TM 562 project Supply Chain Resilience through Industry 4.0
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Central Connecticut State University *
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562
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Industrial Engineering
Date
Jan 9, 2024
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docx
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16
Uploaded by AmalElkassas
Project TM 562
How Industry 4.0 contribute to supply chain resilience
Abstract:
While the potential benefits of Industry 4.0 on Supply Chain Resilience (SCR) have garnered attention, the mechanisms behind these contributions remain largely unexplored. This study presents a comprehensive roadmap elucidating how supply chains can leverage Industry 4.0 functions to enhance SCR. Employing a content-
centric literature review, 16 interrelated Industry 4.0 SCR functions were identified. Findings emphasize the importance of aligning digitalization strategies with the sequence in which Industry 4.0 delivers these functions. Initially, Industry 4.0 contributes
to SCR through data-centric functions such as supply chain automation, information and
communication quality, process monitoring, and visibility. Subsequently, it facilitates enhanced collaboration among supply chain partners in areas like supply chain mapping, complexity management, and innovation capabilities. By augmenting transparency, flexibility, and agility, Industry 4.0 enables more advanced resilience functions, including supply chain responsiveness, adaptive capability, and continuity management. The roadmap details the mutual interactions between each pair of Industry 4.0 SCR functions, collectively fortifying the overall resilience of the supply chain. The study concludes by discussing potential implications and outlining crucial avenues for future research.
1-Introduction:
The recent supply chain disruptions caused by factors such as the Covid-19 pandemic and geopolitical shifts have highlighted the need for supply chain resilience. Industry 4.0, which refers to the digital industrial revolution, offers potential solutions to mitigate these disruptions and enhance supply chain resilience. This includes the use of
technologies such as big data analytics, artificial intelligence, and blockchain, as well as
design principles such as interoperability and decentralization. However, there is a lack of empirical research on how Industry 4.0 can enable supply chain resilience. This study
aims to fill this knowledge gap by identifying and modeling the functions of Industry 4.0 that contribute to supply chain resilience. The study develops a resilience roadmap that outlines the optimal sequence for developing these functions and explores the relationships between them. This research can provide valuable insights for supply chain partners on how to effectively leverage Industry 4.0 to enhance their collective supply chain resilience.
2. Industry 4.0:
Industry 4.0, also known as the fourth industrial revolution, is a concept that originated in Germany in 2011. It refers to the digitalization of manufacturing processes and value chains across various industries. Industry 4.0 involves the use of advanced digital technologies such as artificial intelligence, additive manufacturing, and Cyber-Physical Systems to drive the digital revolution. Design principles, such as vertical-horizontal integration, real-time capability, decentralization, and virtualization, are necessary conditions for businesses to unlock the potential of Industry 4.0. The development of Digital Supply Networks (DSN) is a key aspect of Industry 4.0, involving the digitalization of supply chain components, including suppliers, manufacturers, logistics channels, distributors, and customers. The digitalization of supply chains under Industry
4.0 involves implementing combinations of digital technologies based on each component's strategic needs. DSN offers opportunities for maximizing stakeholder value, such as customer satisfaction, regulatory compliance, improved revenue, and brand responsiveness.
3. Supply chain resilience:
Supply Chain Resilience (SCR) refers to a supply chain's ability to recover and withstand future disruptions and to resist future interruptions, originating from social psychology theory (Tortorella et al., 2022). The recovery aspect involves quickly
restoring optimal operational performance after a disruption, while the resistance aspect
focuses on minimizing disruption impacts proactively. Disruptions can stem from socio-
political shifts, natural disasters, or economic crises, as exemplified by the global supply
chain disruptions during the Covid-19 crisis (Peng et al., 2021; Spieske and Birkel, 2021). The need to build SC resilience capabilities has gained attention in operations and supply chain management.
