative exploration of business practices in the B2B domain. We conducted
expert interviews to understand how firms put digital technologies into action to facilitate
product usage by the customer, enable the development of circular strategies, and boost the
service offering. Due to the novelty of this emergent field, the authors use qualitative research
to provide a more integrative and comprehensive view of business practices. Qualitative
research is well suited to understanding new phenomena due to its logic of discovery and
openness.28 Furthermore, expert interviews are suitable to gain insights into emergent
phenomena because interviewees can provide knowledge related to their specific
3.1 Sample
We focus on pioneering organisations with regard to smart circularity. Based on these
pioneers, we aim at identifying embryonic patterns of current phenomena in expectation of
their evolution in a broader context in the future.30 Firms in the study started the
implementation of digital technologies several years ago. Despite looking into pioneers, the
current level of implementation of digital technologies and related smart circular strategies can
be considered at an early stage of development. There are many partial solutions on the
market that are still under development or being tested.
We selected experts from 27 organisations, including firms, consultancies, and research
organisations. The sample focused on experts from German-speaking countries (the DACH
region), mainly from Austria.31 These experts (and related organisations) were selected after
a preliminary desk analysis of websites, media articles, and other published materials (e.g.,
reports, white papers, flyers, and magazines). This preliminary data allowed us to select
experts from organisations that are involved in either developing smart circular services (e.g.,
producers) or in diffusing their implementation (e.g., consultancies).
In order to disentangle the multi-faceted and interdisciplinary phenomenon of smart circularity,
the authors sought viewpoints from experts working at different positions in the value chain:
manufacturing firms (17) and service providers (7). Furthermore, as part of the framing and
linking level attached to the value chain,32 we covered research organisations and knowledge
brokers (3). Moreover, the authors selected experts working in multiple industries and holding
different managerial roles within their respective firms. Industries were classified according to
28 Flick et al. (2004)
29 Gläser and Laudel (2010)
30 Noci and Verganti (1999, p. 7)
31 We included one expert from an English-speaking country due to their otherwise good fit to the sample data.
32 Fichter (2009); see also "supra-organisational networks" as proposed by Clarke and Roome (1995)
product similarity and function in the market. The sample includes firms in the following
industries (Figure 3):
machine building & heavy-duty vehicles,
components & equipment manufacturing,
services, logistics, & energy management,
academic & non-academic research, and
IT & consulting.
Managerial roles include (Figure 4):
general management,
IT services & digitalisation,
marketing, sales & after sales,
research, innovation & development,
quality & environmental management, and
production & operations.
Figure 3: Industry allocation of interviewed organisations.
Academic &
Machine building &
heavy-duty vehicles
Components & equipment
logistics &
3 (11,1%) 11 (40,7%)
4 (14,8%)
6 (22,2%)
IT & consulting
3 (11,1%)
Figure 4: Managerial role of the interviewees.
3.2 Data collection
We conducted a total of 35 interviews between 2017 and 2019. All interviews were tape-
recorded and transcribed so that the raw data could be systematically and iteratively analysed
(Table 1). A total of 36 hours of recording time was obtained during the interview phase.
Managers and researchers were selected based on their expertise on the subject. As
explained above, different managerial roles were selected to discover a broader set of
practices from different business areas.
At the start of the interview phase, we conducted one pilot interview to ensure the applicability
of the questions and improve the survey. The questionnaire had open-ended questions and
was separated into three main sections:
digital technologies, smart products, and services,
smart circular strategies, and
relevant aspects of the business model such as customer relationships, resources, and
supply chain.
As it can be seen in Table 1, we complemented interview data with relevant archival data (e.g.,
reports from, and media articles about, companies participating in the study).
IT services & digitalisation
Marketing, sales &
after sales
Production &
General management
Quality &
Research, innovation &
6 (14%)
13 (30,2%)
4 (9,3%)
4 (9,3%)
9 (20,9%)
7 (16,3%)
Data Type Quantity Type of Documentation
Formal Interviews 35 Interview Transcripts
Informal Interviews 2 Protocols
- Company presentation and informal discussion 2
Archival Documents 83 Source Files
- Corporate publications (sustainability reports,
environmental policy statements, videos)
- Websites 27
- Press releases 10
- Media articles 7
- Internal documents (work files, presentation slides,
organisation charts)
Observations 6 Protocols
- Site visits 3
- Ethnographic observation of industry events 3
Table 1: Data sources.
3.3 Data analysis
Interviews and documents generated during the desk research analysis (Table 1) were
triangulated and coded based on the framework proposed by the authors.33 First, we identified
a set of smart cross-strategy business practices aiming at building the capabilities and
infrastructure for the operationalisation of smart circularity. Second, business practices related
to the base strategy smart usewere identified. Third, best practices related to specific smart
circular strategies were coded, that is, smart maintenance and repair, smart reuse, smart
remanufacturing, and smart recycling. Finally, considerations about product design, service
business models, challenges, and future developments were added to the findings to
complement the results.
33 Alcayaga et al. (2019)
The results of the study are structured in the framework in Figure 5 and summarised in Table
2. Smart products, smart cross-strategy capabilities, and the related infrastructure used by the
producer (or third parties) are the basis for smart use and the smart circular strategies.
Figure 5: Framework of best practices for a smart circular economy.
As a base layer of the funnel, we introduce smart cross-strategy capabilities such as life
cycle management systems and product passports (Chapter 5). We then introduce the base
strategy smart use (Chapter 6) and the smart circular strategies of maintenance and repair
(Chapter 7), reuse (Chapter 8), remanufacturing (Chapter 9), and recycling (Chapter 10).
Producer / Service Provider
(and Business Model Ecosystem Partners)
Feedback into R&D for circular product (re)design
Condition-based return scheduling
History of products/components
Remote and preventative maintenance
Inventory and warranty management
Customer integration
Augmented technician support
IoT-enabled product return
Passport-based high quality recycling
Data enables circular strategies
Transparency of reuse system
Customer’ care of the product
Asses condition and value
Smart customisation
Cross-Strategy Capabilities
Assure product care,
optimise take-back,
and facilitate
Increase product
reliability and
maximise uptime
Understand the use
phase and improve
user experience
plannability and
Increase quality and
quantity of secondary
Remote access and control
Notifications and communication
Product and process optimisation
Usage data
Condition monitoring
Location tracking
Product life cycle management
Product passport
Product data management
Peripheral devices (e.g., tablets)
Backend and infrastructure
Maintenance / Repair
Smart Strategies Goals Smart Processes
Smart Cross-
Enable smart use
and smart circular
- Enable and manage life cycle management
systems (product passport and life cycle
management applications).
- Manage product data (collection, storage,
integration, analysis) and ensure data security.
- Maintain, troubleshoot, and update the backend
architecture and peripheral devices.
Smart Use Understand the
use phase and
improve user
- Execute and manage condition monitoring.
- Manage usage data and end-
user support
applications for location tracking, remote access
and control, notifications, communication with the
customer, etc.
- Optimise product and processes.
Maintenance /
Increase product
reliability and
maximise uptime.
Execute and manage preventative maintenance
and repair.
- Manage the inventory of spare parts.
- Integrate customers in the maintenance process.
- Enable an extension or limitation of the warranty.
- Assist technicians with smart devices.
Smart Reuse Assure product
care, optimise
take-back, and
- Increase
the transparency of and optimise the
reuse system.
- Use data to change customer behaviour.
- Customise the product using digital enablers.
plannability and
- Use condition monitoring to enable the take-back
- Use product history to improve decision-making.
- Enable data-based technological upgrading.
Smart Recycling
Increase quality
and quantity of
- Enable IoT-based closed-loops.
- Use product passports for high-quality recycling.
Table 2: Summary of results
Smart use and circular strategies are presented in the framework (Figure 5) and summary
of results (Table 2) from top to bottom based on the following criteria:34
Stage in the product life cycle: Smart cross-strategy capabilities, as well as
processes and services for smart use, are the base layer that enables the execution of
circular strategies. The subsequent descending order of the activities in the funnel
reflects the product lifetime. Activities such as maintenance/repair or reuse are
executed earlier in the product lifetime than remanufacturing or recycling.
Frequency of transactions and related data queries: The frequency of transactions
and of related data queries declines along the funnel. For instance, data collection and
analysis to assess the need for maintenance is carried out regularly during the use
phase, while data for recycling is only relevant once, at the product’s end of life.
Priority of circular strategies: Similar to the waste hierarchy, circular strategies such
as maintenance and reuse have greater environmental valueand therefore higher
prioritythan material-level recycling strategies. This represents Stahel’s inertia
principle: “do not repair what is not broken, do not remanufacture something that can
be repaired, do not recycle a product that can be remanufactured”.35
After the smart use and circular strategies, we reflect on feedback into R&D for circular product
redesign (Chapter 11). We also explain the role of the degree of servitisation and related
business model type for advancing smart circular strategies (Chapter 12). Finally, we discuss
current challenges and trends (Chapter 13) and conclude our analysis (Chapter 14).
34 For further information see: Alcayaga et al. (2019); Alcayaga et al. (2020, May); Circular Economy Initiative
Deutschland (Ed.) (2021); Hansen et al. (2021)
35 Stahel (2010, p. 195)
5.1 Goals and characterisation
Firms may have several business goals for the use of digital technologies. These may include
reducing costs, improving the overall efficiency of products and processes, increasing the
quality of their service offer, gaining full product life cycle transparency, and generating
synergies between departments, among others. Building smart cross-strategy capabilities is
crucial to achieving these goals. In particular, when considering the use phase and product
circularity, smart cross-strategy capabilities aim at supporting and enabling the
operationalisation of smart use and smart circular processes. Firms build firm-centric as
well as customer-centric hardware and software to configure the foundational infrastructure of
their operations; this includes the IT and communication infrastructure, the backend
architecture, the overall suite of software applications, and other digital enablers such as smart
boxes or RFID readers. Ultimately, the configuration of these building blocks serves the
purpose of adding value for the customer.
5.2 Smart processes and infrastructure
5.2.1 Product life cycle management system overview
The life cycle management system is the gatekeeper of product lifetime data. It proactively
manages (collects, centralises, harmonises, integrates, analyses, and delivers) product
information, involving different applications, actors, and products in this process. As can be
seen in Figure 6, firms may link the smart product (e.g., through sensors, peripheral devices,
condition monitoring applications) with their life cycle management system, which is also
connected to other (internal or external) systems such as enterprise resource planning (ERP)
and warehouse management systems. Managing product data and ensuring connectivity
is vital to enable effective lifetime management and the operationalisation of smart use
and circular strategies. As explained by an interviewee, a smart maintenance project would
only be effective when the provider can connect the new maintenance tool to the existing
inventory or warehouse management system. The interface between both IT systems provides
identification of spare part availability and order requirements. If they are not connected,
maintenance personnel would have to log in to other applications and order the missing spare
parts manually. In a less favourable scenario, a service technician would have to drive to the
warehouse and personally check for available spare parts. Both cases would increase the
complexity and the costs associated with the new maintenance tool, hindering their
implementation. According to another interviewee, projects aiming at the integration of multiple
systems that enable lifetime management functionality are of high complexity, and these
initiatives are still at an early stage in several industries, as they require the coordinated
interaction of several stakeholders.
