Impacts of Industry 4.0 Technologies on Supply Chain Resilience

Saeed Albeetar

, Concetta Semeraro

and Giovanni Francesco Massari

Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah, U.A.E.

Department of Mechanics, Mathematics, and Management, Politecnico di Bari, Bari, Italy

Keywords: Resilience, Industry 4.0, Industry 4.0 Technologies, Supply Chain, Supply Chain Resilience Drivers.

Abstract: Disruptions in the supply chain are among the most dangerous events. Supply chains increasingly face a

turbulent environment characterized by unpredictable disruptions that threaten the stability of industrial

operations. Most companies face challenges in their supply chains. Resilience will help organizations

transform and adapt their business to dynamic environments and recover quickly from difficulties and

toughness. Recent technological progress, primarily Industry 4.0 (I4.0) technologies, indicates promising

possibilities to mitigate supply chain risk. This paper will study the impact of Industry 4.0 technologies on

supply chain resilience.

1 INTRODUCTION

Today firms operate in complex and turbulent

environments characterized by disrupting events

continuously threatening the stability of their

operations, processes, and performance. Disruptions

may vary in nature, as well as the probability of

occurrence and the impact of their consequences.

However, disruptions can also occur due to natural

catastrophes, disasters, and economic disruptions

such as hurricanes, earthquakes, floods, terrorist

attacks, labour strikes, fuel crises, and financial crises

(Spieske & Birkel, 2021). These events may cause

adverse effects on supply chain operations, including

production, manufacturing, delivery, shipment

delays, customer needs, sales, and market share

losses, negatively impacting the economic

performance of the supply chain. For example, the

ongoing pandemics of COVID-19 is considered the

most severe disruption in the last decades, and during

the pandemic, customers' demand was highly

unpredictable, and suppliers could not meet their

delivery obligations because of stringent rules; this

has led companies to investigate on how industrial

systems can improve resilience during disruptions.

In literature, resilience is recognized as a

multidimensional construct, including a static and a

dynamic perspective. (Acquaah et al., 2011) defined

resilience as "a firm's ability to persist in significant

https://orcid.org/0000-0001-5152-0004

https://orcid.org/0000-0002-1797-3699

changes in the business and economic environment

and survive disruptions on an organizational level."In

literature, supply chain resilience has received

attention from numerous researchers. Some of them

have proposed rigorous definitions. For example,

(Christopher & Peck, 2004) defined supply chain

resilience as: "the system's ability to return to its

previous condition or move to a more desirable state

after being disturbed."

More recently, researchers have started

investigating the enabling factors for SC resilience

development. In this regard, Industry 4.0 technologies

play a determinant role, given their influence on

supply chain operations

However, the existing literature is fragmented,

leaving the understanding of the effect of I4.0

technologies on supply chain resilience not

completely understood. To fill this research gap, we

carry out a Systematic Literature Review addressing

the following research question:

1. How I4.0 technologies favour the

development of SC resilience?

2 RESEARCH METHODOLOGY

We carried out a Systematic Literature Review (SLR)

to address our research questions. This was designed

Albeetar, S., Semeraro, C. and Massari, G.

Impacts of Industry 4.0 Technologies on Supply Chain Resilience.

DOI: 10.5220/0011588700003329

In Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2022), pages 153-160

ISBN: 978-989-758-612-5; ISSN: 2184-9285

153

consistently with previous studies (Colicchia &

Strozzi, 2012). In the following, we describe the

process of material collection and material

refinement.

2.1 Material Collection

This first process is devoted to selecting databases

and keywords to find the articles. There are, arguably,

three major abstract and citation databases: Google

Scholar, Scopus, and the Web of Science. Meanwhile,

Scopus has broader coverage than the Web of

Science, but the latter provides access to older sources

(Farooque et al., 2019). Since we are investigating a

recent phenomenon, accessing older sources offered

by the Web of Science database is not an advantage.

