Why the Front End, You Ask? Most companies establish a supply/demand match in their S&OP process, beginning with the demand statement created by the "front end" of the business, which drives requirements for the products or services to be provided. There are some industries that have a "build it and they will come" mentality, but even they have to be concerned with setting the operating level at a point that won't result in excess inventory.
The following data points show the forecast accuracy advantage that the Best-in-Class (top 20%) have at the product group/family level and the SKU level compared to All Others (bottom 80%).
- Product Family forecast accuracy
• Best-in-Class - 82%
• All Others - 63%
- SKU forecast accuracy
• Best-in-Class - 74%
• All Others - 55%
The product group level accuracy is important for setting operating levels or run rates for a given product line or segment. The SKU accuracy is a good indication of how well the forecasting system and consensus input is working. The question is how does this affect the rest of the organization?
The actual impact of poor forecast accuracy is more easily understood by evaluating from the forecast error perspective, as shown below.
- Product Family forecast error
• Best-in-Class - 18%
• All Others - 37%
- SKU forecast error
• Best-in-Class - 26%
• All Others - 45%
At the product group level All Others have twice the error rate of the Best-in-Class (37% vs. 18%), which means they are correcting the operating levels for twice the volume as the Best-in-Class and the level of disruption and incremental cost arguably twice as great.
At the SKU level, the error rate is 72% higher which means that there are 72% more specific items that must be changed if the organization intends to meet the demand. Change may mean setting aside the unneeded product/services and may require expediting the unanticipated product/service through the process. The set aside product increases the inventory, and the expedited products increase the costs due to more changeovers, setups and expedite costs, plus the time spent performing the reschedule. These rescheduling costs are highly visible, so the impetus to improve forecast accuracy is very tangible as a result.
The hidden cost of forecast error is the impact on suppliers who rely on the schedule as their forecast to execute against. Once a forecast is in effect, any error is propagated to the internal organization and every supplier/partner that provides a product or service. The negative consequence when suppliers are frequently rescheduled is the loss of credibility, one of the toughest things to recover. It's difficult enough to regain credibility internally, but when suppliers/partners lose faith, it's very tough to regain their attention.
When the forecast error is not addressed, the negative ripple effect can be devastating and goes beyond the inability to meet a schedule. Conversely, taking a proactive approach to improving forecast accuracy for an organization will have a positive ripple effect throughout the business at all levels, and suppliers as well, which makes starting with the front end of the business for improvement, an extremely good choice.
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