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.
Keywords: data, forecasting, analytics, agility, automation, efficiency, data-driven demand planning, supply chain agility, forecasting accuracy; advanced analytics, exception-based management, automation in demand planning
Piotr Jasiński is a highly accomplished supply chain management professional, currently serving as the Global Senior End-to-End Supply Chain Planning Manager at Carlsberg Group. With a robust background in planning and analytics, Piotr has demonstrated his expertise across major organisations such as Carlsberg and Nestlé, where he has managed global teams and spearheaded large-scale transformation initiatives. Throughout his career, Piotr has excelled in implementing new tools and technologies, providing comprehensive training and coaching teams to embrace new methodologies. His broad experience spans demand analysis, product forecasting, cost reduction initiatives, stakeholder management and solution architecture. Piotr’s technical proficiency includes Kinaxis Rapid Response, Statistical Analysis System (SAS), System, Applications and Products in Data Processing (SAP) and Python programming. Piotr holds a bachelor’s degree in computer science from the University of London and a master’s in quantitative methods from SGH Warsaw School of Economics, Poland.