Out Of Stock Cost

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Out Of Stock Cost

  1. 1. 5/12/2008 SUPPLY COST OF OUT OF STOCK IN FMCG COMPANIES CHAIN Vinh Nguyen
  2. 2. Contents 1. Introduction ...................................................................................................................................... 3 2. What is out of Stock (OOS) ........................................................................................................ 3 3. Root causes of OOS ....................................................................................................................... 3 4. Why should we pay attention to OOS? .................................................................................. 4 5. Consumer response to OOS situations................................................................................... 5 6. The total costs of OOS .................................................................................................................. 7 1.1. Loss of sales ............................................................................................................................. 7 1.2. Additional effects of OOS ..................................................................................................... 8 1.3. OOS and Increased Shopper Costs.................................................................................. 9 7. Conclusion ......................................................................................................................................... 9 8. References ....................................................................................................................................... 10
  3. 3. 1. Introduction An out-of-stock occurs whenever an item is demanded from a supplier but cannot be delivered because it is temporarily out of stock. In the short run, out-of-stocks may incur backorder and/or lost sales costs. Backorder costs typically include extra costs for administration, price discounts or contractual penalties for late deliveries, expediting material handling and transportation, the potential interest on the profit tied up in the backorder, etc. Lost sales costs include the potential profit loss of the sale if all or part of the sale is lost, contractual penalties for failure to deliver, etc. Besides backorder and lost sales costs, which can be directly measured, a out-of-stock may also incur a less tangible cost in the long run. This cost is related to the loss of customer goodwill. Intuition suggests that a customer who experiences a out-of-stock from a supplier may think twice before placing another order in the future to the same supplier or, even worse, may inform other customers about the disservice he received and influence them into defecting in the future too. In other words, the service level provided by a supplier may influence his future demand and therefore sales. In the short run, sales may fall short of demand when customers experience out-of-stocks and choose not to backorder. In the long run, demand itself may decline as customers who experience excessive out-of- stock shift temporarily or even permanently to more reliable sources. A new study from RIS News and IHL Groupi concludes retailers are losing $93 billion in sales annually as a result of being out-of-stock on the products consumers are looking to buy in their stores. According to the companies, retailers could increase store sales by an average of 3.7 percent if they could manage to keep in-stock. RIS News and IHL came up with their figures after surveying 124 retailers who operate more than 85,000 stores and generate $460 billion in annual sales. The two biggest reasons given by respondents as to why stores run out-of-stock are buyers making planning mistakes and store management failing to execute. 2. What is out of Stock (OOS) As the first and most accepted approach, the OOS rate is measured as a percentage of SKUs that are out-of-stock on the retail store shelf at a particular moment in time (i.e., the consumer expects to find the item but it is not available). In general, studies using this approach begin with the selection of one or more categories to examine. Next, a sample of stores from a single retail chain is selected, and a series of physical audits is conducted at the retailer at specific times during the day over a specified period of time. For each category, the OOS rate is calculated as the average percentage of the SKUs not in stock at the time of the audits. A second and alternative consumer-based definition of an OOS is the number of times a consumer looks for the SKU and does not find it. The percentage rate is calculated as the number of times the consumer does not find the SKU divided into the sum of the times the consumer does find the SKU plus the number of times the consumer does not find it. Instead of relying on