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.

Dr. Simos is Director of Forecasting and Predictive Analytics at e-forecasting.com, a division of Infometrica’s Data Center, 65 Newmarket Road, Durham, NH 03824, U.S.A. and professor of economics at Paul College, University of New Hampshire, www.infometrica.com, eosimos@e-forecasting.com. This report does not purport to be a complete description of global economic conditions and financial markets. Neither the Journal nor Infometrica, 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 individuals or companies who use it. The views presented here are those of the author, and in no way represent the views, analysis, or models of Infometrica, Inc. or any organization that the author may be associated with.

Doug is credited with originating the field of infonomics, developing methods to quantify information’s value. He put forward these ideas in the best-selling book, Infonomics: Monetizing, Managing and Measuring Information as a Competitive Advantage, and lectures on the topic at the University of Illinois Gies School of Business. Formerly, Doug led the Deloitte Analytics Institute and worked as an analyst at Gartner’s research and advisory practice. When he is not teaching, Doug leads the data and analytics strategy practice at the consultancy firm Caserta.

With digitalization there is a need to refocus on new demand management technology that utilizes advanced analytics including artificial Intelligence/machine Learning and takes advantage of cloud based open source technology. The next generation of demand management technology will form part of an analytically driven demand planning and forecasting process that provides a platform for business users and data scientists to predict and plan for demand across all time horizons including daily, weekly, monthly and beyond. This will connect all business functions in realtime, enhancing the accuracy of statistical forecasts and the value of demand plans.

Using means and standard deviations of statistical forecasts has been the default method for demand planners for decades, but there is a key shortcoming with this approach, namely that it assumes that demand is normally distributed, which it rarely is. This incorrect assumption severely impacts forecast accuracy and accuracy of all dependent plans. The solution, and an increasingly adopted method, is probabilistic forecasting. In this article I discuss how probability distributions allow planners to work with the real uncertainty in demand and enjoy more accurate demand plans as a result. I also explore other benefits of this approach and the differences between deterministic and probabilistic forecasting.

This article deals with Artificial Intelligence (AI) technology and its potential role in business decision-making (DM) processes. It addresses whether an AI computer system’s speeding up of processes, approaching real-time decision-making including optimization, will be beneficial. To assess this, it introduces two type types of thinking discussed in the literature. While pre-supposing that AI will surely be useful in a decision support role, it looks at roles it may play in making decisions for the two types, without intervention from managers. Moreover, it warns against using AI output at face-value for the most impactful and strategic decisions to be made.

Shimano North America manufactures and distributes thousands of high-end cycling and fishing components and accessories. The company distributes through a variety of channels including bike shops, sporting goods chains, distributors, and direct to consumer. As retail channels are being redefined, and direct to consumer marketplaces like Amazon have flourished, Shimano needed to optimize product assortments and availability for each channel in what had become a highly dynamic marketplace.

New planning software can bring about greater forecast accuracy, lower inventory and better service levels. But implementing a new planning solution is a major undertaking, and maximizing the new tools requires an approach that goes beyond the implementation itself. In this article I document 4 common behaviors that can limit the ROI of any software investment, or even lead to abandonment of the software. I provide suggestions on how planners can avoid these behaviors and get the most out of newly available tools, both now and for the long term. Key considerations include post-launch training and managing stakeholders’ expectations.

Sir Ralf Speth is a leading figure in the automotive industry, starting his career at BMW where he spent 20 years. After a stint as CEO at Linde, a multi-billion dollar chemical company, he became Director of Production, Quality and Product Planning at Ford Motor Company, before becoming CEO of Jaguar Land Rover in 2010. His tenure has seen an increase in the workforce by 17,000 and established JLR as the biggest R&D investor in the UK. He is an honorary professor at Warwick University and was knighted in 2015 for services to the automotive industry.

Blockchain is an emerging technology based on distributed ledger technology, which is hailed by many as a revolutionary development with positive implications across many industries and business processes. This article explains how blockchain technology works and how it can drive efficiencies in supply chain management and demand planning. Although this technology is still early in its development, it already has many success stories in the food, aerospace and automotive industries. It is already used to drive transparency into companies’ supply chains for better supply planning and, given that blockchain allows for the tracking and capturing of vast amounts of data, it provides a valuable and trusted data source that demand planners can use for better forecasts and insight into consumer behavior.

In this article I aim to help you make the right planning software investment for your company. I discuss how to develop a detailed specification document based on your collaboration, process and functional requirements. Such a document allows you to crosscheck your needs with whatever solution you are considering and helps ensure that any investment serves all internal users of your forecasts and provides an ROI for the long-term. I also reveal the costs of investing in software, and the direct and indirect savings it can provide.

The four most important ingredients of effective S&OP are data, people, process and technology, and with the massive proliferation in data that we are currently experiencing, the role of technology has never been so important in turning that data into actionable insight. In demand planning we have long used technology to identify relationships and create forecasts which facilitate decision making.

Since the e-commerce market is growing by leaps and bounds, companies need a dedicated team within the S&OP process to take advantage of it. It is a different animal and requires a different skillset and strategy to manage and grow demand. It presents major challenges and has already damaged brick-and-mortar retailers, as well as a host of different industries—publishing being a prime example. At the same time, it has provided many new opportunities to grow demand via target and subscription-based marketing.

The success of an S&OP process depends heavily on the behavior of its participants. Unfortunately, the cross-functional teams required for S&OP often exhibit some form of dysfunctional behavior, resulting in poor performance and reduced faith in the process. However, by setting behavioral expectations and refining the process, the effect of these dysfunctional behaviors can be mitigated and reversed, enabling higher levels of performance.

This article deals with the differences between ‘responsible’ versus ‘ethical’ supply chains. It is meant to be controversial. I believe that businesses need to address the seemingly uncivil public culture we are experiencing today, in order to ensure that plans, forecasts and decisions are made collaboratively, as well as consensus-based. We have started to see instances in which employees’ so-called ‘ethical’ opinions about politics and value-systems have fostered dysfunction in some high-profile workplaces. It is important for management to address these issues as they arise, so that ‘responsible’ decisions are made in the interest of the whole company.