Figure 6: Architecture of a life cycle management system.
The life cycle management system consists of the following components:
lifetime database,
digital product and materials passports, and
product life cycle management applications.
Lifetime databases store static and dynamic information.36 Static information refers to the
intrinsic characteristics of a product (e.g., design information, technical data, disassembly
sequence, etc.). It may be altered during the product lifetime and is mainly determined at the
manufacturing stage. Dynamic information represents the changes to product condition over
time (e.g., remaining useful life, maintenance history, part replacement and upgrade history,
financial data, etc.) and is usually generated during the use phase and the circular flows.
Interfaces ensure the correct information flow between the smart product, the life cycle
management system, and other systems such as warehouse management. We describe
results related to life cycle applications in the next section due to their relevance for
5.2.2 Product passports and life cycle management applications
In an ideal world, digital product and materials passports (henceforth referred to as product
passports) act as a registry of product and customer data and deliver qualitative
36 Circular Economy Initiative Deutschland (Ed.) (2021)
Lifetime database
Producer /
(and ecosystem
Life cycle management system
Other systems
Product and customer-centric
peripheral devices
Integrated hardware and/or software/
application components
Traditional product components
Product life cycle
management applications
Information flow
information about the product. This information covers several aspects of the product, such
as the origin, composition, repair and disassembly instructions, end-of-life handling guidelines,
and serves as the basis of subsequent smart strategies such as smart maintenance, reuse,
remanufacturing, and recycling.37 However, our study indicates that in practice, product
lifetime data may be scattered among several databases, applications, and departments
within the focal firm and across their boundaries (customers, suppliers, and partners). Even
within the firm, applications and departments may follow different standards that may hinder
harmonisation. For example, product data can be stored in digital (e.g., different databases)
and analogue formats (e.g., sheets of paper) by different departments of the firm.
In order to overcome these limitations, firms may combine analogue and digital systems. A
manufacturer of recycling machines, saves all communication with the customer in a life cycle
management tool. They record product and customer information gathered manually during
service visits. During these visits, they record information about the materials used by the
customer, how they operate the machine, and the history of maintenance and repair activities.
According to the interviewee, the main difference between their partially analogue system and
a fully digital one with a direct online connection to the product lies in the frequency of data
collection. A fully digital system would ensure gathering data on a daily, hourly, or real-time
basis and having a portrait of the customer and the product at all times.
Each of our products is registered and subject to a life cycle management tool, so to speak. And
every ticket, that is, every case or incident if you like, is registered in this life cycle management
tool to a customer. So, when a customer calls, this information is entered into this tool, and it is
completed with [omitted] smart data where you can see at what hours, with what operating
conditions, etc. something has happened. It also records exactly which components were
replaced, when, and why. It is crucial that we know all about it because if the customer calls and
has a problem, you always have to look at the history. (Head of IT and quality management)
Furthermore, software applications are increasingly modular and practitioners may use one
application suite with different modules to fulfil their lifetime management needs. Both the
product passport and PLM applications could be modules of an integrated suite. For
instance, a manufacturer of construction vehicles stores and manages product and customer
information in their customer relationship management (CRM) system. They add every
troubleshooting, service, and warranty claim they have processed. They also store additional
information about service requests, such as details about the phone calls with or the emails of
the customer. Thus, this CRM system acts as a single and unified source of information when
tracking the history of their products.
37 Circular Economy Initiative Deutschland (Ed.) (2021)
In addition, PLM applications may play a wide variety of roles, and new use cases can be
served with additional software modules. This flexibility allows firms to add new modules when
addressing new use cases. Below, we describe relevant use cases:
PLM applications can be used by firms to create transparency about the product
lifetime. In order to create transparency, products can be scanned multiple times
during their lifetime using RFID readers, ultra high frequency (UHF) antennas,
Bluetooth technologies, cameras, and smartphones. Then, this data can be linked to
the product passport of the firm to document the lifetime of the product. For instance,
a producer of bearings collects information when the customer scans a data matrix on
the product, when service technicians go to customer sites, or when bearings are
returned for remanufacturing purposes.
Firms may use PLM applications and product data to demonstrate improvements
made to their products. For example, the customers of a manufacturer of air systems
receive data on the function, safety, and energy use of the product. The machine
interacts with the customers and delivers reliable information about the improvements
done by the manufacturer over time. A manufacturer of recycling machines uses a third-
party cloud application. The application shows improvements done to the product
during their lifetime.
Applications may help firms improve decision-making and planning new
investments. A manufacturer of construction vehicles uses a life cycle management
tool to plan reinvesting opportunities for their customers. Due to the presence of dealers
and a lack of direct contact with the final customer, using this tool offers the producer
relevant information about customers’ needs. For instance, the firm may know when a
product has been amortised, therefore, they know when they may start discussing with
the customer about a new acquisition.
Firms may run performance analyses on product information and generate reports for
the top management of the customer’s firm. Customers may download the report
directly from the life cycle management application of the firm or receive it via email.
For example, a provider of automation solutions generates reports for their customers
every week. The reports contain all the information generated by the product within the
last week. This procedure enables a better understanding of the functioning of the
product and also encourages decision-making based on actual data.
5.2.3 Sensing and identification technologies
Firms may use sensing and identification technologies to collect data about the product. These
technologies may facilitate product tracking and can contribute to extending the functionalities
of the product. Below, we explain the characteristics of the three most relevant ones: sensors,
unique identifiers, and smart boxes.
Regarding sensors, the product type determines the number of sensors a product can have.
Complex products have a large number of sensors. For instance, an industrial machine can
easily have more than 200 sensors. In contrast, simple products have fewer sensors or none
at all (e.g., smart textiles have only an RFID tag for identification, but no embedded sensors).
Below, we list different business practices related to the usage of sensors:
A manufacturer of plastic injection moulding machines uses specific sensors in addition
to the standard sensors installed in the machine. While standard sensors are used for
the control and functioning of the machine, additional ones interact with the control
system and collect additional data about the acceleration of the components or the
concentration of particles in the oil.
A provider of logistics equipment uses sensors to detect the weight of the boxes running
on a conveyor belt at customer locations. Moreover, the firm can use sensors to know
the specific humidity and temperature conditions of the location where the equipment
is operating.
We in the technical department read defect reports very intensively, which leads us to say:
‘Okay, it would make sense to install more sensor technologythen we would actually detect
this defect earlierand not only when something is broken’ . . . The people in quality assurance
have learned and often bring the suggestion to us. And we coordinate with them and say: ‘Okay,
we will add that now!’. (Head of research and development)
Second, firms may use unique identifiers to serialise their product and components so that
they can achieve accurate identification and tracking. Firms in the study employ several
alternatives for these ends:
Quick response (QR) codes (further information: ISO/IEC 18004:2015)38,
RFID tags,
Near field communication (NFC) sensors (a branch of High-Frequency RFIDs), or
A data matrix code (further information: ISO/IEC 16022:2006)39.
For example, a manufacturer of tools uses RFID tags to identify their products. Similarly, a
lessor of smart textiles uses barcodes and RFID tags to track their textiles. UHF antennas or
NFC readers at different locations may be used to enable more powerful identification and
In addition, firms can link the unique identifier with a product passport where information about
the product is stored. This information may be displayed on an application for the customer.
In the future, all of our equipment will actually have a unique identifier on it. So, it is not only
unique within a facility, but it is unique worldwide . . . All of our equipment is going to have what
they call a QR-code, so it is a 2d barcode type of thing. So that QR-code is going to be tied to
the documentation in the system. (Director of services unit)
Finally, firms may collect data about their products using an external smart box connected to
sensors in the product. A smart box is a hardware device that amplifies the product with smart
functionalities. Both the sensors and the smart box may be sold and installed separately as an
extension of the standard product. The smart box offers several benefits:
It can gather status and location information of the product, as well as several
customisable parameters.
It can determine and document changes in status, production quantities, and cycle
times of industrial machines.
It includes data communication capabilities via the Global System for Mobile
Communications (GSM) or Wireless Fidelity (Wi-Fi).
It can process and amplify diverse standard sensor signals.
The information from the smart box may be summarised, analysed, and displayed to the
customer via a cloud solution.
5.2.4 Product data collection
Firms may have access to different sources of information about their products. Data may flow
from internal sources (e.g., processes and departments), as well as from external sources
(e.g., products and customers). This data may be recorded in digital formats (e.g., software
solutions) and analogue formats (e.g., paper datasheets).
We have identified three main sources of external data in the study:
Traditional channels (analogue): Firms can collect information about the
environment and usage of the product through traditional channels such as customer
contact during reclamations. They can also gather feedback from technicians after
maintenance and repair visits. For example, service technicians of a manufacturer of
air systems document the behaviour of their machines every time they visit the
We collect a lot of customer information. Just in a different fashion, during every test, every
service report, every commissioning . . . I believe that many customers are not aware of how
much information about their machine, their environment, about their things, is already collected
today. Not on a continuous basis, never in the same way as if you would have big data . . . So
we have very good information about the machines. (CEO)
Hybrid channels (analogue and digital): Firms and customers can record information
about the history of the product by manually entering data into a life cycle management
application or product passport.
So, the asset logbook. Every motor, every powertrain, every measuring point, every device on
the machine has its own entry in the asset logbook, its own asset logbook, so to speak. And I
can click on it and make my entries. It is like, if I now have a motor and I have a logbook for the
motor and, I cleaned it on January 5, I replaced the bearing on January 17, there was a
malfunction on January 7 [omitted], and I changed that on January 8. So, really, a register . . .
And that means I get a book about the machine that I, more or less, write myself. I get the error
messages entered for the powertrain. I get the commissioning entered at the beginning. If the
people take good care of it during the operation of the machine, during maintenance, I have a
complete asset logbook. Of course, it always depends on what the customer really enters
himself, how well-behaved he is. If he doesn't enter anything, then the feature is there, but it's
pointless. (CEO)
Individual parameters (digital): Firms can automatically collect information about the
usage and environment of the product using sensing technologies that measure
specific parameters. For instance, they can measure the humidity, heat, weight,
material use, energy use, working time, vibrations, hydraulic resistance, process
speed, the movement speed of particular components, defects, etc.
5.2.5 Backend architecture
When it comes to finding a solution for managing product data, firms in the study may use
traditional data centres, cloud computing solutions, or on-edge architecture. In this
section, we mainly refer to the latter two alternatives as they were portrayed by interviewees
as state-of-the-art solutions.
First of all, cloud services bring about a different paradigm to data management. In
comparison to data centres, they provide high flexibility because they grow according to the
growth of the business. Second, they have the advantage of connecting complex products at
different locations. When using cloud services, all products can automatically upload their data
to the cloud, which can be directly analysed on the cloud without involving any downloads. Any
manufacturer with different locations could organise a cloud system that collects operational
parameters from different production lines. Third, they have a cost advantage over dedicated
data centres. Dedicated data centres require high up-front investments and preparation.