physical audits, the second approach is measured through the use of models that determine OOS rates from store scanner and inventory data. 3. Root causes of OOS Planning Ordering Replenishment STORE • Incongruence between shelf • Data (bad POS data, • Staffing (insufficient or busy capacity and replenishment inaccurate staff). frequency. • records). • Backroom (congested). • Product purchasing frequencies. • Forecasting (inaccurate • Receiving (receiving errors, • Large number of SKUs in forecast, long cycles). inaccurate records). assortment. • Inventory (inaccurate • Shelf replenishment inventory or book-stocks). (infrequent, late or no shelf • Ordering (no order, late filling). order, wrong order, • Planogram (bad execution backorders). and compliance). • Shrinkage (damage, theft). DISTRIBUTION CENTER • Data (bad data, inaccurate • Transportation (shipping, records). loading). • Forecasting (inaccurate • Receiving (loading errors,
  4. 4. forecast). inaccurate records). • Inventory (inaccurate • Storage (put away/break inventory or book-stocks). pack). • Ordering (no order, late • Replenishment (infrequent, order, wrong order, late or no store replenishment). backorders). • Lead times (long and infrequent). • Shrinkage. WHOLESALER / RETAILER HEAD QUARTERS • Assortment (new or discontinued • Data (bad data, inaccurate • Availability (shortage). item). records). • Data and communication (master • Forecasting (inaccurate data). forecast). • Planogram design and • Inventory (inaccurate implementation (shelf allocation). inventory or book-stocks). • Promotions and pricing decisions. • Ordering (no order, late • Advertising and display planning. order, wrong order, • Store layout and service levels. backorders). SUPPLIER • Assortment (new or discontinued • Data (bad data, inaccurate • Availability (packaging, raw item). records). materials and ingredients). • Data and communication (master • Forecasting (inaccurate data). forecast). • Promotions and pricing decisions. • Inventory (inaccurate • Advertising and display planning. inventory or book-stocks). • Ordering (no order, late order, wrong order, backorders). 4. Why should we pay attention to OOS? • Lost Sales & Margin For Manufacturer • Lost Sales And Margin For Retailer • Domino Effect On Categories • Dissatisfied Customers Out-of-stocks remains a large problem for retailers, distributors and manufacturers in the worldwide consumer goods industry. Out-of-stock rates vary wildly among retailers and their outlets depending on a variety of factors, but the majority tends to fall in the range of 5-10 percent. In report “Retail Out-of-Stocks: A Worldwide Examination of Extent, Causes, and Consumer Responses”, that established the worldwide average level of OOS in retail in the FMCG industry to be about 8.3 percent. This report clearly showed the industry that the problem of OOS items was caused primarily by retail practices, and estimated that OOS was costing the industry billions every year. More importantly, in studies that examine faster selling and/or promoted products, the OOS rate regularly exceeds 10 percent. The overall average OOS rate worldwide is estimated at 8.3 percent and is illustrated on Figure 1
  5. 5. Overall OOS Extent (Averages) Worldw ide 8.3 Other Regions 8.2 Europe 8.6 USA 7.9 0.0 2.0 4.0 6.0 8.0 10.0 Percent OOS Figure 1: Overall OOS Extent (Average) 8 percent is really bad. From a shopper perspective, this means that for every 13 items one wants to buy, one will be out of stock. From a management perspective, the study showed that OOS cost retailers 4 percent of sales, and this translates to a similar 4 percent reduction in the average retailer’s earnings per share. OOS is often measured by category. A category is a microcosm of the retail store, and category management principles encourage a focus on retail performance by category. Of the 40 OOS studies that examined the extent of OOS, 14 of these provided reliable OOS data by category. Additional studies measured OOS by category, but only reported the composite findings and did not report by category. In total, 18 categories provided OOS results except for the GMA DSD study, which detailed the top 25 categories. However, in only six of these 18 categories did data come from three or more studies. Thus, the averages were computed and the OOS rates were reported for these six categories only. Figure 2 illustrates the averages and ranges of OOS for the six categories. O O S A v e r a g e s b y C a te g o ry W o rld A vg (1 8 c a t e g o rie s ) 8 .3 S a lt y S n a c k s 5.3 T o i le t T is s u e 6 .6 F e m H y g ie n e 6.8 D ia p e rs 7.0 L a u n d ry 7.7 H a ir C a re 9.8 0.0 2.0 4.0 6.0 8.0 10.0 12.0 P e rc e n t Figure 2: OSS Averages by Category 5. Consumer response to OOS situations Although academic research has identified and categorized up to 15 possible consumer responses to an OOS, typically, managerial researchers measure five primary responses that consumers will make when they encounter an out-of-stock for an SKU that they had intended to purchase. These are:
