Business logistics

Supply chain challenges have been significantly affected by both demand and supply on a global level. The selection of manufacturing countries has become critical to firms and their boards, even more so coming out of the COVID-19 global pandemic. The present study focuses on how firms select countries and regions to de-risk future global apparel sourcing, as countries that have been dependable in the past may not be in the future based on frequent environmental jolts, legacy supply chain failures, shifting government policy, and extreme volatility. The result of this study is a decision model for manufacturing country selection. This research was focused on the apparel industry; however, further research may indicate that it is applicable to other industries. A group of criteria was selected, the relative significance of these criterion was determined using the Analytical Hierarchy Process (AHP). The AHP methodology was applied in a case study as a decision-making tool to enable decision-makers to assess the most suitable countries for manufacturing country selection. The result of this study is a decision model for manufacturing country selection based on multiple criteria weighted by industry experts using Analytical Hierarchy Process (AHP). In developing the model we utilize data from 61 countries representing over 95% of all the global apparel exports, with criteria utilized originating from 10 indices.

In this study we developed a supply chain contract model for multiple scheduling period with dynamic demand patterns of stochastic nature, and with elastic price structures. The model presented here combined and enhanced several supply chain contract models developed previously. It is unique in that it considered multiple periods, dynamic, stochastic and price-elastic demand patterns, and flexible order quantities. Using a linear demand price-elastic relation and normal distribution pattern, optimal solutions for minimum cost, maximum profit, price structure, and order policies for the entire supply chain were derived. Sensitivity analyses performed in this study gave a better understanding of relative importance of various system variables.

The study of this paper on entropy-based methodology for measuring and analyzing the material and information complexity across organizational interfaces within a logistical framework material and Information flows within the supply chain are often complex. The complexity analysis described in this paper can be used to highlight several issues that are critical to effective supply chain management, and for internal control. My methodology is based on the understanding that the level of control and predictability, as well as the dynamic characteristics and static of the flows contribute to the overall level of complexity within the supply chain. This study demonstrated that supply chain complexity is realistically understood, carefully and accurately assessed it can be used to monitor and effectively control the performance of the entire supply chain and delivery system. At the supply chain interface, the methodology can provide quantitative insights into the transfer of complexity along the supply chain.

Our research establishes the platform and modeling for the new trend in today's Global Supply Chain Management Systems (GCOM). The framework consists of various practical policies with respect to booking units, reserve-price, and the preservation of relationship between the outsourcing organization and their contract manufacturers. Mathematical models have been studied and reconciled to constitute the most suitable expression. Monte Carlo Simulation is chosen to describe the outsourcing's phenomena under our given conditions. The study path may be useful for both SCM's analyst and any global organization looking for the offshore outsourcing in order to establish the optimal point in reserving the production's capacity with the cognition in today's fluctuated global demand and currency exchange rate.