Digital supply chain management and technology to enhance resilience by building and
using end-to-end visibility during the COVID-19 pandemic
Berlin School of Economics and Law
Department of Business and Economics
Supply Chain and Operations Management Group
10825 Berlin, Germany
Phone: +49 30 85789155
In practice, an increased interest into end-to-end visibility as a future-oriented driver and capa-
bility of resilient supply chains can be observed. However, the research in this area is in its
infancy. Even less is understood about resilience and the potentials of a digital supply chain in
pandemic settings. Based on an analysis of the relevant literature supplemented with the multi-
ple case studies constructed with the use of primary data, we build a framework that could be
instructive for supply chain managers seeking to manage resilience during pandemic disrup-
tions and using digital technology. Our main methodological contributions are unlocking the
value and potentials of end-to-end supply chain visibility for resilience management in the face
of pandemic disruptions and proposing an associated design and implementation framework
containing multiple dimensions—management, organizational, and technological. The out-
comes of our study offer a conceptual guideline concerning the potentials and implementation
of end-to-end visibility in the management of supply chain resilience.
Keywords: Digitization; Decision support systems; Supply Chain Integration; Manufacturing
supply chain; Digital Economy
Supply chains are exposed to disruptions, both positive ones driven by technological
advances and negative ones triggered by natural and human-made disasters (Dubey et al. 2019,
Bier et al. 2020, Dolgui and Ivanov 2020, Lohmer et al. 2020). The 21st century has already
seen a technological revolution (i.e., Industry 4.0) and a global pandemic (i.e., COVID-19).
However, modern supply chains were designed in an era of lean management and globalization,
and they now face the challenge of adapting to these revolutionary trends (Dolgui et al. 2020b).
These supply chains are predominantly static in their structures and lack resilience and adapta-
bility (Ivanov 2021d, Sodhi et al. 2021). Nevertheless, the transformations have begun and re-
quire thorough examination to help shape future supply chains. In light of the COVID-19 pan-
demic, researchers and practitioners have become increasingly interested in the value of digital
technologies for supply chain end-to-end visibility and its potential uses to increase resilience
(Ivanov et al. 2020, Wamba and Queiroz 2020, Winkelhaus and Grosse 2020, Dubey et al.
2021, Sheng et al. 2021). Visibility can be seen as both a capability and an outcome. In this
study, we define end-to-end supply chain visibility from the capability perspective as the ability
to represent a physical supply chain in a digital space with all relevant data that can be col-
lected, processed, updated and accessed in real-time to support planning, monitoring, and con-
trol decision-making. As an outcome, end-to-end supply chain visibility is materialized as a
digital twin of a physical supply chain.
The COVID-19 pandemic has changed the operational conditions of many firms and
supply chains on an unprecedented scale (Queiroz et al. 2020, Ruel et al. 2021). Firms have
been forced to learn how to operate in a highly unstable and unpredictable environment (Mehro-
tra et al. 2020, Singh et al., 2020, Paul and Chowdhury, 2021). During the pandemic, companies
have dealt extensively with the concept of resilience, which has become one of the central sup-
ply chain management perspectives. This raises the question of how these lessons from the
pandemic can be used in post-COVID-19 supply chain management. Little is known about sup-
ply chain resilience to pandemics, and some recent research has hypothesized the key role of
digital technology in creating new knowledge in this area (Arlinghaus et al. 2019, Das et al.
2019, Hofmann and Langner 2020, Doetzer and Pflaum 2021, Ivanov et al. 2021b).
Christopher and Lee (2004) have pointed to visibility as one of the key determinants in
managing supply chain risks. The real implementation of supply chain visibility began with the
digitalization of supply chains. One of the major results of the state-of-the-art digital transfor-
mation has been the end-to-end supply chain visibility that has been established in the leading
technological corporations. Indeed, the existing research has developed some strong arguments
in favor of end-to-end visibility as a future-oriented driver and capability of resilient supply
chains (Papadopoulos et al. 2017, Panetto et al. 2019, Queiroz et al. 2019, Sharma et al. 2020,
Wamba et al. 2020, Sahoo et al. 2021). However, research in supply chain visibility under pan-
demic settings is still in its infancy.
Despite the remarkable progress in the last few years in theory and practice, considera-
ble ambiguity still exists about the value of end-to-end supply chain visibility for resilience.
Further research is therefore needed to uncover the requirements on data quality needed for the
effective utilization of end-to-end visibility in resilience management. Two challenges for de-
veloping a timely and efficient design and deployment of recovery policies are uncertainty
about the disruption duration and incomplete data about supply chain parameters such as avail-
able inventory and capacities after the disruption. Since it is rather unlikely to achieve a 100%
data accuracy due to both human-made errors in entering data into information systems and
uncertainty of some data (e.g., due to stochastic nature of some events), it becomes crucial to
understand the levels of data inaccuracy and uncertainty that can be allowed for obtaining reli-
able modeling results. How can businesses enhance optimization and simulation methods for
resilience modeling through data analytics? How can businesses use resilience analytics for the
mitigation of disruption impacts and recovery?
