phantom inventory Archives

Google, Same-Day Delivery, and Container Tracking

Logistics Trends | By Adrian Gonzalez • 10/31/2012

Meet Google, your new supply chain and logistics technology provider.

According to a New York Times article, Google has started to test a same-day delivery service in San Francisco with some Google employees and their friends. At least one national apparel retailer is participating in the test, according to a source. Although Amazon and Walmart have made headlines in recent months with their own same-day delivery offerings (see “On Amazon’s Quest for Same-Day Delivery” and Walmart to Go), Google’s plan to offer this service was first reported last December in the Wall Street Journal. Here are some excerpts from that article:

Google is pushing back [on Amazon] with the quick-shipping service, and some retailers are interested in signing up for it, said people familiar with the matter. They said Google has pitched the project to big retailers that it has teamed up with in the past, a group that includes Macy’s Inc., Gap Inc., and OfficeMax Inc.

The quick-shipping service would be based on Google’s behind-the-scenes system that allows shoppers to figure out whether nearby stores have a product in stock and whether they can get that product shipped to them within a day. When shoppers place an order on participating retailers’ sites, Google’s system could kick in to offer them the option of same-day or next-day delivery, said a person familiar with the matter.

The effort could involve partnerships with shippers such as United Parcel Service Inc. and local courier companies, said people familiar with the matter.

Two quick takeaways: First, the success of Google’s approach will depend on the ability for retailers to provide customers searching online with accurate, real-time visibility to SKU inventory by store. This remains a big challenge for many retailers (see “Phantom Inventory Creates Havoc in Retail Supply Chains”), so unless retailers make significant improvements in store-level inventory accuracy, many customers will be scheduling same-day deliveries of products that aren’t available.

Second, it isn’t enough for Google’s system to determine whether it’s possible to ship a product within a day; the system should also balance the delivery costs incurred by the retailer with the pricing and time windows it offers customers to maximize profitability. For example, if a customer’s neighbor already has a delivery scheduled for 5:00-7:00 PM, the system should incentivize the customer to choose the same delivery window, which would allow both orders to travel on the same truck, thus minimizing the retailer’s delivery cost.

Shifting gears, earlier this month Google was issued a patent for a container tracking system. Here is the abstract:

Shipping containers are networked for transferring data between the shipping containers. The shipping containers include sensors for detecting conditions associated with the shipping containers. The conditions sensed by any shipping container whether transported by rail or ship is transmitted from an ad hoc network, via a gateway configured for satellite or cellular communications for example, to a container-tracking application server or equivalent computer system. The computer system is remotely located to the shipping container for central compilation, analysis, and/or display of data regarding the shipping containers.

Under “Field of the Invention,” the document says, “The present invention relates broadly to wireless transceivers, including the design of a wireless transceiver as well as to forming ad hoc networks with wireless transceivers and to communication protocols between wireless transceivers.” In other words, the invention includes wireless tags and readers and a way of networking them. Here is a use case example the document highlights:

A warehouse store such as Costco carries hundreds of brands of products. Nevertheless, all products need to be inventoried on a realtime basis. In accordance with the present invention, a Wireless Reader Tag is attached to each pallet of goods and assigned a class designation that denotes the manufacturer of goods on that pallet (e.g., Pillsbury, Sony, Kellogg’s, etc.). Each Wireless Reader Tag may also include in its class designation or profile, information about the goods on the pallet. Each boxes on the pallet carries a Wireless Tag that is read by the Wireless Reader Tag of the pallet. On demand, each Wireless Reader Tag gathers information about the Wireless Tags on its pallet and relays the information back to the asset-tracking application server. At any given time, an employee of the warehouse store can inventory goods of a selected manufacturer by sending a query that will be received only by Wireless Reader Tags of the selected class corresponding to such manufacturer. Messages directed to a selected class will not appreciably affect the battery life of Wireless Reader Tags of other classes, as only Wireless Reader Tags of the selected class will wake up from standby mode to receive the messages and process the query. The manufacturer classes can be divided further into categories and subcategories, thereby further collectively reduce battery consumption and radio interference.

I don’t know how this differs from systems currently offered by RFID technology vendors like Motorola Solutions and Savi Technology, but here’s my big picture takeaway: Google is paying attention to supply chain and logistics opportunities, and looking for ways to leverage its technology and intellectual property — including tablets and smartphones, which could serve as tag readers — to improve supply chain processes.

At the end of the day, I believe Google will partner with leading supply chain and logistics technology vendors to exploit these opportunities. And maybe in a few years we’ll have a clearer answer to a question I first raised in a February 2009 posting: If Google was in charge of your supply chain, what would it look like?

Phantom Inventory Creates Havoc in Retail Supply Chains

Inventory Management | By Steve Banker • 11/18/2009

In my “What Causes Shelf Out-of-Stocks?” posting last week, I reviewed some research conducted by Walmart on the major causes of shelf-level out-of-stocks. One reason, occurring about 20 percent of the time, is phantom inventory. Phantom inventory occurs when the store system shows inventory available but none really exists. It also occurs when the system shows more inventory than what is actually available in the store; this can be caused by theft, broken products, mis-picks or checkout issues, for example.

When a store system shows inventory that doesn’t really exist, a store manager could easily assume the product is not selling well and thus decide not to reorder it. When the store system shows more inventory than what really exists in the store, this can also create havoc. I did not fully understand why this was so until Cedric Guyot of Retail Solutions walked me through some slides and explained it to me.

Stores have min/max replenishment logic just like other nodes in the supply chain. Imagine that for a particular SKU, the min is seven and the max is fifteen, and that the store reorders every time the inventory level falls to seven or lower. When the store reorders the product it makes sure the delivery will bring the number of items in the store up to 15. These min/max targets are based on a service level analysis – e.g., the minimum target level that will lead to a 99 percent in-stock rate for that product. In other words, with this minimum target level, if the store operations folks do a good job of keeping the shelf replenished and the inventory accurate, there is only a one percent chance, based on the demand history of that product, that a customer will not find this item in stock when they want to buy it.

Phantom Inventory Leads to Lower than Targeted Service Levels (Source: Retail Solutions; click to enlarge) — Reorder Minimum Target Level of 7 Leads to Only a 1% Chance Customer Won’t Find the Product In-Stock (Source: Retail Solutions; click to enlarge)

Now assume that there is some phantom inventory, that there are two less units of inventory in the store than what the system shows. As the chart below shows, the target service level for a min of 5 is only 95 percent. So, if this min reorder point had been chosen, customers coming into the store would not have found that product in stock five percent of the time. Further, if you look at the shape of the service level curve, the more units of phantom inventory, the greater the drop-off in service level. If the store inventory system says there are seven units of the product available, but only two really exist, the service level would be only 69 percent. The shape of this curve is pretty typical. The more phantom inventory you have, the easier it is to have truly unacceptable, yet hard to detect, shelf service levels.

Finally, when the level of phantom inventory becomes greater than the reorder point quantity (eight in the above example), the store stops ordering altogether, creating long out-of-stocks that can only be fixed by an inventory adjustment.

I should point out that the service level curves shown above are hypothetical. Store-level SKU service levels would differ for every SKU in every store based on that store’s/SKU’s demand history.

In conclusion, Mr. Guyot tells me that most studies show 40 to 50 percent of the items in a store have some amount of phantom inventory. This is clearly a serious problem, but a problem that current Demand Signal Repositories are helping to solve.

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.

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