Fourth, the manufacturer can develop their own analytic tools and use the cloud to deploy
them. This allows for higher proximity to the customer because of the closer interaction and
common access to the results of the analysis. Finally, cloud solutions are well suited for firms
that do not have the necessary in-house IT capabilities to offer integral services. For instance,
a manufacturer of plastics recycling machinery uses cloud services from a well-known external
provider because the firm does not have the capabilities to master this process worldwide. The
external provider also takes care of the data visualisation for the final customer on a
Although cloud services entail several advantages, the legal framework and data security
details must be clarified with the customer in advance so that both partners can define a sound
cloud strategy. In some cases, customers prefer to keep ownership of the data, while in others,
they make contractual arrangements and transfer the ownership to the cloud provider or the
On-edge technologies are an alternative to cloud solutions and traditional data centres. They
are implemented by moving hardware and software capabilities directly to the product. For
product types with a large number of sensors, the high volume of data generated by them
renders cloud solutions unfeasible due to the high costs of online data storage and analysis.
Moreover, some products generate huge amounts of data and are located in non-urban areas
with poor connectivity. Therefore, firms may prefer to execute data analysis at the product
before sending the results via the GSM or Wi-Fi networks. In this case, the product would only
send relevant information and store or delete useless data. Two cases illustrate this
A manufacturer of construction vehicles uses on-edge architecture because of the high
volume of data they generate. The firm gathers data from 500 parameters on a 250
millisecond range per vehicle. Each vehicle generates approximately one to two
gigabytes of binary data per hour. In this data range, cloud computing becomes
irrelevant because the operations to transfer, store, and analyse that data volume
would be far too expensive.
A manufacturer of plastic injection moulding machines stores product data at an on-
edge device inside the machine at the customer location. When the device has internet
connectivity, it sends the data to the manufacturer’s servers for analysis. The data is
used by engineers to gain insights into the use and wear of the product and for the
further development of data analysis algorithms.
5.2.6 Product data analysis
Among the different tasks comprised within product data management, product data analysis
is the cornerstone for effective and efficient PLM. Product data analysis occurs on different
layers relative to the amount of data that a firm may have:
. . . just because you are gathering information and it is saying: ‘This particular piece of
equipment wears out sooner. Well, it could have been an original defect that the other parts do
not have, so you have got all of these different things that, I think over time, it will be like peeling
away an onion. You know a layer at a time, where you will get more information, you will use it,
you have to see how it impacts things, and then you get down to the next layer, and you will
learn. (Director of services unit)
The results of the study suggest that data analysis can be divided into three layers:
Layer 1 Running a simple analysis: Firms may understand the status of the product
and assess the performance through a classification of conditions and errors of the
product and their components. For instance, a manufacturer of air systems tracks
several parameters of the machine. They track how much energy is being used and
saved, the resistance of individual components, whether vibrations occur, the emission
rates before and after filtering, etc. Similarly, a manufacturer of plastic injection
moulding machines performs basic assessments of the products without using machine
learning or big data. Then, the firm can use this data to generate recommendations for
their customers.
Layer 2 Generating a holistic view: Some products may have too many
components and parts, and it would be very costly to have a sensor for each
component. For these reasons, the focus shall remain on having a holistic picture of
the functioning of the product and understanding customers’ expectations.
One component of our machines consists of around 100.000 pieces. I would not be able to do
condition monitoring for each piece. That means that I have to understand the overall context of
the machine and learn to make predictions about its behaviour and whether it fulfils customer
needs. (Head of the digital department)
Layer 3 Transitioning towards an integrated analysis: Firms can understand the
usage of the product by aggregating parameters from different products at different
locations. This type of analysis requires a better understanding of the customer, their
application scenarios, and the environment where the product operates. Due to the
complexity of managing larger data sets, this type of analysis may be supported by
artificial intelligence solutions. A particular application of this layer involves customer
clustering. A manufacturer of equipment for metal processing uses data retrieved from
different products and product categories to cluster customers and understand different
operation patterns.
5.2.7 Managing data security
Another relevant point concerning data management is data security. Building trust is a
challenging task. Customers may be reluctant to provide access to their products and systems
because proprietary or sensitive information could be exposed or shared with the competition.
For these reasons, producer firms or service providers may have to offer a safe infrastructure
for data sharing that prevents unauthorised access. In addition, they may need to ensure the
adequate usage of confidential data by their own employees. For instance, maintenance
personnel may accidentally introduce a virus during the manipulation of the product or
mistakenly give access to unauthorised personnel. Some customers in the plastic industry
have their plastic formulas saved in the machines. These might even be patented and shall be
adequately protected against theft. Although the high cost of data security measures may
hinder the development of smart solutions, the competitive pressure in the market drives firms
to offer solutions similar to the competition in order to ensure long-term survival.
Customers are very nervous about providing access to their systems and allowing information
gathering because they may see that as proprietary information or sensitive data that they do
not want to share. (Director of services unit)
That would be an [optimised] business model, but that requires a lot of things. Not only that it is
basically possible to exchange data, but again an essential point is trust, that the customer has
the feeling that I am not using this data to pass on information or anything else to a competitor.
So, I can actually get production information from process data. (CEO)
What is always difficult is to build trust, that no abuse can happen. I have to achieve that! So,
because, of course, there is always the fear when I am connected to the internet, of course, all
sorts of viruses or whatever can possibly paralyse my production process. (Head of the
innovation department)
A key insight into data security reveals that firms only collect data about the operation of
the product in an anonymous way. They do not collect specific details about the final
products or users unless it is agreed upon with the customer. This practice is very relevant
when it comes to implementing smart solutions.
It is really about the abstract description of the use of our products, and there are no conclusions
about the components that the customer manufactures . . . We are primarily concerned with
how often, at what intervals, at what spacing, at what frequency certain axes are moved, and at
what maximum speeds and accelerations they are driven. (Product and marketing lead)
5.2.8 Archiving rules
Product history may be kept for many years in a product passport, and a traditional approach
would be to save every detail of the product history. However, the enormous amount of data
generated by smart products requires firms to reconsider this approach and create rules for
data archiving. As explained by an interviewee, storing every detail is practically
impossible due to the increasing costs of data storage. An approach to solve this issue
would be to keep specific information about changes made to the product related to
maintenance, repairs, or upgrades in the product passport and delete dynamic information
gained from sensors after a period of time. Dynamic data could be used to reveal useful
insights into the use phase and customer behaviour. However, after some months or years,
these insights have already driven changes to the product design, the functioning of the
product, or the internal activities of the firm, and the data becomes practically irrelevant.
6.1 Goals and characterisation
Figure 7: The use phase black-box
The main driver for the implementation of digital technologies is the need to understand the
use phase (Figure 7). Firms usually have little access to their products and do not know how
customers are using them. Firms do not know whether their design, specifications, and
fabrication are appropriate for different users, use cases, and environments. In addition,
gathering accurate information about the use phase helps firms to improve traceability along
the entire product lifetime. Traceability information can be used to generate estimations of
longevity for long-lived products (e.g., industrial machines) and precise lifetime-span
calculations for short-lived products (e.g., textiles).
We do not have enough available knowledge about the behaviour of the customer. We build our
machines according to customer requests, but we do not know what they do out there in the
field. (Head of digital department)
In some cases, manufacturers sell their products through dealer networks, while in others, the
customer operates a fleet management business for third parties. This additional actor in the
value chain has direct access to the final customer but hampers the communication between
the manufacturer and the final customer. This can be seen in the case of cranes and other
products in the construction industry. A distant and inaccessible location of vehicles for
construction may hinder offering timely and high-quality assistance services. In order to solve
these communication and proximity issues, firms may use the data delivered by smart products
to understand customer behaviour and cover their needs with remote assistance services.
With an export quota of 97%, our products [omitted] stand worldwide remotely in the outback,
in non-urban areas . . . far away from someone that could provide them with support. In other
words, the customer is positioned somewhere in the world, and the operator is completely alone.
If there was a problem or if one would want to help him, this would typically happen in the
evening or the next day. By this time, he has already forgotten what exactly happened to the
product. (Head of IT and quality management)
Production Services
Smart recycling
Smart remanufacturing
Smart reuse
End of life
Smart maintenance / repair
Smart use
Use of smart
6.2 Smart processes
6.2.1 Condition monitoring
As can be seen in Figure 8, remote condition monitoring (simply known as condition
monitoring) is a business practice broadly adopted on machines, heavy vehicles, and
components (e.g., bearings, engines). It refers to the continuous remote collection of data
about the operation of the product and its components, and the delivery of results of their
analysis. The objective of this smart process is to understand the use phase and create
value for the customer. In the case of products such as professional textiles and tools,
condition monitoring is limited to location tracking and manual updates on software
applications. Manual input is supported by visual inspections. In this respect, visual
inspections still have enormous relevance to analyse the product condition for all product
Figure 8: Condition monitoring across product types.
Two types of condition monitoringactive vs. passiveexist for medium-cycling and slow-
cycling products of higher complexity, that is, machine components, industrial machinery, and
heavy vehicles:
A product is actively monitored when it autonomously sends data to the producer. For
instance, a manufacturer of plastic injection moulding machines has started to actively
monitor three components of their machines in real-time. These components have
been serialised with a unique identifier. The use of the serial number has been
integrated into the production process. The data received from the product is analysed
Machines and heavy vehicles
Tools and components
Professional textiles
+ Frequency
+ Condition
to estimate the remaining useful life in hours. This is achieved by defining the boundary
values of the parameters measured.
A passive form of condition monitoring implies that the product does not automatically
send data to the manufacturer. The firm needs to collect data through site visits or after
the product has been taken back. Firms may scan the product, directly connect to them
through a cable, or remotely access it through a controlled connection with predefined
permissions. For example, a manufacturer of air systems can regularly log into the
customer’s systems through a virtual private network (VPN) to monitor the product
usage and proactively look for errors. This could be done once a day, once a week, or
even once a month. A significant driver of this alternative is the reluctance of customers
to give direct access to product data. To overcome the lack of remote access,
technicians may manually access the product after a service visit at the customer site
and bring the data back to the firm.
Using condition monitoring to understand the condition of the product during the use phase
may facilitate decision-making. For example, firms can remotely monitor composting or
biogas units to know how full they are and how they are functioning. With condition monitoring,
an operator can receive a short message service (SMS) or a push notification containing
product information on a smartphone or tablet application. The information about the current
capacity of the units can help the operator with decision-making regarding the next steps and
route planning. They can even use an application to change the settings and operate the
machine remotely. If a unit is empty, operators can decide to drive to their location and load
them with materials from the storage. As explained by an interviewee, condition monitoring
reduces transportation time and staff costs, allows for remote operation, and
centralises the delivery of information about the product. In some cases, only one person
can monitor and manage ten units within a radius of 100 km. Without condition monitoring,
additional staff would be required to fulfil this task because the only way for the staff to
understand what is happening with the units is to drive to every unit and do a manual
6.2.2 End-user support applications
In this section, we describe use cases of IT applications with a focus on supporting the
customer and the end-user during the use phase:
End-user support applications can provide the customer with fast and simple access
to product and component information. They may act as a dashboard or cockpit
that displays information about the product. For instance, a manufacturer of recycling
machinery provides an online platform to their customers so they can see complete
documentation of their products. Customers have access to electrical and mechanical
drawings, statistics, the order history, and quality information. Similarly, a producer of
bearings has implemented a data matrix on their products. Customers can scan the
matrix with a smartphone application which directly shows the product information. For
instance, the customer can scan the bearing and verify that the features of the delivered
bearing comply with their requirements. Finally, a producer of tools has installed
barcodes and Bluetooth-enabled smart tags on their tools. The firm can scan their tools
and use an application to display product data.