Various resilience strategies have been proposed, categorized as reactive, proactive, or
a combination of both. Widely accepted strategies include SC visibility, flexibility, postponement, collaboration, information security, and automation (Bag, Dhamija, et al.,
2021; Bag, Gupta, et al., 2021; Belhadi, Kamble, Fosso Wamba, et al., 2021; Belhadi, Kamble, Jabbour, et al., 2021; Ivanov and Dolgui, 2021; Senna et al., 2023). Digitalization is emerging as a key resilience strategy, with studies exploring its role in SC collaboration, innovation, information security, and mindfulness (Zouari, Ruel, and Viale, 2021; Belhadi, Mani, et al., 2021; Dennehy et al., 2021; Lohmer, Bugert, and Lasch, 2020). The interaction between Industry 4.0 and SC resilience is a growing area of research, with scholars examining how Industry 4.0 technologies, such as big data analytics, machine learning, and real-time communication, can enhance SC disruption risk management (Ralston and Blackhurst, 2020; Ivanov and Dolgui, 2021; Spieske and
Birkel, 2021).
Although the literature highlights the positive outcomes of Industry 4.0 for SCR, it falls short in explaining how the various Industry 4.0 resilience functions interact to promote SCR. Further research is needed to explore these interactions and provide a comprehensive understanding of the role of Industry 4.0 in enhancing supply chain resilience.
4.The Four Important Categories in Supply Chain Risk Management:
Uncertainty:
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Definition: Present in systems of significant complexity, uncertainty can either decrease or intensify. It stems from a lack of knowledge or information about future events.
Outcomes: Uncertainty involves unknown outcomes.
Minimization
: Uncertainty cannot be minimized.
Probabilities: Uncertainty does not allow for the assignment of probabilities.
Handling Measures: Uncertainty, in contrast, cannot be measured in quantitative terms as future events are unpredictable, but we can Implement forecasting techniques, scenario planning, and real-time information systems to reduce uncertainty.
Risk:
Definition: Risk refers to the situation involving the potential of gaining or losing something valuable.
Arising from uncertainty, risk is identifiable, analyzable, and controllable. It represents the potential for an event to negatively impact the supply chain, causing disturbance.
Outcomes: In risk, potential outcomes are known.
Minimization: Risk can be minimized by taking necessary precautions.
Probabilities: can be assigned to a set of circumstances in the case of risk.
handling measures: Risk can be measured and quantified using theoretical models, to be controlled through proper measures and precautions.
1-Identify risks through analysis and assessment.
2-Analyze the impact and likelihood of each risk.
3-Develop strategies to mitigate identified risks.
4-Continuously monitor and control risks.
5-Collaborate with stakeholders to manage risks.
6-Consider insurance and other risk transfer options.
7-Continuously improve risk management processes.
By following these measures, supply chain managers can better manage and reduce the negative effects of potential disruptions, making their operations more reliable and resilient.
Seven Different Supply Chain Risks:
Quang and Hara (2017) have classified the following groups of risks into seven categories:
1-External risks: These risks originate from external factors such as economic, sociopolitical, or geographical conditions. Examples include fire accidents, natural disasters, economic downturns, external legal issues, corruption, and cultural differences.
2-Time risks: These risks pertain to delays in supply chain processes.
3-Information risks: This category involves risks associated with communication breakdowns within the project team, complications with information infrastructure, distorted information, and information leakage.
4-Financial risks: These risks involve factors such as inflation, interest rates, currency fluctuations, and stakeholder demands.
5-Supply risks: This category encompasses risks related to suppliers, such as supplier bankruptcies, price fluctuations, and unstable quality and quantity of inputs.
6-Operational risks: This category comprises risks arising from problems within the organizational boundaries of a firm, such as changes in design and technology, accidents, and labor disputes.
7-Demand risks: This category refers to risks associated with demand variability, intense market competition, customer bankruptcies, and customer fragmentation.
To grasp the distinctions between risk and uncertainty, refer to the table below, which succinctly outlines the key differences:
Disturbance (Perturbation Impact):
Definition: An external force affecting the supply chain's regular operations.