This paper is motivated by these open research questions. We contribute to literature by
examining the role of visibility in enhancing preparedness and recovery resilience capacities at
the pandemic times. Based on an in-depth literature analysis supplemented with multiple case
studies, we build a framework of end-to-end supply chain visibility for resilience management
during pandemic disruptions. Our framework is composed of management, organizational, and
technological dimensions. We also propose implementation steps for this framework that can
be used by supply chain managers seeking to manage resilience during pandemic disruptions
using digital technology. In particular, the framework proposed can be useful for design and
coordination of emergency deployment responses, efficient and effective capacity scalability,
and cross-sectoral collaboration by substituting some resources in one sector with capacities in
other sectors (e.g., a collaboration between healthcare and commercial supply chains).
The remainder of this paper is organized as follows: Section 2 presents our literature
review. In Section 3, case-studies are presented. Section 4 describes the conceptual frameworks
we develop. Section 5 outlines the implementation of these frameworks in practice. And finally,
we conclude the paper in Section 6 by summarizing its major outcomes and outlining a future
2. Literature analysis
We build on and contribute to four research streams, i.e., digital supply chain technol-
ogy, supply chain visibility, supply chain resilience, and pandemic disruptions in supply chains.
We organize our literature analysis accordingly.
2.1. Digital supply chain technology and visibility for resilience management
Digitalization and resilience are tightly connected with each other (Sheffi et al. 2015,
Ivanov and Dolgui 2020b, Ralston and Blackhurst 2020). Despite an increased interest in the
design and utilization of end-to-end visibility by means of data analytics, digital supply chain
twins, digital platforms, blockchain, and smart contracting (Frazzon et al. 2018, Queiroz et al.
2019, Brintrup et al. 2020, Roeck et al. 2020), research on the value of visibility in managing
supply chain resilience is in its infancy (Gu et al. 2021, Ivanov and Dolgui 2021, Zouari et al.
If businesses are to design end-to-end supply chain visibility that will increase supply
chain resilience, there is an acute need for rethinking and reinventing supply chain design and
management from the perspective of data-driven, reconfigurable, and viable networks in the
context of two particular transformation drivers—digital technology and severe pandemic-like
disruptions (Gunasekaran et al. 2015, Hosseini et al. 2019, Hosseini et al. 2020, Ivanov 2020b,
Ivanov and Dolgui 2020a). In recent years, supply chains experienced technological transfor-
mations that require highly flexible and adaptable supply networks with structural variety and
multifunctional processes (Kusiak 2020, Roeck et al. 2020, Ivanov et al. 2021). As a result,
digital technology plays an increasingly important role in transformations in supply chains (Cai
et al. 2020).
Procurement, manufacturing shop floors, promotional actions in omnichannel models,
routing optimization, real-time traffic operation monitoring, and proactive safety management
are among the recent application areas of data analytics and artificial intelligence in supply
chain and operations management (Choi and Lambert 2017, Choi et al. 2018). Papadopoulos et
al. (2017) showed how data analytics can be applied to supply chain risk management and dis-
aster resistance. Simchi-Levi et al. (2015) described a supply chain risk management system in
the automotive sector based on a framework that integrates risk databases, a quantitative risk
exposure model, and an output visualization tool. The data sources for risk analysis include the
material requirements planning system, the purchasing database, and sales volume planning
information based on a supply chain mapping methodology (Gusikhin and Klampfl 2012).
In summary, this literature analysis has indicated that the following digital technologies
have been used to establish end-to-end supply chain visibility:
Big data analytics
Track and trace systems
Early warning systems
Digital platforms and collaborative supplier portals
Analysis of the literature suggests that digital technology for supply chain resilience can be
classified as retrospective or prospective. The retrospective data can be used for predicting dis-
ruptions and their consequences, along with contingency plan elaboration. For real-time event
detection systems, acquiring and sharing real-time information is of vital importance for supply
chain recovery planning and the coordinated deployment of recovery policies (Sheffi 2015).
Early warning systems aim to identify deviations or dangers of supply chain disruptions and
prepare supply chain managers for activating contingency plans in a timely manner. In addition,
track and trace systems and blockchain applications in supply chains contribute to transparency
and allow the creation of digital supply chain finance platforms that utilize smart contracts (Ba-
sole and Nowak 2018, Hofmann et al. 2018, Dolgui et al. 2020a).
2.2. Role of visibility in supply chain resilience under pandemic conditions
The COVID-19 pandemic represents a very specific type of supply chain disruption
unlike any seen before (Ivanov 2020a, Mehrotra et al. 2020, Ivanov 2021a, Ivanov 2021c, Na-
gurney et al. 2021, Ivanov and Dolgui 2021b). While research on the utilization of redundancy
and flexibility capabilities (e.g., risk inventory and backup suppliers) to improve supply chain
resilience has become mature over the last two decades (Saghafian and van Oyen 2016, Snyder
et al. 2016, Shen and Li 2017, Pavlov et al. 2018, Lücker et al. 2020, Yoon et al. 2020, Li et al.