These applications also serve as a general control centre for the product. For
example, a producer of automation equipment has created a wizard that helps end-
users select the parameters and settings for an optimal production process.
Furthermore, a provider of automation solutions has developed software for remote
control and management of recycling equipment. The user can visualise the equipment
and select specific components and circuit diagrams. In addition, they can use
indicators to make calculations, change the parameters, and optimise the equipment.
End-user support applications can work as location and geolocation information
systems. An application may display the product location using maps or a product
landscape. For instance, a provider of energy services offers a geolocation application
with augmented reality to their customers, who can use their smartphone and the image
recognition software to project the location of wires and gas pipes in the screen. This
service may be useful for construction firms to avoid gas and energy lines when digging
a construction site. Avoiding damages to infrastructure helps both the firm and the
customer to reduce interruptions in energy and heating systems during construction,
as well as to ensure the safety of construction workers.
Firms can also use location tracking to offer inventory and fleet management
services to their customers. A producer of tools and equipment offers a fleet
management tool to their customers. The application allows the customer to manage
the right mix of tools, even from third-party manufacturers, and use tracking information
to better allocate the tools to tasks and employees.
End-user support applications can send notifications to the control system of the
customer or to a specific person via email, SMS, or a smartphone or tablet application.
Notifications are relevant for condition monitoring services. For instance, the user of a
smartphone application for mobile cranes may receive status notifications and
information about the location of the nearest service centre. The use of global
positioning system (GPS) technologies and product landscapefunctionality may
support these services. In the case of industrial equipment, if certain limits to the
operation of the product are exceeded during production, such as the pressure
tolerance, the equipment can trigger a warning that is sent to the operator via email. If
the pressure exceeds an even higher limit for several minutes, the equipment can
trigger an alarm.
They may serve as a source of training and instructions for customers, users, and
employees. Training about the usage of a product is typically integrated into the sale
contract. However, the emergence of digital technologies requires additional efforts on
both sides (the firm and the customer) to better understand how the product works and
how it is best operated. A producer of trailers has developed an application to show the
truck drivers a list of actions to be taken before driving. For example, they should test
the pressure of the tyres before departure. A producer of construction machines
employs a cloud system containing machine data, videos, pictures, marketing support,
sales support, documentation, manuals, and service forums. Dealers can log in and
download all relevant information so that they can provide a better service to the final
customer. A producer of welding machines has developed a customisable welding
simulator to train and improve the skills of new employees. The simulator accompanies
new employees during the learning process to help them understand the complexity of
smart products.
End-user support applications may inform the customer about the type of final
product that is being produced at a point in time (if the smart product can generate
different types of final products). For example, a machine producing plastic boxes has
different configurations for the size of the boxes produced. There are also open and
closed plastic boxes used for the transportation of bottles and other products.
Information from sensors can be displayed on a dashboard to give a real-time view of
the operation of the machine.
Finally, peripheral devices, such as smartphones, tablets, and smart glasses with
augmented reality or virtual reality capabilities may be used with end-user support
applications to improve the interaction between the user and the product. These
devices may be used by the firm to understand problems during the operation of the
product or analyse the interactions of the operator with the product.
6.2.3 Product and process optimisation Benchmarking
Condition monitoring services and product standardisation allow for better
comparisons between different products. For example, a manufacturer of plastic injection
moulding machines can use the condition monitoring information of their machines to compare
the performance of their products. If a customer has several machines worldwide, the firm
could summarise the information gathered from different product categories at different
locations and compare their performance in terms of the kilograms of materials and viscosity
produced per hour. Moreover, they can compare the operation costs and usage of spare parts.
The manufacturer could use this information to offer recommendations to their customers.
Furthermore, using cloud solutions can facilitate the comparison of products because
the cloud can contain a large data set generated by multiple locations and customers. For
instance, a provider of automation solutions for biogas and composting units can collect data
from their customers on the cloud and create a pool with summarised information. The firm
only collects information relevant to the performance and functioning of the units, without
storing data about the user or the customer. Both the firm and their customer can assess the
performance of the biogas units using a tool on the cloud.
Firms could use digital technologies to track the location of their products and compare
them at different locations and operating conditions. Below, we describe examples of firms
using location technologies:
A producer of construction vehicles may know the location of a specific product using
a real-time map that shows all their products in the market. This product landscape
can be scaled up to the whole world and is particularly beneficial for mobile products
because they are typically located at remote locations.
Another firm, a provider of energy services, uses Google maps to visualise their heating
pipelines, pipe fittings, and instruments.
A producer of tools allows the customer to use their application to track and trace the
location of their products and the ones of their competitors. Every product is tagged
with a barcode and registered in the application using a smartphone or scanner. The
firm may also use a Bluetooth-enabled smart tag that allows for active tracking of the
product location within 30 meters.
Finally, a lessor of smart textiles uses RFID chips and antennas to track and trace their
textiles within their plants and at customer sites.
Firms may benefit from location tracking because information about current operating costs,
spare parts used, availability, or performance might vary according to customer sites,
geographies, or even countries.
Firms can also collect information about their products and components and compare it to a
smart fingerprint. A smart fingerprint is a virtual image of the product made by the producer
before its first use. With this information, they can compare usage data among many products.
In addition, this information could be used as a quality standard or benchmark for the future
behaviour of the product during the use phase. For instance, a manufacturer of construction
vehicles performs a test run of their products and saves information about several parameters
before the products leave the factory. The information is used for future benchmarking. In
addition, a manufacturing firm operated an online solution where customers send data about
specific loops in the production process every six months. The firm compares product data
against the initial setup and gives the customer a recommendation on how to tune the machine
for best performance.
Challenges in product tracking may arise when products do not have an active location
tracking system. Product traceability may be lost after several sales transactions if the
product passport is not updated correctly. For example, machine engines may go through a
wholesaler, a machine manufacturer, and a dealer before arriving at a customer site. Even if
the owner of the machine uses the sensors onboard and the cloud solution of the manufacturer
for condition monitoring, the manufacturer may not know the specific engine they are
communicating with. The manufacturer could get the location and model of the engine but no
specific information because the owner could upload information to the cloud anonymously.
Although the firm in the example has lost accurate traceability of the product, we can observe
how they apply data security best practices to protect the privacy of the customer. Product optimisation
Smart products can be optimised by digital technologies according to their environment and
usage behaviour of the customer. In general, these optimisations apply to products with long
use cycles and a high degree of complexity, such as industrial machines, heavy vehicles, and
components. Improvements can be divided into three main areas: reliability, comfort, and cost
Reliability can be understood as product use stability, high uptime, or long lifetime.
There are different internal and external events like changes in temperature, air
pressure, or material quality that affect the functioning of the product. For industrial
machines, heavy vehicles, and components, firms can use data from sensors to timely
regulate the product and its environment before failure occurs. For tools and textiles,
firms may use product history to better control the materials used for fabrication and
design. Offering long-lived products of high quality is an important element of the
value proposition of the firms under study. High quality and long lifetimes may also
ensure higher levels of availability for all product types.
Reducing the efforts of product operation to a minimum and increasing automation can
offer a higher degree of comfort and usability to the users of industrial machines or
heavy vehicles. Usability and comfort may also increase by offering additional services
over life cycle management applications. Firms may develop assistant systems to
support the user in real-time. The product is optimised using algorithms that
automate specific sequences of operation. The product could perform self-monitoring
up to a certain degree to avoid errors and accidents. This reduces the complexity for
the user, reducing training hours, and allowing employees with general knowledge to
use it without inconvenience. For instance, a producer of mobile cranes has added
functionality to support the crane operator with automatic folding and unfolding.
Furthermore, cranes can automatically monitor their stability using software. Both these
functionalities make the cranes easier and safer to handle. Thus, a rather complex
operation becomes very simple for the operator due to the use of appropriate software
in the background. Usability is improved because the user does not need to be a
specialist on every crane model and can actually have general knowledge to operate a
crane. Consequently, digitalisation reduces the need for further training and enables a
wider range of employees to operate the machine.
Embedding intelligence into products and services is also driven by the cost factor.
Improving reliability, availability, extending lifetimes, and improving the use phase may
contribute to cost savings. Reducing the operational costs of products with long use
cycles through energy efficiency improvements is still relevant for firms in the B2B
domain. However, interviewees mentioned that additional gains in the energy efficiency
of their products are rather marginal without major innovation leaps. Exploring IoT
and digital solutions may bring the necessary changes to the product design to discover
uncharted benefits.
Product optimisation of industrial machines, heavy vehicles, and their components can also be
supported by condition monitoring and remote control. A producer of industrial air systems
can remotely tune their products for the best working conditions. The firm can improve energy
efficiency by changing the parameters of the product according to the working schedule of the
customer. For instance, by reducing the working performance of the machine to a standby
mode, the firm can reduce energy consumption and extend the lifetime of the product during
holidays. Another producer of air systems can determine the status of a specific compressed
air unit. On the basis of the data obtained, the firm can elaborate optimisation concepts, which
in turn result in operational and energy efficiency.
Remote control is also suitable to improve the efficiency of product usage when products are
at remote locations. As explained by an interviewee, operators of snowmaking equipment at
ski areas can save several hours a week by remotely operating the snowmaking equipment
from their smartphones or tablets. They are able to turn it on and off, and change some
settings. These tasks require relatively low data volume and offer considerable cost savings.
Moreover, a producer of special machines for production processes can remotely adjust the
machines for the needs of their customers. For example, they can make changes to the
lubrication when they detect that a cylinder is heating up.
Product optimisation of complex products can also be supported by triggering indicators in
the product itself. For example, a producer of construction vehicles has installed a lighting
system to tell the user how well they are operating the machine in real-time. Operators can
receive automatic feedback on their behaviour in the form of three light bars.
Firms may also assist their customers by providing them with training supported by smart
glasses. Users can enter virtual classrooms and use the products in a 3D virtual world. Users
can even test-drive new vehicles that are still under development. These applications can
reduce travel time and training costs, and allow for better usage of the product. Process optimisation
When considering product types with long use cycles, such as industrial machines, their
interaction with other machines within a production process may affect the stability and
capability of the process, which, in turn, may negatively affect the final product. A producer of
injection moulding machines offers feedback to the operator of the product in order to
improve the production process. The firm uses process simulation to analyse factors like
environment temperature, air pressure, and materials quality. The operator receives feedback
about these problems as well as suggestions to balance the production process in real-time.
The firm is also developing optimisation strategies for the production line using a digital
twin. With this application, the firm could monitor the flow of plastic into the mould and analyse
how it spreads and behaves. Thereafter, the firm could find the best settings so that the
customer can adjust the working parameters and set up an ideal production process. Although
this type of service complements the know-how of the customer during the fabrication of final
goods, it might generate resistance and tension in the relationship with the customer due to
the higher proximity between them.
Furthermore, a provider of solutions for the chemical industry works together with their
customers to improve the quality of the production process. The resulting chemicals are
very diverse, even if the customers have a specific formula, because organic materials and
their cells are not identical. This results in a lot of rejections and considerable losses. The firm
uses sensors to detect the state of the final product at every stage of the production process.
