Assignment 5 JM APUS

8

Supply Chain Risk Management – Risk in the Evolving Supply Chain Process

APUS

TLMT441

September 10, 2021

Contents Contents 2 Abstract 2 Introduction 2 Background of the study 2 Limitations of the study 3 Literature review 3 Methodology 3 Results 4 Discussion 4 Conclusion 4 References 5

Abstract

           Since no one exists in isolation, all businesses are vulnerable to various risk factors. Any supply chain professional is quick to admit that risks are unpredictable, and the market is never stable at any given time. Businesses are inconstant as they are inevitable to environmental changes. Such changes may include leadership changes, climate conditions changes, pricing changes, and business location by migration from one region to the other. All businesses, including big conglomerates and small & medium enterprises, are exposed to similar risks. In this era of technology, a business entity may win customers worldwide as long as due diligence has been conducted to ensure the smooth transition of the goods or services. Unfortunately, a small risk may cause a ripple effect in the supply chain causing friction in all other systems. Many organizations are grappling with the challenge of how to balance demand and supply.

Introduction

If the mismatch in demand and supply chain can be solved adequately, most supply chain risk management problems can be sorted. The business landscape is constantly changing, with sources of raw materials becoming depleted, forcing the constant search for a new alternative, reliable, and sustainable sources. The competition is also burning the midnight oil to acquire the same suppliers that the main organization seeks. At this point, it may be necessary to provide prompt payment for goods or services delivery. The cost should be attractive and competitive to attract the best raw materials suppliers (Schneider, 2008). Dealing with complexity and uncertainty may cause require the integration of more than one processor technology? Structural equation modeling is involved in analyzing data from five (5) types of research.

Background of the study

Supply chain risk management (S. C. R. M) may be defined as the process of identifying the possible risk (s) that may occur in a business entity, assessing the potential extent of the risk, and proactively reducing the possible hazards along the supply chain. Supply Chain Risk Management is mainly either internal or external. The internal threats may be mitigated by applying a stringent code of ethics that must be adhered to avoid human error.

Limitations of the study

 The study is limited to the supply chain risk management function only. The recruitment process must be thorough to ensure only qualified personnel are engaged. The qualified personnel must understand the core objectives of the study.

Literature review

Seven main types of risks may happen in supply chain risk management. The hazards include human behavior risks, project organization risks, legal risks, financial risks, environmental risks, the scope of schedule risk. The importance of risk management is measured based on the value (Pettit, Croxton & Fiksel, 2019). The management must ensure risk management is done according to plan. The importance of risk management is exhibited in the number of wastages that have been significantly reduced in organizations that practice it. Risk management enables the management to repurpose resources to reduce possible wastages. The supply chain risk management may not necessarily stop the risks but may limit their impact on organizational continuity.

Stimulation of the best practices is also possible when there is a supply chain risk management policy. When Supply Chain Risk Management is done, it enables simulation of best practices to improve organizational cultures hence a behavior change. The process should be actively monitored and not passive (Pettit, Croxton & Fiksel, 2019). The organization must find the most cost-effective method to ensure the goods reach the final consumer at the shortest time possible hence a lot of pressure in the already overwhelmed supply chain.

There are many risks affecting business. The following are some of the main dangers faced in supply chain risk management: container ship fire, safety recalls, cargo theft, Brexit, economic uncertainty, global or regional trade wars, pandemic, epidemic, untenable environmental regulations, and climate change risks. The risks may cause a loss of robustness by affecting the resilience.

Some risks happen every day that is managed every day, while others are exceptional happen irregularly. Tools for supply risk management may be evoked to remedy the situation. The following are some of the essential tools that may apply in supply chain risk management: code substantiation solutions, wholesaler risk management solutions, mapping solutions, geopolitical risk management solutions, and environmental risk management solutions (Fan & Stevenson, 2018). Tooling and retooling are necessary because organizations are currently adopting one-day shipping or two-day shipping as Amazon Inc, which has fully applied prescriptive analysis, predictive analysis, and artificial intelligence in supply chain risk management. 

Methodology

The method applied in the study was structural equation modeling which sought to find out how to conduct Supply Chain Risk Management using software from different literature sources. Supply Chain Risk Management is a big gainer from technological advancements because it may apply predictive analysis and Artificial intelligence (A. I) to reduce the chances of loss. Supply chain risk management may be conducted using software such as software supply chain security – Binary SCA & SBOM Management. The connection is performed throughout all the connected devices (Baryannis, Validi, Dani & Antoniou, 2019). The other enterprise resource planning (E. R. P) tools applied in Supply Chain Risk Management include blue yonder/JDA, IFS, and Plex Systems. The right software in Supply Chain Risk Management can be a competitive advantage to the organization. Using the predictive analysis, the organization can predict the risk and prepare to mitigate it long before the effects disrupt its operations (Abdel-Basset & Mohamed, 2020). Artificial intelligence can predict patterns through a thorough analysis of big data to identify disruptive behavior with high accuracy and provide suitable solutions.

Results

The results reveal how to conduct Supply Chain Risk Management. Supply Chain Risk Management can be achieved using the following essential steps. Risk in the evolving supply chain process. As technologies change, there is a need for Supply Chain Risk Management to evolve with it. No human being can accurately predict the future trends in drought, social unrest, weather patterns, temperature fluctuation, natural disaster, and traffic jam patterns. At the same time, the same may be easily achieved using technology—identification and documentation of the risks. The first step is to identify the risks. The risks should then be documented in a manner palatable to the user. The second step involves building a Supply Chain Risk Management framework. The third step is to monitor the risk actively. The fourth step is to institute regular review and governance.

Discussion

The efficiency of a business is dependent on the quality of data it uses in its industry. It may be a grave mistake for a company to make decisions based on historical, incomplete, and unreliable data. Artificial intelligence provides real-time, high-quality, and analyzed data. High-quality decisions can save a lot of management time, making proactive decisions appropriate for the situation at hand. According to a report by Gartner, 40 % of organizations reported having used artificial intelligence in Supply Chain Risk Management. Predictive analytics can use the available high volumes of historical data to predict phenomena on Supply Chain Risk Management. 

Conclusion

It is easier to make reliable decisions with prescriptive analysis, predictive analysis, and artificial intelligence embedded in supply chain risk management software. As we advance, business leaders must proactively observe new business models, bet on new technologies and explore emerging markets to remain afloat. Even though change may look scary, it should not be so because failing to change is fatal. Organizations such as Amazon have actively applied prescriptive analysis, predictive analysis, and artificial intelligence in their logistics to ensure same-day delivery or two-day delivery, which has led to the increase in the share prices and demand for their products. Organizations that failed to adopt technology.

References

Abdel-Basset, M., & Mohamed, R. (2020). A novel pathogenic TOPSIS-CRITIC model for sustainable supply chain risk management. Journal of Cleaner Production, 247, 119586.

Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202.

Fan, Y., & Stevenson, M. (2018). A review of supply chain risk management: definition, theory, and research agenda. International Journal of Physical Distribution & Logistics Management.

Pettit, T. J., Croxton, K. L., & Fiksel, J. (2019). The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience. Journal of Business Logistics, 40(1), 56-65.

Schneider, R. J. (2008). Supply chain risk management: risk in the evolving supply chain process. Industry Week, 27.