Where Should the Forecasting Function Reside?

By Lapide, Larry | The Journal of Business Forecasting Methods & Systems, Winter 2003 | Go to article overview

Where Should the Forecasting Function Reside?


Lapide, Larry, The Journal of Business Forecasting Methods & Systems


(This is an ongoing column in The Journal, which is intended to give a brief view on a potential topic of interest to practitioners of business forecasting. Suggestions on topics that you would like to see covered should be sent via email to llapide@amrresearch.com. Ed)

The Fall 2002 Journal of Business Forecasting publication was a special issue on benchmarking. The publication covered a variety of benchmark data collected by the Institute of Business Forecasting (IBF) in a survey it conducted. One of the most interesting data sets I noticed dealt with results on where respondents stated their company's forecasting function resides. The results showed that across all industries polled the percent of companies where the forecasting function resides by department was:

* Operations/Production: 20%

* Marketing: 20%

* Finance: 14%

* Sales: 12%

* Forecasting: 10%

* Logistics: 9%

* Strategic Planning: 6%

* Other: 9%

While the data is certainly interesting, it is not insightful in helping a company determine where it should put its forecasting function. Essentially it says: "Take your pick"! This type of inconclusive benchmarking result is the reason why I often talk about what is important in deciding into which department a forecasting group should reside.

On the face of it, my usual advice does not at first appear to be much help, because I take the opinion that it depends on a variety of factors, and that there is no one right answer for a company, generally speaking. The right answer to me is to put the forecasting function inside a department that will diligently execute an effective forecasting process in a way it needs to be conducted to ensure the best output possible - namely the most accurate consensus forecast that can be developed and one that is used as the basis for all operational planning activities.

Dr. Lapide is the VP, Supply Chain Strategies at AMR Research. He has extensive business experience in industry and consulting and has a broad range of forecasting experiences. He was a forecaster in industry for many years, has led forecasting-related consulting projects for clients across a variety of industries, and has taught forecasting in a college setting. As a market analyst, he has been tracking the business application software market for over 6 years.

EXECUTING AN EFFECTIVE FORECASTING PROCESS

Executing an effective operational forecast process means setting up and adhering to a set of activities that enables the following to occur:

A finalized demand forecast that incorporates a balanced mix of quantitative and qualitative data. The process should start with the development of a baseline forecast that is based on objective information, often developed using statistical forecasting methods to blend historical information with known factors about the future. The baseline forecast should then be adjusted to incorporate market intelligence.

All stakeholder departments (such as Marketing, Sales, Operations and Finance) provide the market intelligence that is used to adjust the baseline forecast to account for factors not incorporated into it.

A consensus forecast is developed to which all stakeholder departments agree to, as well as are accountable for in their respective ways. This further means that the consensus demand forecast is used as the basis for every department's operational planning - enabling a single number planning best practice.

As long as a department can successfully execute a forecasting process and enables the above to occur, it qualifies as a good place to put the forecasting function. Of course, if the forecasting function can accomplish this without being in any department, then that is another option.

EVALUATION CRITERIA

Not every department has all the qualifications for successfully conducting an effective forecasting process.

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