Centralize or Decentralize the Statistical Baseline Forecast?

Demand Analysts have advanced statistical skills and strong business acumen

Demand Analysts have advanced statistical skills and strong business acumen

I was recently asked by a customer if they should move the responsibility for creating the statistical baseline forecast. They were considering moving it from their regional country offices to their global headquarters. In addtion, they were considering changing the role of their regional demand planners to only make adjustments to the statistical baseline forecasts based on local sales and marketing activities (i.e., sales promotions, marketing events and others).

Many companies are considering creating “Centers of Forecasting Excellence” within their corporate headquarters, particularly, at larger global companies. Furthermore, they are staffing those centers of excellence with “demand analysts”, not demand planners. So, what is the difference? Demand analysts are responsible for creating the statistical baseline forecasts for all the regions/divisions.  Then, they pass those statistical baseline forecasts to the regional/divisional demand planners to refine (make adjustments) the statisitical baseline forecasts.  Those adjustments are based on local sales and marketing activities, such as pricing actions, sales promotions., and others.   

Different skill sets

The skill set of these newly created demand analyst positions are different from the demand planners.  The demand analysts have advanced statistical skills and strong business acumen. They also have strong collaboration skills as they work closely with the regional demand planners. The regional demand planners do not necessarily have a strong statistical skill set, but work closely with the local commercial business teams to refine the statistical baseline forecasts reflecting regional sales/marketing activities (i.e., pricing actions. sales promotions, marketing events, and others).

Another question that always follows is the ratio of demand analysts to demand planners. Based on my experience with customers, we recommend one demand analyst for every three to four demand planners as the optimal mix. Once all the statistical models are generated demand analysts only need to tweak the statistical baseline forecasts on an exception basis requiring fewer resources. The demand analysts also provide ad hoc analysis in support of the global commercial teams to assess business strategic initiatives and tactics.

If you are implementing a demand-driven forecasting and planning process, those statistical baseline forecasts will include key performance indicators (KPI’s) such as price and sales promotions, which the demand planners can utilize to make adjustments through “What If” analysis, not “gut feeling” judgment. Also, POS/Syndicated Scanner data (true demand) can be integrated into the process encouraging the commercial teams to engage in the demand planning process. The goal is to have demand analysts building statistical baseline forecasts that include KPI’s, such as sales promotions, price, advertising, in-store merchandising and more. Then, demand planners working with the commercial teams (sales/marketing) running “What If” simulations adjust the forecasts based on data, analytics and domain knowledge rather than just "gut feeling" judgment.

The goal is to reduce judgment bias by using data, analytics, domain knowledge, and scalable technology, thus minimizing bias judgmental overrides that add error. This requires investment in people (skills/behavior changes), process (horizontal, not vertical) that includes the commercial side of the business, using analytics (not just descriptive, but also predictive analytics), and finally, supported by scalable enterprise technology. Most traditional demand forecasting processes focus only on the process and technology, and then, rely on “gut feeling” judgement to enhance the accuracy of the demand forecast. That traditional process has failed. Furthermore, spreadsheets are not scalable enough to handle thousands of SKU’s across multiple market areas on a global basis. It requires an integrated scalable enterprise solution.

Change requires a Champion

Companies are quickly realizing that an internal “Champion” is needed to drive the change management required to gain “adoption”, because this new process design and added demand analyst role is a radical change for most companies. Also, even if you get adoption, you need for it to be “sustainable”. Many People Process analytics and technologycompanies gain adoption, but cannot sustain it to make it part of the company culture. In order to make it sustainable companies need to incorporate predictive analytics into the process that is supported by a large scale enterprise technology solution with an easy-to-use UI. Also, internal “Champion” involvement will assure this new approach to demand forecasting and planning becomes part of the corporate culture over time.

These interdependencies are also influenced by the strategic intent of your demand forecasting and planning process. In other words, is the intent to create a more accurate demand response, a financial plan, marketing plan, supply plan, or a sales plan (target setting)? These different intentions are all conflicting, and are not really forecasts, but rather plans that are derivatives of the unconstrained demand forecast. By the way none of these plans reflect the original demand forecast, and in many cases are conflicting.  One solution is a set of horizontal metrics that are shared across the supply chain, which includes the commerical teams.

So, does your company have a centralized, decentralized, or a hybrid demand forecasting and planning process?  Also, what is the intent of your demand forecasting and planning process?

Charles Chase author webpage http://support.sas.com/publishing/authors/chase.html


About Author

Charlie Chase

Executive Industry Consultant/Trusted Advisor, SAS Retail/CPG Global Practice

Charles Chase is the executive industry consultant and trusted advisor for the SAS Retail/CPG global practice. He is the author of Next Generation Demand Management: People, Process, Analytics and Technology, author of Demand-Driven Forecasting: A Structured Approach to Forecasting, and co-author of Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation, as well as over 50 articles in several business journals on demand forecasting and planning, supply chain management, and market response modeling. His latest book is Consumption-Based Forecasting and Planning: Predicting Changing Demand Patterns in the New Digital Economy. To learn more, please see his Author page.

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