Journal of Business Forecasting Articles Issues

Dr. Nahavandi is Associate Professor of economics at Pfeiffer University School of Graduate Studies, specializing in Business Economics, International Business, and Healthcare Economics. The information in these forecasts is gathered by the Journal from sources it considers reliable. Neither the Journal nor the individual institutions providing the data guarantee accuracy, nor do they claim that use of the data appearing herein will enhance the business or investment performance of companies or individuals who use them.

During the current pandemic and subsequent demand disruption, we are preoccupied with understanding current consumer behavior and short-term demand. But equally as important is the longer-term picture. In this article I present a framework for understanding how demand for different products will shift, both as we exit this crisis and for the year ahead. I use a categorization system based on product needs, revealing how consumer behavior now and going forward will impact your demand forecasts.

A one-number forecast has some serious drawbacks: it does not always facilitate honest discussion about constraints, it encourages people to agree to numbers they cannot necessarily meet, and certain participants may game the process. There is a better way—what I call the Collaborative Process Forecast allows each function to share what risks they see in meeting the number, and what opportunities exist to exceed it, as well as providing participants with greater visibility into the constraints other functions are facing. While requiring more work, this process drives better performance in key KPIs and fosters greater engagement with the S&OP process.

Rapid demand response forecasting techniques are forecasting processes that can incorporate key information quickly enough to act upon in real time by agile supply chains. This makes it the ideal approach to plan demand during the current disruptions caused by Covid-19. Here I present a case study of using these techniques to assist a major online grocer, employing machine learning and advanced analytics to better predict demand. Combining product attribute data, a range of external data, and historical demand proved to be the best approach to predicting changing demand patterns, protecting this company at this difficult time, and giving it a powerful competitive advantage.

Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller, a New York Times Editors’ Choice, and a New Yorker favorite book of the year. He is coauthor of Algorithms to Live By, a #1 Audible bestseller, Amazon best science book of the year and MIT Technology Review best book of the year. His writing has been translated into nineteen languages, and has appeared in The New Yorker, The Atlantic, Wired, and in scientific journals. His new book, The Alignment Problem: Machine Learning and Human Values, will be published in October 2020. Brian holds degrees in computer science, philosophy, and poetry from Brown University and the University of Washington, and is a Visiting Scholar at the University of California, Berkeley.

Starting an S&OP process from scratch is difficult, particularly in a small organization. In this article I discuss the key steps in the implementation process including securing support from the VP of Sales and other stakeholders. I also address how to overcome the challenge of cultural change by demonstrating the value demand planning brings to each function. The following provides practical guidelines that will serve as a template to ensure successful and sustainable S&OP implementation in your organization.

This column represents the second in a two-part series, comprised of reprints from previous JBF columns. The column—reprinted in the Spring 2020 issue—was a revision of “Forecasting Heroes Catch Turning Points” (Summer 2001). It discussed ways to forecast downturns and get organizational buy-in for them. This second article is a revision of the “The Best and Worse Forecasting Year” column (Spring 2015). Such as in the first, the ideas are largely the same, yet are relevant for today’s forecasters and planners because the world is experiencing a drastic economic downturn caused by the Covid-19 virus pandemic and its ensuing mitigation efforts. This column discusses lessons learned from selling and surviving in an organization living through the realities of a ‘bad news’ annual forecast.

As Covid-19 abates and we emerge from lockdown, businesses are reopening amid an entirely different set of circumstances. Volatility will be around for a while, and supply chains must react accordingly, with specific functions needing to reassess how they add value in this new environment, and what their day-to-day operations should be. Here I present recommendations for supply chain functions to mitigate risk and ensure efficiency going forward based on the 3 V’s: Visibility, Velocity, and Variability.

Forecasting the number of cases and deaths from Coronavirus is challenging because it is new, meaning we don’t have historical data or analog viruses to work with. Therefore, traditional forecasting models and newer cause-and-effect models won’t work. The “S” curve model, the model often used in business to forecast demand for new products, seems to be most appropriate for this kind of forecasting. Here I show how this model works and provide forecasts for both Covid-19 cases and deaths in the USA and New York State. Consistency in the patterns of total cases and deaths gives us confidence in the numbers generated by this model.

Dr. Evangelos Otto Simos is head of predictive intelligence at Kefallonia, Inc., a private research and consulting firm, and professor at the University of New Hampshire. This report does not purport to be a complete description of global economic conditions and financial markets. Neither the Journal nor Kefallonia, Inc. guarantee the accuracy of the projections, nor do they warrant in any way that the use of information or data appearing herein will enhance operational or investment performance of individualsor companies who use it. The views presented here are those of the author, and in no way represent the views, analysis, ormodels of Kefallonia, Inc. or any organization that the author may be associated with. You can contact Evangelos at eosimos@eforecasting.com

This article is a revision of one written almost twenty years ago in the JBF, “Forecasting Heroes Catch Turning Points” published in the Summer 2001 issue. While the ideas are largely the same, it is particularly relevant for today’s forecasters and planners. The U.S. has experienced a long-running period of economic growth and pundits are speculating about an economic downturn. After all, “what-goes-up, must come down” and “what goes down must come up” and we are at a point in the economic cycle where things are likely to go down. For too long, business forecasters and planners have had a relatively easy job forecasting continual growth. A potential downturn is much harder to forecast. This first column of a two-part series deals with potential ways to forecast turning points and get organizational support for them. A second column will deal with selling and surviving in an organization and living through a ‘bad news’ forecast.

This article discusses the importance of establishing strong links between demand planning and inventory management processes. Several steps in the Sales & Operations Planning (S&OP) process can be more effective if this relationship is managed well. For example, linking expected demand patterns with a review of stocking risks and an evaluation of the best stocking strategy. It shows “how-to” examples for integrating demand planning with inventory control to gain a better overall balance across objectives leading to improved financial performance.

Excel spreadsheets have long been a valuable resource to professionals the world over, including Demand Planners. However, spreadsheets cannot scale in today’s digital economy where data streams into repositories every second from mobile devices, websites and IoT connected devices. Over the last decade, a wide variety of other, non-spreadsheet-based forecasting and planning tools, applications, and enterprise solutions have become available that can handle this Big Data. Here I reveal that to succeed in our roles in the digital economy, we need to move away from spreadsheets and towards these more sophisticated, scalable solutions.

The COVID-19 pandemic has quickly disrupted global business operations by impacting resources at multiple points in a complex, interconnected process. From suppliers to transportation, and including huge spikes and deep declines in demand, there are varying degrees of disruption in virtually all industries. While it is impossible to predict a crisis like COVID-19, this article outlines a path to combat volatility with vitality by harnessing the power of AI with best-fit internal and external data to model and better predict demand across short- to long-term horizons.