We, therefore, focused on Scopus. The data were

collected in March 2022 using search criteria in the

article title, abstract, and keywords of peer-reviewed

articles (journal articles, chapters, and books). The

search criteria were defined as the combination of the

keywords related to Industry 4.0 technologies (see

Table 1) with those regarding supply chain resilience

context ("resilience") AND ("industry 4.0" OR "i4.0"

OR "digital technolog*"). After excluding duplicate

publications, we collected 277 records, including 217

journal articles and 60 reviews.

Table 1: List of keywords used for literature search.

Industry 4.0 Technology Keywords

Internet of Things

IoT, Internet of Things

Cloud Computing

CC, Cloud Computing

Big Data Analytics

BDA, Big Data

Blockchain

Artificial Intelligence

AI, Artificial

Intelligence

Cyber-physical system

CPSs, Cyber-Physical

Systems

Additive Manufacturing

AM, 3D printing,

Additive Manufacturing

2.2 Material Refinement

Refinement is a rigorous approach to analysing and

evaluating the selected research articles to exclude

those not explicitly addressing our research purpose.

We refine the collected articles by meticulously

analysing their titles and abstracts and then their full-

length text. After reading titles and abstracts, 185

records were considered out of topic and filtered

based on specific exclusion criteria. Thus, we

collected 24 documents. In the following, we present

the results of the content analysis.

3 THEORETICAL

BACKGROUND

3.1 Supply Chain Resilience

The term resilience is developed and used in many

areas such as engineering, ecology, environmental

science, social, management, economics, and

organisational science (Morisse & Prigge, 2017).

Studies on supply chain resilience conceptualize it as

the supply chain's ability to anticipate and respond to

interruptions, recover quickly and cost-effectively,

and advance to a post-disruption state better than

before is the supply chain resilience; or even as the

ability of a supply chain to decrease the possibility of

facing unpredicted disruptions, resist disruptions by

maintaining control over structures and functions, and

recover and respond by putting in place quick and

efficient reactive plans to deal with the disruption and

return the supply chain to a stable state of operations.

Disruptions can result in a loss of market share,

financial loss, reputational damage, a drop in

shareholder value, or losing market opportunities

(Morisse & Prigge, 2017). Disruption events are low-

probability, high-impact occurrences that vary in

type, scale, and nature, are intermittent and irregular

to identify, assess, and anticipate adequately, and may

have short or long-term negative consequences

(Dolgui et al., 2018).

Thus, developing resilient supply chains

represents an imperative. Given its relevance,

scholars have investigated it widely. Rigorous,

however non-convergent, definitions have been

provided. While some of them point out the ability to

“return to its previous condition or move to a more

desirable state after being disturbed” (Christopher &

Peck, 2004), others focused on that to “anticipate and

respond to unexpected events and recover from

unexpected events by keeping processes connected

and under control of structure and function at the

needed level”. (Bhamra et al., 2011) defined

resilience from a static perspective as “resilience is

related to the system’s ability to absorb disturbance

and return back to the original normal state

maintaining its core functions when shocked”.

Instead (Carvalho et al., 2012) defined resilience from

a dynamic perspective as "resilience is viewed as the

ability to adapt to a disturbance by moving towards

the original but even new, more favorable equilibrium

states”. The development of supply chain resilience

has been explained as occurring during specific

phases. (Hohenstein et al., 2016) argued on the

existence of four phases: readiness, response,

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

154

recovery, and growth. Readiness refers to all pre-

disruption steps that can be taken to lower the

likelihood of disruption and absorb its harmful

consequences. The response and recovery phases

refer to post-disaster activities that aim to manage

limited resources efficiently and effectively when

responding to damage caused by disasters. The

response includes countermeasures performed

immediately after the supply chain disruption is

detected or experienced. The speed must be

prioritised at this stage to avoid negative

consequences for the SC. The recovery phase refers

to post-disaster activities which aim to manage

limited resources efficiently and effectively when

responding to damages caused by disasters; it targets

restoring the SC's performance level. Finally, Growth

measures are to improve SC performance over the

pre-disruption condition. Most research concentrate

on the ready and response stages, which differs from

past research in specific ways.

