In section 2-1, we showed how lost sales due to OOS correlate with product sales velocity. Because of this, many retailers have instituted a “red dot” program where the top identified “never-outs” are identified with a specially-marked shelf tag to alert store personnel to watch for potential OOS situations with those items. However, we view a “red-dot” program as a workaround for a bad design. It works as a medication to soothe the problem, but is not a cure. The cure is a redistribution of shelf space based on demand rather than case-packs. In this section we examine the degree to which demand-based planograms (POG) will result in lower OOS than packout-based POGs.
A. Current Situation: 91 percent of the SKUs are Allocated Shelf Space Based on Case-Pack Size In our examination of several categories across several retail chains, we found that on average, 86 percent of the items had enough inventory on the shelf to last more than 7 days (days of supply, or DOS). This means that only 14 percent of the items need to be stocked more than one-time per week. Looking at the other end of the tail, several items had in excess of two weeks supply on the shelf. If deliveries
Figure 21 Costs of Handling Products
Credit: TU/e Retail Operations Group
Transportation 22%
Handling Warehouse 28%
Inventory Store 7%
Inventory Warehouse 5%
A Comprehensve Gude To Retal Out-of-Stock Reducton In the Fast-Movng Consumer Goods Industry
Chapter 3
Gruen & Corsten 2008
come weekly, the logical question to ask is, “why not give the 14 percent of the items more space, and take away shelf space from the slower-moving items?” The answer is that it is more difficult to do than it seems, and this is due to two reasons. First, most (91 percent) of the SKUs are allocated based on case packout, and this takes up most of the space on the shelf. Since labor is expensive and tracking partial cases in the back room is a nightmare, stocking a full case on the shelf makes sense. Second, there is no additional space, since a slow moving SKU cannot have less than one facing.
An empirical study at various retailers conducted by the TU/e Retail Operations Group showed that retailers have what is termed “Net Shelf Space” (NSS). NSS is calculated as the difference between the shelf space that is required to carry out the current operations with respect to customer service and costs and the shelf space that is allocated to the item or items of interest. To examine the potential excess shelf space (ESS) an item might be allocated, the researchers considered the merchandising guidelines of the retailer as well as the target shelf space advised based on a formula they derived for the maximum inventory level on hand. As expected, case pack sizes, physical dimensions of the consumer units and the shelf depth were the major drivers of ESS. When the ESS of the items is summed, retailers were found to have substantial NSS. The existence of NSS implies that there is space available to reallocate to items that do not have adequate shelf space.
We also know from multiple tests in category management that shoppers respond favorably to minor reductions in choice within a category, especially when there are reasonable substitutes. Thus, an option worth examining is finding additional space for fast moving items at the expense of removing the slowest moving items. The question is how deep can a retailer make this adjustment until the negative effects outweigh the sales gained from fewer OOS events? B. What Would be the Effect on Sales When 14
Fastest-Moving Items Receive More Space, and 14 Slowest-Moving Items are Removed?
Using the 86 percent DOS > seven days average, if the category had 100 SKUs, then a reasonable place to begin to examine the effect of reallocating shelf space would be to find additional shelf space for the 14 items that did not have enough shelf capacity to last seven days by removing the 14 slowest moving items. Let’s make a few assumptions:
• The average product dimensions and the price margins of the 14 fastest and slowest movers will be the same. • The ratio of sales between the fast-movers to slow
movers is, on average, 10:1.
• The OOS rate for the fast-moving items is 12 percent (1.5 times the worldwide average for all items).
Using these assumptions, and using a movement of one unit per week average for the slow mover items, then in a given week, the fast movers would sell 140 units and encounter 17 OOS events, while the slow movers would sell 14 units. Given these assumptions, the retailer would gain three weekly sales in the category. This appears to be somewhat equivocal, until considerations for the total cost of OOS are considered, as well as the reduced labor and tracking of 14 fewer SKUs. Alternatively, there are some customers who wanted to buy the 14 units of the slow movers that are no longer available.
Thus, before embarking on wholesale change, each of the assumptions has to be understood. For example, if the sales velocity ration between the fastest and slowest movers was increased, then the payoff would be greater. Alternatively, if the OOS rate on the items was decreased, then the payoff would be lower.
C. Feedback-based Approach to Reallocating Shelf-Space
Given the above discussion, a very simple solution appears. When considering the 14 fastest and slowest moving items, the results may be equivocal. However, if only a few of the fastest moving items with the greatest OOS lost sales were identified, and additional space was found for these by eliminating a few of the slowest moving items, the results would generally be overwhelmingly favorable.
Using OOS estimation using the POS estimation method, this could be easily implemented. The top three to five OOS items could be identified, and additional shelf space could be allocated to these by eliminating a few slow moving items. A month or two later, the OOS estimation could be made again, and the top OOS items would be examined. If it were any of the original three to five OOS items, then additional space would be added to them, and if not the next items would be considered. Given that there will be diminishing returns, this would continue only until the OOS reductions began to meet the sales level of the slowest moving remaining items.
The approach here is to address the items that create the most OOS. These items can be easily identified using POS estimation, and implementation would involve minor planogram redesign.
D. Considering the Potential of Demand-Based Planograms
Most available computer software ignores peak demand and demand variability. Planogram software by popular providers such as The Nielsen Company are based on mean demand.
A Comprehensve Gude To Retal Out-of-Stock Reducton In the Fast-Movng Consumer Goods Industry Gruen & Corsten 2008