Handling Measures: Building flexibility into the supply chain, implementing agile practices, and maintaining strong relationships with suppliers.
Disruption (Operational Deviations):
Definition: Sudden and unexpected events that significantly impact the supply chain.
Handling Measures: Developing robust contingency plans, establishing backup suppliers, and implementing rapid response mechanisms. Differentiating between delay and disruption is important in the aerospace industry. Analyzing cause-and-effect relationships and documenting actual vs. achievable production are key to successful disruption claims.
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Connection Between the Four Elements:
Uncertainty, Risk, Disturbance, and Disruption are Mutually Connected:
Uncertainty creates the foundation for risk.
Disturbance is a potential precursor to disruption.
Risk mitigation strategies can reduce the impact of disturbances and disruptions.
In summary, the four elements are interconnected in a way that highlights the dynamic and complex nature of supply chain risk management. Addressing uncertainty, managing risks, and preparing for disturbances and disruptions are crucial for building a
resilient and efficient supply chain.
Examples of disturbances and disruptions in supply chains can be categorized into various factors, each with its specific impact:
Terrorism and Piracy:
Example: September 11 attacks, Somali piracy in 2008
Impacts: Closure of five Ford plants for an extended period, breaks in multiple supply chains
Natural Disasters:
Examples: Earthquake in Thailand (1999), Flood in Saxony (2002), Earthquake in Japan (2007), Hurricane Katrina (2006), Earthquake and tsunami in Japan (2011), Floods in Chennai, India (2015)
Impacts: Paralysis of Apple computers’ production in Asia, significant production decrease at VW, Dresden, production breakdown in Toyota’s supply chains (55,000 cars
affected), 10–15% halt in total US gasoline production, massive collapses in global automotive and electronics supply chains, stoppage of academic literature production at
international publishing houses
Manmade Disasters:
Examples: Explosion at BASF plant in Ludwigshafen (2016), Fire at distribution centre of e-commerce retail company ASOS (2005), Fire in Phillips Semiconductor plant in Albuquerque, New Mexico (2000)
Impacts: 15% of raw materials missing for the entire supply chain at BASF, production stoppage for weeks at BASF, one-month delivery stop at ASOS, $400 million potential revenue loss for Ericsson (Phillips's major customer)
Political Crises:
Example: "Gas" crisis in 2009
Impacts: Breaks in gas supply from Russia to Europe, resulting in billions of losses to GAZPROM and customers
Financial Crises:
Example: Autumn 2008
Impacts: Production decrease or closure, breaks in supply chains across industries
Strikes:
Example: Strikes at Hyundai plants in 2016
Impacts: Production of 130,000 cars affected
Legal Contract Disputes:
Example: Volkswagen and Prevent Group contract dispute in summer 2016
Impacts: Six German factories face production halt due to parts shortage, affecting 27,700 workers with some sent home and others moved to short time working.
Explanations of Terms:
1. Big Data Analytics:
Definition: The term "Big Data" commonly refers to the vast volume of unstructured or semi-structured data generated daily by companies, their devices, machines, or products in use. The task of organizing and structuring this data demands significant
effort, and the subsequent analysis, once uploaded into a relational database, requires extensive expertise and labor. The term "big" in Big Data Analytics (BDA) pertains to the
sheer magnitude of data generated, adjusted, or modified on a day-to-day basis. Big Data Analytics encompasses the process of scrutinizing large and intricate datasets to unveil concealed patterns, correlations, and insights, ultimately supporting informed decision-making.
2. Industry 4.0:
Definition: Industry 4.0 represents the fourth industrial revolution, characterized by the integration of digital technologies, such as IoT, AI, and smart manufacturing, to create interconnected and intelligent production systems.