3.2 The Supply Chain Resilience

Drivers

The most studied drivers of supply chain resilience

include agility, visibility, flexibility, and

collaboration (Bode, 2016).

Agility is one of the most important drivers in

supply chain resilience. Supply chain agility refers to

an organisation's readiness and speed to respond to

change and return to normal situation as quickly as

possible (Hohenstein et al., 2016). Supply chain

agility refers to the ability of the supply chain to

quickly adapt the network structure and operations

policies to the customer's dynamic and unpredictable

requirements (Dubey et al., 2018). A company's

ability to implement demand sensing by integrating

information into a company philosophy helps

improve supply chain agility (Hohenstein et al.,

2016). Supply chain agility in a company's capacity

to deliver products or services quickly benefits supply

chain resilience.

Visibility tracks the identity, location, and status

of entities passing through the supply chain and

captures them in timely messages, along with the

scheduled dates and times of these events. Demand

visibility refers to information relating to the

customers' dynamic requirements. Upstream and

downstream inventories, demand and supply

situations, and production and purchase schedules

have also been called supply chain visibility.

Flexibility is the ability of the supply chain to

adopt the change in the environments and

stakeholders with minimal effort and time. Scholars

claimed that flexibility, as a component of supply

chain resilience, defines a ' 'company's ability to

respond to market changes, even though such external

influence is beyond the supply chain ecosystem's

immediate scope and control. According to the

literature, flexibility in terms of transportation,

sourcing, labour arrangements, and postponement

could all help build resilient supply chains (Pettit et

al., 2013). According to (Pettit et al., 2013), flexibility

improves rapid adaption during disruption conditions

and makes the supply chain more resilient. Supply

chains with less sourcing and order fulfilment

flexibility are more sensitive to disruptions and less

resilient.

Collaboration is essential for developing resilient

supply chains (Pettit et al., 2013). Although experts

agree that collaboration can improve supply chain

resilience, it is unclear how it affects it (Hosseini et

al., 2019). The ability of two or more autonomous

firms to collaborate in planning and executing supply

chain activities to meet shared objectives is referred

to as collaboration in the supply. Collaboration allows

partners to build synergies, ease cooperative

planning, and stimulate real-time information

exchange, which helps supply chains to prepare,

respond, and recover from supply chain disruptions

and reduce the impact of disruption on the supply

chains.

3.3 Enabling Factors for Supply Chain

Resilience: Industry 4.0

Technologies

Industry 4.0 support companies in digitalising

manufacturing processes, enabling the

communication between products and their business

environments in different supply chain sectors, such

as manufacturing, warehousing, inventory, logistics,

delivery, and retailing. I4.0 technologies in the

supply chain can record real-time data, which helps

improve the supply chain's visibility and information

sharing (Semeraro et al., 2019)(Ralston & Blackhurst,

2020). Real-time information enables autonomous

decisions throughout the supply chain to best satisfy

demand, including predictive analysis of the future

demand predictions. Digitalization is pushing all the

involved supply chain firms to digitalize their internal

processes, operations, and relationships with partners

through the implementation and integration of digital

technologies (DTs). Thus, a traditional supply chain

turns into an intelligent network of smart things, e.g.,

humans, products, and machines, supported mainly

by artificial intelligence (AI), the Internet of Things

(IoT), cyber-physical systems (CPSs), big data

Impacts of Industry 4.0 Technologies on Supply Chain Resilience

155

analytics (BDA), blockchain (BT), additive

Manufacturing (AM).

These and other DTs make information available

throughout the whole chain with enormous benefits

for all the involved actors. According to the Master

Expert of McKinsey & Company, Knut Alicke,

digital supply chains benefit from the increased

agility, flexibility, modularity, efficiency, and inter-

firm collaboration derived from the implementation

of DTs. AI enable more accurate demand forecasts

through predictive analytics of Big Data, both internal

(e.g., demand, machine status) and external (e.g.,

market trends, weather, school vacations,

construction indices). A more accurate forecast of

demand volume reduces the delivery time and lead

time, avoiding problems of under or over-stocking. It

allows following high-customization customer-based

requirements. Real-time monitoring and planning

make the entire chain more flexible and reactive to

changing demand or supply operating conditions.

