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Data Quality: The Part of Inventory Replenishment that Many Companies Miss

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Why Bad Data May Be “Shrinking” Your Inventory Replenishment System Effectiveness

By Alex Achour, store replenishment and inventory product solution manager

There are many options when forecasting inventory replenishment. Some companies tout special logic for special types of items. Most, however, tout their use of multiple algorithms and their system’s benefit to different markets, products and consumer needs.

Many demand-based replenishment and ordering solutions, though, only consider two basic components when predicting product need: sales and receipts. Each of these components suffers from a significant flaw if not identified quickly. That weak point is a proper inventory system.

Keeping inventory amounts in good order often falls as a burden to each store. And even the most advanced replenishment solution will suffer if the data used to create orders is wrong. Consider the components used for replenishment and how inventory will affect the resulting orders. Sales data that is not clean causes the wrong in-stock amount to be displayed in the program.

Cashiers. Shrink. And Receipts.

Cashiers’ abuse of the “quantity” key on their POS systems – instead of scanning each item – is a common source of this problem. For example, if someone were to buy 20 flavors of baby food and the cashier simply uses the quantity key, one flavor is decremented too much and the others too little. Multiply this effect by the thousands of SKUs in a store and the problem becomes very real.

Another part of the sales process that is not tracked well is shrink due to loss and waste. Products that “walk out the door” are not often reflected in a store’s current inventory. In addition, waste is often handled expediently and without regard to system updates of data.

Receipts of merchandise is also another area where inventory can alter from system values. Missing or substituted products, damaged goods, miss picks and such can cause large fluctuations in reality if not caught quickly.

An Inventory Replenishment That Catches Bad Data

With so much attention going to forecasting, it is surprising that so little attention is paid to leveraging inventory data patterns. A proper replenishment system will include advanced logic to identify patterns of inventory issues. The system should do the following:

•    Alert when inventory patterns exceed basic norms (i.e. a value is negative)
•    Create alerts when an item has a normal sales pattern and that pattern changes (i.e. a fast mover that suddenly stops selling)
•    Prompt for attention when a store repeatedly changes a suggested order. This is often an indication of bad data
•    Create manageable lists of items to count to ensure high-value items are audited frequently, and to report on compliance of these counts
•    Create manageable lists of items to count so you can accomplish cycle counts in departments, categories or stores within a set time period (i.e. count cigarettes every week or every day; count some parts of dairy two days a week; and ensure all items are counted at least once a week, etc.)

With so much computing power available, and so much data at a company’s disposal, neglect of this basic component is a weak spot that many merchants endure.  As the saying goes, “bad data in, bad data out.”

Find out how Redner’s optimized their inventory with Retalix demand driven replenishment solution, click here.

For more information about inventory management and demand-based replenishment, contact Alex Achour at alex.achour@retalix.com.

 

The post Data Quality: The Part of Inventory Replenishment that Many Companies Miss appeared first on Retalix Blog.


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