The demand planning renaissance: A data-driven approach

Abstract

This paper analyses how demand planning in supply chain management is changing through the use of data-driven methods. It emphasises the need to move from traditional, reactive strategies to proactive, data-centric approaches that can predict trends, respond to changes and make informed decisions quickly. It starts by outlining a common supply chain challenge and stressing the importance of agility and responsiveness in demand planning. The paper also points out the drawbacks of manual forecasting and the advantages of using advanced analytics, artificial intelligence (AI) and real-time data to enhance forecasting accuracy and operational efficiency. Readers will gain insights into the key components of data-driven demand planning, including the integration of various data sources, the application of machine learning (ML) for accurate forecasting and the strategic implementation of exception-based management (EBM). Practical examples, such as automating forecast phasing and utilising suppliers, inputs, process, outputs and customers (SIPOC) process architecture, demonstrate how technology and human expertise can collaboratively enhance demand planning processes. By delving into the synergy between automation and human insight, the paper emphasises the balanced approach needed for effective demand planning. It also introduces unconventional forecasting methods like probabilistic forecasting and reinforcement learning, providing readers with a comprehensive understanding of advanced forecasting techniques. Overall, readers can expect to learn how to implement data-driven strategies to achieve improved forecast accuracy, optimised inventory levels, enhanced customer satisfaction, increased profitability and greater business agility. This knowledge equips supply chain professionals with the tools to navigate the complexities of modern supply chain management and drive continuous improvement in their organisations.