Moreover, they use algorithms to ensure the parameters at every stage are delivering optimal
results. For instance, reducing the temperature by some degrees or shortening a production
stage a few minutes may deliver optimal product quality and a reduction of losses during
From a broader perspective that goes beyond the production process, firms may offer
suggestions to improve the IT landscape of their customers or even sell services to manage
it. A manufacturer of plastic injection moulding machines offers suggestions to their customers
regarding network infrastructure, security, and user access. Similarly, a logistics provider has
started to sell services related to processor monitoring and disk space management. The focus
of these services is on maintaining the uptime of the IT landscape of the customer. Finally,
firms may make recommendations to their customers about the integration of cloud solutions
into their IT systems. Consolidation of the IT landscape is very important before implementing
cloud solutions used for maintenance and other services. After international expansion, firms
might have different applications for warehouse and order management that require
integration. If a firm has operations in 50 countries, in the worst-case scenario, it could have
50 different systems in place.
[What] we are starting to sell now as a service is: ‘We will log into your system on a regular
basis, we will gather information, we will look proactively for errors and problems that are going
on’ . . . They [customers] need to depend that our equipment is up and functional and running
all the time. And so, by being able to monitor their system and solve issues before they shut
down unexpectedly, we are able to kind of keep the system up, which helps guarantee that
uptime. (Director of services unit)
Firms may also improve their sales and after-sales processes. Product and customer data
allow for a more proactive and less expensive repeat sales process. A manufacturer of
special vehicles may use product data to know when a customer might be ready to reinvest.
Data may help the firm to prepare a repeat sales strategy in advance and contact the
customer to sell a new product. Firms may also support this process with smart glasses.
Technicians carrying smart glasses can bring the entire product range directly to the customer
and display them in virtual reality.
. . . this is the direction it is now going, to be proactive. Because you cannot, we do not sell
vacuum cleaners, where somebody calls you and asks you: ‘Do you need a vacuum cleaner?’
You cannot just go around asking people things like that; the effort would be exorbitant. And
now the system runs, evaluates the data, identifies the next replacement part, infers from it what
the ideal package is, suggests it and says: ‘Okay, give me the green light or not. And there you
can tell your dealer, your supervising agent: Well, that might be a good deal after all!’. (Head of
IT and quality management)
7.1 Goals and characterisation
We have combined maintenance and repair in a single category due to their similarities. First,
both strategies aim at extending the product lifetime during the use phase by correcting or
preventing failure. Second, repair practices are usually a key component of any maintenance
strategy. Finally, different terms can be used to describe IoT-enabled maintenance and repair
practices. For instance, condition-based maintenance”40 and predictive maintenance41 are
widely used terms within scientific and practice-oriented literature.42 This variety of terms may
lead to confusion among readers and using a single term would simplify the understanding of
this report. For these reasons, we have chosen the term smart maintenance and repair to
encompass the broad set of related business practices.
Firms are increasingly developing maintenance and repair services enabled by digital
technologies to avoid downtime, extend the lifetime of their products, and support their
customers during product use. As explained by an interviewee, the customer firm benefits
from planning maintenance with the support of technology because it brings about lower
operational costs and a longer service life for the product. Changing components regularly
before they break down can contribute to a longer lifetime of a machine. Without maintenance
enabled by condition monitoring, the firm would have to change parts (like a pump wheel) after
a predefined period. Now, they can calculate the maximum lifespan for these parts without
damaging the machine.
As explained in previous chapters, condition monitoring and product passports are key
enablers of circular strategies. In order to execute maintenance and repair operations at the
best possible time, firms need to monitor the condition of their product while it is being used by
the customer. They also need to have a product passport to manage historical information
about the product, and a life cycle management application to communicate with the customer
about their need for maintenance or repair, find their product using location technologies, or
remotely execute some tasks that facilitate maintenance, among others.
7.2 Smart processes
7.2.1 Preventative maintenance and repair
Smart maintenance may be used for the proactive identification of wear, the estimation of
remaining useful life, and the maximisation of resource use. For instance, a provider of
40 Prajapati et al. (2012)
41 Selcuk (2016)
42 Alcayaga et al. (2019)
equipment and services for logistic operations uses sensor information to identify the
environment temperature and the weight of the boxes that are handled by the equipment.
Higher temperatures and heavier boxes might increase the wear of the machinery and
conveyor belts. This information allows for a better estimation of the remaining useful life and
possible wear of the equipment. Detecting wear in advance is crucial to enable maintenance
activities. Second, a producer of construction vehicles uses a sophisticated form of condition
monitoring. The analysis of the data goes through machine learning algorithms. These
algorithms automatically optimise the operating parameters of the machine and detect wear of
the parts and components. Detecting wear can enable the configuration of different machine
setups according to the use case. Thus, these individualised setups can prevent damage
caused by an overload of the machine. Furthermore, the firm can also use geolocation to map
the condition of different products and plan the inventory requirements of each machine before
maintenance is executed. Both these approaches are a more comprehensive understanding
of smart maintenance. They are about reducing downtime and also about optimising the
operation and maximising the use of available resources.
The system can distinguish: ‘User A needs that portfolio of parts after 400 hours, and user B
needs it only after 900but again a different portfolio’. And, of course, that makes a big
difference. In the past, you did not know how many hours the machine had been in operation,
how it was doing, where it was malfunctioning and where it was not. That means you could not
do anything! You could not even call the client. You could call randomly and ask: ‘Do you need
anything?’ And since we have a dealer in between, we do not even knowdid the dealer make
a call? Or maybe not, maybe selling new machines is more interesting than selling spare parts!
And today we can draw a global map and say exactly where approximately how much wear is
occurring—because we know where our machine is, we know all the operating parameters, we
can virtually recognise the wear and can say: ‘Actually, I need a warehouse there, a warehouse
there, and a warehouse there’. (Head of IT and quality management)
Smart maintenance enabled by condition monitoring can also help the manufacturer of the
product in the accurate discovery and management of anomalies in the operation. They
can prevent maintenance actions and ensure high product availability. Below, we
describe some examples of these practices:
A producer of construction machines uses condition monitoring to detect errors or
anomalies in the operation of the product. These errors typically imply more working
hours and high transportation costs if the product is located at a remote location. After
the identification of the anomaly, the manufacturer can send error management
routines to the operator. These routines can also be automatically retrieved by the
machines. The manufacturer can give recommendations to the user to change the
settings of the machine for better operation. Specific settings for different materials can
reduce fuel consumption and improve the processing power of the machine.
Equipment for metal processing is usually in operation 24 hours a day, for three working
shifts, and customers require an availability of around 98%. If the manufacturer detects
a continuous rise in temperature of the engine for several consecutive days, the firm
can contact the customer and suggest a plan of action. The firm could also use sensor
information to detect that a pump or filter is dirty and needs maintenance. Thereafter,
they could contact the customer to execute maintenance during the weekend.
A producer of air systems uses predictive algorithms to permanently monitor their
products and analyse their data. On the basis of observation of machine behaviour
and trends, it is possible to forecast when errors are approaching. For example, air
compressors for snowmaking located at remote locations, such as on a mountain 2,500
metres above sea level, are to be monitored from a central location because the staff
responsible for maintenance may live in a valley far away from it. When failure occurs,
the staff can use available product information, documents, and monitoring data to
classify how critical the failure is and develop a maintenance concept. They can
coordinate required materials, spare parts, and other resources like working hours. In
addition, it is of central importance to identify errors in time to initiate the
corresponding measures before damage and downtime actually occurs.
Downtime prevention is also a cost saving strategy because customers can reduce
inventory reserves.
A producer of special vehicles (e.g., harvesters) measures the condition of their
products to ensure high availability during the high season. The firm has installed
sensors in the neuralgic points of their products. Data is sent to the firm via the internet
using the GSM network in real-time. The firm analyses the data and can determine the
degree of contamination of the oil, which is a direct indicator of wear in hydraulic
parts, and plan maintenance or repair actions accordingly.
The engine is of a certain type; the type of engine that typically runs in this area. And by
comparing this data, which is in fact anonymous, we can identify earlier that there is a problem.
So, the customer now suddenly also uses the data of our other customersin each case with
consent, of coursehe does not know who these customers are, but it is the same type of
engine. And now the customer can consume this knowledge earlier, so to speak, and say, ‘Ah,
if this indicator appears with the other customer, then I have to do something. And often these
are minor things that have to be done . . . so the benefit is quite clearless downtime and a
longer life. Because if maintenance is carried out at the right time, there is no damage. (Service
sales senior manager)
7.2.2 Inventory management for spare parts
Smart maintenance and repair solutions enable better inventory management of spare
parts. This is demonstrated by the following examples:
The use of product data allows a manufacturer of air systems to manage the parts and pieces
that need replacement. The firm can use life cycle data to estimate which filter elements of the
machine need replacement within the next months. Moreover, the firm can react faster to
unplanned downtimes and put targeted measures in place to solve them. As a result, the
service technician can take the parts and pieces required before traveling to the customer site,
thus saving time and costs, and speeding up the repair process. Without sensor data, finding
out the cause of failure would require disassembling the machine, which would increase the
costs and extend its downtime.
This approach on parts replacement is also used by a manufacturer of plastic injection
moulding machines. The firm is using condition monitoring to improve maintenance and repair
operations. The machines are very complex and highly individualised. Having information
about part wear allows the firm to take actions in advance for the production and
distribution of spare parts. By these means, customers will receive the required parts before
the old ones reach a dangerous level, preventing a breakdown of the machine.
A manufacturer of construction vehicles can combine condition monitoring and a product
landscapeto locate each individual product and determine the right portfolio of spare parts
needed by each individual customer.
. . . in terms of future predictive information, we will be able to provide better information, so
people can plan better in terms of how much of an inventory of spare parts should I keep.
Because now I know, instead of keeping a whole bunch of products, I can keep less product on
hand because I am more confident that my equipment will last longer. (Director of services unit)
In general, the topic of predictive maintenanceis a very important matter to us, also for future
developments. Because industrial companies today simply no longer have reserves for every
device. Fail-safe systems and reserves are for cost reasons simply shrinking . . . not only spare
parts. But if you look back, for example, 10 or 20 years ago, there was hardly any industrial
enterprise where there was no replacement for a machine. In other words, if there was a
problem, there was a replacement device to take over the function in the event of a failure.
Today, that is no longer the case. (Head of sales)
Finally, online dashboards and product information could facilitate the acquisition of spare
parts. A manufacturer of recycling machinery provides an online tool with digital drawings of
their products and a spare parts catalogue to their customers. Customers, in turn, can identify
the components of the machine via the drawings and place an electronic order for spare parts.
The tool displays prices and the current stock of spare parts and components.
7.2.3 Customer integration in maintenance and repair
Using product data allows for a proactive participation of the customer in the maintenance
process. Below, we describe some examples of this practice:
A manufacturer of plastics recycling machines provides product information from
sensors installed in their machines to their customers so that they can easily plan
the maintenance intervals. In particular, the firm could monitor the performance and
wear of the screw extruder of a recycling machine. When the screw extruder wears out,
the output of the machine decreases, and maintenance is required. However, the signal
used to measure the wear might not always be accurate. As explained in the beginning
of this report, firms are still learning about the data they generate and need time to
analyse it. Providing accurate information about product wear and longevity is an
evolving phenomenon and customer involvement may help the producer to refine
the results of the analysis.
