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• W258284920 abstract "EXECUTIVE SUMMARY | Benchmarks are an important part of the culture of continuous improvement that enables companies to compare their performance against best-in-class companies. This forecasting benchmark study is the most comprehensive one that examines forecasting performance of promotions, new product introductions, and fast- and slow-moving products of consumer products companies. It covers virtually all items and locations for the North American warehouse-delivered business of nine multinational companies.The Consumer Packaged Goods industry faced another year of challenges from economic turbulence and weak consumer confidence in 2011. North American shipments contracted slightly, and the study shows evidence of a heightened reliance on marketing activities to drive sales. Promotions continued to play an important role in 2011, with promotional volume climbing by 8%. Innovation was impressive in 2011, with new products accounting for onethird of all items. Furthermore, almost half the items of the collective dataset have less than two years of history, making it difficult to forecast with a reasonable amount of accuracy using traditional techniques of forecasting.Given the volatility and rapid pace of promotional activities and product innovation, the gradual increase in forecast error and bias since 2009 comes as no surprise. Traditional forecasting techniques provide more or less the same performance across companies, with average weekly error rates of 53%. The spread in bias among companies is more pronounced - top performers achieve weekly bias of 3%, less than half the average level of 7%.Surprisingly, there is little evidence that cost pressures are driving much network consolidation. Far from calls for SKU rationalization, the industry experienced considerable growth in items, especially in non-food categories. Most of the growth occurred in lowvolume items, which is a matter of concern, given that they make up 80% of items and tend to have disproportionately high inventory costs.Demand Sensing provides consistently high performancethroughout the turbulence, across all segments and business activities, including promoted products, new and seasonal products, and fast- and slow-moving products. This underscores the value of an algorithm that dynamically adapts to current market realities, instead of relying solely on seasonal patterns. There is also considerable opportunity to add value where there are extreme error conditions - when sales exceed or fall short of forecasts by two times or more. These events are some of the most disruptive and costly for supply chains. With market volatility expected for the foreseeable future, the Demand Sensing tool plays an increasingly central role in achieving forecast excellence. For an overview of Demand Sensing, see Mr. Byrne's article Beyond Traditional Time Series: Using Demand Sensing to Improve Forecasts in Volatile Times, in the Summer 2012 issue of the Journal of Business Forecasting.STUDY METHODOLOGYThe study encompasses 90,000 items stocked in 475 locations, 4 billion physical cases, and more than $100 billion in annual sales. There is good representation of items across the key consumer packaged goods sectors: Food & Beverage, Personal Care, and Home Care. Some of the nine companies included are Procter & Gamble, Unilever, Kraft Foods, Kimberly-Clark, General Mills, ConAgra Foods, and Campbell Soup.To ensure comparability, consistent sets of metrics and standards were applied to the entire dataset. All measurements are according to the following protocol:* Demand Planning refers to each company's demand planning system, such as SAP Advanced Planner and Optimizer (APO) or JDA Manugistics. These are not baseline statistical projections but the final forecasts published and used for product planning, i.e., statistical forecasts first tuned by planners and then adjusted during the sales and operations (S&OP) process. …" @default.
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• W258284920 date "2012-10-01" @default.
• W258284920 modified "2023-09-28" @default.
• W258284920 title "Forecasting Performance for North American Consumer Products" @default.
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