Journal of Business Forecasting Articles Issues

Generative AI (Large Language Models that analyze large data sets and create outputs for various applications) have taken hold across many industries and disciplines, including Supply Chain Management. The next frontier in AI technology is Agentic AI that goes beyond creating outputs to manage processes independently, such as gathering and managing data and connecting different platforms and business functions. In this article, I reveal how agentic AI can combine with generative AI to build a framework that facilitates the integration of all required information, people, technology and processes to create digital twins that perform scenario planning and generate forecasts. The challenges of building such a framework are shared, along with the ‘human’ competencies these models must possess to be effective in a VUCA environment.

The tariffs on goods coming into the USA are creating great uncertainty regarding the cost of materials and product price inflation. Even if a tariff percentage is known, it can be difficult to quantify the impact on the cost to build or assemble a product across wide portfolio of SKUs. The Finance & Accounting function is an invaluable asset in assessing the costs associated with tariffs. In this article, I reveal how a team lead by your company’s Cost Accountant can identify the direct costs associated with tariffs on each product, create iterative scenario models that allow for different assumptions, and lay the groundwork for contingency action plans that allow for effective cost control responses.

My last column, “Strategic Planning for Global Supply Chains in a Fragmented World” was written to advise managers regarding recent tariff initiatives. This article seeks to explain how we got here, and what incentivized executives to outsource to the Far East. I reveal how a focus on financial metrics like Return on Assets has driven the move to outsourcing at the cost of American workers and in-house manufacturing expertise that, once outsourced, is difficult to regain. Strategic downsides to such moves are discussed, including the tendency for foreign countries to acquire the Intellectual Property of outsourcing countries and replicating their products, as well the risk of critical supply shortages the likes of which were seen during COVID.

With thousands of new book launches per year, forecasting demand in the book publishing industry is challenging. Finding the balance between meeting market demand and avoiding the costs of unsold inventory requires a robust planning process that incorporates industry knowledge, cross-functional involvement, and advanced forecast modeling. In this article, I reveal the approach used by a global publishing giant behind some of the world’s most popular books. I detail how publishers are responding to new market dynamics like spikes in demand caused by TikTok influencers, and how we are building new models that combine internal and external comparable sales data, POS data, lead times, and publicity-driven spikes.

In today’s complex business environment, Sales and Operations Planning (S&OP) is evolving from a tactical coordination tool into a strategic, AI-supported framework for organizational alignment. This article examines how ERP platforms, collaborative planning tools, and predictive analytics underpin an effective S&OP process. From enhancing forecast accuracy to improving agility during disruptions, we explore how technology can make S&OP more dynamic, resilient, and impactful. Special attention is paid to how ERP systems function and how they support various aspects of the planning process.

Is AI changing hiring trends? Companies are certainly interested in hiring AI-related team members. However, those roles tend to be more stand-alone positions in support of other functions. For example, one client of ours hired a Data Scientist specifically to aid the demand planning team. In some organizations, these resources act as a shared service across the business. In larger businesses, these resources will specialize in working with a specific team, sometimes as part of a Center of Excellence.

Future-proofing pharmaceutical supply chains is critical in an increasingly complex and unpredictable business environment. While redesigning the asset footprint is often impractical due to high costs and stringent regulations, pharmaceutical companies can ensure their supply chain is future-ready by selecting a fit-for-purpose technique. This article explores three data-driven strategies of future-proofing supply chains in the pharmaceutical industry: scenario modelling, allocation optimization, and capacity expansion – along with illustrative examples based on real-life projects in the Life Science industry.

Customer service is not always included in S&OP, but it should be. This often over- looked function has insight into customer behavior and issues that salespeople do not, and can highlight factors impacting demand that help us to plan better. This qualitative insights allows us to refine statistical forecasts for more accurate demand plans. In this article I reveal five reasons to include customer service in your planning meetings, including identifying their seasonal buying behavior, highlighting upcoming promotions and events, and revealing whether individual customers are ahead or behind the planned sales volumes.