Customers of another manufacturer of machines in the plastic industry buy condition
monitoring for specific pieces of the machine so that they can perform maintenance by
themselves. The additional hardware and the algorithms that analyse the data are
included in the price, as the customer pays a one-time fee for the solution. When the
boundary values are met, the customer receives push-notifications or emails with the
results of the analysis. The customer can also log in to a platform on a browser to see
the information. The results do not include any raw data, but they include suggestions
in the form of measures for the customer.
Finally, customers of a producer of automation machinery can do maintenance by
themselves using predictive maintenance tools either by performing their own data
analyses or by signing a contract so that product data is sent to the cloud and analysed
for them by the producer.
The evolution of technology has also led to the automation of data analysis services to
improve maintenance. These services were not available in the past or were simply too
expensive. For instance, a provider of automation solutions can run analyses of the data that
customers load into their cloud applications. When there are indicators outside the normal
range, the customer receives a notification with this information so they can plan maintenance
and repair activities. The application can also compare data from different customers to deliver
better analyses.
In order to increase user participation, firms may offer repair routines that can help the
operator fix simple issues by themselves. A producer of mobile crushers offers a smart
solution that involves a smartphone or tablet application. If the condition monitoring solution
finds errors while reading the parameters of the machine, these errors can be automatically
linked through codes with guided repair routines. In most cases, the customer can solve the
problem using these guides based on videos and pictures without needing to undergo lengthy
troubleshooting procedures.
Firms may use product monitoring and assessment to identify the reasons for complaints
and have full traceability of repair activities. A lessor of smart textiles collects damaged
textiles in special bags. These bags are sent to the repair crew that visually analyses them and
decides on whether to repair, run an additional washing cycle, or dispose of them. The type of
damage is booked into the ERP system of the firm, allowing for identification of the reasons
behind the problem, as well as what department or product types are involved in the problem.
This analysis has allowed them to identify seasonal fluctuations or problems associated with
specific teams or new personnel.
Smart products can directly notify the customer about the need for maintenance and
repair. A producer of tools has installed a red light in their tools. The light turns on after the
product has been used for a defined number of hours. After an additional number of hours of
use, the tool cannot be used anymore, and the user has to return it to the producer for
maintenance and repair services. The producer has also implemented tags on their tools to
track their products when they are returned. Thanks to both of these practices, they can know
exactly how many times and how often a tool has been repaired.
A producer of transportation vehicles and trailers has developed a smartphone and tablet
application that drivers can use to locate service centres. The application uses geolocation
to show the driver the nearest partner firm where they can bring the vehicle in case they
experience a problem on the road. The driver gets the contact details of the service partner
and can make a call to ask for spare parts. The firm has around 350 service partners in Europe.
7.2.4 Warranty extension and rejection
The availability of condition monitoring and smart maintenance may enable an extension of
the warranty. For example, customers of a producer of vehicles for the construction industry
could opt for an extension of the warranty period from two to three years with the precondition
that they use the smart capabilities developed by the firm. The customer shall also commit to
a certain level of data exchange with the firm. In order to make an attractive offer, the firm
offers a package that includes a smart application, condition monitoring, and the extension of
the warranty. Due to the access to product information during the operation of the vehicle, the
producer can perform maintenance before failure occurs and ensure the financial viability of
the warranty extension.
The incorrect usage of a product could generate downtime or failure, and trigger a warranty
claim for repair services. In this respect, product data could help producers control for the
correct usage of the product and avoid or reject warranty claims, and thus, reduce the
need for repair services. A producer of plastic injection moulding tools uses sensors to
measure the pressure, temperature, and speed of the injection mould. The warranty offered
includes the production of a predefined number of parts at a specific speed, for instance, 1
million parts at 30 seconds per part. If the customer runs the machine at a faster rate, for
example, at 25 seconds per part, there would be a gain in efficiency, but the injection moulding
tool would not be able to cool down properly and would wear out faster. In the long-term, the
tool might get damaged before reaching the 1 million parts promised in the warranty, or it may
produce parts at a lower quality. Using sensors and collecting data about the use phase of the
machine allows the producer to ensure the quality of the final product and the correct usage of
the product. This, in turn, would ensure meeting the targets defined in the warranty and avoid
over-heating, wear, and unnecessary repairs.
Even in the case that a producer firm can analyse product lifetime data only after the product
has failed, they can still know how the user has operated the machine and decide whether to
accept or reject the reclamation and warranty claim. If the reclamation is related to errors in
the operation, the firm can charge for the required spare parts and repairs. For instance, a
producer of special vehicles can use sensors to measure the pressure executed by the
hydraulic cylinders of their machines. If the specification of the operation recommends 300
bars for a cylinder, but the customer operated it considerably beyond this threshold until the
cylinder breaks down, the provider can object the reclamation using product data and charge
the customer for the repair services.
7.2.5 Smart devices assisting technicians’ smart maintenance and repair
Firms may use devices such as tablets or smart glasses to facilitate the tasks of
technicians doing maintenance and repair. Firms may combine digital twins with tablets and
virtual reality headsets to display information about the product or channel recommendations
from experts to internal or external technicians in the field.
A manufacturer and provider of logistics solutions uses smart glasses to allow remote
communication between technicians and the service desk. Technicians at customer sites
can call the hotline, pull up schematics, take pictures, exchange data with the service desk, or
use the camera to livestream their environment. Technicians may not have deep expertise in
the field, but they can get help from the service desk to achieve their tasks. The service desk
might use a visualisation in real-time of a particular product component to guide technicians
through complex processes and give them maintenance or repair instructions. They might help
technicians accomplish their tasks by annotating pictures and sending them feedback. They
might also use the livestream to help them accomplish their tasks directly.
Reclamations are another scenario for the usage of smart glasses. When products have
a unique identifier, employees of the producer firm can scan a code and find all the information
about that product on the glasses. They can view maintenance schedules or error messages
and receive information from other systems. When employees receive damaged products from
suppliers, they can immediately scan them, make pictures, annotate vital information about the
problem and submit a claim using the glasses. They can use that data to ask for repairs or a
product exchange.
A provider of energy solutions uses tablets to support their technicians while they are on
duty on the road. The firm has created a solution that they call “mobile maintenance”.
Technicians can use their tablets to view maps with product geolocation, check diagrams and
documentation, execute orders, receive notifications, communicate with colleagues, review the
maintenance history, and remotely control products, among others. These possibilities expand
the concept of smart maintenance, increase the efficiency of the employees, and allow for a
longer product lifetime.
8.1 Goals and characterisation
Reuse is about taking a product or component back from a customer andafter a quality
inspection and sometimes minor refurbishments (e.g., polishing, minor repairs, repackaging)
delivering it to another or even the same customer for an additional use cycle. Over a product
lifetime, a product can have multiple use cycles. Reuse extends the lifetime of a product and
saves about 75 per cent of the energy embodied in a product.43 Reuse may allow firms to
reduce costs, attend to customer needs, and capture value by decoupling economic activity
from the consumption of resources.
Reuse can be operationalised with multiple good types, in various schemes, and with varying
service degrees of the business model:
Fast-cycling non-durable goods vs. slow-cycling durable goods: Reuse can be
applied to non-durable, fast-moving consumer goods (e.g., packaging, textiles) and
durable goods (e.g., vehicles). Regarding the former, the reuse strategy is particularly
important for replacing single-use products (e.g., disposable packaging, protective
wear in hospitals) with relatively more durable reusable goods (e.g., reusable glass
packaging; washable protective wear)—both in consumer and professional markets.
Internal and external smart components of product design differ between durable and
non-durable goods (see Chapter 2).
Isolated reuse for secondary markets vs. continuous reuse system: In its most
basic form, reuse happens in a rather isolated fashion when a user does not need a
product anymore (e.g., ceased use case, fashion obsolescence) and returns it to the
retailer or producer, or sells it directly in formal (e.g., B2B sharing platform) or informal
markets (e.g., flea market). Thereafter, the product gets into a second, or sometimes,
additional use cycle(s) with a new owner. Because each use cycle is usually relatively
long, the total number of use cycles within a product lifetime remains rather low.
Sometimes, financial incentives like deposits are provided to motivate users to return
the product to the retailer, producer, or related economic actor. The length of each use
cycle is not planned and fully depends on the user’s needs and behaviour. Therefore,
the reverse flow of goods is not steady. In contrast to these rather isolated and
uncoordinated reuse cycles, continuous reuse systems are characterised by a frequent
sequence of use cycles where each cycle is clearly demarcated (e.g., empty bottle or
packaging), and product handover and the related return are always linked to financial
43 Stahel (2010, p. 194)
incentives (e.g., deposit, system fee) or rules (e.g., use time frame). This results in a
high total number of reuse cycles within each products’ lifetime. As continuous reuse
systems manage a high number of cycles, smart enablers become more important.
Product vs. use/result-oriented business models: To operationalise isolated vs.
continuous reuse, the service business model plays a crucial role. Isolated reuse
usually happens within the context of conventional product-oriented business models.
In this transactional sales model, products are taken back from the current owner and
are then remarketed to a new customer by a double change of ownership”44.
Continuous reuse systems can also be run based on product-oriented business
models; solely relying on financial incentives such as deposits. However, relational
sales approaches based on use-oriented (e.g., rental, sharing) and result-oriented
business modelswhere ownership remains with the provider or “fleet operator”45
are more appropriate. Leasing, rental, or sharing contracts include definitions for each
use cycle (e.g., timeframe, return process) and related fines for non-compliance leading
to a coordinated reverse flow of goods. For such fleet operators, products become
assets, and it is in their best interest to maximise the number of use cycles and,
relatedly, extend the product lifetime. Use and result-oriented business models
increase utilisation of (durable) goods by reducing idle time and, consequently, drive
Reuse only works within the context of a reuse infrastructure or system. Hence, organisations
require a set of business processes that enable the circular flow of reusable products. For
example, after a reusable professional textile has been used at a hospital, a rental textile
provider could take the textile back, wash it, prepare it for another use cycle, and deliver it back
to the hospital. The same happens with glass bottles that are collected at the supermarket in
the B2C market and with other product categories such as tool rentals for both the B2C and
B2B markets. A rental business model of professional textiles would not be effective without
the extensive infrastructure that supports the reuse system.
Digital technologies can facilitate both reusable products and their related reuse infrastructure.
Smart enablers are particularly important in continuous reuse systems with elaborate
infrastructures. Firms can streamline reuse if they have timely information about the identity,
condition, location, and history of the product. Moreover, information from condition monitoring,
preventative maintenance, and other strategies introduced in the previous chapters can feed
the product passport with reuse-related data and enable smart reuse.
44 Stahel (2010, p. 222)
45 Stahel (2010) distinguishes OEM from non-OEM fleet operators.
8.2 Smart processes
8.2.1 Increasing transparency and optimising the reuse system
Firms may use digital technologies to increase the transparency of the reuse system. By
acquiring accurate information on the usage, condition, and location of their products, firms
may measure product circulation, inventory, losses, failures, and customer needs. For
instance, a lessor of professional textiles can measure the speed of circulation of their textiles.
