TERRA S FORECASTING BENCHMARK STUDY HIGHLIGHTS

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1 2012 TERRA S FORECASTING BENCHMARK STUDY HIGHLIGHTS 2013 FORECASTING BENCHMARK STUDY HIGHLIGHTS Now in its fourth year, this remains the most comprehensive study of demand planning performance, including virtually all items and locations of the North American warehouse-delivered business for eleven multinational consumer packaged goods companies. The scope encompasses 500 distribution centers, 450,000 item-locations, close to 5 billion physical cases and more than $130 billion in annual sales. Key Findings As the economy continued to recover in North America, supply chains became considerably more complex while shipments for the group as a whole remained essentially flat. One would have hoped for just the opposite rising volume and a stable or decreasing level of complexity. Proliferation of items, base codes and locations grew by up to 10% in 2012 for the group, but differed significantly between companies. Non-food companies drove much of the complexity, with the growth in nonfood item-locations since 2009 outpacing the growth in food item-locations by a factor of 10. There was also a pronounced upswing in the number of seasonal items, which rose almost 20% in 2012, compared to only 5% for non-seasonal. Complexity from innovation reversed a three-year trend of improved forecast value-added which dropped slightly in Average weekly forecast error also dipped slightly from 53% in 2011 to 51% in Bias for the group remained unchanged at 7%, with the consumer packaged goods industry consistently over-forecasting for every year encompassed in the study. Increased reliance on promotions and new products drove sales, but made forecasting and execution more challenging with elevated error levels and bias 4-5X higher than regular sales. Throughout this changing environment, Demand Sensing continues to provide consistent value across all velocities and business activities, including new products, promotions, seasonal and regular turn volume. This highlights the importance of new mathematics that automatically adapts to changing market conditions whether the economy is experiencing a downturn or a recovery. This whitepaper identifies trends in network complexity, demand planning error and bias, extreme error and forecast value-added. It also summarizes the reduction in error manufacturers are seeing through the use of demand sensing. To learn more, request a full copy of the Forecasting Benchmark Study report at terratechnology.com. 1 OF 8

2 Network Complexity The dataset confirms that networks are rapidly becoming more complex. The number of items has grown at a rate of almost 10% per year since Meanwhile, aside from an initial rally in shipments following the aftermath of the 2009 recession, volume has remained flat. As a result, volume per item declined significantly. Decisions to close warehouses following the economic downturn were realized in 2010, only to rebound the following year as companies pursued growth. The relationship between volume and items for all companies follows the 80/20 rule, with just over 20% of the items contributing 80% of the volume. Top performing companies (in terms of managing slow-moving items) have a smaller tail, comprising only 72% of items compared to 79% for the average. The high number of low-velocity items across all companies every year suggests a continued opportunity for SKU rationalization. CHANGE IN NETWORK COMPLEXITY ITEM DISTRIBUTION BY VELOCITY To study the characteristics of fast-and slow-moving products, the dataset has been divided into five quintiles, each comprising 20% of the volume. Velocity 1 refers to the fastest-moving items and Velocity 5 the slowest. Slowest-moving products continued to drive SKU proliferation, accounting for about 90% of last year s growth in the number of item-locations. The constant growth of items and locations reveals that, as an industry, we continue to add complexity instead of remove it. 2 OF 8

3 Demand Planning Error The slight upward trend in forecast error observed in previous years appears to have stopped in Error was 51%, down from 53% the year before, but up since The spread between top performer figures and the average dropped by almost half, from 8% in 2009 to 5% in Overall, forecasting is becoming more challenging. WEEKLY ERROR BY YEAR Error for low-velocity items remained disproportionately high at 73%. In comparison, high-velocity items had an average forecast error of 45%. However, the large number of slow-moving items that make up the tail, combined with their elevated forecast error, create a considerable inventory burden for the industry. Since safety stock is roughly proportional to forecast error, these slow-moving items require 60% more safety stock to ensure the same fill rates as Velocity 1 items. Top performers have a considerable advantage across all velocities. WEEKLY ERROR BY VELOCITY This year reversed the trend of growing error, with more than half of the companies experiencing net gains in forecast accuracy. 3 OF 8

4 Demand Planning Bias Weekly and monthly bias remained positive in the 6% to 7% range, unchanged from 2011, with the consumer packaged goods industry consistently over-forecasting for every year encompassed in the study. Top performers had a consistent bias advantage across all years. WEEKLY BIAS BY YEAR Bias for the slowest-moving products was three to four times higher than for the faster items. With Velocity 5 products accounting for only 20% of the volume, it is natural to expect planners to focus their limited resources on the 21% of items that make up the bulk of the shipments. As such, high forecast error for Velocity 5 items comes as no surprise, but the same logic does not hold true for bias. The consistently high bias for these items exposes an unjustified optimism for products that make up the long tail and reveals a weakness in the S&OP system that results in high inventory levels. The top performer bias advantage is significant across all velocities, cutting bias by almost two to five times. WEEKLY BIAS BY VELOCITY Top performers have less than half the bias than the group as a whole, highlighting opportunities to improve S&OP. 4 OF 8