2021), little is known about the value and best use of visibility to manage resilience. Even less
is understood about supply chain resilience in pandemic settings. End-to-end visibility has been
seen as a crucial capability for coping with massive disruptions when multiple supply chain
echelons and markets are affected on a long-term scale (Ivanov et al. 2019, Dubey et al. 2020b,
Hofmann and Langner 2020, Ralston and Blackhurst 2020, Ivanov 2021b). Thus, there is an
acute need to examine how to enhance the existing redundancy and flexibility resilience capa-
bilities (Knemeyer et al. 2009, Yu et al. 2019, Pavlov et al. 2020, Sawik 2020) by using end-
to-end supply chain visibility.
2.3. Analysis frameworks
Ivanov et al. (2020) proposed a 3D (three-dimensional) framework for analyzing the
impacts of Industry 4.0 on supply chains. Their framework is composed of management, or-
ganization, and technological perspectives. We follow this idea in developing our framework
in this study. Ivanov and Dolgui (2020b) developed the notion of a digital supply chain twin—
a computerized model that represents network states for any given moment in real time using
end-to-end visibility. Their results suggested that the combination of model-based and data-
driven approaches allows achieving a novel quality in disruption modeling and performance
impact assessment. The supply chain shocks and adaptations amid the COVID-19 pandemic,
along with post-pandemic recoveries, provide indisputable evidence of the urgent need for dig-
ital twins to map supply networks and ensure visibility. Dolgui et al. (2020b) developed and
tested a new model for smart contract design in a supply chain with multiple logistics service
providers and using blockchain technology. The constructed model and the developed experi-
mental environment constitute an event-driven dynamic approach to task and service composi-
tion when designing smart contracts. This approach is also of value when considering the con-
tract execution stage. Ivanov et al. (2019) analyzed the impact of digitalization and Industry 4.0
on the ripple effect and disruption risk control analytics in supply chains. It was one of the first
studies to reveal the relations between digitalization and supply chain risks.
Dolgui et al. (2020a) and Ivanov and Dolgui (2020a) developed conceptual frameworks
of the viable and reconfigurable supply chain with a specific focus on aligning resilience, sus-
tainability, profitability, and digitalization aspects. Ivanov and Dolgui (2021) and Ivanov
(2020a, 2020b, 2021a, 2021b) addressed different aspects of the digital and reconfigurable sup-
ply chain with a specific focus on the COVID-19 pandemic. They observed the specific aspects
of the pandemic that made it a distinct instigator of supply chain disruptions. They also deduced
and quantified four major adaptation strategies in the wake of the COVID-19 pandemic: inter-
twining of commercial and healthcare supply chains, capacity scalability in logistics and pro-
duction, repurposing, and substitution.
2.4. Insights from the literature analysis and contribution of this study
Despite the remarkable progress in the last few years in theory and practice, considera-
ble uncertainty still exists about the value of end-to-end supply chain visibility for resilience.
Our study aims to address this open research question. The outcomes and findings of our study
could both progress state-of-the-art on supply chain resilience and be utilized by industry and
researchers alike to advance the decision support systems guiding supply chains through a pan-
demic and toward long-term recovery and viability. Our expected findings aim to depict how
visibility can enhance resilience management and help supply chains cope with the disruptions
and ripple effects during pandemics.
Through literature analysis, we have found that applications of digital technologies to
enhance supply chain resilience have been seen primarily in the areas of supply, demand, and
process risks. It has been observed that firms and their supply chains are evolving toward tech-
nology-driven networks and digital ecosystems and implementing technologies for digital sup-
ply chains such as procurement, manufacturing, and finance platforms. The central questions
addressed in both academia and practice are related to the data needed for resilience manage-
ment and modeling, as well as the information availability required to use end-to-end visibility
for decision-making (Frazzon et al. 2021).
Our analysis has shown how the existing literature addresses the potentials of digital
technology applications to enhance supply chain resilience management; however, these efforts
and their generalizations have not been collated. Our study aims to fill this research gap.
3. Case studies
Case studies have been considered an effective method in the supply chain literature for practi-
cal contextualization and generalization of some real-life observations (Wu and Choi 2005,
Chen et al. 2019, Ivanov 2021d). Building on the results of our literature analysis, we now
illustrate major supply chain end-to-end visibility dimensions identified in Section 2 using mul-
tiple case studies constructed with the use of primary data. Through the case studies, we aim to
illustrate the practical context and supplement the literature analysis to derive relevant determi-
nants for building of the conceptual framework in Section 4.
