[Implementation of an Advanced Planning and Scheduling System in a pharmaceutical context]. / Mise en place d'un système de planification et d'ordonnancement avancé dans un contexte pharmaceutique.

Optimizing its supply chain has now become more than a necessity for any company seeking to expand its national and international market, so that it is able to continue its progress and fulfil its obligations towards its employees and its customers, particularly in the pharmaceutical context. The Covid-19 pandemic has shown the importance of resilience in the pharmaceutical industry to deal with unexpected disruptions and high demand from patients and authorities. Better production planning based on data management and predictive analysis, through the use of new Industry 4.0 tools, improves operational performance in terms of productivity and flexibility in relation to the vagaries of the request. It is in this vision that we approach the implementation of an "Advanced Planning and Scheduling -APS" system, in a pharmaceutical laboratory. It is a leading company in the Tunisian pharmaceutical market that seeks to expand its national and international market. In this work, we describe its project to implement an "Advanced Planning and Scheduling" system and its integration with its already functional "Enterprise Resource Planning" software system, as complementary decision-making tools.

A new rolling forecasting framework using Microsoft Power BI for data visualization: A case study in a pharmaceutical industry.

CONTEXT AND OBJECTIVES: Demand forecasting is a vital step for production planning and consequently, for supply chain efficiency, especially for the pharmaceutical (pharma) supply chain due to its unique characteristics. Numerous models and techniques that are proposed in the literature but little in concrete and generic framework to forecasting process, mainly for pharmaceutical supply chain. Unlike studies in the literature, this study not only perfectly predict the sales of a pharma manufacturer, but also visualize the results via a developed dashboard using modern information technology and business intelligence. MATERIAL AND METHODS: In this research, a rolling forecasting framework comprising of different steps and specialized tools is proposed that can assist supply chain managers to perform an accurate sales forecasting and consequently a better performance and specifically patient satisfaction. The proposed generic framework combines the use of Visual studio C++ software to extract optimal forecasting and the Power BI software to monitor the accuracy of the obtained sales forecasts. Three exponential smoothing methods are integrated in the proposed framework, which is open to adding more new forecasting methods. RESULTS: The proposed framework is tested for many data sets from a pharmaceutical manufacturer company, and the results obtained show superior performance, especially a clear decline in both forecast errors, which can reach 75% and a drop of stock level to 50%. Therefore, the company is currently using it and a future integration with their ERP is being carried out. CONCLUSION: The proposed rolling forecasting framework contributes to insightful decision-making through the visualization of accurate future sales and turnover, and consequently, an efficient stock management and effective production planning.