Journal of Business Forecasting Articles Issues

Digital tools and artificial intelligence can be your best ally for engaging all the functions of your company into a single Integrated Business Planning process and, therefore, for breaking down silos. Artificial Intelligence-based forecasting allows you to decrease human bias, boost end-to-end automation and maximize business impact. However, on this digital journey you must not underestimate the crucial importance of having empowered people and robust processes. In this article, we explain our recent experience of implementing a digital-based Integrated Business Planning process at Sandoz, a multi-billion dollar pharmaceuticals company, and share some change management tips that facilitate successful outcomes.

Over 4 years ago I wrote a column with Eric Wilson to start a dialogue about a new concept called Quick Response Forecasting (QRF). The column discussed speeding up responses to downstream and social media predictive demand signals. It also discussed rapid response to catastrophic events such as hurricanes and earthquakes. My research during COVID-19 has led me to believe that QR can play an important role in forecasting and planning following catastrophic events, because managers are faced with having to plan under uncertainty. This column discusses lessons learned during the pandemic and introduces a Hierarchy Demand-Supply Planning Framework that includes QR under uncertainty, separately run from the S&OP process.

Forecast Value Added is a useful metric to identify which parts of the planning process are adding value and which are not. This is valuable in not only improving forecasting and planning KPIs, but in illustrating the value of demand planning to senior leadership. Launching a metric in a large planning organization is challenging, however. In this article, we reveal Hilti’s journey in FVA implementation, detailing how we launched it across demand planning teams in multiple markets, and maximized successful adoption through the use of training, collecting feedback from planners, and setting clear expectations.

Building trust of our stakeholders in the cross-functional planning process is an important part of ensuring that decision making is guided by data, and not siloed perspectives. Trust in the value of data-driven insight, however, is not a given, rather it is earned over a period of time. In this article I present 5 ways to facilitate positive relationships within demand planning, including knowing your products and customers, being prepared to balance data with experience, and — crucially — leaving one’s ego at the door.

What did you study at college? I graduated in 2005 from Neoma Business School, a French Business school, where I obtained a master’s degree in Business Administration specialized in International Marketing and Distribution. What was your first job after graduation? I was a coop at Danone during my studies and was hired ‘on the field’ as a sales representative at Danone in their biscuit division in 2005.

In 2017, Orchid Orthopedic Solutions rolled out a new IBP process. 2 years into this journey, COVID hit, requiring us to take our fledgling planning process and adapt it to manage unpredictable and unprecedented supply constraints. The challenges of demand planning didn’t end as COVID eased however, as demand plans based on COVID sales data caused a backlog of orders as the market picked back up. In this article, I reveal how we responded to fluid demand in the post-pandemic environment by centering our demand planning around switching from constrained to unconstrained demand data for our forecast inputs, allowing us to provide a better demand signal to supply planning.

Many companies have made efforts to implement Artificial Intelligence (AI) forecasting techniques in recent years. But most companies have not been able to translate these initiatives into improvement in KPIs like forecast accuracy, inventory turns or cash flow. It is not because that forecasting with AI cannot achieve higher accuracy than traditional cause-and-effect models, but because that Demand Planners do not have adequate understanding of how and where it adds value. In this article I propose 4 approaches that allow us to fully leverage the power of AI forecasting.

Expectations for the U. S. economy in the second half of 2022 suggest modest growth well into the first half of 2023. Consensus thus expects the nation’s GDP growth rate to remain in the neighborhood of 1.65%. The consensus contains differing viewpoints, however. Dr. Ray Perryman of the Perryman Group expects the economy to face significant headwinds, with supply chain woes exacerbated by slowdowns in Chinese production and the Ukraine war weighing heavily on the oil and gas and grain markets.

Supply Chain seeks to optimize the triangle of service, cost and cash in a way that best serves enterprise strategy and maximizes a company’s competitive advantages. And, given it manages how resources are utilized, Supply Chain is critical to the success or failure of a company as measured by Return on Capital Employed (ROCE). In this article, I discuss how this function must take control of enterprise planning, from helping to develop enterprise strategy to delivering on tactical execution. Further, I highlight the redundancy of duplicate planning processes, advocating alignment under Supply Chain.

Sales & Marketing want inventory on hand and ready to sell, while Production is focused on low cost through long production runs and few changeovers. Typically, neither is held accountable for inventory performance resulting in negative impacts on inventory costs and/or customer service. In this article I provide practical methodologies to optimize inventory management using Coefficient of Variation for Volume Variance Analysis. This allows Demand Planners to identify the optimal inventory and manufacturing strategy for each product category, thereby keeping sufficient inventory to maximize sales while controlling production and carrying costs. Alan is an IBF Certified Professional Forecaster (CPF).

This column provides a primer on how planning decisions might be made under uncertainty, when the probabilities of future outcomes are unknown, versus under risk where probabilities of future outcomes are known. I discuss how several companies leveraged their understanding of these concepts to respond effectively to the COVID-19 pandemic. I also include a Payoff Matrix that facilitates effective decision-making in uncertain environments according to the amount of risk a company is willing to undertake. A hypothetical COVID-19 analysis is provided with three decision styles that can help your demand and supply planning during the pandemic and for any disruptions the future may hold.