demand signal repository Archives

What Causes Shelf Out-of-Stocks?

Inventory Management | By Steve Banker • 11/09/2009

The other day I received a package from Manhattan Associates (an ARC client) via Priority Mail. I had to use a knife to cut through the shipping tape, and when I opened the package there was a pretty little blue box, very Tiffany-like, inside with a white ribbon on it. I had to use scissors to cut the ribbon off, and after all that effort, when I finally opened the box, I found an insert inside that read, “Sorry Steve, OUT OF STOCK”. When I unfolded the insert it asked, “Disappointed?” Damn right I was disappointed!

That was the point of this purposely-irritating marketing campaign. Out-of-stocks disappoint customers and cost retailers money. Manhattan launched a website tied to this “Zero Disappointment Retail” campaign where you can download an ROI calculator to determine what out-of-stocks cost retailers.

I’m kicking off a market study of Demand Signal Repositories (DSRs), and as part of this effort, I recently spoke with Cedric Guyot, Vice President of Marketing at Retail Solutions. It turns out that Retail Solutions has some interesting data on the root causes shelf out-of-stocks (OOS). The statistics are based on a study Walmart conducted with five of its leading suppliers. Walmart examined the root causes of out-of-stocks for 400 stock keeping units (SKUs), across 25 stores, for 6 months. On a daily basis, observers went into stores to see what was out of stock, and to determine the root cause. Retail Solutions was able to analyze this data because it assisted four of the five suppliers with this study.

I will tackle the easy-to-explain root causes first.

Root Causes of Out-of-Stocks (click to enlarge)

The major reason a SKU was not on the store shelf, occurring 40 percent of the time, is that while there was inventory in the store, the shelf was not replenished or not replenished in a timely manner, the planogram was altered (no more space for the product), or the product was in the wrong location, hidden or not available for sale as intended.

The least likely reason for store OOS, occurring only 5 percent of the time, was an upstream supply chain failure. In this instance, shipments were not sent from the DC to the store in a timely manner, or what was supposed to be sent was not sent in the proper quantities.

Another major reason for OOS is phantom inventory. Phantom inventory occurs when the store system shows inventory when none really exists. It also occurs when the system shows more inventory than what really exists in the store. This is caused by theft, broken products, mis-picks, or checkout issues (among other factors). When a store system shows inventory that doesn’t really exists, a store manager could easily assume the product is not selling well and thus decide not to reorder it. This type of phantom inventory can also create additional havoc, which I’ll explain in more detail in a future posting.

The final reason for OOS is incorrect store-level replenishment settings. One way this comes about is when a SKU is supposed to be sold at a store (“traited”) but the store’s Computer Assisted Ordering System has the SKU flagged as not valid (cannot be ordered). There is a second way this can happen. If a store is not selling as much product as it should, and the problem is attributed to a lack of demand instead of various execution problems, then the system will calculate store-level reorder minimums that are too low.

In conclusion, shelf OOS is not easy problem to solve despite its prevalence. It will require better collaboration between suppliers and retailers, between store operations, merchandising, and supply chain teams, and Demand Signal Repositories with advanced supporting applications.

The First Moment of Truth: Part II

Consumer Packaged Goods (CPG) | By Steve Banker • 04/29/2009

Yesterday, I talked about how I’m a fan of the “moment of truth” concept, and I highlighted how we have gone through three phases in trying to solve the “out of stock” problem, but that a new phase is emerging. This new phase, which has begun only recently at some leading manufacturers, includes making a better link between merchandising and supply chain operations.

Merchandising involves maximizing sales using product design, packaging, pricing, and displays in a way that stimulates consumers to spend more. Merchandizing services can be performed by a third party that visits retail stores to determine whether a manufacturer’s products are in stock and on the shelves; whether they have the agreed upon facings; and whether promotional displays have been built and stocked correctly.

In yesterday’s posting, I also mentioned that many manufacturers have only recently begun to realize how their ability to achieve good in-stock and on-shelf performance is being hampered by inaccurate store-level inventory data and poor in-store execution of promotions. These problems are starting to get addressed with fancy new math and better analytics. Companies like Retail Solutions (RSi) and TrueDemand use advanced math to calculate what should be selling at the SKU/store level and compares it to what is actually selling. If you are running a promotion and a particular store is not selling anything, while the other stores are selling great quantities of the promoted item, it suggests that either the data coming from the store is wrong or store employees are not pulling the promoted items from the back room to the front of the store.

