You are driving to work as a supermarket supply-chain manager. The hot weekend weather has continued, it’s a lovely Monday morning, yet you’re surprised when your boss calls so early. Agitated, he shares how he just discovered from the CEO that all stores were reporting a massive sales loss because they had sold out of strawberries. “Why didn’t you order more?” he asked. “Didn’t you realize it was going to be a blistering hot weekend?”
Herein lies the dilemma facing every retailer selling fresh and/or seasonal products: how much volume are you prepared to lose in order to optimize profits? Put another way, what level of on-shelf availability for the shopper should you plan for—100 percent, 95 percent, 90 percent?
With a focus on fresh products, the ECR Europe Shrinkage & On-shelf Availability Group commissioned Eindhoven University of Technology to research and explore this relationship between lost sales, on-shelf availability, and waste to create a model that retailers could use to identify the sweet spot where profits could be optimized.
Research Objectives and Methodology
The primary aim of the research was to model the relationship between lost sales, food waste, and cost minimization. Three European supermarket retailers agreed to participate in the research and share their sales, waste, on-shelf availability, case size, shelf life, and other data inputs for three fresh categories: meat, fruits and vegetables, and convenience (ready-made meals and ready-to-cook vegetables). In total, 17,093 store-item combination data points were analyzed across the three categories in 27 stores, where one item in one store equals a one store-item combination.
Waste Is a Choice. The research set out to use the data from the three retailers to mathematically calculate the minimum level of waste that could be expected for any targeted level of on-shelf availability.
To meet an on-shelf availability target, for each store-item combination the model created a reorder trigger point—the inventory level below which a signal is sent back up the supply chain to initiate a replenishment order.
The model, using Microsoft Excel, was able to identify minimal waste levels for each of the three categories (Figure 1), and as can be seen, the output looks like a “frontier.” For each category, you can determine the level of waste that would be associated with each on-shelf availability target.
For example, if you were to target an on-shelf availability rate of 88 percent on fresh meat, a waste rate of 2 percent of sales value would be predicted. However, if you determine that the on-shelf availability target should be 96 per cent, then a waste rate of 4.3 percent of sales would be predicted.
While this data is interesting, and without considering some of the interventions that may shift the frontier to the right, the question the business will ask is what to consider as the right choice.
As the organization reflects on this question, there will be different voices. Those in the buying function will look at the dilemma through the shopper lens and will argue that it is unacceptable to lose any sales through poor on-shelf availability. On the other hand, those in other functions, such as finance, will see a higher waste number as unacceptable.
To assist with these decisions, additional features and inputs in the model take into account the product gross profit margin (measured as the retail sales values less the cost of goods from the supplier and the supply-chain costs), the extent to which shoppers will substitute this item, and the cost of those lost sales. Together with cost of waste, a total cost per euro of demand can be calculated for every on-shelf availability target (Figure 2).
In the graph, the cost per euro of demand is plotted for the cost of lost sales, waste, and the total costs. Total cost is defined as the cost of waste, which increases as on-shelf availability improves, plus the cost of lost sales, which decreases as on-shelf availability improves. In Figure 2, the total cost per euro of demand is lowest when the on-shelf availability targets are set between 94 and 96 percent.
The lowest total cost per euro of sales is unlikely to always translate into the profit sweet spot; however, it is a good guide, and for our supply-chain friend, this analysis could help inform a discussion on the extent of risk exposure that management is prepared to take with perishable products when they next place an order for strawberries.
Fresh Case Cover (FCC). The research developed a simple metric that can identify the store-item combinations most vulnerable to waste.
To calculate the FCC, the minimum order quantity in consumer units is divided by the expected daily sales of the item multiplied by the number of days of shelf life. In many situations, the minimum order quantity will be set at the case level, rather than consumer unit level, implying that even the lowest order quantity will be more than one consumer unit.
By way of illustration, let us take an apple item, say Golden Delicious, where one consumer unit is a tray of four apples. This tray is then packed into a plastic tote. The vendor can fit eight trays into one tote. The store can order one, two, three, four, or more totes, which in terms of consumer units is equal to eight, sixteen, twenty-four, thirty-two, or another multiple.
The case pack size (or minimum order quantity) in this example is equal to eight, which is the number of consumer units in one plastic tote. The tote has been designed to protect and organize the trays on their journey through the supply chain.
For this item, the daily sales are two consumer units and the shelf life, once on the shelf will be six days. The FCC is calculated by dividing 8 (the minimum order quantity) by 12 (two sales a day multiplied by shelf life of six) which creates a score of 0.67.