Using advanced manufacturing systems, materials

(e.g., pallets, boxes, single pieces) can be processed

(e.g., received, unloaded, packed, shipped)

automatically and flexibly. Cloud manufacturing

technologies support customers, suppliers, and

buying companies through shared logistic

infrastructure partners can decide to tackle supply

chain tasks together to save admin costs, leverage best

practices, and learn from each other, thus improving

inter-firm collaboration among supply chain partners.

Different studies confirm the positive effect of

DTs on supply chain operations. Big Data positively

affects supply chain and organizational performance

(Dubey et al., 2018). It can influence the production

network by promoting operational brilliance, cost

reserves, and consumer loyalty. Supply chain leaders

can use BD to improve their organizations'

relationships with customers and suppliers and

increase replenishment through improved inventory

management. Big Data is useful in terms of helping

managers to understand supplier performance. Big

Data provides better forecasts, increases supply chain

visibility, and strong supply chain relationships

(Dolgui et al., 2018). Big Data in production planning

and control is developing. Big Data is useful

technology for the supply chain, especially for

demand forecasting, procurement, inventory, and

reverse logistics.

Internet of Things (IoT) in supply chains helps

build up warehouse operations' efficiency, reduce

unnecessary processes, and increase inventory time.

The Internet of Things (IoT) makes supply chain

management more effective and efficient. For

example, the IoT enables improvements in cost

savings, inventory accuracy, and product tracking

(Ben-Daya et al., 2019). The IoT can also improve

products, services, customer experience, and safety.

Cloud computing (CC) is most important for

logistics management, database management, and

demand forecasting and planning. Adoption of CC

would promote collaboration among supply chain

members. It would improve resource and information

sharing. It would also improve adaptability to

changes in demand (Manuel Maqueira et al., 2019).

Cyber-physical systems (CPSs) are the basis of

I4.0 because they enable the digital integration of

physical processes through integrated computers and

networks to monitor and control these physical

processes. In this context, these systems can create

intelligent industries. CPSs contributes to optimizing

inventory and production control.

Blockchain technology is leading to a new way of

thinking about supply chain management. Blockchain

is already helping to transform traditional business

models and create new opportunities across the

supply chain. Blockchain technology allows data to

be tracked and shared more quickly, and adaptability

can be provided instantly. Companies can conduct

real-time exchanges via a blockchain-powered

inventory network. BT (Bitcoin) takes a critical role

as it tends to counter security breaches while

improving supply chain availability. BT is hack-proof

and carefully designed to ensure automatic

traceability. Blockchain technology improves the

tracking of goods and passengers, from their origin to

overall supply chain management. It helps eliminate

disclosure and accountability issues. Advanced

manufacturing, particularly 3D printing, is being used

to produce technical prototypes.

Advanced manufacturing, especially 3D printing,

is used to produce engineering prototypes. It can

massively customize goods on a large scale. 3DP

helps reduce excessive inventory. 3DP's flexibility

can reduce the number of suppliers and increase

product quality. Similarly, product variety, shorter

lead times, efficiency, and better inventory control

can be achieved (Ivanov et al., 2019a). Augmented

reality (AR) refers to the overlay of PC reproduction

models with the physical design of a current

environment. AR improves the effectiveness of

current supply chain processes. Most normal types of

AR incorporate some sort of glassy and visual

representation that a carrier can utilise during the time

of expanding profitability and execution. Expanded

reality is utilised to give a sense of scene recognition

when picking orders. Radio Frequency Identification

(RFID) enables real-time identification, real-time

material flow, and tracking, which increases data

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

156

quality (Ivanov et al., 2019a). The great promise of

RFID technology is to reduce costs and provide

information that helps companies better understand,

predict, and respond to customer demand.

