Systemically Reduce Out-of-Stocks through the Elimination of False Positives
Few consumer goods manufacturers need to be told that retail out-of-stocks (OOS) take a real bite out of sales and profits. The fact that the problem persists means there is still more work to be done.
This shouldn’t be surprising given the retail industry has long sought to increase sales while simultaneously reducing inventory costs. Retailers and manufacturers who strive for both must engage in a delicate balancing act to avoid running out of inventory and missing sales opportunities.
Over the past 10 years, many in the industry have worked to address the problem and achieved some success. But OOS continue. The cost is illustrated through a 2012 study of U.S. supermarket chains identifying OOS as the single largest factor causing shopper satisfaction to decrease by 11 percent.
While both retailers and manufacturers feel the pain of OOS, they feel it in different ways. Retailers are focused on the shopper’s store loyalty. When the shopper goes to another store or competitor’s web site, it’s a concern; when the shopper buys a different brand at the same store, it’s not.
Consumer goods manufacturers, on the other hand, are focused on the shopper’s brand loyalty, but not when a shopper switches stores or goes online to buy the same brand. This conundrum complicates the retailer-manufacturer collaboration goal of reducing OOS.
A Spectrum of Causes
Why are OOS still such a nagging issue? Traditional approaches to addressing OOS have focused on ensuring timely and accurate shipments to the retailer, as well as timely and accurate stocking of shelves once products are at the store’s loading dock.
That’s helped, but it’s not enough because it doesn’t begin to cover the spectrum of causes fueling OOS. Those causes may be traced to the retailer, manufacturer, DCs or specific stores — and even to issues related to shoppers. Here’s a sampling of some issues:
Because there are so many potential causes for OOS, manufacturers need guidance on where to look: at themselves, retail policies, store execution, and so on. More than that, they need exception-based guidance on what they’re looking for, so they can predict and correct problems quickly and efficiently.
Ironically, manufacturers also need to know when and where not to look for OOS problems. Technology solutions can inadvertently generate false positive alerts, identifying store problems that don’t, in fact, exist. This is a huge concern in the industry, accounting for up to 50 percent of all alerts generated by some systems.
The problem can be traced to solutions that ignore systemic causes of OOS — causes that are driven by the retailer’s corporate-level data, rather than store-level, or that fail to account for OOS accurately.
It’s an expensive problem because systemic issues are responsible for the most widespread OOS. So, when systemic issues are not addressed in a timely fashion, the opportunity to have the biggest impact on sales and profitability is missed. Also, the high cost of sending personnel into stores to fix problems, especially problems that originate elsewhere, contributes to the financial loss caused by false positives.
Because systemic issues create the most widespread OOS problems, an analytics system should start with real-time demand signals and be designed to identify true OOS, not false positives. The analytics system should then proceed in a tiered approach, examining warehouse insights and moving down to store-level, i.e. POS, inventory, replenishment flags, etc. Working with this methodology enables retailers and their suppliers to collaboratively maximize ROI at every tier from production to consumption.
*This is an excerpt from a Retail Velocity white paper “Solving the Out-of-Stock Challenge.” If you would like a copy of the complete white paper, please click here.
ABOUT THE AUTHOR
Jennifer Beckett is the VP-Sales and Marketing at Retail Velocity (www.RetailVelocity.com). She has over 25 years of experience in category and supply chain management including a Supply Chain Management undergraduate degree from Michigan State University and a MBA in International Management from Loyola Marymount University. She also moderates two LinkedIn groups focused on resolving OOS for the retail industry. Please contact her if you’d like to join either group:
Jennifer Beckett, CPIM
VP – Sales & Marketing
Retail Velocity
[email protected]
www.RetailVelocity.com
This shouldn’t be surprising given the retail industry has long sought to increase sales while simultaneously reducing inventory costs. Retailers and manufacturers who strive for both must engage in a delicate balancing act to avoid running out of inventory and missing sales opportunities.
Over the past 10 years, many in the industry have worked to address the problem and achieved some success. But OOS continue. The cost is illustrated through a 2012 study of U.S. supermarket chains identifying OOS as the single largest factor causing shopper satisfaction to decrease by 11 percent.