Smart textiles tagged with RFID chips flow between the firm and their customers in a closed
loop. The firm uses antennas installed at different locations to read information about their
textiles. With this information, they can calculate how many textiles have not returned to the
firm and accurately establish losses and current inventory after a period of time. The firm can
also determine the condition of their products by storing information about the number of use
cycles and repairs a textile has gone through. Overall, this allows for an optimisation of the
reuse system and the internal processes associated with it. The firm may generate the
delivery orders based on textile stock, order additional stock based on arrival information, and
improve the overall efficiency of the process by having deep information on the circulation at
A producer and lessor of tools uses passive and active tracking systems to locate their
products. When using passive tracking, employees shall manually enter the current or last
known location of the tool in an application. Active tracking is based on Bluetooth technology
and the process runs automatically. The firm can collect this information while tools are at the
customer sites, when they are returned for maintenance and repair, or after a use cycle. This
information increases transparency on the products and enables a better reuse system.
Firms may also use product data to generate reports for the top management of the firm.
The quality management department of a lessor of smart textiles can generate monthly and
yearly reports containing information about the textiles in circulation, reclamations, deliveries,
washing cycles, number of damaged and discarded textiles, repairs, and textile losses, among
others. All this information can be sorted by product category, customer, or internal
department. The firm can reach high levels of transparency because the information can even
be obtained at the article level. Thereafter, top management can use the information for
decision-making on several domains such as future contracts, tendering, or order
8.2.2 Data-based feedback to help improve customerscare for the product
Firms may increase customer awareness by using product data and by giving them
recommendations on how to improve the whole reuse system. Digital enablers allow for
a continuous exchange of information between the smart product and the producer or service
provider. This information can facilitate early analyses of the actual use phase (see Chapter 6)
and the complete reuse system. In other words, using product data may help the firm to
understand what the customer actually does with the product. This involves knowing at what
times, temperature, environmental conditions, and extreme situations the product is being
used. After analysing the usage data, the firm can take a proactive role and try to influence
customer behaviour, and thereby, help improve customers’ care for the products in the reuse
system. For instance, a lessor of smart textiles has used product lifetime data to raise customer
awareness due to the misuse of rental textiles. Product data may reveal differences in the
inventory of arrivals and deliveries. RFID readers may detect towels in the rubbish containers
because they were used as cleaning cloths. In order to solve these issues, the firm has
provided training on inventory management to their customers, as well as hired personnel to
organise the inventory at the customer locations as part of the service package. Reducing
losses due to misuse at customer sites could bring cost savings and resource efficiencies for
both parties.
8.2.3 Smart product customisation by the producer or service provider
Customer requirements are very diverse, leading organisations to offer broad product
portfolios. A key challenge for reuse is matching used products to new (and sometimes the
same) customers. The broader the product variety, the higher the total number of products in
the reuse pool must be to match heterogeneous customer requirements at any given time.
Similarly, the reuse utilisation diminishes as many used products may not find related customer
demand temporarily. In response, firms may introduce smart product customisation. This term
refers to the combination of standardised hardware with software-based customisation
controlled by the producer or provider before it is delivered to the customer.46 This allows
firms to remarket the same product to customers with different requirements without making
changes to the hardware of the product. For example, a producer of cranes may use the same
standard engine for several product types. The digital components in the product can
electronically configure the lifting capacity, speed, and range before delivering the crane to the
customer. With software-based customisation accessible to the provider, the firm may
practically use the same crane to offer individualised service to different customers. If a fleet
operator has a customer that wants to rent a crane with a range of 10 meters, but they do not
have any in stock, they can use a longer crane and digitally reduce its maximum range to 10
meters for this specific user. Smart product customisation is particularly helpful in the context
of reuse and related services (e.g., leasing, rental) because the product pool can be
considerably decreased, whereas product reuse can be significantly increased.
46 This practice does not refer to dynamic customisation during the use phase.
9.1 Goals and characterisation
Remanufacturing is a complex process of returning a product “to at least its original
performance with a warranty that is equivalent or better than that of the newly manufactured
product47. For this reason, firms may create a special business unit for remanufacturing.
Due to the nature of remanufacturing, related operations are usually physically separated from
the original manufacturing processeseither in a separate area of the same plant or at an
entirely independent plant. As explained by an interviewee, the economic incentives and
related expertise developed by a new business unit can increase the identification rate of old
machines and facilitate the remanufacturing process. The interviewee also commented that
remanufacturing is different from the manufacturing of new machines because there is a
different working rhythm and a different way of thinking. Remanufacturing is highly manual and
more pragmatic. It requires an inspection of the product, and a negotiation with the customer
to understand their needs and how much money they are willing to invest. After the negotiation,
the business unit can define which parts need to be replaced and what the quality level of the
remanufactured product will be.
Remanufacturing processes can also be split across components, with some of the
components being processed through partnerships or outsourcing. In particular, firms may
outsource the remanufacturing of digital components to business partners. For instance, a
producer of heavy construction equipment remanufactures their equipment such as forklifts or
excavators. They take care of the mechanical parts internally, while the (external) business
partner upgrades the electronic and software components such as displays or control units.
As explained by the interviewee, both firms dedicate their efforts to their own core business
and share the remanufacturing process. One challenge for the business partner is the relatively
shorter lifetime of electronic components in comparison with mechanical parts. New electronic
components installed in the remanufactured construction equipment have to be compatible
with the old machine as well as with the remaining electronic components that are still
operating properly. For example, a new display has to be compatible with the old control unit
if the unit is not replaced.
To execute remanufacturing, firms may take used products back and resell them to the same
or a different customer. They may also offer the remanufacturing service as part of a rental or
pay-per-use business model. Products may be brought back to their initial quality standard or
remanufactured with higher specifications to achieve better performance. Visual inspections
are still relevant to the remanufacturing process, as they reveal key information about the
47 British Standards Institution (2009, p. 4)
actual condition of the product and their components. However, inspections can be supported
by information from condition monitoring technologies and product passports. Due to the
complexity of the remanufacturing process, product history information shall go beyond the
overall condition of the product and include the condition and history of the components.
9.2 Smart processes
9.2.1 Monitoring-based take-back decisions
Firms may use condition monitoring to enable decision-making on remanufacturing.
There are different alternatives to analyse the condition of the product before remanufacturing.
Firms may use traditional inspections, analyse vibration and lubrication parameters, or apply
condition monitoring to detect early damage. Early damage analysis implies a higher
probability of effective remanufacturing, lower operating costs, and longer product lifetimes.
A producer and remanufacturer of bearings uses sensors to enable online condition
monitoring. Using digital technologies, the firm can detect faults in advance and replace the
bearing before severe failure occurs. Bearings are then transported from the customer site to
a central location where they are remanufactured. In this sense, technology allows a reduction
in remanufacturing costs and increases the likelihood of remanufacturing. Bearings can be
remanufactured several times and therefore become considerably cheaper. This enables
attractive pricing and competitive advantage. In addition, the firm can use the data from the
bearing to generate additional sources of income by taking on additional services, such as the
overall maintenance of the bearings.
9.2.2 Product history-based inspection and remanufacturing decision
Firms may document all the changes made to the product in a product passport so that
remanufacturing is possible towards the end of the lifetime. For example, if a machine using a
particular engine stops working because the engine has failed, the firm would need all the
documentation of the machine and the engine before performing an upgrade.
So, you build the engine, send it out, then something may break, then you change something,
then you want both statesthe old state and the new stateand it goes on like that. And at
some point, you might want to recycle or remanufacture it, right, or you do a retrofit because
you replace something. You have to be careful . . . [and ask yourself], am I doing this? Is
somebody else doing this? Do I have the PLM data? But ideally you would get the data from the
PLM system and you would always know what the latest status is . . . So, even before I go to
my service assignment, I need the data from the PLM systemthe current documentation. This
means that someone may have replaced a motor in this machine once. Then I need to know
that maybe 5 years later I will have to replace a motor that no longer exists. What properties
must the motor have and what installation space do I have available? How can I mount it so that
the motor works, and where can I get a motor quickly? Namely, one that is quickly available and
corresponds to these values . . . Then I install it, this motor has new tasks, new maintenance
tasks, it looks different, it needs a different connection, it needs a change perhaps in the control
system, in the electrical connectionall this is documented again. (Service sales senior
A producer and remanufacturer of bearings combines several methods to store product
information at the customer site and after take-back. The firm uses condition monitoring,
visual inspections, and image documentation to determine the condition of the bearing as well
as to record product history on the passport. For example, damage points are marked and
registered after product take-back during the arrival inspection. This information enables the
analysis of the condition of the product and informs the decision on which remanufacturing
steps shall be taken.
Firms may use product history to improve decision-making during the entire
remanufacturing process. A producer of recycling machines may use the information in their
product passport to know the condition of the product before they re-buy the machine. The firm
can access information about used spare parts, machine hours, and even about the visual
condition of the machine if service personnel has performed a maintenance service. This
information can be used to estimate a buying price.
The firm may sell the remanufactured product to the same customer or to new ones in the
market. Before remanufacturing the machine, the firm may offer different price alternatives to
the customer to match their investment strategy. These different alternatives will signify a
different quality of the machine after remanufacturing.
A producer of plastic moulding machines can use the product history collected through sensors
to know how the customer has used the product and assess their current quality. If the product
has always been run within the specification, it is probably worth more than a product that has
constantly been run above the specification limits.
9.2.3 Data-based technological upgrading
Firms may adapt the existing product via technological upgrading to achieve higher
performance after measuring product data during the use phase. A producer of air
systems offers upgrading services to improve product performance. The firm may upgrade a
ventilator with new features to control the speed according to the customer’s needs. For
instance, the customer may have an old filtering system that always runs at 100 per cent
performance, but the analysis of product data indicates that the customer would only need
variable levels between 50 and 90 per cent. After the upgrade, the filtering system
automatically regulates the speed according to the performance needs. Such upgrading
requires an additional investment by the customer but achieves lower total cost of ownership
due to the reduced energy consumption and extension of the components’ lifetime.
10.1 Goals and characterisation
Recycling aims at extracting raw materials from products and components at the end of their
life cycle to use them in new products.48 In comparison with other circular strategies where the
energy and labour already embedded in products and components is maintained (e.g.,
remanufacturing), recycling involves the destruction of the integrity of the product, and
therefore, it is the least preferred circular strategy and it should only be applied when other
strategies are not viable anymore.49 Current recycling processes typically reduce the quality
and utility of the materials (i.e., downcycling) and require considerable energy input for
collection, sorting, and the actual recycling process.50 We focus on high-quality physical
recycling.51 This is based on closed-loop recycling (in comparison with open loops), where
secondary materials can actually replace virgin materials in producing the same goods or
goods with similar material performance requirements.52 Regarding the inputs to recycling, this
approach requires advanced efforts in material separation. For instance, the heavy vehicles of
a producer in the sample consist of several tonnes of steel which need to be separated from
the aluminium, electronic components, and other materials. Regarding outputs, high-quality
recyclates require quality assurance of their physical, chemical, biological, and toxicological
properties and the related transparency.53 In this report, we focus on how smart solutions
contribute to high-quality recycling. In particular, on how IoT-enabled closed product loops
ensure product return in relevant quantities and how digital product passports enable smart
recycling. Product passports provide information about the disassembly, incorporated
materials, and material characteristics of the product, and therefore, may improve high-quality
10.2 Smart processes
10.2.1 IoT-enabled closed-loop product cycles as a basis for recycling
Dedicated recycling practices can be established by the focal actor only when products return
in relevant quantities. Using IoT technologies to drive the reliability of closed product loops
whether based on rental business models or other financial incentives for reliable product
returnis not only relevant for smart reuse and smart remanufacturing but also the basis for
48 Benoy et al. (2014)
49 Den Hollander et al. (2017)
50 Circular Economy Initiative Deutschland (Ed.) (2021)
51 Schlummer et al. (2020); Umweltbundesamt (2020)
52 For the benefits of closed over open loop recycling see: Hansen and Revellio (2020, p. 1266)
53 Circular Economy Initiative Deutschland (Ed.) (2021)
smart recycling. We observed this particularly in the case of goods with short use cycles and
high circulation frequency (e.g., tools, textiles).