Artificial intelligence (AI) techniques are being

used for planning in cellular manufacturing systems.

In addition, AI is being used in industry through

machine vision and autonomous applications. Using

predictive technologies to model future scenarios and

develop a comprehensive understanding of supply

chain interactions will improve business

performance.

According to (Büyüközkan & Göçer, 2018) ten

strategic objectives characterize a Digital Supply

Chain. These are speed (ability to react quickly to

demand), flexibility (agile reaction), global

connectivity (internet-enabled SC), real-time

inventory (continuous monitoring of stocks levels),

intelligence (self-learning smart products), cost-

effectiveness (use of technology to increase

organisational performance), transparency (adjusting

networks to changing scenarios), scalability

(optimisation and duplication of processes),

innovation (in pursue of competitiveness and

excellence), proactivity (anticipating issues before

occurrence).

Figure 1: I4.0 technologies in Supply Chain Resilience.

4 RESULTS

Cloud Computing is decentralised nature enables

easy data collection, storage, processing, and

exchange among many entities, improving overall

data accessibility and management within and across

organisations. Therefore, Cloud Computing is

generally considered to enhance the performance of

SC (Queiroz et al., 2019). In a supply chain resilience

context, Cloud Computing risk data can be collected,

analysed, and interpreted more quickly, enabling

more efficient planning of supply, transportation, and

demand. Cloud Computing the program's

effectiveness in improving supply chain’s

performance has been empirically demonstrated. For

example, a study in the automotive industry found

that cloud-based SMEs are more resilient than their

non-cloud-based competitors.

IoT can help the supply chain resilience track

items and determine key metrics such as temperature

and pressure across the supply chain; this can

improve the process and overall risk knowledge and

strategies in the supply chain (Birkel & Hartmann,

2020). Specific application areas for Big Data in

supply chain resilience include risk event prediction,

proactive response planning, and reactive real-time

control (Ralston & Blackhurst, 2020)(Ivanov &

Dolgui, 2020). For example, Big Data can support the

development and execution of continuity plans during

periods of supply chain disruptions. In addition, Big

Data can be combined with traditional simulation

techniques to create digital supply chain twins

(Ivanov & Dolgui, 2020). These models can help

understand complex supply chain resilience

problems, identify possible solutions, visualise

dynamics, and test alternative scenarios.

Autonomous robots and vehicles play an essential

role in Cyber-Physical Systems, as they can facilitate

or even take over the work of personnel to reduce

potential risks and errors from human labour,

especially during a pandemic. As a specific supply

chain resilience example, an automated system for

detecting and transporting test samples from the

assembly line to a laboratory mitigates potential

quality risks more reliably and efficiently than human

workers ever could (Ralston & Blackhurst, 2020)

By using Additive Manufacturing, potential

sources of risk in the supply chain can be reduced,

such as, the number of production steps, suppliers,

and transportation links (Ivanov et al., 2019a).

In the context of supply chain, Blockchain can be

used to verify the accuracy of the information and to

track locations and ownership (Ivanov & Dolgui,

2020). Supply chain resilience can benefit

significantly from these application areas, as

blockchain improves open communication,

coordination, and trust across organisational

boundaries.

Most of the identified papers deal with Big Data

solutions, which supports (Ivanov et al., 2019b) as

Big Data is considered mature in research and

industry. All other enabler technologies do not show

a high number of contributions. Many researchers

expect significant improvements in supply chain

resilience from I4.0. (Ivanov et al., 2019a)(Ralston &

Impacts of Industry 4.0 Technologies on Supply Chain Resilience

157

Blackhurst, 2020). This shows that research has yet to

uncover the potential and detailed application areas of

each I4.0 enabler technology in supply chain

resilience.

4.1 Impacts of I4.0 Technologies on

Agility Driver in Supply Chain

In this context, the Internet of Things perception

capabilities are based on a range of identification and

tracking technologies that allow for remote

monitoring of physical things, increasing visibility.