3.1. Sample selection
We developed a two-stage case study analysis. First, we used our research results drawing on
cases of using digital technology in supply chain resilience in the pre-pandemic world (2018-
2019), relying on Gottlieb et al. (2019) and Das et al. (2019). This helps us to justify the selec-
tion of the digital technologies for supply chain visibility and relevant risks in terms of frame-
work development in Fig. 1 and Fig. 2. Second, we developed a new case study on a global
corporation with an advanced utilization of digital technology in their supply chain. For validity
purposes, we used the same protocol in both 2018’ and 2021’ case-studies whereas in the 2021’s
case-study, we added some additional questions especially focusing on pandemic disruptions
and digital technologies. The case-study analysis results are used for validation/tuning of the
frameworks (Fig. 1 and Fig. 2) and for supporting arguments on practical implementation in
The case studies have been developed by interviewing (in person and via questionnaires) key
decision-makers in supply chain management of several organizations that have extensive ex-
perience in dealing with disruptions and digital technologies. The data were collected through
semi-structured interviews in order to achieve a certain degree of comparability, and at the same
time ensure an unimpeded flow of narration. Both some pre-defined multiple-choice questions
(e.g., lists of technologies which potentially contribute to supply chain visibility or lists of sup-
ply chain disruptions) and open-ended questions were used to collect qualitative text data (Ap-
pendix 1). This approach offered participants opportunity to elaborate and contextualize their
experiences with resilience management and digital technologies. Structured questions pro-
vided ranking options. Interview questions were formulated based on identified gaps in the
literature. Data confidentiality and respondent anonymity were properly assured. Data collec-
tion began with identifying detailed knowledge through filled out questionnaires. Subsequently,
the questionaries’ results were clarified with the experts in some follow-up discussions (in-
person, telephone, and video calls). The data analysis was conducted by analyzing the individ-
ual cases and comparing them with one another. The questionnaire responses were reviewed
and cross-compared to identify similarities and differences in order to deduce recurring pat-
3.2 Cases 2018-2019
Based on the pre-pandemic sample collected in 2018, nine case-studies have been developed.
They have been reported in Gottlieb et al. (2019) and Das et al. (2019), and so we present a
condensed summary of three (most representative) of them in this paper particular focusing on
the end-to-end visibility of supply chains.
In the Case A (semiconductor industry), “the company strives for a self-optimized supply chain
with a perfect mix of automated processes and human decision-making processes according to
the roadmap being descriptive, diagnostic, predictive, and prescriptive. The supply chain has
evolved from rigid patterns to a global and highly flexible supply network as the smart factory
approach has improved the manufacturing flexibility realized through superior planning pro-
cesses” (Das et al. 2019). The respondent at company A has been Head of Innovation with more
than 20 years of practical experience.
In Case B (automotive supplier industry), the company “operates the world's largest production
plant for automotive electronics. Products for eight different business units are manufactured
there, from sensor systems and transmission controls to engine components. A total of around
360,000 units leave the plant every day, which supplies major German and international auto-
mobile manufacturers. The leading German automotive supplier group has 427 locations in 56
countries with a total of more than 3000 suppliers”. The respondent at company B has been
process and risk manager with 7 years of practical experience.
In Case C (automotive industry), the supply chain network is organized around over 20 auto-
motive plants worldwide and 5,000 suppliers. The respondent at company C has been a supply
chain risk manager for the entire German automotive group with over 15 years of practical
The respondents were first asked about the use of digital technologies in their supply chains
and how these technologies support decision-making in case of disruptions. The respondent at
company A named advanced communication tools, advanced global detailed visibility, predic-
tive analytics, tracing and tracking, and simulation as most important digital technologies for
coping with disruption risks. Besides the technologies used so far, respondent B would benefit
from a risk pre-warning system in supply chain risk management, so that they can react quickly
to earthquakes, hurricanes, and capacity bottlenecks and form emergency teams. This respond-
ent mentioned that “in the recent past there were several earthquakes in Japan, which led to
big crises. The earthquakes impacted a very important supplier. An early warning system would
be helpful to react and to set up recovery management teams in a faster way in case of earth-
quakes, hurricanes etc. There is a need for a system which can integrate all supply chain data,
identifying risks upfront and assessing their impact. Data accuracy in the systems used is man-
datory for being fast in reaction”. The risk manager at company C explains that data analytics
helped him to react more quickly when a plant caught fire. The analytics system informed him
about the event one day earlier than the media. That provided him a time advantage over com-
petitors in buying components from distributors.
When thinking about possible barriers to adoption of digital technologies in supply chain resil-
ience management, the respondent at company B mentioned that barriers for early warning
system establishment concern costs and data provision. Both the company itself and its suppli-
ers would have to exchange data with associated issues of trust and data security. This confirms
literature analysis findings and the challenge of upstream supply chain visibility (Dubey et al.
3.3. Case 2021
3.3.1. Company and supply chain description
The company is a global technology corporation with a broad variety of manufacturing and
service supply chains. They see the value-creation for customers and the competitive edge in
connecting physical and digital worlds. The company is represented worldwide. We performed
detailed interviews with two senior managers. We talked to the Head of Logistics Digitalization
of a business area (> 25 years of experience in logistics) and Head of Plan Source and Digital-
ization at one of the business units (> 10 years of experience in logistics). The physical supply
chain of the company is a complex, global, and multi-echelon network. The digital supply chain
is composed of big data analytics, artificial intelligence, additive manufacturing, ERP, Industry
4.0, and collaborative supplier platforms.
