4 Inventory Management Best Practices—GEODIS Insights

Pay attention to barcodes

For decades, one-dimensional (1D) UPC/EAN barcodes have provided product and pricing information at points of sale. When it comes to product traceability and transparency, however, they don’t offer visibility to this type of data.

Two-dimensional (2D) barcodes, such as QR codes, can hold much more information to help improve inventory management and consumer engagement. For example, they can carry data about batches and lots, shipment and expiration dates, and serial numbers to support recall readiness, stock rotation, and even forecasting. They can also connect consumers to online resources, content, and experiences. This can take inventory management to new levels for warehouses, distribution centers, and retailers.

Because 1D barcodes are scanned with traditional laser scanners and 2D barcodes must be read with optical scanners (like a camera), many retailers believe they’ll need to overhaul POS systems to take advantage of this technology. But that isn’t always true.

“If your registers can read QR codes, then they already have 2D-read capabilities,” Nuce-Hilton said. “Using 2D barcodes could be one of the most cost-effective things you can do technology-wise. They’re powerful because they unlock machine readability, and that’s how you scale automation.”

For ordering and distribution in logistics and transportation, serialized shipping container codes—more formally known as GS1-128 barcodes—are also something to consider. Placed on pallets and cartons by shippers or logistics providers, they act as license plates. “We’ve seen people ignore those barcodes and place their own barcodes right over them,” said Nuce-Hilton. “But these barcodes tie the products to electronic notification of what’s being shipped—so warehouses, distribution centers, and retailers can prepare to receive the items and prioritize their receiving process accordingly.”

Try a new cycle-counting process

Aligning the number of products in the warehouse with the expected inventory level recorded in the warehouse  management system (WMS) is key to maintaining accurate inventory levels, identifying and correcting discrepancies, managing product shrinkage, and preventing overstocking or out-of-stock items.

To do this, some retail and 3PL warehouses rely on standard cycle counting methods: counting products on a regular basis (at least once a year) and reconciling that count against the WMS. While this can be a dependable way to maintain inventory accuracy, it also has drawbacks. It requires time-consuming manual effort, creates the potential for human error, and doesn’t allow for the best use of resources. Following this approach, slow-moving products with unchanging inventory levels still need to be counted; even locations that remain empty for extended periods of time need to be cycle counted.

A new model—predictive analytics cycle counting (PACC)—can rectify many of these issues. PACC analyzes historic inventory data and uses algorithms to indicate which products and warehouse locations should be counted next based on their likelihood of being incorrect. Using actual data from warehousing operations, PACC classifies cycle count locations that have a high probability of mismatch based on metrics like real client inventory data, previous cycle counts, location, pick front activity, SKU order volumes, and item retail value.

This allows inventory control teams to focus on potentially problematic inventory locations and SKUs while reducing manual effort and labor hours. It also supports early identification of problems and maintains more accurate inventory. With PACC, warehouses can replenish stock on a reasonable schedule based on available data instead of ordering excess inventory to mitigate the risk of unexpected stockouts.