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A novel I4.0-enabled engineering method and its evaluation

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Frederick Prinz
Frederick Prinz
Frederick Prinz
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Michael Schoeffler
Michael Schoeffler
Michael Schoeffler
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Armin Lechler at University of Stuttgart
Armin Lechler
Alexander Verl
Alexander Verl
Alexander Verl
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Abstract

Recent trends show that products are becoming more complex and multi-variant. Therefore, future production systems need to become more advanced in terms of reconfigurability, flexibility, and transformability. To achieve these advancements, future systems must be highly changeable and support plug-and-produce approaches. The majority of today’s engineering methods focus on static workflows based on predefined assets and setups. As a consequence, changes in the production system come with high costs, especially during production process execution. Therefore, new engineering methods are required which are explicitly designed for highly changeable production systems. To contribute towards fully changeable production systems, an I4.0 framework is proposed that covers the entire engineering process. The focus is set on presenting a graphical I4.0-enabled engineering method that enables dynamic workflows with varying assets and setups. Moreover, in order to evaluate the method, a user study was conducted, in which participants were asked to solve multiple engineering tasks by utilizing the presented I4.0-enabled method as well as a conventional approach. The results indicated that the proposed I4.0-enabled engineering method significantly outperformed the conventional method in terms of required engineering times and subjective ratings.
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The International Journal of Advanced Manufacturing Technology (2019) 102:2245–2263
https://doi.org/10.1007/s00170-019-03382-1
ORIGINAL ARTICLE
A novel I4.0-enabled engineering method and its evaluation
Frederick Prinz1
·Michael Schoeffler1
·Armin Lechler2
·Alexander Verl2
Received: 30 August 2018 / Accepted: 21 January 2019 / Published online: 30 January 2019
©Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract
Recent trends show that products are becoming more complex and multi-variant. Therefore, future production systems need
to become more advanced in terms of reconfigurability, flexibility, and transformability. To achieve these advancements,
future systems must be highly changeable and support plug-and-produce approaches. The majority of today’s engineering
methods focus on static workflows based on predefined assets and setups. As a consequence, changes in the production
system come with high costs, especially during production process execution. Therefore, new engineering methods are
required which are explicitly designed for highly changeable production systems. To contribute towards fully changeable
production systems, an I4.0 framework is proposed that covers the entire engineering process. The focus is set on presenting
a graphical I4.0-enabled engineering method that enables dynamic workflows with varying assets and setups. Moreover, in
order to evaluate the method, a user study was conducted, in which participants were asked to solve multiple engineering
tasks by utilizing the presented I4.0-enabled method as well as a conventional approach. The results indicated that
the proposed I4.0-enabled engineering method significantly outperformed the conventional method in terms of required
engineering times and subjective ratings.
Keywords Industry 4.0 ·Industrial internet of things ·Changeability ·Framework ·Workflow engineering ·Business
process modeling and notation ·User study
1 Introduction
Significant impacts on current production systems are
expected by advancements related to the so-called fourth
industrial revolution, often denoted by the terms Industry
4.0 (I4.0) and Industrial Internet of Things (IIoT) [1].
Industry 4.0 is a term based on an initiative of the German
government [2–4], while IIoT is an enabling technology for
Industry 4.0 [1]. IIoT is a sub-term of the more general
term Internet of Things (IoT) [5], which describes the
networking of smart components to exchange and aggregate
a huge amount of data [6,7]. IoT systems are to be found
in almost any domain, such as autonomous driving, smart
Frederick Prinz
frederick.prinz@de.bosch.com
1Corporate Sector Research and Advance Engineering,
Robert Bosch GmbH, 71272 Renningen, Germany
2Institute for Control Engineering of Machine Tools and
Manufacturing Units (ISW), University of Stuttgart,
70174 Stuttgart, Germany
cities, and industrial environments [8,9]. Moreover, they are
also referred to as cyber-physical systems (CPS) [10,11].
Respectively, cyber-physical production systems (CPPS)
refer to systems that focus on industrial applications [12].