We asked both managers to fill out a questionnaire (Appendix 1) and supplemented the data
collection by personal in-depth discussions to clarify particular questions. The Head of Logis-
tics Digitalization explains: “When the coronavirus outbreak and associated supplier shut-
downs began in China in January 2020, we had to confront with the following questions: how
much of capacity will be unavailable in the upstream part of the supply chain? How should we
adapt the production planning and demand-supply allocations during the lockdown and capac-
ity shutdown periods? How much capacity should we add into the global supply chain once the
Chinese factories are recovered? Decision-making in these areas strongly depends on the end-
to-end visibility and timely data availability. We were lucky to benefit from the digital supply
chain twin built in our organization in 2017-2020.” The Head of Plan Source and Digitalization
adds: “We were blessed in disguise. Late in 2019, we anticipated an explosion of a volcano in
Indonesia and develop some recovery plans for the cases when operations at global logistics
hubs in South-Asian regions would be disrupted by this volcano explosion, similar to the vol-
cano explosion in Iceland in 2011. These recovery plans helped us a lot during the coronavirus
outbreak early in 2020”.
Both managers strongly support the value of digital technology and end-to-end visibility for
managing supply chain resilience and disruptions. The Head of Logistics Digitalization says:
„The continuously changing situation has been the main challenge when managing the supply
chains during the COVID-19 pandemic. Frequently, we did not exactly know where the deliv-
eries are. The information about availability/non-availability of different regions in the world
arrived with delays leading to delivery returns. We created visibility through so called “Order-
to-Cash” dashboards. If our delivery has arrived at a particular location, this region was
marked green; otherwise – red. So we could understand the lanes in our supply networks ca-
pable of supply using on-time delivery and delivery capability performance indicators. With
that said, a lot of these efforts were done manually. Digital twin-based simulations would be of
great help.” The Head of Plan Source and Digitalization gives an example: “During the
COVID-19 pandemic, limitations of logistics capacities have been the most severe disruption
for us impacting all tiers in the supply chain”.
While both managers identified a lot of benefits associated with the end-to-end visibility in
supply chains, they also observed several problems and shortcomings and articulated require-
ments on digital supply chain twins. The Head of Logistics Digitalization says: “It is very com-
plicated to create a transparency across the overall supply chain network. The digital twin
should provide a kind of decision-support matrix for situational demand-supply re-allocations
when supply is scarce. Most important, we should be able to observe financial impact of differ-
ent decisions and use the transparency for developing and timely communicating a solution for
our customers in disruption cases, e.g., a delivery at a later date.”
Both managers point to the differences in building visibility upstream vs downstream in the
supply chain. In particular they mentioned that “while the downstream visibility is realistic to
establish, especially using collaboration with logistics service providers, the upstream visibility
is much more difficult due to highly decentralized supplier bases. Even the downstream visibil-
ity usually ends at the ports or other end terminals in the supply chain and does not always
reach the last-mile operations”.
The Head of Plan Source and Digitalization points that “The post Covid-19 pandemic-induced
uncertainty is the major challenge we face. Management capabilities like building redundancy
by backup sourcing and production facilities, alternative transportation routes, risk mitigation
inventory, and capacity reservations are imperative but all this requires effort and generates
costs. Therefore acceptance and investment analysis is necessary. Managing crisis is still hu-
man’s business and experience can disappear when employees leave the company. Knowledge
management and learning systems and lessons learned along with the experiences are the key
drivers for managing the crisis and the recovery. Digitalization helps in decision-making sup-
port for recovery management. Resilience and sustainability will become key sales instruments
in the future.”
4. Framework for building and using end-to-end visibility during the COVID-19 pan-
In this section, we merge the findings of our literature and case-study analysis into a
framework for digital supply chain management and the use of technology to enhance resilience
by building and using end-to-end visibility during the COVID-19 pandemic. The main objective
of this framework is to unlock the value and potentials of end-to-end supply chain visibility for
resilience management during pandemic disruptions and propose an associated design and im-
plementation framework that contains management, organizational, and technological dimen-
Fig. 1. End-to-end supply chain visibility framework for resilience management: Decision-sup-
In Fig. 1, we provide a comprehensive decision support taxonomy of an end-to-end sup-
ply chain visibility framework for resilience management. Using the classification of supply,
demand, and process risks (Christopher and Peck 2004), our first objective was to identify the
specific cases of such risks during the COVID-19 pandemic and then identify key decision-
making support that end-to-end visibility can provide with the help of the different digital tech-
nologies identified in Section 2.
Regarding supply risks, our literature and case-study analysis revealed delayed deliver-
ies from suppliers, partial supplier unavailability (due to lockdowns), and full supplier unavail-
ability (due to the bankruptcy of suppliers) as specific pandemic risks (Ivanov 2020a, Ivanov
2021d). Demand risks during the pandemic include severe backlogs or shrinkages due to surges
in demand (Govindan et al. 2020, Mehrotra et al. 2020). As process risks, we identified insuf-
ficient capacity in manufacturing and logistics due to lockdowns and quarantine measures, in-
sufficient cash liquidity in the supply chain due to unbalanced financial and material flows, and
inventory instability (shortages and overages) due to unbalanced demand and supply and inher-
ent uncertainty about them in both the short and long term (Choi 2020, Paul and Chowdhury
2020, Nagurney 2021).
