Shoplifting is at record levels ($115 billion from July 2008-June 2009) and this is causing supply chain problems, not only for the retailers, but also for consumer goods manufacturers.
The Centre for Retail Research (CRR) published its annual Global Retail Theft Barometer report on November 10th. CRR interviewed 1,069 large retailers with total revenue of $822 billion. A summary of the report is available at here.
Here are some of the key points:
- The study monitored the costs of shrinkage and crime in the global retail industry between July 2008 and June 2009, and found that the rise in shrink occurred in all regions surveyed, with the greatest increase in North America (+8.1%), Middle East-Africa (+7.5%) and Europe (+4.7%).
- Employee theft is perceived as the biggest contributor to shrink in North America, responsible for 44% of all losses. The average employee in North America stole nearly $1,900 per incident.
- In 2009, some of the highest average shrinkage rates were found in apparel/clothing and fashion/accessories (1.84% of revenues) and in cosmetics/perfume/beauty supply/pharmacy (1.77% of revenues). In apparel/clothing and fashion, the highest shrinkage losses were seen in accessories (3.85% of revenues) and in fashion/tailored clothing (3.64% of revenues). For food items/groceries, the highest shrinkage was reported in fresh meat with 3.38% of revenues.
So, why does this matter to supply chain groups?
As I’ve written about in the past (see “Should Logistics Personnel Work in Retail Stores?”), there is a growing trend in the retail industry towards having supply chain personnel work in stores, responsible for front-of-store replenishment. This means that supply chain organizations are increasingly becoming responsible for policing employees not only in warehouses, where that job is easier, but also in stores, where the task becomes more difficult.
But theft also creates problems for consumer goods manufacturers. Long term forecasting, replenishment, and the sales and operations planning process (particularly surrounding promotion planning and new product introductions) are all more accurate if consumer goods manufacturers use the most granular downstream data available. This means having access to store inventory, POS, and even syndicated and store loyalty data. In short, when it comes to running an effective supply chain, using “sell-through” data (what customers are actually buying from stores) is far better than using “sell-into” data (what manufacturers are shipping to retail distribution centers).
Take the forecasting problem. Apparel companies, which experience the highest shrinkage rates according to the CRR survey, benefit from POS data for sales forecast purposes, even if their supply chains are too long to benefit from more effective replenishment (most apparel supply chains begin in China/Asia and have lead times of up to six months). At the end of a selling season an apparel company might see a sales balance of zero for a particular SKU sold through a particular retail chain. But this does not tell the company whether that SKU sold out early at full price or whether it had been marked down. Similarly, POS data can help a clothing company improve its forecasts by geography and type of customer.
But that data needs to be both clean and accurate. Theft that occurs at retail outlets contributes to inaccurate perpetual inventories at the stores.
But even if theft levels were reduced substantially, it would not solve the other big contributor to inaccurate sell-through data: poor scanning discipline at the cash register. The other day, for example, canned soup was on sale, so I bought ten cans, but three different varieties. All the cans were priced the same. At checkout, the clerk took one can and scanned it ten times (I hope she scanned it ten times and not eleven or twelve). This saved time, but the resulting sell-through data was inaccurate. Very few retail food chains have POS discipline (7-Eleven Japan is among the few exceptions I’ve come across). This discipline is much more common among apparel retailers because they are not dealing with volume checkout issues.
So, what can an upstream manufacturer do about this? From a technology standpoint, there are Demand Signal Repository applications from vendors such as Retail Solutions and TrueDemand that use advanced predictive analytics to predict when in-store perpetual inventory and sell-through data is wrong.