IoT applications and Cyber-Physical Systems

significantly impact tracking and tracing the material

flow and improving risk transparency in the supply

chain. Tracking and tracing systems are used parallel

with radio frequency identification to offer real-time

information about process execution. The tracking

and tracing systems can help at the recovery stage to

monitor and predict disruptions. Also, the tracking

systems are designed to detect disruptions or the

threat of disruptions in supply chains as soon as

possible, analyse disruptions, provide alerts about

what disruptions have occurred or may occur, and

develop management measures to restore supply

chain operations. Big data is discussed in the context

of digital supply chain twins; the data from different

sources are processed and combined with simulation

techniques to represent the real-world supply chain.

4.2 Impact of I4.0 Technologies on

Visibility Driver in Supply Chain

Big Data helps improve market visibility through

predictive analytic techniques, which leads to more

reliable demand and sales forecasts. Artificial

Intelligence can help achieve supply chain resilience

by having access to real-time tracking of every

ingredient that goes into a product and highly

accurate inventory counts. Artificial Intelligence can

help find supply routes and fulfilment processes to

shorten delivery times. Also, Artificial Intelligence

helps decision-makers use demand data to make more

accurate demand forecasts. Blockchain can help

supply and demand visibility through real-time data

sharing. Adopting analytical models can transform

raw data into valuable forecasts and optimised

outcomes. As a result, practically all products in an

IoT-enabled environment, including products,

machinery, and devices, have sensors and are

connected to the Internet; the advanced level of

connectivity can lead to enhanced visibility and

considerably improve supply chain resilience by

enabling real-time access to information.

4.3 Impact of I4.0 Technologies on

Flexibility Driver in Supply Chain

Flexibility is the only supply chain resilience driver

that all I4.0 technologies support. Supply chain

flexibility is the ability to rapidly change the process

to achieve the same goals. I4.0 will allow the work

and operation to be performed faster while changing

any process, leading to improved lead times. Big Data

can detect risks and possible disruption occurrences

earlier, executing mitigation measures faster, leading

to supply chain resilience. The Artificial Intelligence

helps replace labour-intensive and time-consuming

operations with automatic information processing

and interpreting capabilities. Cloud computing

enables flexibility by creating a reliable, real-time

data platform that allows companies to assess supply

chain inventory and accelerate and reroute as needed,

and the faster access to data generally leads to better

decisions during disruptions. Finally, Cyber-Physical

Systems radically affect the supply chain and

manufacturing processes and enable more responsive

and flexible production.

4.4 Impact of I4.0 Technologies on

Collaboration Driver in Supply

Chain

Real-time information tracking using blockchain has

the potential to change the way that the information is

shared between supply chain partners. For instance,

Internet of Things sensors can be used to send real-

time data about storage and traffic conditions. The

data is permanently stored on the blockchain and

transmitted to other areas of the supply chain such as

logistics and transport. This will allow firms to take

action during disruptions. Big Data and Artificial

Intelligence applications can use a massive amount of

data generated from the supply chain operations to

help build a resilient supply chain by sharing

important data on risks and supply chain disruptions

to all relevant supply chain members faster, the faster

data access to supply chain members generally leads

to better decision-making and performance

advantages during disruptions. Cloud Computing can

play an essential role in sharing and processing

information, for example if a project needs multiple

interactions from different entities in the supply

chain, cloud computing can be used to give

employees, managers and contractors access to the

same files which will lead to easier information

sharing. Information sharing can be enhanced by

integrating detailed data gained from IoT devices.

IoT's ability to collect real-time data increases

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

158

communication in the supply chain and simplifies

redistribution activities, allowing businesses to better

manage supplier-buyer relationships during

disruptions. Cyber-Physical Systems can help supply

chains by improving communication and information

sharing, resulting in more smooth and adaptable

operations that lead to supply chain resilience.

Cyber-Physical Systems for example, can offer a

better knowledge of the requirements along the

supply chain and improve collaboration and

cooperation between them by providing a high level

of integration and information exchange. As a result,

supply chain decision-making and responsiveness

may increase, resulting in enhanced product delivery

and customer satisfaction.