For each of these crucial problem areas, we then derived associated decision support
solutions stemming from the use of end-to-end supply chain visibility (see Fig. 1). The literature
and case-study analysis indicated that digital technology can allow faster and more reliable
detection of potential disruptions (i.e., by means of early warning systems) (Ivanov et al. 2019,
Sharma et al. 2020, Zouari et al. 2021), real-time recognition of real disruptions (i.e., using
blockchain and track and trace technology) (Dolgui et al. 2020a, Roeck et al. 2020, Wamba and
Queiroz 2020, Nguyen et al. 2021), analysis of disruption reasons, prediction of their conse-
quences, and elaboration of recovery strategies (i.e., with the help of big data analytics and
artificial intelligence) (Arlinghaus et al. 2019, Ralston and Blackhurst 2020, Sheng et al. 2021),
and collaborative decision-making support on disruption management and recovery (i.e., by
means of digital collaborative supply chain platforms) (Cai et al. 2020, Ivanov and Dolgui
In addition, we validated Fig. 1 through the 2021’s case-study. The experts pointed to visibil-
ity as one of the key determinants in managing supply chain risks at pandemic times: “Within
our organization, main challenges we faced during the COVID-19 pandemic are as follows:
• Managing capacity in manufacturing and incomplete end-to-end visibility in multi-tier
• Logistics bottlenecks due to lockdowns, quarantine measures and scarce capacities
both in sea and air freight,
• Inventory instability (shortages and overages) as well as delayed deliveries,
• Partial supplier unavailability (due to lockdowns)”.
The Head of Logistics Digitalization says that “it is very important to detect potential supply
shortages well in advance. This allows to secure supply and transportation capacities. Since
major suppliers are delivering to different industries, disruptions at such suppliers usually re-
sult in high competition about materials and logistics capacities once the disruption is an-
nounced officially. Early signals are extremely helpful and save costs for recovery. Low sup-
ply chain visibility is the major barrier. Prevention is much less expensive as treatment – we
focus of early disruption recognition since it saves a lot as compared to recovery. Visibility
plays here the key role.” He continues bringing an example of how their digital supply chain
twin created benefits: “Recently, a quality problem with one charge led to a situation where
we would need to recall about 2.3 millions of products from the market creating enormous
costs. Due to visibility, we were able to exactly detect at which place in the supply chain the
quality problem occurred and which charge was affected. So we recalled only 180 items in-
stead of 2.3 millions”.
Finally, we present the conceptual view of our framework that integrates management,
organization, and technology perspectives and so extends the decision-making view toward a
holistic perspective (Fig. 2).
Fig. 2. End-to-end supply chain visibility framework for resilient management: Conceptual
Linking the end-to-end visibility technologies identified in Fig. 1, Fig. 2 introduces and
integrates associated perspectives of management and organization. The management dimen-
sion frames the perspective of using end-to-end visibility for supply chain resilience manage-
ment. It contains performance management and indicators for digital resilience analysis
(Behzadi et al. 2020) along with the use of end-to-end visibility in mitigation and recovery
strategies. Organizational dimension of resilient decision-making develops guidelines for han-
dling cases of disruptions that include both an operative and a strategic component. Disruptions
are usually identified by operative level employees (e.g., production plant managers) while the
decisions about recovery procedures are frequently made by strategic global supply chain man-
agers. For example, a disruption at a major supplier may be noticed by a production planner,
who could observe through our models that this disruption will affect production at major lo-
cations globally in a week and requires reconfiguration of demand-supply allocations through-
out the whole network. Such decisions are within the purview of a global supply chain manager.
To this end, clear instructions about how to use end-to-end supply chain visibility for such
strategic-operative settings are needed, along with consideration of managers’ risk aversion. A
data-driven learning system could be proactively used to help managers learn from past events
and improve resilient supply chain design and planning in the future. Finally, it is important to
consider the technological dimension depicts the instrumental environment for end-to-end sup-
ply chain visibility. It is composed of the digital technologies that enable end-to-end visibility.
In addition, we validated Fig. 2 through the 2021’s case-study. With regards to the cost-benefit
analysis, the Head of Logistics Digitalization points that a 100% visibility is very expensive.
One should strive a balance between investments in visibility and its value. Besides, the visi-
bility fosters utilization of artificial intelligence-based models and algorithms. Humans tend to
rely on data and using them for decisions without understanding of what algorithms have been
used and how reliable their results are. One can name this effect “data blind flight”. At the
management decision-support level, visibility and digital twin applications are concerned with
combination of artificial and human intelligence. Decision-making is still human’s business and
experiences/competences can disappear if allowing for fully artificial intelligence-driven con-
The framework we have developed constitutes a conceptual and implementation basis
for proactive, resilient supply chain design in anticipation of disruptions and structural-para-
metrical adaptation in the event of disruptions. Building a resilient supply chain in practice is a
MAMMOTH project—a combination of MAnagement, Modeling, Organization, and TecH-
nology. We summarize in this section the main aspects of building resilient supply chains with
end-to-end visibility using our frameworks and own practical experiences in projects with dig-
ital supply chains and resilience in companies.
Usually, a project for building supply chain resilience is composed of four major stages
that aim to answer four major questions:
- What happens in the supply chain if it is hit by disruptions?
- Why does this happen?
- How can we mitigate the disruption and recover?
- How can we learn from the disruptions and improve performance?
