An analysis and mitigation of demand variability on external supply chains

Abstract

In the pharmaceutical industry, increasing product complexity, shifts towards specialty medicine and growth in emerging markets have resulted in increased forecast variation and manufacturing complexity for new products. In the past six years, AstraZeneca has outperformed its peers in research and development productivity, increasing the number and speed of product launches. The resulting demand variability and shifting operational environment have led to financial and non-financial impacts, such as poor inventory performance and strained supplier relationships. The objective of this research is to identify processes and procedures that amplify the impact of demand variability and the areas in the end-to-end operation that are significantly impacted. The secondary objective is to identify process improvements in the existing system and develop strategies to mitigate the risk of demand variability.

This thesis presents an analysis of the impact of demand variability on the external manufacturing and supply chain operations for new products. A case study approach is used to assess its impact on the forecast processes, manufacturing systems and supplier relationships. A simulation tool was developed as a method to measure financial impact based on inventory performance. The simulation was expanded for use as a decision assist tool to evaluate test cases developed from the current state analysis. The research illustrates that the end-to-end manufacturing and supply chain operation is experiencing significant bullwhip effects for new products. The primary sources of financial impacts are the policy stock requirements tied to monthly demand and segmentation of the supply chain causing different forecasts to be used for certain stages. Non-financial impacts include loss of trust with suppliers, manually managed complexity and limited communication resulting in the bullwhip effect.

The short-term and long-term recommendations focus on increased operational transparency and scenario-based forecast planning to mitigate the impact of demand variability on the system. Pilot programs for statistical process control implementation in drug substance manufacturing and development of a future state commercial partnership model were defined as follow-up work to this research