Supply Chain Management Analysis

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Supply Chain Management Analysis

Summary

The process of supply chain management is concerned with the control of materials and information throughout the entire supply chain, from suppliers to the producers of components and subsequently to store shelves. Therefore, optimization is an essential factor of supply chain management that can ensure that all of the processes are completed in the most efficient way possible, thus reducing costs and increasing business capabilities. Linear optimization can be effectively used in the management of the supply chain because of the process is associated with linear connections between stages (Anderson et al., 2016). Moreover, the model is a method used for achieving the best outcome, which is beneficial for the supply chain management.

The article by Alawneh, Alrefaei, Diabat, Al-Aomar, and Faisal (2014) is useful for understanding the ways in which linear programming may be used for supply chain management optimization to reach efficiency within the supply chain.

Using the example of a steel company in Qatar, the researchers showed that linear programming could be used to provide answers in regards to the optimal amount of new materials necessary to request from suppliers, the optimal number of products to be delivered, as well as the optimal inventory level of new materials (Alawneh et al., 2014, p. 3). The approach implemented in the study is associated with a comprehensive deterministic linear programming model that was implemented for the purpose of cutting costs within the supply chain at the steel company through optimizing the processes associated transportation.

The results of the study show that linear optimization modeling can be useful in managing the supply chain through the use of available data as parameters. The number of materials purchased from suppliers could be determined with the help of the model, which could reveal the difference in costs that different suppliers establish. This is important for revealing the prices that end consumers would have to pay for the product. Therefore, in the context of a steel company, linear optimization can be effectively used for determining the optimal amount of resources necessary for ensuring the optimal level of inventory and material distribution within the supply chain.

Reaction

The article by Alawneh et al. (2014) is useful for understanding how real-life businesses can use linear optimization modeling within the supply chain. The researcher validated and solved the model with the help of GAMS software, while the sensitivity analysis was conducted for concluding factors contributing to supply chain efficiency. Throughout the research, it became clear that linear optimization can be effectively embedded in supply chain management by creating a set of Key Performance Indicators. These KPIs are developed for characterizing the supply chain performance in regards to ensuring efficiency, responsiveness, and utilization.

In their study, the researchers analyzed several cases associated with supply chain management to show that linear optimization can be applied in different instances. Depending on the availability of raw materials and the demand for particular products, it is possible to make conclusions regarding the distribution of resources. Overall, the proposed approach was successful in optimizing supply chain management at the steel company and showing an example of how companies can use the method in their contexts. Nevertheless, research on linear programming within the supply chain is limited, which points to the need to study the subject in further detail to facilitate its better understanding.

References

Alawneh, A., Alrefaei, M., Diabat, A., Al-Aomar, R., & Faisal, M. (2014). An LP model for optimizing a supply chain management system for steel company. IMECS, 2. Web.

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. L., Fry, M. J., & Ohlmann, J. W. (2016). Quantitative methods for business with CengageNOW (13th ed.). Boston, MA: Cengage Learning.

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