Journal of Business Forecasting Articles Issues

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.

The purpose of this article is to dispel misconceptions surrounding outliers and highlight their significance in statistical modeling. Outliers often yield crucial insights into the underlying relationships in the data being modeled, particularly in consumer behavior modeling. In this article I discuss how planning professionals tend to discard these anomalies or average them out, mistakenly believing it enhances the data’s structural integrity. I discuss how this false assumption can lead to the removal of valuable insights from the data, and provide a practical framework for treating outliers in statistical modeling.

Helmut is a senior Supply Chain practitioner and IBP expert, having led S&OP/IBP at several multinational companies in the FMCG, medical devices and pharmaceutical industries including Johnson & Johnson, Merck, and Smith+Nephew. Since 2019 Helmut has been running his own business, HELIBLICK, assisting corporations with IBP and S&OE implementation and other business critical transformations. Helmut is a founding member of the CSCMP Roundtable in Switzerland and founder of the IBP/IBSing practitioners’ network, a peer group of more than 300 S&OP/IBP experts from 15 industries for learning and knowledge sharing. He holds a master’s degree in Industrial Engineering from Technische Universität Graz, Austria.

The Demand Review is critical part of the S&OP process and is where a picture of demand is created by incorporating viewpoints from different stakeholders into a statistical demand forecast. An effective De- mand Review lies in the preparation—underlying this is data and information gathering, particularly from Sales and Marketing. In the pre-Demand Review meetings, we must discuss high level issues impacting clients, the market, and products, which will build a framework for more detailed discussion in the Demand Review itself. In this article, I reveal the keys to a successfully navigating this stage in the S&OP process, with an emphasis on data, collaboration and the right discussion points in the meeting.

During the COVID-19 pandemic I noted that the traditional business planning processes including demand forecasting and S&OP were rendered largely useless in managing uncertain futures and severe supply shortages. I realized that at least two special ad hoc planning processes needed to be added to the Hierarchy Demand-Supply Planning Framework: 1) ‘Quick Response under Uncertainty’ and 2) ‘Supply-in-Mind Demand Optimization under Risk.’ This column discusses these two processes and provides an illustration for a fictitious (yet realistic) carmaker using the Linear Programming algorithm to do Demand Optimization planning under severe supply shortages.

According to the latest data from the Bureau of Labor Statistics, the Consumer Price Index (CPI) increased 2.5% over the prior year in August. Dr. Dhawan of the Economic Forecasting Center at Georgia State University believes the moderation in inflation will allow the Fed to aggressively cut rates to mitigate the weakness in the labor market, necessary for the economy to return to its trend level by late 2025. His estimates for the GDP growth rate are 2.50% in 2024 and 1.20% in 2025.

Mature demand planning is a complex affair and difficult to achieve for even the most well-resourced organizations. One emerging solution to this challenge is outsourced demand planning, known as Third- Party Planning, that can handle one or more planning activities. Companies can rely on such providers to generate forecasts, develop materials for Consensus meetings, provide performance metric and give advice on how to improve. This article reveals how such providers fill a crucial skills gap, allowing companies to level up their planning capabilities in a flexible way that meets their specific requirements.

Technology innovations are poised to dramatically improve the value and effectiveness of S&OP and IBP processes, in both small and large manufacturers. New solutions are combining these traditional pro- cesses into fully integrated financial and operational planning and performance management. What results are incre- mental capabilities that can reduce the cost of planning by up to 50%, while simultaneously improving profits by up to 5% of sales. In this article, I make the case for leveraging these new technologies to fully integrate all elements of plan- ning across geographies and legal entities for a better view of business performance.

Since the pandemic, S&OP has seen further adoption and is increasingly recognized as a proven method to manage demand and supply variability. But have we stretched the scope of this planning process too far? Recently, many articles have been published that advocate for S&OP’s management of FP&A, Master Planning, New Product Development, and even commercial strategy. In this article I make the case that S&OP is designed to be the connective tissue between different planning processes and that S&OP adds the most value by integrating stand- alone functions and facilitating transfer of information, rather than setting the agenda for the entire enterprise.

The agricultural chemical marketplace is very complex. The end customers—growers— require a highly responsive and localized supply chain while suppliers must make purchase and production decisions long before variations in weather, pricing, pestilence, or prevalence of crops are known. The inherent conflict of long lead times and responsiveness is a nearly intractable supply chain problem. In this article, I outline how successful Agri- science suppliers try to optimize supply, demand and other influences to improve these crucial decisions, which ulti- mately impacts how produce is being brought “to the table”.

Despite increasing pressure to reduce working capital across many industries, many multinationals still set their safety stock targets as a number of weeks of demand depending on an item’s ABC/XYZ- segment, ignoring important aspects like lead time and replenishment quantities. This leaves room for improving costs, customer service and working capital. This article explains how to set up ABC/XYZ-segmentation in the right way and how to successfully implement inventory policies and service level differentiation in your organization. A real- life case study of a manufacturer of cargo securing systems is used to highlight best practices and the benefits gained.