Risk Management in Supply Chain Processes

Introduction

Supply chain risk management relates to the procedure through which organizations employ strategic steps to discern, evaluate, and mitigate threats within their end-to-end supply chain. Two primary risks could disrupt one’s supply chain; including external and internal risks (Prakash et al., 2017; Son, 2018). Threats in the supply chain typically emerge due to

• Operational fluctuations, for instance, price variability, demand uncertainties, and supply variability.
• Natural occurrences include epidemics, cyclones, and earthquakes.
• Human-made crises such as economic recessions, unethical business operations, and terrorist attacks.
• Political, infrastructural, and cultural differences and trends towards lean practices, single-sourcing, and outsourcing have also increased the supply chain’s susceptibility to risks.

Supply chain (SC) innovation is a complex procedure that integrates technology and process innovation to offer solutions for consumer needs and distinguish new ways to improve processes. This approach enhances better operational capacities and facilitates the augmentation of risk management capability (Ho et al., 2015). It serves as a catalyst to enhance several activities’ efficiency, including forecasting, monitoring, purchasing, and planning in complex SC practices. This presentation provides insights into the recent innovations in SCRM.

SCRM Innovations

Radio Frequency Identification Device (RFID)

RFID relates to an automatic discerning technology used to identify specific aspects and collects pertinent information without data entry or human intervention. Some of the major features associated with this innovation include

It is wireless.

Offers a specific object identification.

It tracks and traces objects.

This gadget has three primary capabilities: supply chain visibility, closed-loop tracking, and advanced procedure automation. According to Dmitry et al. (2019), the above-mentioned RFID technological capacities yield three significant risk management abilities: high-quality decision-making processes and increased response speed and monitoring capacity.

Big Data Analytics

Big data analytics refers to the complex procedure of evaluating big data to identify information such as consumer preferences, market trends, correlations, and hidden patterns that could help a company make informed operational or business decisions.

According to Birkel and Hartmann (2020), it integrates relevant techniques and technologies to offer an efficient means for analyzing data sets and uncovering new information deemed crucial during the decision-making process.

Big data analytics allows organizations to translate the significant amount of information generated by the supply chain into valuable insights. This helps to distinguish both opportunities and issues and transform the firm’s business approach from reactive to proactive (Wang et al., 2020).

Supply chain analytics integrates the use of quantitative approaches and data for better decision-making. The outcomes or insights obtained from analytics become critical for supply chain risk management, particularly during this era of increased interconnectivity (Orger et al., 2018). New threats such as cyber threats typically emerge alongside conventional ones, increasing the supply chain’s vulnerability.

Big data plays a crucial role in detecting and preventing or eliminating cyber-related hazards. This procedure can also trigger significant customer service improvements by preventing shipment delays because of unforeseen situations and increasing better product preservation during transportation.

An effective SCRM also depends on a predictive and proactive approach enhanced by big data analytics. Discerning and mitigating threats prior to the onset of their negative impact help minimize financial and operational losses (Birkel & Hartmann, 2020). The key elements included in the management of the supply chain risks using big data are as follows:

• Increased control and visibility over suppliers’ networks: This innovation can offer insights into each purveyor’s performance for improved risk management. This element is essential and useful for organizations working with several suppliers.
• Alignment and integration of supply chain: Typically, SCRM may be conducted separately by every member of the supply chain. Under this circumstance, data analytics can help transform the procedure into a coordinated effort to ensure the entire supply chain shares in the benefits instead of single entities (Zhao et al., 2020).
• Increased resilience and agility: Merging supply chains and big data analytics can be instrumental in the attainment of a specific resilience level within the supply chain through the analysis of huge data volumes.

Big data plays a crucial role in detecting and preventing or eliminating cyber-related hazards. This procedure can also trigger significant customer service improvements by preventing shipment delays because of unforeseen situations and increasing better product preservation during transportation.

An effective SCRM also depends on a predictive and proactive approach enhanced by big data analytics. Discerning and mitigating threats prior to the onset of their negative impact help minimize financial and operational losses (Birkel & Hartmann, 2020). The key elements included in the management of the supply chain risks using big data are as follows:

• Increased control and visibility over suppliers’ networks: This innovation can offer insights into each purveyor’s performance for improved risk management. This element is essential and useful for organizations working with several suppliers.
• Alignment and integration of supply chain: Typically, SCRM may be conducted separately by every member of the supply chain. Under this circumstance, data analytics can help transform the procedure into a coordinated effort to ensure the entire supply chain shares in the benefits instead of single entities (Zhao et al., 2020).
• Increased resilience and agility: Merging supply chains and big data analytics can be instrumental in the attainment of a specific resilience level within the supply chain through the analysis of huge data volumes.

