COVID-19 Challenges: Can Industry 4.0 Technologies Help with Business Continuity?
Abstract
:1. Introduction
1.1. SCs and Epidemic Outbreaks
1.2. Industry 4.0
1.2.1. Industry 4.0 Technologies
- Functional Compatibilities: The intelligent machines are capable of exchanging information between mechanical components and people through the internet and internet of things, forming an intelligent factory, also known as a smart factory.
- Decentralized Controlling Capability: The separate components in the smart factory are capable of making independent decisions in the direction of circumstances without requiring local or individual control [25].
- Virtual Capability: The digital twin of the industry is the virtual replica of the smart factory produced by the big data generated from sensors installed in the production processes. This helps monitor the production and other related metrics for management.
- Data: Data is the most valuable asset for an industry. Industry 4.0 provides the opportunity to gather and analyze data for effective decision-making in real-time scenarios [27].
- Smart Services Orientation: The digitally integrated systems help customers track the data while using the products, and integrates the maintenance and other important checks for smart users.
- Flexible Systems: The smart factories are capable of mass customization in everchanging requirements by using mass production and planning for customized solutions [24].
- Modularity of Systems: The smart factories adapt the changing requirements by expanding or replacing individual modules with respect to the job.
- Rapidly recover revenue.
- Rebuild operations.
- Rethink the organization.
- Accelerate digital adoption to enable reimagination.
1.2.2. Potential Benefits of Using Industry 4.0 Technologies during COVID-19
2. Research Problem and Objectives
3. Methodology
- Experts’ opinions and the calculation of the arithmetic mean direct relationship matrix.
- Normalize the Direct Relation Matrix (X).
- Develop the full Direct/Indirect Relationship Matrix.
- Calculate the Total Relationship Matrix (T).
- Produce the cause and effect relationship diagram from T.
4. Analysis and Discussion
4.1. Delphi-Based Identification of the Challenges
4.2. DEMATEL-Based Analysis of Challenges
4.3. Domain-Oriented Insights
4.3.1. Strategic-Level Supply Chain Experts
4.3.2. Industry 4.0 and Industrial Systems Experts
5. Conclusions and Related Implications
5.1. Empirical and Theoretical Implications
5.2. Managerial Implications
5.3. Ecological and Social Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Participant ID | Expertise | Current Affiliation |
---|---|---|
ID-1 | Workplace design, lean and green production | UET Lahore, Pakistan |
ID-2 | Engineering management | UET Lahore, Pakistan |
ID-3 | Supply chain management, network design, sustainability | UET Lahore, Pakistan |
ID-4 | Industry 4.0, resilience | University of Strathclyde, UK |
ID-5 | Robotics and automation, Industry 4.0, digital twins | Siemens Energy, Denmark |
ID-6 | Sustainable manufacturing, industrial automation | Shandong University, China |
ID-7 | Scheduling in Industry 4.0 | University of Lahore, Islamabad |
ID-8 | Industrial sensors, energy | Tianjin University, China |
ID-9 | Supply chain management, network design, sustainability | NUST, Islamabad |
ID-10 | Lean production in Industry 4.0, decision analytics | LUMS, Lahore |
ID-11 | Industrial system reliability, predictive maintenance | Beijing Institute of Technology, China |
ID-12 | Technology road mapping | UNIDO, Islamabad, Pakistan |
ID-13 | Project planning, supply chain management | TEVTA, Pakistan |
