Demand Forecasting – Proven bang for the buck on the bottom line

Solving business problems and helping our partners create future profits – Demand Foresight is completely focused on these goals. We focus on delivering upon these through improving demand planning and overall S&OP operations and helping support transition to being a demand driven company.  Our experience in this work helps us quantify the impact of what we do – a minimum 5% improvement in pre-tax profitability.  But obviously we are not the only game in town and there is a tremendous body of work around demand forecasting and the impact of improving the forecast on various components of the supply/value chain.  It was brought up that we had never really highlighted some of this research so we went back and took a look at some of the source inspiration for our focus.  I hope you find this of value.

First up is Dr. Hau Lee.  He is the Thoma Professor of Operations, Information, and Technology at Stanford University; Co director of The Stanford Global Supply Chain Management Forum;  Director of Managing Your Supply Chain for Global Competitiveness Executive Program.

Dr. Lee’s signature idea is the “bullwhip effect,” a concept he co-developed in the 1990s, which has become a basic tenet in both academia and industry. When a person cracks a bullwhip, the small movements at the wrist produce huge waves at the other end of the whip, which describes how information on demand becomes increasingly exaggerated and distorted as it moves up the supply chain from customer to manufacturer to supplier, driving up costs and hurting efficiency.

There are several components to the cause, effect, and solution to the bullwhip effect which you can start to explore through the attached links but one key is that the demand forecasting capability plays a large role in the bullwhip and the effects can be devastating to a company’s value chain.  Specifically:

“Distorted information from one end of a supply chain to the other can lead to tremendous inefficiencies: excessive inventory investment, poor customer service, lost revenues, misguided capacity plans, inactive transportation, and missed production schedules.”

Given Dr. Lee’s long term research and cooperation with client companies, the impact of improving forecasting and focusing on demand drivers is significant.

“I have already seen some companies using new software tools to manage their businesses based on demand, with results ranging between 50% and 100% in net profit increases, which in turn can easily be translated into enormous increases in shareholder and market values.”

Next up is Dr. John Mentzer.  Dr. John T. (Tom) Mentzer was the Harry J. and Vivienne R. Bruce Chair of Excellence in Business in the Department of Marketing, Logistics and Transportation at the University of Tennessee. He was a prolific researcher and author with 5 books on value chain excellence and competitive differentiation not to mention hundreds of articles to his name.

One of his key areas of research highlighted competitive differentiation and included was a fairly definitive summary of what it took to develop a competitive differentiation in demand forecasting and demand planning.

Specifically, here are Dr. Mentzers’ 7 Keys to better forecasting:

  1. Understand what forecasting is and is not

  2. Forecast demand, plan supply

  3. Communication, cooperation, and collaboration

  4. Eliminate islands of analysis

  5. Use tools wisely

  6. Make it Important

  7. Measure, measure, measure

Most important of all was that Dr. Mentzer extended his research, through the help of many colleagues and collaborators, to the measurable impact (following his own guidance – measure) on a company’s performance.  There are a number of components to the result most of which you can begin to explore through the included links but from the point of view of an executive and/or member of the vaunted c-suite with specific fiduciary responsibility to all stakeholders of a company – the single most clarifying result of improved forecasting highlight by Dr.  Mentzer:

An increase of shareholder value of 15% or more!

Lastly, we referred to our friends at Garter which includes the recently merged business/supply chain analysts from AMR Research.  There are a host of strong practitioners such as Noha Tohamy, Tim Payne, Mike Griswold, Dennis Gaughin, and Steve Steurterman just to name a few. Since January 2011 they have published a large number of research papers and articles that highlight the impact of improved demand forecasting.  Unfortunately here we cannot include the source documentation links as that goes against policy and legal agreements, but please feel free to contact Gartner directly (  As they are a potentially important source of information for companies looking to make strategic investments in demand forecasting, sales and operations planning, and demand driven supply chains and networks.

Relevant to this blog post, their cumulative research has pointed to some significant findings.

Gartner’s measurable impacts of improved forecasting and demand planning:

  1. 1% to 3% revenue increases

  2. 15% to 30% inventory reductions

  3. 20% to 30% order fill rate increases (Demand Foresight note – important to do 2 and 3 simultaneously)

  4. 10% to 15% decreases in obsolete inventory

  5. 3% to 5% increases in gross margin

Independent of Demand Foresight and its experiences working with it partners, there is great research ongoing that undeniably highlights the benefits of focusing on improving demand forecasting and demand planning.  However, one consistent anomaly (kind of an oxymoron – A consistent anomaly?) that we notice is that while this highlighted research above as well as others all emphasize the importance of demand forecasting and measure the benefits of improved demand planning, very few of them talk about specific impacts based on higher percentages of accuracy.  Our suggestion and potential contribution to the field is what is the impact of improving accuracy by 5%? 10%?

Or as we ask all of our partners and potential partners – what would a minimum 25% reduction in forecast error mean to your bottom line?  Judging from our experience and the research– a huge amount!


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