Journal of Business Forecasting Articles Issues

Artificial Intelligence (AI) is revolutionizing demand planning. The key objective of demand planning is to grow and manage demand. AI provides tools to achieve both. To increase demand, AI provides tools such as dynamic pricing, personalized offerings, new channels of distribution, and Augmented Reality. To manage demand, we need to balance supply and demand, make the supply chain more agile, retain more customers, and optimize the product portfolio. In this article, I discuss how we can use AI tools to do all these much more effectively.

There is little debate as to the efficacy of AI-based demand forecasting when implemented sucessfully, but most implementations fail. Failure is due in large part to organizations being unprepared for such a wide-ranging and transformative initiative and not understanding the foundational elements required to support it. Three pillars underpin successful AI/ML planning transformation, namely Data, Technology, and Organizational Readiness. In this article I discuss the nuances within these pillars and how they combine to form an effective AI-forecasting and planning ecosystem. Key questions to ask are provided, designed to help you assess your own readiness for AI / ML-based forecasting.

Applying AI to forecasting and planning can seem a daunting, even overwhelming, challenge. However, applying this game-changing technology is often far simpler than many Demand Planners believe. Tools like Power BI and Excel features allows us to start small with AI forecasting, gaining small wins and allowing us to integrate AI into our existing planning processes over time. In this article, I reveal how to take your first steps in AI, how new platforms no longer require data science or coding skills, and how this technology, far from replacing Demand Planners, elevates our value.

Forecast Value Added (FVA) Analysis, a tool for assessing the effectiveness of demand forecasting processes, has traditionally focused on understanding how each step in a forecast process contributes to or detracts from overall accuracy. With the advent of generative AI and large language models (LLMs), new possibilities for enhancing FVA analysis are emerging. This article examines how generative AI and LLMs can be integrated into FVA to improve forecasting accuracy and process efficiency. Importantly, it considers current limitations and potential future developments which may remedy those limitations.

AI will shape everything we do, and an organization’s AI payoff hinges on doing the hard work of changing its culture. Becoming an AI-driven company requires a business-led roadmap, a purpose-driven vision, the right organizational culture, talent management, agile processes, and data and technology. It is imperative that executive leadership lead AI transformations and that use cases focus on measurable business impact. In this article I reveal a practical framework for becoming an AI-driven company capable of supporting effective AI implementation.

In an environment where demand planning is increasingly difficult, machine learning (ML) models help generate accurate forecasts and allow us to better plan the business. Machine learning for demand fore- casting goes beyond traditional statistical models using just sales data. In this article, I discuss how ML models can use a variety of data inputs including consumption data, promotions, social media, weather, economic data, and more. I also reveal the importance of ‘forecast explainability’ when using ML. This avoids a ‘black box’ approach and allows us to understand the individual components of the forecast and their contribution, helping us to understand and better communicate the demand drivers.

There are numerous challenges facing the current supply chain sector, including unpredictable demand and a lack of stability among suppliers, both of which impact efficiency. Accurate prediction of supplier Lead Time is crucial for efficient inventory management, production scheduling, and service level improvement. This article presents an innovative solution by combining multi-source information with artificial intelligence (AI) algorithms to enhance prediction accuracy, to generate more reliable predictions of supplier delivery times. This integration improves inventory management and production planning, ultimately enhancing overall supply chain resilience.

There are numerous subcategories of Artificial Intelligence. Robotics, machine vision and natural language interfaces are all subsets of AI. Expert systems are also AI. I could argue most supply chain planning solutions are expert systems by nature. When I hear folks talking about AI in planning, the ‘function’ or ‘task’ most often mentioned is the leveraging of machine learning algorithms to statistically forecast or optimize inventory, or to integrate and analyse large volumes of data into logical constructs.

The nation’s GDP growth rate from Q1 to Q4 of 2025 is expected to grow by 1.47% according to Consensus. Similarly, Wells Fargo predict real GDP growth of only 1.30% on a Q4/Q4 basis in 2025 with growth weakening in the second half of 2025 with export activity and consumer spending softening. Similarly, Dr. Perryman of the Perryman Group indicates that the election and its aftermath have brought uncertainty to the US economic outlook and as such he anticipates modest growth in 2025.

Predicting demand is one thing; shaping it is another altogether. Mature planning organizations have frameworks to shape demand up or down according to their strategic objectives. In this article we discuss the levers planning teams can pull to bring demand and supply into equilibrium, starting from either a position of excess or insufficient inventory. Techniques include pricing, launching/shortening marketing promotions, product substitution, and changing internal incentives etc. Crucially, we explore how to measure the efficacy of demand shaping activities by incorporating Forecast Value Add KPIs such as accuracy, error and bias with examples of real life applications.

While forecast error is an intrinsic part of demand planning, what separates advanced planning teams from the rest is how forecast error is measured, how the causes of error are identified, the remedial actions to improve forecasting processes, and the downstream supply chain decisions taken in light of forecast error. In this article, I examine the different types of forecast error metrics used in Japan, the common causes of error, and crucially, steps to reduce error with a focus on reviewing demand forecasting processes and customer and market analysis. I call this multi-faceted approach Ambidextrous Forecast Accuracy Management.

Supply chain organizations typically plan purchase orders without regard to distribution center receiving capacity on planned delivery dates. This leads to bottlenecks on some days and underutilized labor on others. In this case study of a $10B+ quick service restaurant chain, a novel methodology we call Master Purchasing Receipt Scheduling (MPRS) provides a solution. The methodology schedules deliveries at the time of purchase order creation, resulting in a steady volume of deliveries and lower planning and logistics costs.