Machin learning strategies can be used across the supply chain to perform various tasks; however, its efficacy is impacted by accessibility to adequate data amounts and the appropriate data type. Deep neural networks, neural network programming, and deep learning are among the upcoming technological advancements which would act as machine learning subsets (Wang et al., 2020). These innovations will facilitate the A.I. system’s capacity to learn, take commands, and improve its behavior. Machine learning systems typically offer the following features:

• Explainability: The capacity to elucidate the functioning of the A.I system (Baryannis et al., 2018).
• Interpretability: This refers to the level to which an individual can predict an occurrence if a change is initiated to the system’s input or algorithm parameters. It is the capacity to interpret the system’s functioning (Baryannis et al., 2018).
• Auditability: This component highlights the ability to audit the data, system, and the utilization of information (Layak et al., 2019).
• Transparency: This element underscores the A.I system’s ability to offer full access to the system’s operations and guarantee stakeholders’ trust (Layak et al., 2019). The utilization of A.I. techniques, especially machine learning methodologies, could be leveraged to facilitate the organization’s capacity to harness fast and big data from the supply chain, forecast threats within the supply chain, and outline mitigating approaches.

• A.I’s future and its impact on SCRM typically depend on: predictive analytics (the data analytics system’s capacity to distinguish information patterns and anticipate prospective events) and prescriptive analytics (the system’s capability to take a command to address predefined goals).

Machin learning strategies can be used across the supply chain to perform various tasks; however, its efficacy is impacted by accessibility to adequate data amounts and the appropriate data type. Deep neural networks, neural network programming, and deep learning are among the upcoming technological advancements which would act as machine learning subsets (Wang et al., 2020). These innovations will facilitate the A.I system’s capacity to learn, take commands, and improve its behavior. Machine learning systems typically offer the following features:

• Explainability: The capacity to elucidate the functioning of the A.I system (Baryannis et al., 2018).
• Interpretability: This refers to the level to which an individual can predict an occurrence if a change is initiated to the system’s input or algorithm parameters. It is the capacity to interpret the system’s functioning (Baryannis et al., 2018).
• Auditability: This component highlights the ability to audit the data, system, and the utilization of information (Layak et al., 2019).
• Transparency: This element underscores the A.I system’s ability to offer full access to the system’s operations and guarantee stakeholders’ trust (Layak et al., 2019).

The utilization of A.I. techniques, especially machine learning methodologies, could be leveraged to facilitate the organization’s capacity to harness fast and big data from the supply chain, forecast threats within the supply chain, and outline mitigating approaches.

A.I’s future and its impact on SCRM typically depend on: predictive analytics (the data analytics system’s capacity to distinguish information patterns and anticipate prospective events) and prescriptive analytics (the system’s capability to take a command to address predefined goals).

Conclusion

SCRM-related innovations play a crucial role in enhancing an organization’s capacity to identify and manage risks within its supply chain. They serve as a catalyst to enhance several activities’ efficiency, including forecasting, monitoring, purchasing, and planning in complex SC practices. Some of the recently developed technologies used to improve the SCRM process include RFID, big data analytics, and A.I. systems such as machine learning. The efficacy of these technological advancements relies on their response to decisions and proactive status.

References

Baryannis, G., Validi, S., Dani, S., & Antoniou. G. (2018). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2102.

Birkel, H. S., & Hartmann, E. (2020). Internet of Things – the future of managing supply chain risks. Supply Chain Management: An International Journal, 25(5), 535-548.

Dmitry, I., Alexandre, D., & Boris, S. (2019). The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production, 57(3), 829–846.

Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research, 53(16), 1-56. Web.

Layak, M. W., Goudz, A., Noche, B., & Artif, M. (2019). The impact of digitization on tactical procurement and its risks management. International Academic Journal of Procurement and Supply Chain Management, 3(1), 217-234.

Son, C. E. (2018). Supply chain risk management: A review of thirteen years of research. American Journal of Industrial and Business Management, 8, 2294-2320. .

Orger, R., Benaben, F., Lauras, M., & Montreuil, B. (2018). Towards decision support automation for supply chain risk management among logistics network stakeholders. IFAC-PapersOnLine, 51(11), 1505-1510.

Prakash, S., Soni, G., & Rathore, A.P.S. (2017). A critical analysis of supply chain risk management content: A structured literature review. Journal of Advances in Management Research, 14(1), 69-90.

Wang, M., Asian, S., Wood, L. C., & Wang, B. (2020). Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era. Modern Supply Chain Research and Applications, 2(2), 83-98.

Zhao, J., Ji, M., & Feng, B. (2020). Smarter supply chain: A literature review and practices. Journal of Data, Information and Management, 2, 95–110.