Journal of Business Forecasting Articles Issues

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.

It seems intuitively obvious that the companies who figure out how to best engage with their consumers will get more than their fair share of growth. As a result, integrating consumer demand into the demand forecasting and planning process to improve shipment (supply) forecasts has become a high priority for many companies. Most supply chain professionals are quickly realizing that their supply chain planning solutions have not driven down costs and have not reduced inventories or speed to market. Consumption-based modeling using a process called, “multi-tiered causal analysis” (MTCA) which links consumer demand to supply (downstream data to upstream data), using a process of nesting advanced analytical models. Although this process is not new in concept, it is new in practice. Consumption-based forecasting using the MTCA approach is a simple process that links a series of causal models through a common element (consumer demand) to model the push/pull (sell in/ sell out) effects of the supply chain. It is truly a decision support system that is designed to integrate statistical analysis with downstream (POS and/or syndicated scanner) and upstream (shipment) data to analyze the business from a holistic supply chain perspective.

Sir Ian Davis is the Chairman of Rolls-Royce Holdings-the world’s second largest manufacturer of aircraft engines-and is a non-executive director at Johnson & Johnson and BP. Ian spent much of his career in management consulting, joining McKinsey & Co. in 1979 and becoming Managing Director in 2003. He was knighted in 2019 for services to business.

Safety stocks are a crucial part of meeting customer demand. They are an important buffer that protects businesses from unexpected spikes in demand, allowing companies to have inventory on hand to deliver to the customer without waiting for (often lengthy) production lead times. Inventory, however, is expensive, and holding excess stock puts companies in a position where inventory may become obsolete. It is crucial, therefore, that demand management and inventory management professionals strive to protect their company’s bottom line and cash flow by finding the right balance. Here I present effective safety stock calculation methods that, supported by forecasting and S&OP, can effectively reconcile the need for high customer fulfillment rates with the need to minimize costs.

Dr. Eric Siegel is the founding chair of the Predictive Analytics World conferences series, is the author of the award-winning book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, and is the host of the Dr. Data Show on YouTube. He is a former Columbia University professor and has become a defender of civil liberties in the age of Big Data. He is also the editor of The Predictive Analytics Times.

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.

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. 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.

Programmatic advertising allows companies to advertise to target markets and understand demand drivers and consumer behavior like never before. What’s more, this kind of advertising allows demand planners to shape demand in near real-time. By connecting programmatic advertising with demand and supply planning, we can use it to boost demand at will. It represents the next stage of demand planning and analytics, and offers forward-thinking companies the opportunity to gain a competitive advantage in the years to come.

This article asserts that standard integrated planning implementation requires three generic templates: a process model, a transformation framework, and an information systems roadmap. It makes the case for S&OP and IBP program managers to tailor these templates to their own organizations in order to assess current capabilities and guide the next phase of their business transformation. The results of implementation are improvements in financial results and key performance indicators.

This column is a modified version of an article I wrote, “Sales and Operations Planning (S&OP) Mindsets,” published in the JBF back in April 2007. While the content is largely the same as the one written 12 years ago, it is probably even more relevant today. Back then, S&OP was not as widely used as it is now but many of the challenges of running a well-functioning S&OP process remain the same. The major recommendation here is that one should establish clearly defined roles for various functional managers on an S&OP team—ones that are based on their psychologies or mindsets. I present a framework for understanding different stakeholders’ personalities in the S&OP meeting that can foster collaboration and help in achieving consensus.

The digital economy refers to an economy that is based on digital computing technologies where business is conducted through online and mobile devices using the internet-of-things (IoT). In the digital economy, value is created through the technology-enabled links between people, machines, channels and organizations. All this is giving rise to an awareness and willingness to apply analytics to everything, not just to strategic initiatives, but to day-today tasks. Advanced analytics aided by machine learning algorithms will automate the repetitive work demand planners do regarding managing data and information as well as uncovering key insights allowing them to work smarter and more efficiently. As such, digitalization of the supply chain will require companies to manage product replenishment based on actual consumption rather than transactions.