For many manufacturers, the business landscape has changed dramatically over the past several years. Not only have we endured a downturn in the economy, causing increased competition, but we have now been put in the position to deal with soaring increases in raw material, energy and transportation costs.
So how can food manufacturers, in particular, gain a competitive advantage while maintaining high service levels and healthy margins? Certainly brand management, quality, and even culture are long-standing core competencies. But from a process perspective, the ability to accurately predict demand in the wake of inflation can make the biggest difference.
When speaking to food manufacturers around the world, demand forecasting seems to be a prominent issue that keeps them up at night. Yet, while many companies consider demand forecasting important, several still are not addressing it through improvement programs. The problem sometimes stems from a lack of understanding of how to improve forecast accuracy (or that it can be improved), as well as what the improvement is worth in terms of profit and asset utilization.
Demand forecasting helps companies in several readily apparent areas, such as production, scheduling and customer service. Food manufacturers have traditionally found that there is usually an obvious increase in inventory associated with customer service level improvements. However, being able to rebalance or reclassify inventory as a result of improved forecast accuracy can produce significant improvements in customer service without increasing overall inventory value.
The ability to produce these improvements even as raw material costs increase is a real attention-getter for several food manufacturers, including a recent Silvon Software customer for which raw materials represent the second largest cost after labor. This food manufacturer has a lot of perishable product moving from plants to distribution centers to stores around the country. If its forecast is off by as little as one percent, the company’s fuel and packaging costs can soar, quickly bleeding the bottom line. This is especially true today, since the wildly fluctuating cost of oil affects not only transportation costs, but also the cost of film used to package products. Just because this company’s fuel costs may be rising, that doesn’t mean it can go to a mass merchandiser, such as Wal-Mart, and raise its prices. The end result would be lost shelf space, as competitors who did a more effective job of demand management are able to deliver product to the mass merchandisers at a lower cost.
Another issue is promotional periods. If a food manufacturer underestimates forecasts for key promotional periods such as holidays, mass merchandisers will quickly turn to other suppliers for product - and those same merchandisers will remember the manufacturer’s shortfall the next time a promotional period hits. For instance, ricotta cheese is very popular just before Easter and Thanksgiving. For a company in the dairy business, if it doesn’t hit those promotional periods on time, it can lose its shelf space quickly – and perhaps, for a long time.
How important is it to accurately predict demand during promotional periods? According to a recent Grocery Manufacturers of America survey, stockout rates for promoted items are two times the level of the stockout rates for non-promoted items. Food manufacturers who are not using demand management tools to predict the lift they receive from a promotion are often very surprised when they find out they have underestimated the forecast of consumer demand. Some other stats from the survey that stand out:
• Consumers cannot find promoted items 7.4 percent of the time due to stockouts.
• If an item is out of stock, 62 percent of shoppers will substitute for it with another product. That kills customer loyalty.
• If a customer cannot find a product, 23 percent of them will go to another retailer just to buy the product they were looking for.
• Bottom line – the GMA survey found that stockouts result in $6 billion in losses annually – all because companies underestimate the demand forecast and can’t supply the customer.
But while many food manufacturers are familiar with the perils of underforecasting, many are also hurt just as much by overforecasting. For instance, one food manufacturing customer of Silvon Software produces a product with a supermarket shelf life of seven days. If it overestimates forecasts, the product has to be thrown out within a week. The substantial costs related to this makes it imperative that the company not overestimate forecasts of this product.
Another problem of overforecasting is wasted packaging. If a product doesn’t sell and 4 million impressions of film for that product have been ordered, the company is stuck with years of packaging supply. That’s a big problem made even worse when changes in labeling rules result in some companies having to completely throw out millions of dollars in packaging…all because forecasts were overestimated.
The approach many food processors are adopting is an internal collaborative demand forecasting process, driven by a statistical forecasting model. In today’s world of Supply Chain tools, users need only a rudimentary knowledge of data analysis and statistics. They should be expected to deal primarily with exceptions to the forecasting process, driven by predetermined business rules, such as absolute forecast error.
By no means is demand forecasting a black-box approach (nor will it ever be), but the analytical heavy lifting can be done with software tools. Not only that, but the results can be measured to determine the trust to place in the process and when to use internal experts. In fact, measuring forecast accuracy at all levels of detail will, in and of itself, drive improvements.
Surprisingly, many food manufacturing companies still have not made demand forecasting a core competency. Those food manufacturers that have are more competitive, more agile and more profitable in today’s demand-driven world.