ID-14 | Industrial waste management, production planning | Al-Aziz Packages, Faisalabad |
ID-15 | Supply chain management, production planning | Waves, Lahore |
ID-16 | Applied artificial intelligence in Industry 4.0, big data | KAIST, South Korea |
ID-17 | Industrial automation, artificial intelligence | UET Lahore, Pakistan |
ID-18 | Additive manufacturing | Hong Kong University, Hong Kong |
ID-19 | Project engineering, HSE, operations management | Shell, Islamabad, Pakistan |
ID-20 | Systems integration, flexible manufacturing | Tsinghua University, China |
ID-21 | Cloud computing | Saudi Aramco, Saudi Arabia |
ID-22 | Artificial intelligence, lean transformation | University of Punjab, Lahore |
ID-23 | Internet of things, big data | General Electric, Canada |
ID-24 | Cloud computing, augmented/virtual reality | University of Cambridge, UK |
ID-25 | Lean production system, agile and green manufacturing | Mahmood Group of Textile Industries, Pakistan |
ID-26 | Flexible manufacturing, engineering management | University of Strathclyde, UK |
ID-27 | Internet of things, autonomous robotics | Coca Cola International, Pakistan |
ID-28 | Engineering management, artificial intelligence | Boeing, Canada |
ID-29 | Autonomous robotics | Vivo, Pakistan |
ID-30 | Big data analytics, artificial intelligence | University of Cambridge, UK |
ID-31 | Engineering management, production planning and control | Honda Motor, Japan |
ID-32 | Artificial intelligence, production planning, engineering management | Style Textile, Pakistan |
ID-33 | Engineering management, digital planning | IGI Global, Pakistan |
ID-34 | Additive manufacturing, systems optimization | University of Leeds, UK |
ID-35 | Robotics and automation, Industry 4.0, digital manufacturing | Hong Kong University, Hong Kong |
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Sr. No. | Industry 4.0 Technologies | Brief Introduction | Potential Benefits in the Pandemic | Ref. |
---|---|---|---|---|
1 | Autonomous robotics |
| In the times of COVID-19 pandemic, robots could be used to serve medication to patients, to affirm that people are maintaining social distancing, in industries perform repetitive tasks to create an environment of collaborative workplace and assist in logistical activities in hospitals as well as in industries. | [18,22] |
2 | Big data analytics |
| During pandemic like situations, analytics based on big date helps to analyze and forecast the impacts of disease on people and supply chains. Moreover, it helps in tracking and collecting the real time data which equips medical practitioners, businessmen, analysts, and other personnel. In addition to this, it also improves the policy deployment through sentiment analysis of response of public as well as provides efficient transparency to business and supply chain networks. | [22,31] |
3 | System integration |
| Systems integration across different departments backed by artificial intelligence can help in predicting and minimizing the spread of disease. Moreover, it helps to navigate and take down wrong news to refrain the public from panic. Also, the trail phase could be systemized and helped by creating virtual support through investigating medicine effects on tangible implants and analyzing them across the system. Moreover, the merger of physical world, robots, and simulation based virtual environment can help in analyzing more critical situations. It also helps in manufacturing healthcare related equipment at the time of need. | [13,18,23,32] |
4 | Internet of things |