For instance, a producer and lessor of tools runs an IoT-enabled fleet management
business and recycles several hundred thousand of their used products. When old tools
cannot be repaired anymore, they are disassembled and recycled by certified business
A producer of industrial bearings uses condition monitoring to take their components
back from the customer before failure occurs and forwards the end-of-life component
to recycling if remanufacturing is not possible.
However, in most cases of industrial machinery, heavy vehicles, and components, the long
use cycles of these products (in many cases more than 10 or 20 years of use) make producers
operate rather “open” product cycles characterised by a low maturity of use of digital
technologies. These producers usually lose track of their products and do not perform
recycling themselves. In these cases, recovering end-of-life products to enable recycling is
rather practised accidentally when the customer returns to the producer or the service provider
to buy a new product. Then, the producer may take back the old product for a small fee and
outsource recycling to third parties. Thus, for product types operating in open product loops,
the customers’ individual decision whether and where to return the product plays an important
role in the focal actor’s recycling practices.
Although we showed insights into how firms operating closed product loops recycle parts of
their product portfolio, we find little evidence that digital technologies are used in the actual
recycling process.
10.2.2 Use of product passports to improve high-quality recycling
Lifetime information of components, parts, and materials stored on the product passport can
be used to support the recycling decision and plan the related processes (e.g., collection,
disassembly). Similar to remanufacturing, the final decision to recycle or even dispose of the
product relies not only on product data from the passport and related databases but also on
the expertise of the staff unfolding in visual inspections.
Before the recycling process starts, firms may remove the product instance from current
lifetime databases, PLM applications, and the product passport to avoid inventory
problems. Firms may use a unique identifier to facilitate the process by either scanning it or
entering it into an application. After the product has been removed from inventory, recycling
can be initiated.
We distinguish between direct feedback into R&D at companies responsible for the product
design and indirect feedback on product redesign via changes in the product portfolio and
related procurement decisions of third-party actors (e.g., fleet operators or retailers).54
11.1 Direct feedback to improve circular product design
Data from products collected from smart use, maintenance/repair, reuse,
remanufacturing, and recycling is the basis for producer’s R&D initiatives to improve the
circular design of newer product generations and to develop entirely new products and
services. This is exemplified by a producer of plastic injection moulding machines. The firm
collects product data during the use phase to understand under what conditions specific
components suffer excessive wear. This information is used to change the development of
next-generation components to extend their service life.
Accessing product data during the use phase has important implications for the further
development of the product, and firms may choose to collect usage data by default. For
instance, a producer of construction vehicles has included access to product data as a default
point in the service contract with their customers in exchange for benefits in terms of the
warranty extension.
Data generated during customer complaints and reclamations could be used to improve
future product design. First, firms may use the information from reclamations and customer
feedback to redesign their products. This information is typically generated by the after-sales
department. For example, a producer of engines uses the information for continuous product
development and makes new releases every year. Reclamations and customer feedback may
be stored on a passport for internal use.
So, you could also mention the customer complaint report. They will be stored in the IT system
and processed, and will somehow reveal a trend and a difficulty with the customer that will lead
to a product improvement. (Head of research and development)
And the third department that is now very significantly involved is the after-sales department,
which inevitably receives the most feedback on broken, worn components, where you say:
'Okay, that could have been recognised earlier'. We now have to react extremely quickly in the
after-sales department so that this machine is available again to the customer as quickly as
possible. It costs money if they have to interrupt production somewhere, if they have to add
something, and therefore, these departments are all very important. We have a platform where
all these defect reports flow in, where the distribution lists are set up in such a way that the users
54 Hansen and Revellio (2020, p. 1267)
know who has to read a script and who has to return a message. (Head of research and
Second, firms may collect product data from maintenance and repair activities. This is
illustrated by a producer of automation equipment. Service personnel uses an error logging
system to gather maintenance and repair information. Moreover, the firm involves the service
personnel in the development of newer generations because they can better understand this
So, we usually have maintenance partners, service partners, who support us, but the knowledge
about the problems, that goes naturally one-to-one into our development. With every new
product development, every new product generation, this was a very important piece of
information that was obtained. What were the problems and what were the components?
Components anyway, because this is done via our error system. Such errors are recorded
anyway. If we produce components ourselves, that is, the processors or the flat modules that
we manufacture, it goes right down to production, down to the batches that are blocked or
whatever. That is clear. Of course, there are also devices that are returned, repaired and sent
back again. Repair cases are all collected, and of course, analysed before we build a new
generation. (Head of innovation management)
Firms may create prototypes of their services using information from their R&D labs. For
instance, a manufacturer of plastic injection moulding machines has deliberately driven the
components of a product until end of life to obtain product lifetime data. These activities have
considerably improved the learning curve about product behaviour and have allowed them to
create a condition monitoring service.
Another alternative is to develop new services directly with the customer. Firms may
establish a business relationship with a specific customer to pilot a newly created service
business model. For instance, a manufacturer of plastics recycling machinery has started a
partnership with a customer to test a service business model. The manufacturer rents out the
machine to the customer and uses product data to improve the service for future sales. They
are also focused on understanding the effects of the service business model for the firm before
scaling up the solution.
11.2 Indirect feedback to improve circular product design
Firms may use data obtained from both the use phase and circular strategies to improve
product procurement decisions and enable proactive supplier management. For
example, a rental textile operator uses lifetime data from tagged textiles to revise procurement
decisions and give feedback to their suppliers. With these actions, the firm aims at procuring
more durable products (e.g., increased amount of cotton or other fibres in the textiles). The
suppliers, in turn, can use the feedback to make changes to their product design.
12.1 Role of the service business model for integrating circular strategies
Successfully advancing circularity does not usually come from a single (smart) circular
strategy. Successful firms combine strategies in the sense of circular configurations to profit
from their integration and coordination.55 Whether a firm is able to address multiple smart
circular strategies and generate synergies mainly depends on the scope of the service level
agreement with the customer (i.e., basic service levels may only include smart use, while
advanced levels cover maintenance and guarantee product uptime). Although service level
agreements are company and customer-specific, and therefore, too manifold to look at in
detail, we find that the fundamental differences in scope of various service level
agreements are linked to the service business model underlying the contractual
For instance, a producer of metal processing equipment offers maintenance services to their
customers in addition to product sales. The firm offers simple maintenance contracts up to all-
inclusive packages that include spare parts and annual safety inspections. Similarly, a
manufacturer of construction vehicles offers three service levels for their smart use,
maintenance, and repair services. Both these cases reflect the limitations of a business model
based on product sales. The change in ownership of the product hinders the development of
further services. In contrast, a manufacturer of air systems offers an operator model where
customers only pay for the compressed air they require. Although this business model may
imply higher investments for the focal firm, the firm may extend the scope of the service
portfolio towards all circular strategies.
As can be seen above and in previous sections, our results suggest an intensification of the
service offering among the firms under study. In order to satisfy diverse customer needs and
better manage products across the entire life cycle, pioneering firms are combining products
and services, similar to what has been observed more broadly in sustainability-oriented
innovations.57 We distinguish three main types of service business models with intensifying
service degree which are relevant for an IoT-enabled circular economy:58
1. product-oriented (product sales with additional services),
2. use-oriented (e.g., rentals), and
3. result-oriented (integrated solutions such as performance contracting).
55 Blomsma and Tennant (2020); see also broader "loop configurations" by Hansen and Revellio (2020)
56 Circular Economy Initiative Deutschland (Ed.) (2021)
57 Hansen et al. (2009)
58 For the adoption of these types in the context of the circular economy and digitalisation see: Alcayaga et al.
(2019); Circular Economy Initiative Deutschland (Ed.) (2021); Tukker (2004)
Figure 9: Service degree of business models and smart circularity.
12.1.1 Product-oriented business model
As can be seen in Figure 9, the service degree of the business model influences the potential
for smart circularity. Firms with a product-oriented service business model may sell their
products and offer additional services to their customers for a fee. These business models are
usually limited to achieving satisfactory levels of smart circularity. They can pursue smart
use services such as condition monitoring, product benchmarking, and process optimisation,
but they struggle to expand their service offer because their customers have data security
concerns, lack skilled personnel to handle smart services, or have a rather conservative
attitude towards the adoption of new technologies. These barriers are extended to smart
maintenance and repair services. Some customers may have technological literacy, but they
may not share their data and would prefer to execute maintenance by themselves. Circular
strategies such as reuse or remanufacturing may be partially implemented. These limitations
are related to the transactional style of their sales operations and the characteristics of the
market. In many cases, firms do not know what happens with their products after they have
been sold to the customer. Even if they advance towards digital services and condition
monitoring, they may not be able to influence the customer’s reinvestment decision. Indeed,
as seen in this report, some firms with this type of business model offer reconditioning or
remanufacturing services, but these activities are not executed systematically and are not a
part of their core business. Hence, when looking at the full scope of circular strategies, basic
product-oriented service business models only allow for a limited extension of the service
Smart Circular Strategy
Service Degree of Business Model
Firm’s achievement of
smart circularity:
Synergies and information
transfer between strategies:
C: Product-oriented business
model hinders synergies and
information transfer.
D: Although reman exists as a
product sale, low integration in
the business models hinders
these activities.
E: Partnerships with external
recyclers allows recycling but
low synergies and low
information transfer.
AA<B: Higher proximity to the
customer and access to the
product in “B” implies superior
achievement of smart
maintenance and repair.
12.1.2 Use-oriented business model
Firms with a use-oriented service business model may become fleet managers in a reuse
system based on renting, leasing, or sharing their products. They achieve an intermediate
level of smart circularity because they can fully integrate services such as smart monitoring,
location tracking, or preventative maintenance to their value proposition. This integration is
possible because they assure closed product loops by keeping the ownership of their products.
From a customer’s perspective, these solutions represent outsourcing of administrative tasks
related to product ownership (e.g., continued procurement, inventory management, managing
maintenance processes), making it easier for them to focus on their core business. In the case
of textiles or tool rentals, service providers take back their products to close a use cycle and
achieve reuse (or even remanufacturing). This implies higher levels of resource efficiency,
longer product lifetimes, and better recovery of materials at end of life. For instance, a lessor
of tools is able to collect their products when they are not fit for rental anymore. These are then
sent to external recyclers, and materials are recovered for the production of new tools.
12.1.3 Result-oriented business model
With result-oriented service business models, service providers can achieve superior
levels of smart circularity. Full-service business models