Both I4.0 and IIoT share a similar vision of future CPPS
[1]. One aspect of this vision is to cope the new challenges
related to product versatility and market volatility [13]. In
particular, future production systems are expected to be
more advanced in terms of “mass customization” [14–16].
Mass customization targets at low costs per product unit
combined with a maximum of flexibility for individual
customization. Moreover, the overall changeability of
future production systems is presumed to be significantly
increased which will, e. g., further reduce ramp-up times for
new products [17–19]. Assets of future production systems
(such as sensors, devices, machines, or stations) can be
rearranged for new products during runtime or at least
within short retooling time frames.
Unfortunately, the majority of today’s engineering
methods do not support such a high degree of changeability.
These engineering methods rely on static arrangements with
predefined assets, i. e., assets including their input/output
signals must be preconfigured within the engineering
method. Moreover, assets cannot be added or removed from
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... The smart manufacturing system represents the epitome of digital transformations on the factory floor, emphasizing integration across manufacturing assets, communication, resilience, and transparency of systems (Bhatia & Diaz-Elsayed 2023;Prinz et al. 2019). Transparency is a critical Content courtesy of Springer Nature, terms of use apply. ...
... For example, transparency in smart manufacturing systems allows for real-time visibility of products, enabling the identification of defective parts or anomalies that can be traced, leading to necessary corrective actions as required (Abualsauod 2023). One application of transparency is the monitoring of process parameters using IoT-based sensors and the estimation of the overall equipment effectiveness (OEE) in real-time (Li 2018;Moktadir et al. 2018;Prinz et al. 2019). System transparency facilitates the real-time acquisition of data, often referred to as big data, which is then subjected to analytics algorithms to derive actionable insights (Luo et al. 2019;Raut et al. 2021). ...
... System transparency facilitates the real-time acquisition of data, often referred to as big data, which is then subjected to analytics algorithms to derive actionable insights (Luo et al. 2019;Raut et al. 2021). The choice of analytics depends on the type of data, application, and observations, emphasizing the importance of integration and ubiquitous connectivity in achieving real-time monitoring, traceability of parameters, and transparency in business operations (Kim et al. 2015;Prinz et al. 2019;Sung 2018). ...
Evaluating the transparency capability of smart manufacturing systems
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Transparency encompasses the potential to monitor operations instantaneously so that required corrective actions can be taken as needed. Transparency entails the ability to track processes in real-time, enhance the visibility of the operations, and require a seamless network for improved communication for smart manufacturing systems. However, there is a lack of proper metrics to assess the transparency of smart manufacturing environments. This paper contributes to the assessment of transparency by proposing a metric for its evaluation. In doing so, we found that the assessment of transparency takes the quantification of traceability into account. Hence, a step-in assessment is conducted by initially developing a mathematical model for traceability, followed by a model for transparency. The model is validated by analysing the sensitivity and applicability through simulation-based experimentation. The results demonstrate the level of traceability followed by transparency with the implementation of smart manufacturing systems. A point of inflexion that determines the variability in the offerings of traceability at a given set of inputs was found. This is one of the few works that focus on the development of a metric for quantifying transparency through the traceability of smart manufacturing systems. Furthermore, it investigates the behaviour by analyzing the sensitivity of the model through simulation-based approaches, which is a unique addition to the realm of the smart manufacturing literature. Managers can refer to this study's findings to design the deployment of smart manufacturing systems with informative trade-offs to maintain their required traceability and transparency capabilities.
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... Smart manufacturing is characterized by the integration across manufacturing assets, integration, communication, interoperability, and transparent systems (Kusiak 2019;Prinz et al. 2019). The integration leads to real-time monitoring that results in identifying an anomaly during risk occurrence for resilient control. ...
... For instance, the process parameters of the machines in monitored in real-time and under circumstances of uncertainties, the system is designed to be reactive for robustness of the performance resulting in resilience (X. Li et al. 2017;Moktadir et al. 2018;Prinz et al. 2019). The integration and seamless connectivity effect the systems being prone to viruses and malware, resulting in cyberthreat (Aazam, Zeadally, and Harras 2018;Kamble, Gunasekaran, and Sharma 2018). ...