While both retailers and manufacturers feel the pain of OOS, they feel it in different ways. Retailers are focused on the shopper’s store loyalty. When the shopper goes to another store or competitor’s web site, it’s a concern; when the shopper buys a different brand at the same store, it’s not.
Consumer goods manufacturers, on the other hand, are focused on the shopper’s brand loyalty, but not when a shopper switches stores or goes online to buy the same brand. This conundrum complicates the retailer-manufacturer collaboration goal of reducing OOS.
A Spectrum of Causes
Why are OOS still such a nagging issue? Traditional approaches to addressing OOS have focused on ensuring timely and accurate shipments to the retailer, as well as timely and accurate stocking of shelves once products are at the store’s loading dock.
That’s helped, but it’s not enough because it doesn’t begin to cover the spectrum of causes fueling OOS. Those causes may be traced to the retailer, manufacturer, DCs or specific stores — and even to issues related to shoppers. Here’s a sampling of some issues:
- Manufacturer. Sales, production, distribution planning and forecasting may be off by wide margins, resulting in OOS at the retailer or the manufacturer’s own warehouses. The number and location of their warehouses can have a big, but often unconsidered, impact on OOS. Additionally, forecasting based on historical sales trends that contain OOS patterns and/or excludes retailer inventories will only compound the issue.
- Retailer. Flags, reorder points and other triggers in a retailer’s replenishment system can affect OOS across a region or the entire chain. The open sharing of promotional schedules and predicted quantities also has an effect on in-stock performance at the store-level.
- Store. There are many store-level causes for OOS to consider: inventory misplaced in backrooms, planograms not set, shrinkage, missing shelf tags, hijacked space, phantom inventories, and insufficient reorder levels.
- Shopper. When an assortment isn’t localized for a specific store, the result can be an otherwise avoidable OOS. Demographic mismatches between shoppers and products are a problem, as are planograms that put products where shoppers don’t think to look for them.
Because there are so many potential causes for OOS, manufacturers need guidance on where to look: at themselves, retail policies, store execution, and so on. More than that, they need exception-based guidance on what they’re looking for, so they can predict and correct problems quickly and efficiently.
Ironically, manufacturers also need to know when and where not to look for OOS problems. Technology solutions can inadvertently generate false positive alerts, identifying store problems that don’t, in fact, exist. This is a huge concern in the industry, accounting for up to 50 percent of all alerts generated by some systems.
The problem can be traced to solutions that ignore systemic causes of OOS — causes that are driven by the retailer’s corporate-level data, rather than store-level, or that fail to account for OOS accurately.
It’s an expensive problem because systemic issues are responsible for the most widespread OOS. So, when systemic issues are not addressed in a timely fashion, the opportunity to have the biggest impact on sales and profitability is missed. Also, the high cost of sending personnel into stores to fix problems, especially problems that originate elsewhere, contributes to the financial loss caused by false positives.
Because systemic issues create the most widespread OOS problems, an analytics system should start with real-time demand signals and be designed to identify true OOS, not false positives. The analytics system should then proceed in a tiered approach, examining warehouse insights and moving down to store-level, i.e. POS, inventory, replenishment flags, etc. Working with this methodology enables retailers and their suppliers to collaboratively maximize ROI at every tier from production to consumption.
*This is an excerpt from a Retail Velocity white paper “Solving the Out-of-Stock Challenge.” If you would like a copy of the complete white paper, please click here.
ABOUT THE AUTHOR
Jennifer Beckett is the VP-Sales and Marketing at Retail Velocity (www.RetailVelocity.com). She has over 25 years of experience in category and supply chain management including a Supply Chain Management undergraduate degree from Michigan State University and a MBA in International Management from Loyola Marymount University. She also moderates two LinkedIn groups focused on resolving OOS for the retail industry. Please contact her if you’d like to join either group:
Jennifer Beckett, CPIM
VP – Sales & Marketing
Retail Velocity
[email protected]
www.RetailVelocity.com