  6. 6. • Buy item at another store (store switch). • Delay purchase (buy later at the same store). • Substitute – same brand (for a different size or type). • Substitute – different brand (brand switch). • Do not purchase the item (lost sale). All five of the responses include negative consequences and result in direct and/or indirect losses to both retailers and manufacturers. However, some actions place greater direct losses on either the retailer or the manufacturer. Direct Losses First, the retailer faces a direct loss of the potential sale when a consumer faces an out-of-stock because the shopper purchases the item at another store or does not purchase it at all. Similarly, the manufacturer faces a direct loss of the potential sale when a consumer faces an out-of-stock because the shopper substitutes another brand or does not purchase the item at all. Additionally, when a substitution is made, the retailer also loses an additional portion of the potential sale because the shopper tends to switch to smaller and/or cheaper substitutes. Data examined from the studies conducted by Data Ventures shows that consumers are risk averse when making substitutions and, therefore, more commonly substitute a smaller and/or cheaper item. The following table (Figure 3) demonstrates the losses to the manufacturer and to the retailer for each consumer action. Figure 3: Who bears the direct loss for consumer reaction to an OOS Indirect Losses In addition to the direct losses, both the retailer and the manufacturer incur additional indirect losses due to decreased customer satisfaction that results in less overall reliance on the particular retailers and brands. When an OOS leads to purchase at another store, this provides the consumer an opportunity to try a different store. Consumer behavior theory argues that trial precedes adoption, and, thus, an OOS sets the stage for possible permanent store switching. When an OOS leads to purchase of a competing brand, the consumer trial can lead to possible permanent brand switching as well. A second key source of indirect losses comes in the form of supply chain inefficiencies. Consumer switching of brands, sizes and stores as well as delays of purchases provides an inaccurate picture to managers, who seek to have the supply chain deliver accurate levels and mixes of products to retail shelves. Systems dynamics research has shown that inaccurate signals from the retailer become amplified up the supply chain. Indirect losses are demonstrated in Figure 4.
  7. 7. Figure 4: Indirect loss due to OOS Finally, it is important to consider that the overall willingness of a consumer to purchase from another store as opposed to switching an item or brand at the store is related to the overall number of out-of- stocks that the shopper encounters during the shopping trip. When consumers only find one item out-of- stock, they will be more likely to delay or substitute. If, however, there are multiple items that the Shopper cannot obtain the odds of going to another store increases. Similarly, the overall willingness of a consumer to entirely switch stores is dependent upon the cumulative number of times the consumer encounters an out-of-stock at the same store. 6. The total costs of OOS The impact of OOS extends well beyond the lost sales of the OOS item alone. A variety of strategic and operational costs apply to both retailers and suppliers including decreases in store and brand equity and attenuated impact of promotions and trade promotion funds. OOS creates a ripple effect by distorting demand and leading to inaccurate forecasts. Retailer costs also include the time employees spend trying to satisfy shoppers who ask about a specific OOS item. For a typical U.S. grocery store, the cost amounts to $800 per week. The corollary for shoppers is the amount of time spent waiting for resolution that could be spent more productively for the retailer in shopping—an estimated 20 percent of the average time for a shopping trip. 1.1. Loss of sales S a le s L o s s e s D u e to O O S W o rld Ave ra g e 3 .9 E u ro p e 3 .7 U SA 3 .8 O th e r R e g io n s 4 .0 B y R e g io n S a lte d S n a c k s 2 .1 T o ile t T is s u e 2 .4 L a u n d ry 3 .2 F e m in in e H yg ie n e 3 .5 D ia p e rs 3 .8 H a ir C a re 4 .5 B y C a te g o ry 0 .0 1 .0 2 .0 3 .0 4 .0 5 .0 E s tim a te d P e rc e n ta g e L o s s Figure 5: Estimated Percentage Loss
