Erin Marchant

Erin Marchant

This month’s interview is with Erin Marchant, Senior Analyst in Demand Management at Moen, Incorporated.

Erin has over ten years of varied experience in Supply Chain, from strategic sourcing to material planning, production scheduling, and demand management. She is also Moen’s Demand Planning and Analytical Systems Power User. Erin is a graduate of Heidelberg University, and also earned her MBA from Ashland University and M.S. in Information Architecture and Knowledge Management from Kent State University.

Moen is in the early stages of an FVA pilot, and Erin shares her experience to date.

Mike: When did you first hear about FVA, and how did you introduce the FVA approach to your management?

Erin: Our new Director of Demand Management brought the concept of FVA to us early in his tenure. He had learned about FVA from your book and articles, and in turn asked my boss and me to try to pilot the concept with our small segment of forecast responsibility.

Since our Director is the one that introduced the idea to us, we didn’t have to get buy-in from our organization. However, there is a need to get commitment from our business unit partners. In our pilot, we led the conversation to show the business unit why we wanted to start analyzing this data and the potential benefit of its measurement on the forecast process.

One pivotal element of FVA that we have had to reiterate time and time again at all levels of the organization is that FVA is not a measurement of who has the “right” forecast. FVA is a measurement of the value added at each step of the forecast process. Restating that fact constantly helps keep the discussion from devolving into an “us versus them” proposition.

Mike: Was the data needed to do FVA analysis readily available? Or did you have to start collecting it?

Erin: Yes and no. We have always collected the data for each layer (or, in the APO-DP world, “key figure”) of forecast data in our BI systems, so it was possible to determine the value of each step of the forecast. However, until the introduction of FVA, we had never been particularly disciplined about what kind of information went into each key figure. We have now assigned a purpose to each key figure, so that when the data is transferred to our BI systems it is very easy to determine FVA for each process step.

Mike: What are the steps in your forecasting process?

Erin: Process steps are:

  • Statistical Forecasting models run
  • Analyst override at item/DC and mid-level
  • Collaboration meetings to get business unit input
  • Tie to business unit topline forecast $ by fiscal month

Mike: What forecasting performance metric are you using and at what level do you measure?

Erin: In terms of FVA, we measure T-3 (3-month) Topline (Percent) Error and WAPE. While the data is available at the Item level, only a “mid-level” and topline FVA is published for both of these metrics. In the FVA pilot, that mid-level was defined as Product Line.

Mike: Are you measuring forecast bias? What are your findings?

Erin: We do not measure bias in terms of each forecast step defined in FVA, but we do measure a 3- and 6- month rolling MPE at the item, mid- and topline level. This is more of an internal metric that our analysts use to adjust item and mid-level forecasts. The idea of measuring bias in our forecast process something I am now curious to analyze!

Mike: Are you comparing performance to a naïve model?

Erin: Yes – we use a 3-month rolling average of actual sales by customer requested ship date as our naïve forecast.

Mike: What FVA comparisons are you measuring?

Erin: We are comparing:

  • Naïve to Statistical Forecast
  • Statistical to Analytical (Analyst Override)
  • Analytical to Collaborative
  • Collaborative to Topline

Mike: What FVA analyses / reports do you use?

Erin: Because we are so early in the process of using FVA, our reports are pretty rudimentary. We have set up some mapping in our BI tools so that the calculations are updated automatically. The output populates a very simple Excel file.

Monthly Report:



Month-Over-Month Tracker (WAPE Example):



Mike: Can you point to any specific results / benefits / improvements from using FVA at your organization?

Erin: We are new to this process, but even now I am seeing the benefits of FVA in one very profound area: measuring our forecast process in this manner takes the emotion out of the conversation and allows Demand Management to start a dialogue with our partners about our forecast process that is compelling and data-driven.

For example, if you look at the charts above, you notice that tying to a topline forecast number significantly de-values our forecast. I think that the people in Demand Management have thought this for a long time, but have not been able to put a measurement around their assumption that would make a strong case when talking through the issue to the business unit. Now, we can see how each action we complete has an impact on our forecast, and that is a powerful message. Demand Management can be a rather politicizing activity at times, but FVA helps to temper that.

Mike: Any process changes as the result of FVA findings?

Erin: Not yet! The Demand Management department is building a compelling case for some process changes based on the FVA data. But we haven’t been measuring long enough to make changes as of yet.

Mike: Anything else you’d like to say about FVA? Including advice for other companies considering the application of FVA?

Erin: If your company is considering applying the FVA concept, I would stress yet again how critical it is to not make the conversation an “us versus them” proposition. FVA is NOT a tool for Demand Management to take to the business unit and say, “Look how much better we are at forecasting than you.” FVA is a measurement of process – and Demand Management should be a unified, collaborative process.

If your FVA data shows that collaborating with the business unit, for example, is a step in the process that de-values your forecast, don’t let the data become the flame to burn the business unit with. Use the data as the start of a conversation on how the groups can collaborate better or differently in order to achieve a more accurate, shared forecast.

Yes, FVA data can pinpoint steps in your process that can be eliminated, but it is not an excuse for Demand Management to create the demand forecast in a silo, either. When used properly, FVA is a useful tool that can refine and unify the organization’s forecast process.

Willing to share your experiences with FVA? Please contact IBF to arrange an interview for the blog series.