The improvement of supply chain performances through process modeling and multivariate analysis

Abstract

In the supply chain management (SCM), the ability to fulfill the highly fluctuative demand in the most efficient way without compromising the product and/or service quality is seen as a strong value added that can contribute to the organization's financial performance and reputation. This study will explore the significance of the fluctuative demand towards the supply chain KPI performances. As an industry that is prone to demand fluctuation, a pharmaceutical case study company will be used as part of the empirical study.

The method will be implemented through business process modeling and simulation using ExtendSim 9 scenario analysis, followed by multivariate analysis using SAS. The objective is to understand how the seasonal demand fluctuation statistically impacts the SCM system and how can it be handled better to sustain and improve the SCM performance level.

The results for this study is that both demand and process variation have statistically shown significance in affecting the KPI performances. It furthermore shows that both production methods that are done within the organization's internal location are more sustainable against the demand increase in comparison to the toll out manufacturing system.

The minimization use for toll out manufacturing is seen as strongly beneficial in the long run as the system has shown high vulnerability, and an investment to increase the in-house production capacity is seen as pivotal move in order to provide a greater manufacturing flexibility in the long run.