... The significance of analytics algorithms is to understand the type of any risk that has happened, assess the risks for its severity, and suggest corrective actions for its neutralization (Kiel, Arnold, and Voigt 2017;Muhuri, Shukla, and Abraham 2019). The descriptive analytics shows the type of risks and the severity potential, the predictive analytics predicts the occurrence of the risks based on historical observations and the prescriptive analytics suggests the necessary corrective actions that are taken to manage the risk of the system (Kim, Denno, and Jones 2015;Prinz et al. 2019;Sung 2018). During the breakdown of the uncertainty such as covid 19, the organizations deploying smart manufacturing technologies managed to withstand the pandemic by facilitating on-time delivery and flexibility of production (Fatorachian and Kazemi 2018;Uva et al. 2018). ...
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... A challenge of manufacturing today is adapting to an increasingly fluctuating environment and diverse changes to meet the demands of the market. Product life cycles are getting shorter while production batch sizes are getting smaller with dynamic product variants associated with increasing complexity, which is challenging the traditional production systems (Benabdellah et al., 2019;Kuhnle et al., 2021;Ma et al., 2017;Prinz et al., 2019;Windt et al., 2008;Zhu et al., 2015). To manage these dynamics, the industrial concept of Industry 4.0 has come about and has been accepted in both research and industry, a trend linked to digitalization and smart systems that could enable factories to achieve higher production variety with reduced downtimes while improving yield, quality, safety, and decreasing cost and energy consumption (García-Magro & Soriano-Pinar, 2019;Järvenpää et al., 2019;Napoleone et al., 2020;Oztemel & Gursev, 2020;Park & Tran, 2014). ...
... While Haber and Fargnoli (2019) applied the Kano model to prioritize and classify customer requirements into four different categories-must-be, one-dimensional, attractive, indifferent-for the segmentation of customer value propositions, Wang et al. (2018) parametrically linked the customer's emotional responses-physical and psychological-to the properties and characteristics of a product and/or service. If these methods focus on a particular process in design (requirement elicitation converted into engineering characteristics), Prinz et al. (2019) highlighted the use of BPMN to represent workflows-a graphical modelling language for all kinds of business processes. The BPMN is useful for examining a graphical description of design processes to different levels of granularity and discovering inconsistencies and/or differences in sequential steps, conflicting names, or acronyms, to name a few. ...
... The second group is the engineering objectives of design (RR1) that are converted into key performance indicators to quantify the effectiveness of the proposed models or frameworks. Around 23% of the case studies indicate that their proposed solutions achieve the engineering objectives: avoidance of ergonomic risks (Caputo et al., 2019;Ceccacci et al., 2019), improvement of productivity and simultaneously biomechanical workloads (Gualtieri et al., 2020;Wojtynek et al., 2019), production performance in terms of quality and engineering time (Pacaux-Lemoine et al., 2017;Prinz et al., 2019). Furthermore, Wu et al. (2013) proposed a multi-function and modular method for design focusing on human anthropometrics-the branch of ergonomics that deals with measurements of the physical characteristics of human beings (Pheasant, 1990)-and extending products' service life towards sustainability. ...
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The transition to industry 4.0 has impacted factories, but it also affects the entire value chain. In this sense, human-centred factors play a core role in transitioning to sustainable manufacturing processes and consumption. The awareness of human roles in Industry 4.0 is increasing, as evidenced by active work in developing methods, exploring influencing factors, and proving the effectiveness of design oriented to humans. However, numerous studies have been brought into existence but then disconnected from other studies. As a consequence, these studies in industry and research alike are not regularly adopted, and the network of studies is seemingly broad and expands without forming a coherent structure. This study is a unique attempt to bridge the gap through the literature characteristics and lessons learnt derived from a collection of case studies regarding human-centred design (HCD) in the context of Industry 4.0. This objective is achieved by a well-rounded systematic literature review whose special unit of analysis is given to the case studies, delivering contributions in three ways: (1) providing an insight into how the literature has evolved through the cross-disciplinary lens; (2) identifying what research themes associated with design methods are emerging in the field; (3) and setting the research agenda in the context of HCD in Industry 4.0, taking into account the lessons learnt, as uncovered by the in-depth review of case studies.