3. Blockchain:
Definition: Blockchain, initially developed for Bitcoin security, revolutionizes Supply Chain Operations Management (SCOM). It serves as a decentralized, tamper-resistant ledger across a computer network, ensuring security through consistency checks. In SCOM, it boosts visibility, efficiency, and secure data sharing, storing vital asset information. Blockchain's simplicity makes it ideal for sourcing processes, reducing wastage and expediting regulatory procedures. Overall, it is a decentralized, distributed digital ledger enabling secure and transparent transaction recording.
4. Digital Twins:
Definition: The concept of a "digital twin" involves crafting a virtual duplicate, with engineers generating a digital representation during the product development stage. This digital counterpart undergoes continuous enrichment with additional data throughout manufacturing, product usage, and maintenance phases. Sensors facilitate the exchange of status data between the virtual and real product, collecting information across the product's entire lifespan. Essentially, digital twins are virtual replicas of physical objects, processes, or systems, enabling real-time monitoring, simulation, and analysis of their corresponding physical entities.
5. RFID (Radio-Frequency Identification):
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Definition: RFID is a technology that uses radio-frequency signals to identify and track objects, such as products, throughout the supply chain.
6. Tracking and Tracing Systems:
Definition: Tracking and tracing systems involve the use of technologies like RFID, GPS,
and sensors to monitor and trace the movement of goods throughout the supply chain, providing real-time visibility.
These digital technologies play a crucial role in enhancing efficiency, visibility, and decision-making across various stages of the supply chain.
Here below Digital Supply Chain, Smart Operations, and Industry 4.0
Examples of how digital technologies can be applied to each element of the SCOR Model - plan, source, make, deliver, and return:
Plan:
1-Demand Forecasting with Artificial Intelligence (AI):
Application: AI-driven demand forecasting tools can analyze historical data, market trends, and external factors to predict future demand more accurately.
SCOR Relation: This technology enhances the planning phase by providing more precise demand forecasts, allowing organizations to optimize inventory levels and production plans.
2-Advanced Analytics for Network Optimization:
Application: Advanced analytics tools can analyze various supply chain network scenarios, considering factors such as transportation costs, lead times, and production capacities.
SCOR Relation: By leveraging analytics, organizations can optimize their supply chain network design during the planning phase, leading to more efficient and cost-effective sourcing and distribution.
Source:
1-Blockchain for Supply Chain Transparency:
Application: Blockchain technology can be used to create a transparent and traceable supply chain by recording every transaction and movement of goods in a secure and immutable ledger.
SCOR Relation: Enhances visibility and traceability in the sourcing phase, ensuring the authenticity and origin of products, and mitigating risks related to counterfeiting or fraud.
2-Supplier Relationship Management (SRM) Platforms:
Application: SRM platforms provide a centralized digital space for managing relationships with suppliers, enabling collaboration, performance monitoring, and risk assessment.
SCOR Relation: Supports the sourcing process by improving communication with suppliers, evaluating their performance, and mitigating risks associated with the supply base.
Make:
1-IoT-Enabled Predictive Maintenance:
The Internet of Things (IoT) plays a pivotal role in facilitating predictive maintenance. Embedded IoT devices within machinery and equipment continuously gather real-time data, including information on temperature, vibration, pressure, and usage. Subsequently, machine learning algorithms and predictive models analyze this data to discern patterns indicative of potential failures or anomalies in the system.
Application: IoT sensors on manufacturing equipment can collect real-time data, allowing for predictive maintenance to reduce downtime and extend the lifespan of machinery.
SCOR Relation: Improves the make phase by minimizing disruptions in production, ensuring equipment reliability, and optimizing overall operational efficiency.
2-3D Printing/Additive Manufacturing:
Application: 3D printing enables on-demand production of components, reducing lead times and enabling more flexible and localized manufacturing.
SCOR Relation: Enhances the make process by enabling agile and decentralized manufacturing, allowing to produce customized or low-volume items.