  8. 8. In Figure 5, it has become abundantly clear that the direct sales loss, which the report estimated to be up to 4 percent—substantial as it is for both retailers and suppliers—is only one part of the expense OOS items produce. The chart shows that overall sales losses are similar worldwide, with a narrow range from 3.7 percent to 4.0 percent. However, category sales losses vary dramatically from 2.1 percent to 4.5 percent. Regardless of how the data are cut, the implication is still the same: Both the manufacturer and the retailer have created value for the consumer, but nearly 4 percent of this effort is wasted because the retailer cannot extract the value from the consumer due to OOS items Formula for loss of sales: Formula Example OOS Rate _______% Avg OOS rate 8% x X Category Avg MFR Avg Loss 30% Lost Sales _______% X x Category Sales $1B Total Category/ = Organization Sales $_____ Lost sales $24,000,000 = Sales Lost to OOS $_____ Typical Retailer Sales Loss/$1B total sales is about $24 million 1.2. Additional effects of OOS Figure 6 provides an overview of several additional effects of OOS. The total costs are both operational and strategic, and these affect both suppliers and retailers. • From a services delivery perspective, an OOS item indicates that a number of service failures have occurred, and these service failures point to lowered customer satisfaction, decreased store and brand loyalty and increased shopper costs. • From an operations and supply chain management perspective, OOS distort inventory information that is required for ordering and replenishment of the store and shelf. In addition, treatment of OOS items requires extra process steps that could be avoided if systems were in place that would eliminate OOS. • From a marketing and sales forecasting perspective, the presence of OOS items distorts the baseline on which demand forecasts are made. Since true demand is unknown due to OOS items, some items are under-forecasted, while other items are over-forecasted. Manufacturers Retailers • OOS Lowers Impact of Promotions and • OOS Distorts True Shopper Demand thus Trade Promotion Funds Decreases Forecasting and Ordering Accuracy • OOS Distorts True Store Demand, thus • Operational Costs are Increased through Personnel Operational Forecasting, Category Management Looking for OOS Items, Providing “Rain Checks” to and Related Efforts are Less Accurate Shoppers, Unplanned Restocking, etc. (could be and Effective $1.0 Million for 100 stores) • OOS Increases Overall Costs of Relationship with Retailer (Increased Post-Audit Activity, Irregular Ordering) • Direct Loss of Brand Loyalty and Brand • Direct Loss of Store Loyalty Equity • Decreased Customer Satisfaction Strategic • OOS Encourages Trial of Competitor • OOS Encourages Trial of Competitors’ Stores Brands • Permanent Shopper Loss Rate is Undocumented, • Lowered Overall Effectiveness of Sales but Annual Cost is US$1 Million per Every 200 Team Resources Shoppers Figure 6: Additional Effect of OOS First, the impact is much larger than lost sales alone. Second, in the retailer operation quadrant, there is a method to estimate the personnel costs. The Personal Cost calculator shows how to make an estimate of the actual time spent on tracing OOS by personnel. This only looks at the cost of looking for items when asked by a customer. While OOS does not directly drive additional cost of this labor, this labor could be deployed to productive efforts of the store. Let say an average transaction size about $27.34, estimate that 40 percent of the shoppers will encounter at least one OOS (consistent with a 10 item list), conservatively assume that only one of every
  9. 9. 10 will contact an employee about the OOS, and the average wage and benefits cost for the employee is $18.00 hourly. The average weekly sales volume for supermarkets is slightly under $300,000, and we estimate that the store employee will spend six minutes on average searching for the requested item. In the small volume store example, the average weekly sales are approximately 20 percent of that of the large volume format, the average transaction size is slightly more than half the large volume size, and that the employee only spends four minutes on average per customer request. In the large volume store example, the weekly cost per store is about $800 per week, and for the smaller volume store, the cost per store is about $200 per week. When these figures are annualized across all stores in a chain, the total costs are substantial. In both the large and small volume examples, these conservative estimates quantify a typically non-documented cost caused by out-of-stock items, where they redirect scarce store labor away from productive activities. Retailers can construct this simple spreadsheet to match their specific situation Formula to calculate personal cost Personal Cost Calculator Large volume example Small volume example Avg $ volume/week (000) 298 60 Avg $ transaction $27.34 $15.00 Avg # customers/week 10.900 4,000 Shoppers encountering 1 or more OOS 40.0% 40.0% # shoppers encountering OOS 4,360 1,600 % times shoppers involve store labor 10% 10% Avg minutes spent by store employee 6 4 Avg. Hourly wage rate $18.00 $18.00 Avg Cost/week/store $785 $192 # stores in chain 100 100 Weekly cost labor in chain $78,478 $19,200 Annual cost labor in chain $4,080,878 $998,400 1.3. OOS and Increased Shopper Costs OOS events clearly increase the total shopping trip cost to the shopper. The measurement of the aggregate shopper costs in terms of increased transaction costs, lost time, increased decision making requirements, and a host of other social and psychological costs (for example the lower confidence of having to use an untested substitute) has never been calculated—nor are the total effects understood. If we consider the “flip side” of the examples shown in the previous section, in a single large volume grocery store OOS add some amount of increased shopping costs to more than 4,000 shoppers weekly, or more than 200,000 shoppers annually. The costs to each shopper vary. The shopper who makes a quick decision to substitute a similar size and priced item in a category will incur low time and costs, and depending on the item, it can have low psychological costs as well. Alternatively, the shopper who is pressed for time may purchase a more expensive substitute if the psychological substitution costs are high. A customer with high substitution costs will go to another store to get the product, and this is a very expensive proposition for the shopper. Shoppers who ask store personnel to locate an item waste time in their shopping trip, having to find personnel as well as waiting to see if the item is available. With the average shopping trip being 28 minutes, a six-minute wait represents more than 20 percent of the entire shopping trip time, precious minutes that the shopper might spend examining a new item or attending to other merchandising efforts of the retailer. In short, while the shopper is actually in the store, the presence of an OOS forces the shopper to allocate minutes to activities other than those that retail management would most prefer them to engage. In an age of increased consumer understanding of the cost of time, and the availability of the internet as an alternative channel, retailers need to consider ways to make shopping as convenient as possible (or at least eliminate the unnecessary inconveniences). Shoppers not only have alternative stores for shopping, but now also have entirely new alternative shopping channels. 7. Conclusion In spite of heavy investments to improve supply chains, worldwide OOS levels still average 8%, or from the shopper’s perspective, for every 13 items a shopper plans to purchase, one will be OOS. For promoted items, OOS levels average 16%, which translates to one OOS item for every 7 promoted items a shopper plans to purchase. Thus, in an industry heavily dependent upon promotions, the impact of one-seventh of promotional dollars is reduced. Sales velocity always affects the rate of OOS.
  10. 10. All of the studies examined point to a common concern: OOS has been, is and will continue to be a problem. The aggregate extent we found of 8.3 percent (and the similar results found through other industry studies) continue to – and should – raise alarms throughout the industry. OOS is costly. While the total costs to the supply chain have not been investigated, some finding along with others, have assessed the likely sales losses to the average retail store. We found that the worldwide average sales loss due to OOS is 3.9 percent. Knowing the expenses the company has to lose in case of OOS, the company will find the root causes and have them solved accordingly. 8. References i http://www.scdigest.com/assets/NewsViews/08-01-30-1.php?cid=1460 Campo, Katia, Els Gijsbrechts and Patricia Nisol (2000), “Toward Understanding Consumer Response to Stock-Outs,” Journal of Retailing, 76 (2), 219-242. Corstjens, Judith and Marcel Corstjens (1995). Store Wars: The Battle for Mindspace and Shelfspace. West Sussex, England: John Wiley and Sons. Reference especially Chapter 9, pp. 196-218. Emmelhainz, Margaret, James Stock and Larry Emmelhainz (1991), “Consumer Responses to Retail Stock-Outs,” Journal of Retailing, 67 (2), 138-147. Fisher, Marshall L., Anath Raman and Anna Sheen McClelland (2000), “Are You Ready for Rocket Science Retailing?, Harvard Business Review, July-August, 115-124. Fitzsimons, Gavin (2000), “Consumer Response to Stock-outs,” Journal of Consumer Research, 27 (September), 249-266. Peckman, James O. (1963), “The Consumer Speaks,” Journal of Marketing, October, 21-26. Raman, Ananth, Nicole DeHoratius and Zeynep Ton (2001), “Execution: The Missing Link in Retail Operations,” California Management Review, 43 (3, Spring), 136-152. Schary, Philip B. and Martin Christopher (1979), “The Anatomy of a Stock-out,” Journal of Retailing, 55(2), 59-70. Walter, C.K. and John R. Grabner (1975), “Stockout Cost Models: Empirical Tests in a Simulation,” Journal of Marketing, 39 (July), 56-68.

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