Demystifying Forecasting: Myths versus Reality

It has been an exciting time for the field of demand forecasting. All
the elements are in place to support demand forecasting from a fact-based perspective. Advanced analytics has been around for well
over 100 years and data collection has improved significantly over the
past decade, and finally data storage and processing capabilities have
caught up. It is not uncommon for companies’ data warehouses to
capture and store terabits of information on a daily basis, and parallel
processing and grid processing have become common practices. With
improvements in data storage and processing over the past decade,
demand forecasting is now poised to take center stage to drive real
value within the supply chain.
What’s more, predictive analytics has been gaining wide acceptance globally across all industries. Companies are now leveraging predictive analytics to uncover patterns in consumer behavior, measure
Chase, C. W. (2013). Demand-driven forecasting : A structured approach to forecasting. John Wiley & Sons, Incorporated.
Created from Apus on 2021-12-08 10:30:27. Copyright © 2013. John Wiley & Sons, Incorporated. All rights reserved.



the effectiveness of their marketing investment strategies, and optimize financial performance. Using advanced analytics, companies can
now sense demand signals associated with consumer behavior patterns
and shape future demand using predictive analytics and data mining
technology. They can also measure how effective their marketing campaigns are in driving consumer demand for their products and services, and therefore they can optimize their marketing spending across
their product portfolios. As a result, a new buzz phrase has emerged
within the demand forecasting discipline: sensing, shaping, and responding to demand, or what is now being called demand-driven forecasting.
With all these improvements, there has been a renewed focus on
demand forecasting as the key driver of the supply chain. As a result,
demand forecasting methods and applications have been changing,
emphasizing predictive analytics using what-if simulations and scenario planning to shape and proactively drive, rather than react to,
demand. The widespread acceptance of these new methods and applications is being driven by pressures to synchronize demand and supply
to gain more insights into why consumers buy manufacturers’ products. The wide swings in replenishment of demand based on internal
shipments to warehouses and the corresponding effects on supply can
no longer be ignored or managed effectively without great stress on
the upstream planning functions within the supply chain.
New enabling technologies combined with data storage capabilities
have now made it easier to store causal factors that influence demand
in corporate enterprise data warehouses; factors may include price,
advertising, in-store merchandising (e.g., displays, features, features/
displays, temporary price increases), sales promotions, external events,
competitor activities, and others, and then use advanced analytics to
proactively shape future demand utilizing what-if analysis or simulations based on the parameters of the models to test different marketing
strategies. The focus on advanced analytics is driven primarily by the
need of senior management to gain more insights into the business
while growing unit volume and profi t with fewer marketing dollars.
Those companies that are shaping future demand using what-if analysis are experiencing additional efficiencies downstream in the supply
chain. For example, senior managers are now able to measure the
effects of a 5 percent price increase with a good degree of accuracy
Chase, C. W. (2013). Demand-driven forecasting : A structured approach to forecasting. John Wiley & Sons, Incorporated.
Created from apus on 2021-12-08 10:30:27. Copyright © 2013. John Wiley & Sons, Incorporated. All rights reserved.
and ask additional questions, such as: What if we increase advertising
by 10 percent and add another sales promotion in the month of June?
How will that affect demand both from a unit volume and profit perspective? Answers to such questions are now available in real time for
non-statistical users employing advanced analytics with user-friendly
point-and-click interfaces. The heavy-lifting algorithms are embedded
behind the scenes, requiring quarterly or semiannual recalibration by
statisticians who are either on staff or hired through outside service