| Internet of things is helping by providing drone-based surveillance, mask detection and wearing enforcement, segregating affected ones from business and other operations places and helping medical practitioners through diagnosing the disease. It helps industries by keeping social distancing between employees, partially automating the work, and providing the safe and secure operations environment. Moreover, it helps the medical teams in relief operations to monitor patients remotely and provide them medication. | [13,23,31] |
5 | Flexible manufacturing |
| Flexible manufacturing can help in large scale production of essentially required items during pandemics. It happens to support governments through modern technologies such as virtual reality, robots, flexible systems, and industrial designing capabilities. In addition, the systems are backed with the sensors such as biosensor in the time of health crisis (providing essential regulatory measures). These help in carrying out mass production without any hindrance through easily employable, sensitive, and economic support. | [19,23,31] |
6 | Augmented reality |
| The technologies of virtual and AR integrate remote groups working on specific tasks. The technology can help during pandemic and lockdown situations when people’s safety is important and can be maintained through social distancing. It also increases group’s productivity, decreases travelling cost, and consequently, the absenteeism of employees.In addition to above benefits, the technology has also helped in the form of holography. The organization of conferences, meetings, educational activities, and other training sessions for employees, it has helped a lot by providing ultra realistic experience. | [23,33,34] |
7 | Cloud computing |
| During the term of social isolation during health crisis, all services and business went online providing services on internet on the place of physical support. The data collected and managed through these industries increased exponentially which put them in another challenge to analyze the dynamic and live data. Similar is the case with the industries, due to rise in automated systems, the data exponentially increased. Therefore, cloud computing technologies can help in analyzing real time data and optimizing the services and operations experience. Similarly, the data obtained from various hospitals related to disease diagnostic can also be analyzed remotely and transferred to other units for further processing. | [13,22,35] |
8 | Additive manufacturing |
| The additive manufacturing also known as 3D printing can be employed in critically unprecedented conditions. This technology can help in manufacturing the medical aids, providing mass production of surgical masks and ventilator parts, producing medicines, and printing human implants. | [19,31,36] |
No. | Key Challenge | Brief Narrative | References |
---|---|---|---|
1 | Low system flexibility | Decreased throughput rate, expanded lead time, inadequate responsiveness to market fluctuations | [12,19,23,36,40] |
2 | High mismatch between supply and demand | Extremely uncertain demand, decreased logistical activities, delays and supply shortage | [12,17,19,40] |
3 | Elevated consumer generated bullwhip effects | Customer behavior, increased stockpiling, retailers selling monopoly, retailers, and consumer behavior with factory outlets | [9,17,29] |
4 | No responsibility and trust issues | Competency and integrity within SC stakeholders, mismatched deliverables, double-crossing under uncertain environments, contactless deliveries between consumers and retailers | [14,17,38,40] |
5 | Inadequate viability of SC network | Survival during unprecedented circumstances, less resilient systems, low adaptability, inadequate capacity to improve networks to ensure sustainable system | [17,40,41] |