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... The various indicators to evaluate hard measures are assessed next. The machine reconfiguration deals with removing/adding components from and to the machine to enable them to perform different operations (Mourtzis et al. 2019;Prinz et al. 2019;Gumasta et al. 2011) (Gumasta et al. 2011;Mittal et al. 2019). It is essential to make the devices present in the manufacturing system adaptable enough to perform the various task at a limited investment (Garbie, Parsaei, and Leep 2008). ...
... The reachability measures the capability of the system to detect any observation of the processes (Brad, Murar, and Brad 2018;Prinz et al. 2019;Ghobakhloo 2018). R is determined through the number of interactions the operator needs to perform to trace the desired observation of the processes. ...
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The deployment of smart manufacturing technologies like communication, computing, sensors, cyber-physical systems, simulation, and data sciences has enabled the operational transformations of the factory ecosystem. Therefore, it is essential to monitor the alterations commenced by the business due to smart manufacturing implementation. Smart Manufacturing Performance Measures (SMPMs) are used to evaluate operational transformations realized through digitalization of systems. However, there is no reported work focusing on the quantification of SMPMs. This study addresses the research gap by defining potential indicators to quantify SMPMs referred to as smart manufacturing performance indicators (SMPIs) identified through literature review methodology. The SMPIs are discussed in detail besides their mathematical expression advocated through in-depth literature study. Further, a conceptual framework for decision-making in smart manufacturing environment based on SMPIs is proposed. The conceptual framework provides guidelines to plan and select the preferred focused manufacturing output and the relevant set of SMPIs contributing to the outputs for expediting effective smart manufacturing implementation. The research findings are beneficial for the managers and consultants to gauge the potential structure of measurement that can be used to evaluate smart manufacturing systems' performance. ARTICLE HISTORY
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... This tier can also be referred to as the edge tier, where preprocessing of data takes place before transfer. The layer is responsible for initiating reliable, efficient and ubiquitous communication between the server and the host through communication protocol (Liu et al., 2018a;Prinz et al., 2019). The data transfer occurs between the two servers by identifying the IP-specific host and transmitting the data using transmission control protocol (TCP). ...
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Purpose The advent of Internet of Things, cloud computing and advanced computing has endowed smart manufacturing environments with resilience, reconfigurability and intelligence, resulting in the emergence of novel capabilities. These capabilities have significantly reshaped the manufacturing ecosystem, enabling it to effectively navigate uncertainties. The purpose of this study is to assess the operational transformations resulting from the implementation of smart manufacturing, which distinguish it from conventional systems. Design/methodology/approach A list of qualitative and quantitative smart manufacturing performance metrics (SMPMs) are initially suggested and categorized into strategic, tactical and operational levels. The SMPMs resemble the capabilities of smart manufacturing systems to manage disruptions due to uncertainties. Then, industry and academia experts validate the SMPMs through the utilization of the Delphi method, enabling the ranking of the SMPMs. Findings The proposition of the SMPMs serves as a metric to assess the digital transformation capabilities of smart manufacturing systems. In addition, the ranking of the proposed SMPMs shows a degree of relevance of the measures in smart manufacturing deployment and managing the disruptions caused due to the COVID-19 pandemic Research limitations/implications The findings benefit managers, consultants, policymakers and researchers in making appropriate decisions for deploying and operationalizing smart manufacturing systems by focusing on critical SMPMs. Originality/value The research provides a metric to assess the operational transformations during the deployment of smart manufacturing systems. Also, it states the role of the metric in managing the potential disruptions that can alter the performance of the business due to the COVID-19 pandemic.
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... Koroniotis et al. (2020) mostraram a incorporação de sistemas de IoT aos processos de negócios de aeroporto, chamados "aeroportos inteligentes" por meio de serviços habilitados por sensores e sistemas IoT. Prinz et al. (2019) abordaram o avanço no mercado na implementação de processos para transformação e inovação na indústria, onde novos métodos de engenharia são necessários e necessitam ser explicitamente projetados para sistemas de produção altamente mutáveis. Mazzola, Kapahnke e Klusch, (2018) aplicaram uma ferramenta intitulada ODERU (Optimization tool for DEsign and RUntime) em duas instituições e obtiveram resultados satisfatórios. ...