In the remainder of this section, we will analyze these four stages in detail and with
regard to end-to-end supply chain visibility. To answer the first question, a stress test of the
existing supply chains is usually performed with the help of simulation and optimization mod-
els. Some nodes and arcs in the network are assumed to be disrupted, and we can observe the
impact of these disruptions on production-supply-logistics operations, as well as on overall sup-
ply chain performance. However, it can be difficult to define these disruption scenarios because
we cannot stress test all the nodes and arcs, so we must select the most critical ones. This se-
lection is usually based on the existing experience in the company and supported by network
analysis techniques to reveal the weakest parts of the chain. Data visibility can be of great value
for designing potential disruption scenarios and early warning systems. Our frameworks devel-
oped (Fig. 1 and Fig. 2) offer a structured guidance for stress-testing the supply chains from
management perspective (i.e., performance-based assessment of mitigation and recovery strat-
egies), organizational perspective (i.e., development of contingency plans), and technological
perspective (i.e., identification of contributions of different digital technologies with regards to
specific supply chain risks).
While answering the first question—“What happens?”—is important and desirable in
many settings, in the next step we seek to understand why it happens. Why would the supply
chain performance decline when some nodes/arcs are hit by disruptions that may induce ripple
effects? Why do some production-distribution operations become disrupted? To answer these
and other questions, a deeper analysis of simulation/optimization results is needed. The reasons
can be usually found in a lack of preparedness for disruptions, lean processes (e.g., single sourc-
ing, low inventory, or single-channel distribution systems), and lack of end-to-end supply chain
visibility. Data analytics and artificial intelligence can help uncover the reasons for disruptions
(Fig. 1). Moreover, as shown in the organizational dimension (Fig. 2), analysis of structural and
process organization along with disruption learning system can help in detection of disruption
causes in both management and organizational areas.
The analysis of the second stage and its key question (“Why do disruptions affect my
supply chain?”) leads us to the third stage—the development of disruption mitigation and re-
covery capabilities. As suggested in our framework, these capabilities include management,
organizational, and technological components. Management capabilities include building re-
dundancy such as backup sourcing and production facilities, alternative transportation routes,
risk mitigation inventory, and capacity reservations. Organizational capabilities are concerned
with creating organizational structure for resilience management, embedding resilience man-
agement in everyday business processes, and developing contingency plans and clear instruc-
tions for different managers and organizational units for emergency cases. Finally, the techno-
logical dimension includes the development of end-to-end supply chain visibility, early warning
systems, and automation in manufacturing and logistics processes to increase their adaptability.
As pointed by Dubey et al. (2020) and confirmed by our own discussions with supply chain
managers, especially the upstream supply chain part represents a challenge to achieve the end-
to-end visibility due to multi-tier network designs. In the downstream part, the visibility has
been developed by many companies based on closer coordination with logistics service provid-
ers. These differences between upstream and downstream visibility can be explained by several
factors. In the upstream part, data is rather messy and uncertain. Besides, data ownership rep-
resents a hurdle. On the contrary, trusted data and control over data is usually observed down-
stream the supply chain.
The fourth and last stage of building supply chain resilience is the learning and perfor-
mance analysis stage (see the organizational dimension of our framework in Fig. 2). Each com-
pany has experienced managers that have coped with different and severe crises in the past and
thus can help in the analysis at stages 1–3. However, this experience can disappear when em-
ployees leave the company. Thus, it is critical to develop knowledge management and learning
systems to accumulate the lessons learned from disruption handling experiences. A critical role
in this area is played by performance management systems, which are also crucial for the anal-
ysis at stages 1–3. Both retrospective performance indicators and proactive, predictive perfor-
mance indicators should be developed. Additionally, collaboration with suppliers and custom-
ers is critical at the learning and performance analysis stage to ensure information communica-
tion and data visibility, which are of utmost importance for disruption mitigation and recovery.
Decision-making in building supply chain resilience and its continuous improvement according
to stages 1–4 can be conveniently supported by a digital twin-based engine for planning, ana-
lytics, and control (Ivanov and Dolgui 2020b).
The COVID-19 pandemic has shown that firms with end-to-end supply chain visibility
and inherent adaptability (i.e., utilization of resilience capacity during business-as-usual times)
were able to cope with the pandemic’s impacts in a more responsive, efficient, and resilient
manner and were thus able to ensure supply chain viability (Dolgui et al. 2020b, Ivanov and
Dolgui 2020a). The end-to-end visibility at the pandemic times is of utmost importance for
First, supply chain facilities and markets can open and close at different echelons with
different timing, i.e., simultaneously or subsequently. The supply chain mapping is thus a key
instrument to identify the available capacities and demands in the supply chain to adapt accord-
ing to pandemic dynamics. Second, adapting supply chain operations to long-lasting crisis with
hardly predictable scaling and uncertainty about both short-term and long-term future requires
visibility of network and operational data in the supply chain and its environment. Our frame-
work contributes to understanding of technologies, management principles and organizational
requirements to support adaptation decision-making.