Deliver:
1-Real-Time Shipment Tracking with RFID and GPS:
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Application: RFID tags and GPS technology provide real-time visibility into the location and condition of shipments, improving tracking accuracy and delivery performance.
SCOR Relation: Enhances the deliver phase by providing end-to-end visibility, reducing the risk of delays, and improving overall supply chain responsiveness.
2-Automated Last-Mile Delivery Solutions:
Application: Autonomous vehicles and drones can be employed for last-mile deliveries, reducing delivery times and costs.
SCOR Relation: Optimizes the delivery process by increasing efficiency in the last leg of
the supply chain, improving customer satisfaction through faster and more reliable deliveries.
Return:
1-Reverse Logistics Optimization with Analytics:
Application: Analytics tools can analyze data from returns, identifying patterns and reasons for product returns, allowing for better decision-making in reverse logistics.
SCOR Relation: Enhances the return process by optimizing reverse logistics operations,
reducing costs, and improving the management of returned products.
2-Digital Returns Management Platforms:
Application: Dedicated digital platforms can streamline the returns process, facilitating communication between customers, retailers, and logistics providers.
SCOR Relation: Improves the return phase by providing transparency and efficiency in handling returns, allowing for faster processing and resolution.
These examples showcase how digital technologies can be strategically applied to each
element of the SCOR Model, contributing to the optimization and efficiency of supply chain management processes.
Case Study: Smart Manufacturing and Supply Chain Resilience
BMW Group embraces Industry 4.0, focusing on customer preferences, flexibility, and quality. Digitalization includes smart data analytics, logistics, automation, assistance systems, and additive manufacturing, enhancing efficiency throughout production. Smart data analytics uses algorithms for error identification, virtual reality optimizes processes, and intelligent data analysis improves quality. In smart logistics, BMW pioneers with autonomous tugger trains, smart transport robots, and a connected distribution network. Innovative automation aims to complement human flexibility with intelligent solutions, relieving employees and leveraging robotic strengths.
Industry 4.0 Technologies Implemented:
IoT Sensors and Predictive Maintenance:
The company installed IoT sensors on critical machinery in manufacturing plants.
Real-time data on equipment health, temperature, and performance were continuously monitored.
Predictive maintenance algorithms analyzed the data to predict potential failures.
Blockchain for Transparent Supply Chain:
Blockchain was employed to create a transparent and traceable supply chain.
Every transaction, from raw material sourcing to final product delivery, was recorded in an immutable ledger.
This increased visibility and reduced the risk of counterfeit products.
Big Data Analytics for Demand Forecasting:
Big data analytics tools were utilized to analyze historical data, market trends, and external factors.
Accurate demand forecasting helped in optimizing inventory levels and production plans.
RFID and GPS for Real-Time Shipment Tracking:
RFID tags and GPS technology were integrated into the supply chain to provide real-
time visibility into shipments.
This reduced the risk of delays and improved overall responsiveness.
Results:
Reduced Downtime:
Predictive maintenance led to a significant reduction in unplanned downtime.
Machinery issues were identified and addressed before they could cause major disruptions.
Improved Visibility:
The transparent supply chain, enabled by blockchain and real-time tracking, improved overall visibility.
Supply chain managers could proactively address potential disruptions and make informed decisions.
Enhanced Risk Management:
The combination of technologies allowed for better risk management.
The company could identify and assess risks in real-time, allowing for timely mitigation strategies.
Agile Response to Disruptions:
With accurate demand forecasting and real-time tracking, the company could quickly adjust production schedules and distribution plans in response to disruptions.
Customer Satisfaction:
The agile and resilient supply chain resulted in improved customer satisfaction.
The company could meet customer demands even in the face of unforeseen challenges.
Conclusion:
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This case study illustrates how Industry 4.0 technologies, when strategically implemented, can significantly enhance supply chain resilience. By leveraging real-time data, predictive analytics, and transparent tracking, companies can build agile and responsive supply chains capable of withstanding various disruptions.