Neglecting the quality of Master Data leads to various business and system challenges. For companies aiming to leverage AI, high-quality Master Data is crucial.
High-quality Master Data should encompass all aspects of a company, including the warehouse. Frequently, we encounter product Master Data that omits essential details, such as pack sizes. Often, product data only reflects the single unit (each), neglecting:
- pack/shrink,
- carton,
- layers (in FMCG),
- and pallet sizes.
This oversight can result in missed opportunities to enhance warehouse performance.
Consider a warehouse scenario where an order requires a warehouse associate to pick items. They receive a request to pick 157 pieces of a product. Upon reaching the storage location, they open the carton and count out 157 pieces. The pick is confirmed, and they proceed to the next location. While the associate may be aware of the product pack sizes, let’s envision a more efficient scenario.
Now, picture the same situation with pack sizes provided:
- Each = 1
- Shrink = 10
- Carton = 100
- Pallet = 5000
In this instance, the associate would pick 1 carton, 5 shrinks, and 7 single items, resulting in just 13 picks instead of 157 items. With a Warehouse Management System (WMS) in place, the system offers the associate three picking instructions, directing them to various locations based on pack sizes.
This method also reduces the packing requirements for shipping. Besides fewer items to count, the carton of 100 units needs only a shipping label, while only 12 items require repacking. This optimises packing materials, decreases processing time, and lowers the risk of picking and shipping errors.
When these efficiencies accumulate across your orders, you’ll notice considerable enhancements in operations. Reflect on the impact your Master Data can have in your warehouse. How detailed is your Master Data?