Is 0.67 good? As a guide, a score of 1.0 is a signal to management that in theory, and when you know the demand in advance, there is a balance. Put simply, the sales rate suggests that the shelf will be empty at just about the same time as the final tray of apples reaches their shelf life date.
It follows that when the FCC is over 1.0, that there will still be inventory in the store yet to be sold that will go beyond its shelf life, leading to the risk of waste. Conversely, when the FCC is below 1.0, the implication is that the inventory will be sold within its shelf life period.
Actually, the data from the three product categories in the 27 stores suggests that the retailers that want to aim for a waste rate of less than 10 percent of sales should target an FCC of below 0.4. This is illustrated in Figure 3, where you can also see that an FCC of 0.7 delivers a waste rate of 17 to 18 percent, while a much lower FCC of 0.1 returns a predicted waste rate of less than 5 percent.
The value of this measure lies in its ability to predict vulnerabilities to waste and lost sales. It also helps prioritize improvements by asking key questions: How can the minimum order quantity and case size be reduced? How can the shelf life be extended? And finally, how can the daily sales rate be increased?
Using another tool developed in this project (the easy-to-use, Excel-based “sell more, waste less”), the impact of some of the factors in the FCC can be predicted. For example, it predicted adding one day of shelf life to all 17,093 item-store combinations used in this research would deliver a 42 percent reduction in waste. Similarly, by reducing the minimum order quantities and case sizes, waste was modeled to reduce by 32 percent.
In sum, the opportunity for retailers and manufacturers is to adopt the FCC metric as both a line of defense against food waste and a source of
inspiration for new interventions on shelf life, delivery frequency, and minimum case size that can prevent waste before it happens in the store.
Remove Store-Level Variation in Waste. While the interventions that can lower the FCC have the potential to shift the waste and on-shelf availability frontier to the right for the companies as a whole, the research also looked at variation in waste by store. Traditional approaches to comparing waste across stores have been limited as a significant number of the reasons lie outside of the store manager’s control, such as case sizes and delivery frequency. So the researchers identified a different approach: they compared the actual performance of a store to the theoretical “efficient frontier” (EF) for that store.
Variation was then defined as the distance between the actual and theoretical EF. In this example, Store Y has a greater gap and potential for improvement than Store X.
Overall, the research found that if the gap could be removed completely, then there was a potential for a 48 percent improvement in waste across the 27 stores in the study.
On-Shelf Availability Research Outcomes and Next Steps
Given the findings generated by this research, what might some of the policy and organization-wide applications be?
Improve Visibility to On-Shelf Availability Data. The problem of food waste in retail is visible. Stores record their waste losses daily, and the value of markdowns is easy to see via the point-of-sale (POS) system. However, the same could not be said for the on-shelf availability metric. This problem is harder to track, and the negative impact on sales less tangible to measure.
The risk to the business is that the more visible and tangible measure will likely get prioritized and managed ahead of the less visible measure.
For the ultra-fast sellers, many retailers leverage POS-sales-based exception reports and alerts, where the most recent store-item sales are compared to other stores and to the past. When unusually low store-item sales combinations are identified, retailers code these as out-of-stock incidents, and when aggregated, they create for their organization a proxy measure for on-shelf availability.
For the slower sellers, defined as store-item combinations below five units per day, the statistics-based approach is not possible because you cannot distinguish “low sales” from randomness. To get around this problem, many retailers adopt inventory-based measures of on-shelf availability. For example, if there is an inventory level of less than one day of sales, then the system would record these store-item combinations as out-of-stock incidents and again would aggregate up to get to a proxy on-shelf availability metric. Retailers will also invest in third-party shelf audits that measure the number of gaps on the shelf or indeed ask their own store associates to complete these gap checks on a daily basis to then inform an on-shelf availability measure.
With trusted and visible on-shelf availability data and clarity on the bottom-line impact of an out-of-stock, managers will be able to make more informed choices between on-shelf availability and waste.
Change the Mindset of Your Organization. A starting point would be to take this research, create a training plan around the learnings, and deliver it to all those who are part of the fresh process, from buying, planning, logistics, and through to the shelf.
A step further would be to establish clarity on what an organization-wide approach to the problem looks like. What are the respective responsibilities for each function in the delivery of the waste target? Being the single “owner” of the food waste budget is hard to do, but many retail organizations default to having just one function accountable. In some, this is the commercial team. In others, the budget rests with the stores, or even with the supply chain.
The limitations to just one function holding the budget is that they can each have a certain bias; the buying team towards more waste, so as to sell more; the stores towards reducing the on-shelf availability target so as to waste less; and finally, the supply chain may prioritize reducing supply chain costs over lost sales or waste.
To navigate these possible silo management obstacles, defining a specific set of responsibilities for each function and holding them accountable for compliance with those standards is one way to deliver a company-wide improvement effort.