Third, one important pandemic characteristic is that recovery is performed in the pres-
ence of a disruption taking potential crisis recurrence and setbacks into consideration. Both our
literature analysis and case studies show that data analytics and artificial intelligence supple-
mented by real-time monitoring systems can help to timely identify short-term deviations and
prepare a course of recovery actions. Fourth, cascading effects of disruptions through the supply
chain networks (i.e., the ripple effect) should be taken into account. Our framework suggests
that mapping upstream and downstream supply chain parts with manufacturers, utilization of
collaborative digital supply chain portals, and technologies for real-time disruption identifica-
tion are the powerful tools for detection and mitigation of the ripple effect in supply chains.
In summary, the framework developed could be instructive for supply chain managers seeking
to manage resilience during pandemic disruptions and using digital technology. Our main meth-
odological contributions are unlocking the value and potentials of end-to-end supply chain vis-
ibility for resilience management in the face of pandemic disruptions and proposing an associ-
ated design and implementation framework containing multiple dimensions—management, or-
ganizational, and technological. The outcomes of our study offer a conceptual guideline con-
cerning the potentials and implementation of end-to-end visibility in the management of supply
The primary objective of this study was to investigate the potentials and develop rec-
ommendations for implementation of end-to-end visibility to enhance supply chain resilience
in order to be better prepared for future disruptions. The framework we have developed contains
some innovative insights into the principles of designing end-to-end visibility and its utilization
for resilience management in supply chains during the COVID-19 pandemic. In particular, we
have closed existing research gaps by developing conceptual guidelines for digital supply chain
management and the use of technology to enhance resilience by building and using end-to-end
visibility in pandemic conditions. We extended the analysis of digital technology applications
to supply chain resilience from instantaneous, single-event disruptions to pandemic settings
using the COVID-19 example. Through this effort, we have offered novel insights into how to
help firms prepare their supply chains for possible future pandemics or severe pandemic-like
More specifically, our findings have indicated how end-to-end visibility can enhance
resilience management and help businesses cope with the disruptions during pandemics. The
outcomes of our study underly our proposed multidimensional framework composed of man-
agement, organization, and technology perspectives. We have stressed that end-to-end visibility
can help improve supply chain resilience in an efficient manner without building excessive and
expensive redundancies. We have provided a comprehensive taxonomy of crucial problem ar-
eas during the pandemic and associated solutions from digital technology. And in particular,
we have identified and classified pandemic risks according to supply, demand, and process
areas, and suggested associated value-added and implementation steps for end-to-end visibility.
Limitations of our study are related to its conceptual nature. Design and analysis of in-
dustrial case-studies would help to better understand each of the framework dimensions and
digital technologies at different levels of supply chain disruptions. Framing this research out-
comes from the perspective of cyber-physical systems can help use end-to-end visibility to cre-
ate a digital twin for supply chain resilience modeling and management. With regards to future
research, practical context of utilization of end-to-end visibility in resilience management
across different industries and services should be developed. One challenge for the timely and
efficient design and deployment of recovery policies is uncertainty about the duration of a dis-
ruption and data incompleteness about supply chain parameters such as available inventory and
capacities after the disruption.
In future research, one can seek to understand some levels of inaccuracy and uncertain-
ties that can be allowed without having a significant impact on the modeling quality. In sum-
mary, each of the dimensions identified and described (i.e., management, organization, and
technology) should be explored further individually and combined to deduce novel and relevant
synergetic effects. More granular pandemic specifics can provide deeper insights into digital
technology adoption for resilience management across different pandemic stages (e.g., first
wave, second wave, and third wave). Moreover, new technological developments such as 5G
require a thorough analysis and methodological underpinning with regards to supply chain vis-
ibility. End-to-end visibility is also crucial for creating records of sustainability in the supply
chain. Finally, the analysis of visibility value on the level of business ecosystem viability
through interrelations of resilience and sustainability is a promising research overview. For ex-
ample, the Supply Chain Law in Germany (Lieferkettengesetz in German) makes companies
responsible for maintaining human rights and environmental rules in the supply chain. To create
a record of sustainability through tracking of contractors’ activities on a global scale, end-to-
end visibility across the entire supply chain is mandatory. Investigation of the relations between
viability and end-to-end visibility can be considered as an important part of fostering future
developments in digital supply chain research.
We thank three anonymous reviewers for their invaluable comments which helped to improve
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Appendix 1. Interview questions (fragment)
Please provide a brief description of your company's supply chain design (e.g., how many echelons (suppliers,
factories, distribution centers) is your supply chain comprised of?).
What are the most important supply chain disruption risks that your company is concerned about?
Which disruption risks did your company experience in the past? Rank them in terms of importance
What have been the most important supply chain disruptions during the COVID-19 pandemic? *
What are the reasons for the disruptions experienced at your company?
Which of the following digital technologies do you use in your company’s supply chain operations?
What are organizational challenges in designing and using end-to-end supply chain visibility for resilience man-
Structural and Pro-
and Technology Adop-
Please summarize management benefits and disadvantages of end-to-end visibility for resilience and disruption
management in terms of performance management, mitigation and recovery strategies in terms of performance
management, mitigation, and recovery. Please consider the pandemic times specifically.
Please define the interrelations of how the digital technology used helps to improve supply chain resili-
* In the 2021’s case study only