Journal of Business Forecasting Articles Issues

In many organizations, the monthly forecast accuracy report shows improvement while the planning system continues to struggle. Inventory still swings unexpectedly and supply teams still expedite orders. How can this happen if we’re hitting our forecast accuracy targets? In this article, I reveal why forecast accuracy is a less-than-perfect KPI for planning teams because, by the time supply teams execute the number, it reflects more than demand alone. I discuss how consumption signals are adjusted for distribution changes, promotions, and commercial expectations before being converted into shipment requirements. I advocate for planning leaders to view accuracy as one metric, not as judgement of planning quality.

Over the past decade, the way supply chain Management (SCM) professionals and Demand Planners have engaged with AI has continuously evolved. As classification/prediction-oriented AI has developed into generative AI and now agentic AI, with these technologies increasingly being applied in business, SCM professionals must reconsider their approach to these tools. In this article, I propose a framework to effectively utilize generative and agentic AI, along with the key skills we need to develop as demand planning and supply chain professionals.

In recent years tech company executives have pushed back against employee activism and pressure to act ethically, with Google firing more than 50 employees who protested the company’s contracts with Israel in the wake of the Gaza war. It raises the question of whether companies should act ‘ethically’ or ‘responsibly’. I discuss the recent dispute between AI company Anthropic and The Pentagon, the former taking a moral stance on the use of its technology, which ended in The Pentagon designating the firm as a supply chain risk. I argue that corporations should favor acting responsibly, taking accountability for an outcome and managing its consequences rather than reducing everything to a black or white moral issue.

After decades of working in consulting for companies across a range of industries, I know that proper analysis of the right data can address many complex business challenges. Through four real-world case studies from my own experience, I discuss how the real challenge lies not in the lack of data but in identifying what is relevant, and then extracting, analyzing, interpreting, and applying it effectively. I also highlight the importance of being willing to challenge core assumptions and generate practical, actionable insights rather than purely theoretical conclusions.

Forecast accuracy is an important metric in demand planning and while error metrics like Mean Absolute Percent Error (MAPE) have value, they are not enough. In this article I discuss how the same MAPE can tell very different stories about demand and have very different implications for the performance of a business depending on the industry and product type. I make the case that forecasts should be measured on bias and stability, and aim to meet the needs of the business rather than treating accuracy as the goal.

How did you end up working in demand planning? I moved into a demand planning role when I moved from working in retail to working for a manufacturing company. I worked for many years in retail replenishment and order forecasting. So when I made the change, I had to move from managing and planning orders in the near-term of a few days to looking “over the horizon” and anticipating future demand 18-24 months in the future. It took me a couple of years to fully make the transition and develop the basic tools required. Fortunately, I had some very talented teammates who helped me develop a new way of looking at the business.

In many companies, demand forecasting and inventory management are often separate processes. With teams on either side of the demand/supply equation working towards to their own score-cards, supply chain performance can suffer. To solve this problem I propose a proactive monitoring framework designed to bridge this “perspective gap” between demand planning (focused on forecast accuracy) and inventory management (focused on turns and coverage). By evaluating demand and inventory signals simultaneously at the SKU level, the framework transitions supply chain operations from retrospective reporting to automated, actionable recommendations.

Real GDP expanded at a 2.00% annualized rate in the first quarter of 2026, reflecting a generally solid pace of economic activity prior to the onset of the Iran conflict. The labor market has remained relatively resilient, with the unemployment rate holding steady at 4.30% in May. At the same time inflation has edged higher, rising to 4.20% in May, the highest in three years, highlighting persistent underlying price pressures despite moderating economic growth.

In today’s environment of rising tariffs, frequent supply disruptions, and persistent demand volatility, companies can no longer rely on one-size-fits-all planning and execution. This article shows how a unified segmentation approach of categorizing items and customers by profitability, volume, and variability can focus resources, mitigate risk, and align fulfillment strategies to drive measurable improvements in margin, service, and working capital. The article also explains how to embed these insights into IBP performance metrics and use them to guide targeted pricing, part rationalization, and demand shaping actions that strengthen cash flow and asset utilization.

In an era where agility and accuracy are vital for supply chain resilience, this case study highlights the transformation of a global packaging manufacturer’s demand planning process—from fragmented, manual forecasting methods to a centralized, data-driven and machine learning approach. Faced with inconsistent accuracy, reactive planning, and excess inventory, the company implemented a scalable solution that integrated key data sources and introduced more consistent, forward-looking forecasts. The transformation improved forecast accuracy, streamlined planning cycles, and enabled better alignment across sales, operations, and finance. As a result, the organization strengthened its ability to anticipate demand, reduce costs, and support future growth with greater agility.

A review of a new book titled Could Should Might Don’t: How We Think About the Future, casts doubt on whether current discussions about the future would be useful to strategic planning in business. In this article, I debate how the relevance of potential future scenarios for the supply chain planning field including the impact of Artificial Intelligence. I reveal the debates futurists have over to what extent the future lies within our control, and how far we can look into the future with any degree of accuracy or usefulness.

Post-COVID supply chains have prioritized multisourcing to build resilience, yet this expansion has introduced significant hidden costs and complexity. While critical for high-volume materials, multisourcing the “long tail”—the 5–10% of spend comprising 80% of transactions—is often inefficient. In this article, I assess the pros and cons consolidating this incidental spend through third-party partners. By outsourcing these low-volume purchases, organizations can reduce onboarding costs, leverage cost-plus pricing, and simplify transactional loads. This strategic shift allows procurement teams to refocus on strategic priorities while maintaining supply security and operational simplicity.

Despite abundant data, many S&OP cycles remain tactical and slow. Making strategy visible—through visual management and collaborative, Obeya-style boards—creates shared context, clarifies trade-offs, and accelerates decisions. This article presents a practical model for linking S&OP to strategy through the use of visualization boards integrated into the S&OP process. A case study of a railway vehicle manufacturer is shared, detailing how S&OP was launched and how visual boards facilitated decision making, fostered accountability and cadence, and shortened meeting time from 4-5 hours to 90 minutes.

In today’s dynamic business environment, new companies are bringing innovative products to market at an unprecedented rate. Startup enterprises experiencing rapid growth realize that a key competitive differentiator is effective S&OP, but balancing demand and supply and maintaining stable operations while maintaining the agility startups require, is fraught with challenges. This article explores how startups can effectively implement S&OP to bridge the gap between vision and execution, with lessons learned from my experience building and managing S&OP at a range of multinational firms including Amazon, Honda, Estée Lauder and more.