These solutions also provide analytics to determine why a SKU is not selling – for example, incorrect store-level inventory, failure to move inventory from the backroom to the shelf, or replenishment failure from the retailer’s DC. TrueDemand can then trigger execution tasks for the retail account team or in-store merchandising team. Despite several attempts, I have not been able to talk to a manufacturer that is using this new math, so I can’t verify how effective it is at detecting phantom inventory.

However, I did talk to one of Retail Solutions’ manufacturing clients that is using the analytics from the RSi Demand Signal Repository (DSR). In this particular case, a key retailer has installed a DSR from RSi and the manufacturer pays RSi for POS and inventory data by store/by product/by week, and for daily promotional item data.

This manufacturer also uses over 250 people from third party merchandiser Acosta to visit this retailer’s stores. The merchandisers check for out of stocks, make sure the end caps are up, and perform other merchandising activities. This manufacturer can now query things like: Which stores have not sold any of product X in the last 12 weeks? Which stores have more than 20 pieces of any item? They then feed this information to Acosta which uses it to determine which stores to visit. In short, if you have the merchandising analytics, and run the right queries, the advanced math may be unnecessary.

I also talked to a second RSi manufacturing customer that recently began paying for DSR data from a large retail customer. They anticipate that the data will allow them to better phase in and phase out products. With this particular retailer, they pay 50 percent of the base product cost to the retailer once the phase out date has passed. If the product costs $2, they pay the retailer $1 for the effort involved in pulling the product off the shelf and disposing of it or returning it to the manufacturer. This manufacturer can now start monitoring the inventory positions at the stores six months prior to the phase out, and take steps to reduce costs for both the merchandising and the supply chain organizations.

In the supply chain, we have typically viewed marketing, and their ongoing promotions that disrupt the supply chain, as the enemy. But they don’t have to be. Rumor has it that for a very large retailer, better merchandising analytsics was a key impetus for their “green” initiative. This retailer tracks its shelf space data using a metric that is dollars/square inch/time period. In order to improve this metric, the merchandising teams at some manufacturers are developing concentrated products, like detergents, that have the same potency, but contains much less water, thus enabling more units to fit in the same space. This also results in less packaging and more efficient transportation. In short, it was better merchandising decisions that led to a “greener” and more efficient supply chain.

The First Moment of Truth

Consumer Packaged Goods (CPG) | By Steve Banker • 04/28/2009

I’ve always been a fan of Procter & Gamble CEO A.G. Lafley’s two moments of truth concept. The first moment of truth is what a consumer sees on the store shelf; the second is what the consumer experiences after they have bought the product. Today, I want to focus on the first moment of truth, on what consumers see on the store shelf, assuming there’s even a product there for them to see. It strikes me that we have gone through three phases in trying to solve the “out of stock” problem.

In the first phase, we mostly assumed that if a product was out of stock, it was the supplier’s fault-i.e., if only the manufacturer could forecast better, the problem would largely disappear. Or we thought, if only our supply chain was more nimble, if we did not have such long manufacturing runs and planning cycles, we could do a better job of replenishment.

In the second phase, many manufacturers came to the realization that they could not forecast accurately without more timely and granular data from retailers. Companies like WalMart and Target custom built what are now called Demand Signal Repositories (DSRs) to provide suppliers with near-real time granular data, such as POS sales by SKU by store, that made their historical forecast data less lumpy and less prone to the bullwhip effect.

This phase is still taking place. However, now instead of the retailer spending tens or even hundreds of millions of dollars to build these DSRs, companies like Retail Solutions (RSi) build the DSR for retailers like CVS and Walgreens for “free”. Suppliers who want access to the retailer’s sales and inventory data then pay RSI for it. There seems to be a tradeoff between the proprietary DSRs offered by WalMart and Target, and the off-the-shelf solutions offered by the RSI retailers. WalMart and Target make this data available to their suppliers for “free,” but they also have very high expectations regarding replenishment performance, and they are more likely to apply charge backs for “poor” performance. In contrast, Walgreens appears to have less demanding replenishment performance expectations, at least for now, but their suppliers have to buy the data.

In the third phase, we came to realize just how inaccurate in-store inventory systems are. At many retailers, in-store inventory accuracy is only about 65 percent. We also came to realize that poor in-store execution is a huge problem in achieving on-shelf availability, particularly for promotional items. Very often the inventory is present in the store, but not stocked on the shelves or end-of-aisle displays. RFID was supposed to solve this problem, but it hasn’t for a variety of reasons. It has, however, made this problem more visible.

The next phase, which is just beginning at some leading manufacturers, will include a better link between merchandising and supply chain operations. That phase will be the subject of tomorrow’s posting.

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