Fig. 2 summarises the relationship between

supply chain resilience drivers (Layer 1) and I4.0

technologies (Layer 2). The following paragraph

summarise the impact of the I4.0 technologies on

each driver and how they can be affected.

Figure 2: Impact of I4.0 Technologies on Supply Chain.

5 CONCLUSIONS AND FUTURE

WORK

Disruptions in the supply chain are the most

dangerous events, so one of the most important

aspects is developing a resilient supply chain.

Previous studies suggest that this can be addressed

effectively using Industry 4.0 technologies because

these can effectively influence the supply chain

resilience drivers: agility, flexibility, visibility, and

collaboration. I4.0 in supply chain resilience focuses

on solutions supporting the first two supply chain

resilience phases, the readiness and response phases.

While solutions for recovery and growth phases are

still limited, I4.0 will become a fundamental basis for

improving supply chain resilience. There is no

formalized knowledge that describes the relationship

between I4.0 and supply chain resilience and what is

the effect of I4.0 technologies on the Supply chain

resilience drivers. For this reason, the paper analyses

the impacts of the I4.0 technologies on supply chain

resilience. Industry 4.0 technologies influence the

organizational and operational practices to enhance

supply chain resilience at each stage of the supply

chain. For the future work, after concluding that there

is no clear formalized knowledge that describes the

relationship between I4.0 and supply chain resilience

and what is the effect of I4.0 technologies on the

supply chain resilience drivers, so the need for

creating an ontology becomes important, so it can

help the managerial levels in the supply chains in

decision making during disruptions. After creating

the ontology, it is planned to apply it to a case study

and then evaluate the performance of the firm.

REFERENCES

Acquaah, M., Amoako-Gyampah, K., & Jayaram, J. (2011).

Resilience in family and nonfamily firms: An

examination of the relationships between

manufacturing strategy, competitive strategy and firm

performance. International Journal of Production

Research, 49(18), 5527–5544. https://doi.org/10.1080/

00207543.2011.563834

Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet

of things and supply chain management: a literature

review. International Journal of Production Research,

57(15–16), 4719–4742. https://doi.org/10.1080/002075

43.2017.1402140

Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience:

The concept, a literature review and future directions.

International Journal of Production Research, 49(18),

5375–5393. https://doi.org/10.1080/00207543.2011.56

3826

Birkel, H. S., & Hartmann, E. (2020). Internet of Things –

the future of managing supply chain risks. Supply Chain

Management, 25(5), 535–548. https://doi.org/10.1108/

SCM-09-2019-0356

Bode, C. (2016). Stages of Supply Chain Disruption

Response : Direct , Constraining , and Mediating

Factors for Impact Mitigation. Decision Sciences,

00(0), 1–39.

Büyüközkan, G., & Göçer, F. (2018). Digital Supply Chain:

Literature review and a proposed framework for future

research. Computers in Industry, 97, 157–177.

https://doi.org/10.1016/j.compind.2018.02.010

Impacts of Industry 4.0 Technologies on Supply Chain Resilience

159

Carvalho, H., Barroso, A. P., MacHado, V. H., Azevedo, S.,

& Cruz-Machado, V. (2012). Supply chain redesign for

resilience using simulation. Computers and Industrial

Engineering, 62(1), 329–341. https://doi.org/10.1016/

j.cie.2011.10.003

Christopher, M., & Peck, H. (2004). Building the Resilient

Supply Chain. In The International Journal of Logistics

Management (Vol. 15, Issue 2, pp. 1–14).

https://doi.org/10.1108/09574090410700275

Colicchia, C., & Strozzi, F. (2012). Supply chain risk

management: A new methodology for a systematic

literature review. Supply Chain Management, 17(4),

403–418. https://doi.org/10.1108/13598541211246558

Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect

in the supply chain: an analysis and recent literature.