6 | Poor access to real customer and lack of infrastructure | Inadequate penetration to market and access to information, inefficient logistical infrastructure, unavailability of capacity enhancement and subsequent storage opportunities | [12,17,23,40] |
7 | Insufficient human resource availability and security | Shortage of labor and emergency medical support, inefficient security protocols for employees to ensure safer work environment and consumers’ safety | [12,17,19,23] |
8 | Unavailability of medical capabilities for safer workplace | Shortages of disinfectants, safety tools and sanitization amenities | [8,31,38,41] |
9 | Governmental policies and support | Inadequate incentives, insufficient support in logistical activities, lockdowns and enforced restrictions on business retails, no cost-sharing opportunities for special production targets | [6,19,20,38,40] |
10 | Inadequate system transparency and communication | Openness between SC nodes, miscommunication between organization, governmental authorities, and local bodies | [17,19,33,40] |
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | |
---|---|---|---|---|---|---|---|---|---|---|
C1 | 0 | 3 | 2 | 1.5 | 1 | 2 | 0.5 | 0.5 | 0.5 | 1.5 |
C2 | 1.67 | 0 | 3 | 2 | 1 | 1 | 1.5 | 0.5 | 0.5 | 1 |
C3 | 2.33 | 2 | 0 | 1 | 1 | 1.5 | 1 | 1 | 0.5 | 0.5 |
C4 | 1.33 | 1.67 | 2 | 0 | 1.5 | 1.5 | 0.5 | 1 | 0.5 | 0.5 |
C5 | 1.33 | 2.33 | 2.33 | 1.5 | 0 | 2.5 | 2 | 1 | 1 | 1 |
C6 | 2 | 3 | 2 | 2.67 | 2 | 0 | 2.5 | 3 | 0.5 | 2.5 |
C7 | 1.33 | 2.67 | 1.67 | 2.67 | 2 | 1.33 | 0 | 1 | 0.5 | 1.5 |
C8 | 0.67 | 0.33 | 1 | 1 | 1.67 | 1.33 | 2 | 0 | 2 | 2 |
C9 | 1 | 1.67 | 1.67 | 1.67 | 1.33 | 2.67 | 2 | 3 | 0 | 2 |
C10 | 1 | 1.67 | 1.33 | 1.67 | 2 | 2.67 | 2 | 1 | 1.33 | 0 |
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | |
---|---|---|---|---|---|---|---|---|---|---|
C1 | 0 | 0.148 | 0.099 | 0.074 | 0.049 | 0.099 | 0.024 | 0.024 | 0.024 | 0.074 |
C2 | 0.082 | 0 | 0.148 | 0.099 | 0.049 | 0.049 | 0.074 | 0.024 | 0.024 | 0.049 |
C3 | 0.115 | 0.099 | 0 | 0.049 | 0.049 | 0.074 | 0.049 | 0.049 | 0.024 | 0.024 |
C4 | 0.065 | 0.082 | 0.099 | 0 | 0.074 | 0.074 | 0.024 | 0.049 | 0.024 | 0.024 |
C5 | 0.065 | 0.115 | 0.115 | 0.074 | 0 | 0.123 | 0.099 | 0.049 | 0.049 | 0.049 |
C6 | 0.099 | 0.148 | 0.099 | 0.132 | 0.099 | 0 | 0.123 | 0.148 | 0.024 | 0.123 |
C7 | 0.065 | 0.132 | 0.082 | 0.132 | 0.099 | 0.065 | 0 | 0.049 | 0.024 | 0.074 |
C8 | 0.033 | 0.016 | 0.049 | 0.049 | 0.082 | 0.065 | 0.099 | 0 | 0.099 | 0.099 |
C9 | 0.049 | 0.082 | 0.082 | 0.082 | 0.065 | 0.132 | 0.099 | 0.148 | 0 | 0.099 |
C10 | 0.049 | 0.082 | 0.065 | 0.082 | 0.099 | 0.132 | 0.099 | 0.049 | 0.065 | 0 |
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | |
---|---|---|---|---|---|---|---|---|---|---|
C1 | 0.136 | 0.317 | 0.267 | 0.225 | 0.178 | 0.243 | 0.159 | 0.136 | 0.092 | 0.186 |
C2 | 0.206 | 0.175 | 0.297 | 0.236 | 0.170 | 0.192 | 0.190 | 0.128 | 0.088 | 0.154 |
C3 | 0.223 | 0.253 | 0.152 | 0.184 | 0.161 | 0.202 | 0.162 | 0.143 | 0.084 | 0.130 |
C4 | 0.177 | 0.231 | 0.238 | 0.130 | 0.179 | 0.199 | 0.138 | 0.141 | 0.083 | 0.124 |
C5 | 0.225 | 0.327 | 0.313 | 0.259 | 0.159 | 0.296 | 0.253 | 0.185 | 0.130 | 0.191 |
C6 | 0.293 | 0.410 | 0.355 | 0.359 | 0.297 | 0.237 | 0.320 | 0.302 | 0.138 | 0.295 |
C7 | 0.215 | 0.328 | 0.276 | 0.298 | 0.241 | 0.238 | 0.151 | 0.171 | 0.104 | 0.201 |
C8 | 0.163 | 0.202 | 0.216 | 0.206 | 0.214 | 0.224 | 0.233 | 0.121 | 0.167 | 0.216 |
C9 | 0.226 | 0.321 | 0.307 | 0.291 | 0.248 | 0.332 | 0.282 | 0.295 | 0.102 | 0.260 |
C10 | 0.208 | 0.299 | 0.270 | 0.268 | 0.253 | 0.307 | 0.257 | 0.189 | 0.146 | 0.146 |
