Forecast Errors
Volume 2, Issue 2, January 12, 2015

A forecast error is the difference between the actual and predicted value. The consequences are expensive inefficiencies that can be resolved with lean manufacturing technology.

Forecast Errors Examined via Backorders

One of the top customer inquiries and complaints is the status of a backorder, which costs customer service time, it also costs to ship the product once it arrives in the distribution center. With the average cost priced at $13 per backordered unit of merchandise, very quickly those expensive additional costs impact the bottom line.  F. Curtis Barry & Company, a national consulting firm for catalog, e-commerce, and retail businesses, suggests analyzing backorders to improve the accuracy of inventory forecasting. Jeffrey Barry also suggest that the customer order fill rate should be reviewed and improved without being out of stock or overstocked and gave an example of backorder costs: A typical catalog with a 20% backorder rate averaging two items per order processed 200,000 orders for a total of 400,000 units of merchandise. Calculated at 20%, 40,000 customer orders had backorders. Estimating backorder cost on the low end at $7.37 per order, the catalog will have to absorb $294,800 to make up for backorders. See more in our whitepaper.

Forecast Errors Minimized for Food & Beverage

Ordering for food and beverage can be quite complex, depending on the source. Deciding what strategy to use for ordering item/store level versus ordered to maximize turn or to maximize economics can be quite a dilemma. RetailX, a division of NCR Retail, blogged that an item ordered to maximize turn has the benefit of lowering inventory investment at the expense of handling and delivery cost. While this is a common strategy for multi-day delivery stores, it may not be cost effective for small and fragile items such as small items like toothpicks, fragile items and broken case items. Some items make more sense to order in a case pack or larger multiple to reduce handling charges. For those items, determining an economic order quantity that might exceed a turn goal is more cost effective.  When ordering to maximize economic investment and reduce handling costs, consideration must be allowed for space and shelf life. It may make more sense to order a lot of Styrofoam cups, but there may not be space for the product. Milk might be more economical to order in larger lot sizes than turn based, but that increases the amount of spoilage and reduces customer perception that a merchant carries the freshest product. Balancing economics with turn is a strong starting point to reducing costs while increasing profits on the current product mix for any food and beverage merchant such as grocers and convenience companies.

Forecast Errors Big Problem if not a Big Box Retailer

Alex Woodie has IT Jungle recently published an article and asserted that wholesaler distributors that fail to update demand forecasts are often left holding the bag when sales patterns change downstream or retailers cease carrying a product. Woodie referenced the importance of advanced demand forecasting and inventory optimization for a variety of industries with cloud-based solutions.  He acknowledged that in exchange for vast volumes, big-box retailers like Wal-Mart and Home Depot extract deep concessions from suppliers and often require them to maintain high service levels and in-stock percentages. When retailers add or drop products from their shelves, or demand unexpectedly changes, that makes it more difficult for distributors to maintain that "Goldilocks" zone between overstocking and understocking.

Forecast Errors Eliminated Impact on Bottom-line

Rick Morris, a Certified Supply Chain Professional wrote in Supply House Times that while improving fill rates, improved forecast accuracy also lowers inventory levels measured in days of sales and improved forecast accuracy simultaneously improves fill rates and lowers inventory. He suggested this translates into increased profitability. When analysts have studied companies that were best-in-class in demand forecasting, they found these companies average, according to AMR Research, 15% less inventory; 17% higher perfect order fulfillment; 35% shorter cash-to-cash cycle times; and 1/10 the stock outs of their peers. In addition, every 3% increase in forecast accuracy increased profit margin by 2%.  These improvements in inventory efficiencies then translate into improved financial metrics (according to AMR Research), including 10% improvement in earnings per share; 5% increase in return on assets; and a 2.5% gain in profits. See more here.

Move from forecast errors to demand driven accuracy:

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