Planning Optimization Driven by Time Series Forecasting Approach in the Life Sciences Industry

Company X, a leader in the Life Sciences supply chain, specializes in producing high-quality medical and laboratory equipment. Given the dynamic market influenced by trends, seasonal variations, economic shifts, and global events such as pandemics, traditional forecasting methods often fall short in accuracy. This work seeks to enhance demand forecasting for highly demanded products by implementing advanced time series models and developing a comprehensive planning model that integrates these methods. Utilizing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, the work systematically approaches the data-driven challenges of demand and supply management. Each product time series is first statistically classified to determine the presence of seasonality, guiding the selection of appropriate forecasting models. For non-seasonal products, ARIMA, Holt’s Linear, ARIMA-SVR, and Facebook Prophet were evaluated, while seasonal series employed SARIMAX, Holt-Winters, SARIMAX-SVR, and Prophet models. The results indicate that the ARIMA-SVR and SARIMAX-SVR models consistently out-perform others, reducing forecasting errors by approximately 35.3% for seasonal products and 40.4% for non-seasonal products compared to traditional methods. This initiative has significantly enhanced demand forecasting accuracy, thereby improving customer satisfaction and resource optimization in a fluctuating market environment, with strong endorsement from the client.