Rank | |||||
---|---|---|---|---|---|
C1 | 1.944 | 2.076 | −0.132 | 4.020 | 8 |
C2 | 1.842 | 2.867 | −1.024 | 4.709 | 2 |
C3 | 1.698 | 2.696 | −0.997 | 4.394 | 4 |
C4 | 1.644 | 2.461 | −0.816 | 4.106 | 7 |
C5 | 2.343 | 2.104 | 0.238 | 4.447 | 3 |
C6 | 3.011 | 2.475 | 0.535 | 5.486 | 1 |
C7 | 2.227 | 2.151 | 0.075 | 4.378 | 5 |
C8 | 1.966 | 1.816 | 0.150 | 3.782 | 10 |
C9 | 2.668 | 1.138 | 1.529 | 3.807 | 9 |
C10 | 2.349 | 1.908 | 0.441 | 4.257 | 6 |
Challenges as Causes | Challenges as Effects | ||
---|---|---|---|
Challenge No. | Rank | Challenge No. | Rank |
C9 | 1 | C2 | 1 |
C6 | 2 | C3 | 2 |
C10 | 3 | C4 | 3 |
C5 | 4 | C1 | 4 |
C8 | 5 | ||
C7 | 6 |
Case 1 | Case 2 | Case 3 | Case 4 | |
---|---|---|---|---|
Expert 1 | 0.33 | 0.25 | 0.25 | 0.50 |
Expert 2 | 0.33 | 0.25 | 0.50 | 0.25 |
Expert 3 | 0.33 | 0.50 | 0.25 | 0.25 |
Challenge No. | Case 1 | Case 2 | Case 3 | Case 4 | ||||
---|---|---|---|---|---|---|---|---|
Rank | Rank | Rank | Rank | |||||
C1 | 4.020 | 8 | 4.120 | 4 | 3.416 | 10 | 3.674 | 6 |
C2 | 4.709 | 2 | 4.497 | 2 | 4.396 | 3 | 4.308 | 2 |
C3 | 4.394 | 4 | 4.276 | 3 | 4.251 | 4 | 3.673 | 7 |
C4 | 4.106 | 7 | 3.598 | 9 | 3.900 | 6 | 3.608 | 8 |
C5 | 4.447 | 3 | 4.025 | 5 | 3.806 | 8 | 3.705 | 5 |
C6 | 5.486 | 1 | 4.956 | 1 | 5.079 | 1 | 4.471 | 1 |
C7 | 4.378 | 5 | 3.834 | 7 | 4.409 | 2 | 3.766 | 4 |
C8 | 3.782 | 10 | 3.215 | 10 | 3.701 | 9 | 3.299 | 10 |
C9 | 3.807 | 9 | 3.703 | 8 | 3.831 | 7 | 3.541 | 9 |
C10 | 4.257 | 6 | 3.836 | 6 | 4.171 | 5 | 3.892 | 3 |
Research Question | Reference | |
---|---|---|
RQ 1 | What are the potential challenges faced by supply chains specifically manufacturing sectors during COVID-19 pandemic to carry out sustainable operations? | The output of Delphi approach stated in Table 2 highlights the potential challenges. |
RQ 2 | How can these challenges be prioritized and categorized from a strategy perspective under the paradigm of sustainability? | The DEMATEL analysis priortized and categorized the said challenges as termed in Table 7. |
RQ 3 | Which major challenge should be resolved on a priority basis by supply chains specifically manufacturing sectors for sustainable business models under disrupted conditions? | The ranking shown in Table 9 demonstrats the major challenge. The sensitivity analysis results C6 to be the major challenge in said perspective. |
RQ 4 | Which major challenges can be resolved by industry 4.0 technologies? | The causal group formed through DEMATEL analysis are the most priortized challenges which need immediate focus and evaluation through Industry 4.0 lens as illustrated in Figure 3. |
RQ 5 | What is the potentiality of industry 4.0 technologies to resolve these challenges for long term sustainability? | The output of Domain-experts focus on the potentiality of technologies to resolve the selected challenges as shown in Figure 4 and Figure 5. |
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Hussain, A.; Farooq, M.U.; Habib, M.S.; Masood, T.; Pruncu, C.I. COVID-19 Challenges: Can Industry 4.0 Technologies Help with Business Continuity? Sustainability 2021, 13, 11971. https://doi.org/10.3390/su132111971
Hussain A, Farooq MU, Habib MS, Masood T, Pruncu CI. COVID-19 Challenges: Can Industry 4.0 Technologies Help with Business Continuity? Sustainability. 2021; 13(21):11971. https://doi.org/10.3390/su132111971
Chicago/Turabian StyleHussain, Amjad, Muhammad Umar Farooq, Muhammad Salman Habib, Tariq Masood, and Catalin I. Pruncu. 2021. "COVID-19 Challenges: Can Industry 4.0 Technologies Help with Business Continuity?" Sustainability 13, no. 21: 11971. https://doi.org/10.3390/su132111971