Appoint a Chief Freshness Officer. For grocery retailers, shopper insights from Oliver Wyman, a global management consultancy practice, the significant majority of consumers in the United States, UK, France, and Germany say that access to the best-quality, fresh products is the most important consideration when choosing where to shop (see Table 1).
Given the relevance and importance of freshness to the shopper, and the scale of the waste problem, one idea could be for retailers to consider the appointment of a chief freshness officer. This new appointment would be the single leader in the organization who has the decision rights on the choice between waste, supply-chain costs, and on-shelf availability.
The inspiration for this new role has been the discipline of quality management in the engineering and manufacturing sector. Major organizations such as Toyota appointed chief quality officers to lead their companies’ improvement strategies and delivery. The chief quality officer role is accountable for the management of the total cost of quality, typically defined as the cost of bad quality (recalls, trust, brand reputation, and so forth) plus the cost of good quality (more safety checks, investment in people, control measures, and so forth).
In retail, the total cost of shelf quality could be defined as the cost of good quality (days of freshness, waste, range, delivery frequency, and so forth) plus the cost of bad quality (lost sales, lower loyalty, store switching, and so forth). To date, however, few retailers have progressed this idea. It could be because it is fundamentally flawed, or it might be that this new research can prompt a re-assessment in your organization.
What Could You Do Next as a Loss Prevention Leader?
Based on industry benchmarks, some loss prevention leaders are accountable for waste, so this section should be of definite interest. However, for loss prevention leaders not accountable for waste, the findings should be relevant for two reasons. First, unrecorded waste will be in the shrinkage number. In fact, some retailers have shared with this author that when they conduct a thorough root cause analysis of their unknown loss number, they have discovered that roughly 20 percent of their unknown losses can be accounted for by waste that had not been properly recorded. The second reason could be that the research may inspire new thinking on how to manage losses on high-theft items.
Here are three next-step suggestions:
1. The first step could be to gather support to utilize the Eindhoven model and the overall thinking in your organization on fresh products.
2. The second step, which could be taken independently of the above analysis, would be to adopt the FCC metric. Using your own data set, you could identify FCC scores for all store-item combinations. These could then be ranked to help prioritize improvements.
3. The final step to consider would be to assess whether there are high-shrink items, such as cosmetics, spirits, and batteries, where if stores were “allowed” to lose more, the organization as a whole would generate higher profits from those items.
A case in point would be the data from one supermarket retailer in Holland. In this case, the high-shrink items were batteries. In the project, they discovered that sales increased by 87 percent when they moved batteries from behind the customer service desk to self-selection at the checkout. But because batteries were now easier to steal, shrinkage loss increased by twenty-fold, leading to additional shrink costs and a higher shrink rate relative to sales. For the store manager, while they liked the extra sales, the increase in the value of shrink and the higher rate was of concern, as it would make it harder for them to manage their annual shrink budget.
Seen at a total company level, the profits from the new greater level of sales minus the cost of the higher shrink, valued at cost, led to the company profit increasing by 83 percent.
So how do organizations manage this apparent conflict in incentives and rewards between the store manager and the total company? One approach could be to convince suppliers to move to scan-based trading; this is possible in some markets, in some categories, and with some suppliers, such as with magazines in United States. However, the majority of suppliers may not be willing to support scan-based trading. How could some new thinking in the design of incentives and rewards create different approaches to this dilemma?
Another approach could be to offer the stores a larger shrink allowance on high-theft products, either temporarily around seasonal peaks or permanently.
Finally, a more systemic approach would be to plan the next year’s shrink budget category by category, aligning with all parts of the organization the “right” shrink target per category and, ultimately, the “right” shrinkage percentage for the store. This approach could help secure the store managers’ support behind the growth of high-shrink categories such as cosmetics, growth behind which might be limited if the store managers continued to see the category as a threat to the delivery of their shrink budgets.
What I am sure of is that you as loss prevention leaders will have both experience and other ideas on this dilemma along with other next step ideas from this research. Perhaps one of the team could take the lead to present the research and the possible indicated actions for your organization at your next team meeting?
No More—I Don’t Like Mondays
Regrettably, no one can guarantee our supply-chain manager happy Mondays forever and the freedom from the wrath of his or her bosses. However, maybe this new research can start to bring data and evidence to the debates as to how organizations seek to manage the risks of lost sales and food waste, taking the appropriate strategies to manage and prevent those risks, starting first with an agreement on the right on-shelf availability target.
As with all ECR Europe research, this research is free to download online at ecr-shrink-group.com.
This article was originally published in LP Magazine EU in 2016 and was updated February 21, 2017.