8 Key Design Considerations for Optimizing Your Demand Planning Process (Part 1)
Categories: Guest Post, Supply Chain Management | Tags: Process and Best Practice, The Hackett Group
Spend Matters welcomes a guest post from Josh Peacher, manager at The Hackett Group. This is the first in a two-part series.
Demand planning was once an overlooked element of supply chain management. However, more and more companies are beginning to understand how essential this component is to overall operational well-being. After all, a demand forecast is the genesis of the supply chain process. If poor demand signals are being sent through the system, it becomes extremely difficult to manage raw material and finished goods inventories, execute an efficient manufacturing process, provide effective customer service, and ultimately drive an accurate financial forecast.
So if your organization hasn’t already taken a long, hard look at improving its demand planning process, it’s time to begin. As a starting point for your journey, let’s take a look at the eight key design considerations for optimizing your demand planning process. In this first installment, we’ll focus on the four most basic design considerations and then move to more advanced ones in the second installment.
1) Start with statistical forecasting and exception management.
Statistical forecasting should always drive the original forecast. A simple set of formulas such as exponential smoothing, weighted moving average, and Holt-Winters can deliver more accurate, reliable, and efficient forecasts across the entire SKU base than manual forecasts. This can often be a change management challenge for many organizations as demand planners feel a pride of ownership over their forecast and have trouble with relinquishing control to a set of arithmetic functions. This is where exception reporting comes into play.
Exception reporting uses a set of pre-defined criteria to identify SKUs that are not ideal candidates for statistical forecasting. Since the strength of statistical forecasting comes from identifying patterns in demand history, highly erratic and/or variable SKUs are not good candidates and require manual intervention of the forecast. While exception criteria are customizable, common filters include frequent zero demand periods, high variance between the last six months and the six-month forecast, high variance in month-over-month demand history, and frequent shortages. Exception reporting is also an excellent way for demand planners to prioritize their time across the SKU set and focus their efforts on the SKUs that truly require attention.
2) Select the right software tool
In today’s environment of SKU proliferation and real time information, it’s become a necessity to use a demand planning tool to assist with the demand planning process. Software solutions such as Manugistics, SAP APO, and Logility all have their strengths and weaknesses. Key criteria to evaluate when selecting a solution include the following:
- Customer service reputation of the provider
- The tool’s ability to handle forecasting nuances (i.e., 5-4-4 calendar recognition and promotional forecasts)
- Transparency and reliability of the generated statistical forecast
- Forecast performance reporting and exception reporting capabilities
- Flexibility to forecast at multiple levels (e.g., SKU, customer, category, business unit)
3) Track the right metrics.
Demand planning metrics should serve two purposes: identifying improvement opportunities and driving accountability. The appropriate metrics will vary based on the characteristics of the industry and company in question. However, a few core metrics are routinely found in leading organizations. These include:
- Weighted absolute percentage error (WAPE) – In my opinion, WAPE is the most balanced and telling measure of forecast error. Some professionals will advocate for mean absolute percentage error (MAPE). However, MAPE doesn’t effectively account for volume as the forecast error percentage for each period is treated equally.
- Bias – This is similar to forecast error. However, bias provides a measurement of whether your forecast tends to be above or below actual demand, thus signaling a forecasting over/under “bias.”
- Period-over-period error trend – You’ll want to understand whether your demand planning process is improving or digressing. Measuring the forecast accuracy over time will also help to identify meaningful changes occurring in the business.
4) Use the correct data.
Statistical forecasting and exception management will help to get a reasonably accurate forecast. However, to drive forecast error down to best-in-class levels, demand planners must use external information.
As the graphic above shows, there is an abundance of information that demand planners could call upon to help them adjust their forecast. The real art of demand planning is knowing which of these data sources to use and when. Over time, your organization will get a sense for which information streams are most relevant and can begin to build a rules-based process for the use of external information.