5 Extreme Error Cases of extreme error when forecasts exceed shipments by two times or more (extreme undersell) or shipments exceed forecasts by two times or more (extreme oversell) are disruptive and costly to supply chains. Extreme oversell error imposes hardships on human resources, erodes margins through transships, expedites and/or unplanned production changes, and risks service levels. Extreme undersell error has less of an impact on staff, but significant financial consequences stemming from high levels of excess inventory, poor use of working capital and ongoing finance and carrying costs. Consistent with positive bias and the industry s optimistic outlook, the volume of extreme undersell error was higher than oversell across all velocities. Overall, the slowest-moving Velocity 5 items had more than twice the volume of extreme undersell error than those in Velocity 1. On a weekly horizon, 15% of Velocity 1 forecasts exceeded shipments by two times or more. VOLUME OF EXTREME OVERSELL AND UNERSELL ERROR BY VELOCITY VOLUME OF EXTREME ERROR In 2012, extreme error affected one-third of all forecasted volume. Weekly volume of extreme undersell error remained flat at 20%, meaning that 20% of the forecasted volume was greater than two times the actual shipments. Weekly volume of extreme oversell error was 14%, meaning that 14% of the actual volume was more than two times greater than what had been forecasted. High levels of extreme error represent a significant cost to the industry and present a real opportunity to improve supply chain efficiency through the use of better forecasting algorithms. 5 OF 8

6 Forecast Value-Added Forecast value-added is measured as the normalized difference between monthly naïve and Demand Planning errors at the base code-location level. Since baseline error differs by company, normalizing the difference is a helpful way to compare the relative value-added contributed by planners across companies. The naïve forecast used in the study is a simplistic forecast based on a seasonally-adjusted moving average so it is a little less naïve than simply using the last period s actuals as this period s forecast. Top performers had almost twice the valueadded than the group as a whole, suggesting opportunities to improve demand planning across the industry. FORECAST VALUE-ADDED TOP PERFORMER ADVANTAGE The effect of planners efforts at each company is illustrated by the difference between the Demand Planning and naïve forecasts (visible area in blue). The figures above the bars represent the percent value-added by planners. While forecast value-added has been inching up slightly since the beginning of the study, 2012 saw a slight dip, dropping 1 percentage point to 13%. FORECAST VALUE-ADDED BY YEAR Top performers had almost twice the value-added than the group as a whole. 6 OF 8

7 Demand Sensing WEEKLY ERROR FOR ALL ITEMS In 2012, Demand Sensing reduced average weekly error for all items from 48% to 30%, for a 38% reduction in forecast error. For items on promotion in 2012, Demand Sensing cut average weekly error from 53% to 33%. Since 2009, Demand Sensing cut forecast error by a total of 36%. For new items, Demand Sensing cut weekly error by one-third, from 59% to 40%. It is noteworthy that Demand Sensing error for new items continues to be considerably lower than Demand Planning error for existing items over the same period. Weekly Demand Planning error for existing items was 49% +/- 1% between 2009 and Demand Sensing also clearly provides relief for extreme forecast error. The shift from companies clustered in the lower-left corner to the desirable top-right quadrant supports customer claims that Demand Sensing helps lower instances of transships and expedites and significantly assists supply chain professionals with meeting customer service levels. WEEKLY ERROR FOR PROMOTED ITEMS WEEKLY ERROR AS ITEMS AGE Demand Sensing performs consistently across all business activities, underscoring the value of new mathematics that automatically adapts to changing market conditions. WEEKLY VOLUME OF EXTREME ERROR BY COMPANY 7 OF 8

8 Accurate. Current. Consistent. LEARN MORE To request a full version of the report with additional details and analysis, please visit terratechnology.com. About Terra Technology Terra Technology uses better mathematics to sense demand, optimize inventory and predict transportation and warehousing requirements for some of the world s best-known companies including Shell, Procter & Gamble, Unilever, Mondel z International, Kimberly- Clark, AkzoNobel, Kraft Foods, ConAgra Foods, General Mills, Kellogg and Campbell Soup. Terra invented demand sensing in 2002 and offered the first solutions to use retailer data systematically to improve supply chain efficiency, enhance service, cut inventory and reduce waste. Information on how Terra enables a truly integrated supply chain can be found at terratechnology.com. HEADQUARTERS HEADQUARTERS NORTH AMERICA EUROPE ASIA Headquarters 20 Glover Avenue North America Chicago, Illinois Europe Bracknell, England Bangalore, India 20 Norwalk, Glover Avenue CT USA Chicago, Seattle, Illinois Washington Bracknell, England Norwalk, CT USA Seattle, Washington Terra Technology. All rights reserved. Reproductions in whole or in part are prohibited except by written permission. requests or feedback to info@terratechnology.com. 8 OF 8