Consensus expects a modest increase in GDP growth of 1.16% from Q3 2025 to Q2 2026. This suggests a general belief in continued, albeit perhaps not robust, economic expansion. Wells Fargo, on the other hand, presents a more pessimistic near-term outlook, forecasting a “bumpy ride” and an outright decline in GDP growth in Q3 2025. This projected dip is attributed to a decrease in consumer and business spending, following an anticipated pre-tariff spending surge that likely extended into early Q2. This implies a potential pull-forward of demand that will then subside.

As AI in demand forecasting begins to gain traction, we must manage trade-offs between improvements in forecast accuracy and explainability. In this article I reveal the importance of being able to explain how a forecast is derived so executives can make informed decisions without relying on a black box approach. I provide a matrix revealing the trade-offs between performance, explainability, interpretability and accuracy for different forecasting methods of varying complexity. I also introduce three frameworks to explain statistical models, black models, and advanced AI models to facilitate understanding and adoption of your forecasts among decision-makers.

For many of us in the demand planning profession, the pursuit of consensus has become futile. It is a political morass, often unmoored from reality and centered on presenting a number that everyone wants to believe in, even if it makes no sense at all. In this article I reveal the problems with aiming for consensus and how most adjustments to the unconstrained forecast are negative value-add. I provide practical advice to take control of S&OP meetings, including bringing a data-driven forecast, being forearmed with product and business knowledge, holding commercial teams’ assumptions to account, and focusing only on high-value SKUs.

Artificial Intelligent (AI) driven by machine learning has generated significant excitement in the realm of demand forecasting due to advancements in data collection, storage, and processing, as well as techno-logical improvements. Machine learning (ML) can certainly be integrated as another method within the suite of existing forecasting approaches. It’s important to note that there isn’t a single tool— whether a mathematical equation or an algorithm—solely dedicated to demand forecasting. This article delves deeper into Recurrent Neural Networks (RNNs) and their variants, which have gained significant popularity in recent years due to their remarkable ability to handle unstructured sequential (time series) data. These models are called “recurrent” because they process data that unfolds in a sequence, such as text and time-stamped information.

There is currently a trend away from global free trade as more countries impose tariffs on imports. The World Trade Organization (WTO) has expressed concern because it is seeing a bifurcation of trade into two geographically divided blocs. It is estimated that were this to happen, it would result in a loss of 6.4% in global GDP. Previously, as part of the findings from the MIT Supply Chain 2020 Project, I postulated there might even be three or four major trading blocs in the future. This column discusses a strategic planning approach based on “Decision-Making Under Uncertainty”. It recommends using three future scenarios developed by the SC2020 project team as the basis for strategic scenario planning projects aimed at aligning a company’s future global supply chains to the trading blocs that might arise.

I am a Gen Z supply chain professional. Being a part of Gen Z has always had its fair share of negative connotations, “All you kids want to do is stick your nose in a cell phone”, or “You guys have no idea what it was like without a GPS to tell you where to go”, are common among the phrases I heard growing up. While some of these statements may have an element of truth, they don’t define the Gen Z workforce. With the Gen Z population entering and maturing into the work force at a surging pace, it is an appropriate time to understand the nuances of my generation’s values and psychology. In this article I reveal what makes this generation tick, and how to better manage them.

The relationship between sell-in and sell-out in the pharmaceutical industry is a critical one. Sell-in represents the volume of product sold to manufacturers, retailers or distributors while sell-out represents the actual volume sold to the end consumer. Minimizing the delta between the two is key to avoiding burdening different channels with excess stock and maximizing profitability. This article reveals how S&OP and planning tools can be used to minimize the difference. By collaborating with marketing and conducting an effective demand review, the impact of marketing promotions and other demand factors can be identified, allowing for better and more agile inventory decision making.

This article details machine learning for demand forecasting in the cyber security industry. I reveal a standard approach to using machine learning for demand forecasting and compare it to the improved methodology I employed at a leading cyber security firm, which integrates Hierarchical Time Series Forecasting. I discuss how this methodology works and the KPI improvements it yielded relative to the existing approach, plus the strategic decision making benefits it provides.