International Journal of Production Research, 56(1–2),

414–430. https://doi.org/10.1080/00207543.2017.138

7680

Dubey, R., Altay, N., Gunasekaran, A., Blome, C.,

Papadopoulos, T., & Childe, S. J. (2018). Supply chain

agility, adaptability and alignment: Empirical evidence

from the Indian auto components industry.

International Journal of Operations and Production

Management, 38(1), 129–148. https://doi.org/10.1108/

IJOPM-04-2016-0173

Farooque, M., Zhang, A., Thürer, M., Qu, T., & Huisingh,

D. (2019). Circular supply chain management: A

definition and structured literature review. Journal of

Cleaner Production, 228(July), 882–900.

https://doi.org/10.1016/j.jclepro.2019.04.303

Hohenstein, N.-O., Feisel, E., & Hartmann, E. (2016).

Research on the phenomenon of supply chain resilience

A systematic review and paths for further investigation.

International Journal of Physical Distribution &

Logistics Management, 46(2), 153–176.

Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of

quantitative methods for supply chain resilience

analysis. Transportation Research Part E: Logistics

and Transportation Review, 125(February), 285–307.

https://doi.org/10.1016/j.tre.2019.03.001

Ivanov, D., & Dolgui, A. (2020). Viability of intertwined

supply networks: extending the supply chain resilience

angles towards survivability. A position paper

motivated by COVID-19 outbreak. International

Journal of Production Research, 58(10), 2904–2915.

https://doi.org/10.1080/00207543.2020.1750727

Ivanov, D., Dolgui, A., & Sokolov, B. (2019a). The impact

of digital technology and Industry 4.0 on the ripple

effect and supply chain risk analytics. International

Journal of Production Research, 57(3), 829–846.

https://doi.org/10.1080/00207543.2018.1488086

Ivanov, D., Dolgui, A., & Sokolov, B. (2019b). The impact

of digital technology and Industry 4.0 on the ripple

effect and supply chain risk analytics. International

Journal of Production Research, 57(3), 829–846.

https://doi.org/10.1080/00207543.2018.1488086

Manuel Maqueira, J., Moyano-Fuentes, J., & Bruque, S.

(2019). Drivers and consequences of an innovative

technology assimilation in the supply chain: cloud

computing and supply chain integration. International

Journal of Production Research, 57(7), 2083–2103.

https://doi.org/10.1080/00207543.2018.1530473

Morisse, M., & Prigge, C. (2017). Design of a business

resilience model for industry 4.0 manufacturers.

AMCIS 2017 - America’s Conference on Information

Systems: A Tradition of Innovation, 2017-Augus, 1–10.

Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring

supply chain resilience: Development and

implementation of an assessment tool. Journal of

Business Logistics, 34(1), 46–76. https://doi.org/

10.1111/jbl.12009

Queiroz, M. M., Pereira, S. C. F., Telles, R., & Machado,

M. C. (2019). Industry 4.0 and digital supply chain

capabilities: A framework for understanding

digitalisation challenges and opportunities.

Benchmarking, 28(5), 1761–1782. https://doi.org/

10.1108/BIJ-12-2018-0435

Ralston, P., & Blackhurst, J. (2020). Industry 4.0 and

resilience in the supply chain: a driver of capability

enhancement or capability loss? International Journal

of Production Research, 58(16), 5006–5019.

https://doi.org/10.1080/00207543.2020.1736724

Semeraro, C., Lezoche, M., Panetto, H., Dassisti, M., &

Cafagna, S. (2019). Data-Driven Pattern-Based

Constructs Definition for the Digital Transformation

Modelling of Collaborative Networked Manufacturing

Enterprises. IFIP Advances in Information and

Communication Technology, 568, 507–515.

https://doi.org/10.1007/978-3-030-28464-0_44

Spieske, A., & Birkel, H. (2021). Improving supply chain

resilience through industry 4.0: A systematic literature

review under the impressions of the COVID-19

pandemic. Computers and Industrial Engineering,

158(June), 107452. https://doi.org/10.1016/j.cie.20

21.107452

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

160