Perspectivas das práticas de gestão de processos de negócio no contexto das tecnologias da Indústria 4.0: uma revisão sistemática da literatura
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p>Diversos setores da economia sejam públicos ou privados têm obtido sucesso na aplicação de métodos integrados para a melhoria de seus processos, a partir da gestão de processos de negócio associada às novas tecnologias da Indústria 4.0. Tal fato tem despertado interesse de profissionais e pesquisadores que buscam na literatura científica abordagens sob diferentes aspectos relacionadas a estes temas. Assim, o objetivo deste artigo é apresentar uma revisão sistemática da literatura que responda a seguinte questão de pesquisa: “Quais as práticas de BPM são aplicadas no contexto das tecnologias da Indústria 4.0 no segmento industrial?”. As análises efetuadas em vinte e seis estudos apresentaram as tendências atuais de pesquisas relacionadas ao tema abordado, assim como os principais periódicos que publicam estas pesquisas. Os resultados apontaram uma relação das práticas de BPM ao tipo de tecnologia da Indústria 4.0, assim como o segmento industrial de aplicação. A partir dos resultados obtidos foi possível identificar uma forte tendência de pesquisa associada à Análise e Modelagem de Processo a partir da utilização da Internet das Coisas (IoT).</p
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... Already in 1989, Davis introduced this trend, referring to it as a production strategy focused on the broad provision of personalized products and services and naming it "mass customization" (MC) (Davis 1989). However, the fulfilment of such a paradigm requires the definition of new production concepts in order to achieve the requested increased level of flexibility, reconfigurability, and performance (Dashchenko 2006; Many scientists have identified the fourth industrial revolution, known as Industry 4.0 (I4.0), as a response to these new challenges Brad et al. 2018;Prinz et al. 2019).In the I4.0 context, information technology (IT) merges with operations technology (OT) to make possible the achievement of the expected performance targets in a highly customized market scenario (Grassi et al. 2020a;Thames and Schaefer 2016). This link is made possible thanks to the main innovations introduced by Industry 4.0, identifiable in the concepts of cyber-physical systems (CPS) and the internet of things (IoT) (Jeon et al. 2020;Liu and Xu 2017). ...
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Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors.
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... The cluster "BUSINESS-PROCESS-MODELLING-AND-NOTATION" (Figure 6) is also a motor theme with high density and strong centrality. Its appearance is justified by works in the context of intelligent manufacturing that use BPMN or extensions of the BPMN language to model production processes (Mazzola et al., 2018;Prinz et al., 2019). The theme "COMPUTER-SOFTWARE" has strong centrality and density, and the related studies focus on modeling and managing knowledge and information about manufacturing processes (Yang et al., 2018;Zhou et al., 2017) to improve the efficiency and dynamism of organizational processes. ...
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Purpose The purpose of this paper is to identify the relationships between process modeling and Industry 4.0, the strategic themes and the most used process modeling language in smart factories. The study also presents the growth of the field of study worldwide, the perspectives, main challenges, trends and suggestions for future works. Design/methodology/approach To do this, a science mapping was performed using the software SciMAT, supported by VOS viewer. Findings The results show that the Business Process Model and Notation (BPMN), Unified Modelling Language (UML) and Petri Net are the most relevant languages to smart manufacturing. The authors also highlighted the need to develop new languages or extensions capable of representing the dynamism, interoperability and multiple technologies of smart factories. Originality/value It was possible to identify the most used process modeling languages in smart environments and understand how these languages assist control and manage smart processes. Besides, the authors highlighted challenges, new perspectives and the need for future works in the field.
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Unlocking Digital Transformation: How Portuguese Companies View Industry 4.0 Benefits, Barriers, and Success Factors
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Industrial Internet of Things: Cybermanufacturing Systems
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Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments
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Today´s factory involves more services and customisation. A paradigm shift is towards " Industry 4.0 " (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment.
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