Stock-outs hurt retailers’ revenue and consumer experience, particularly when dealing with unknown stock-outs caused by ghost inventory. Previously retailers may have opted to overstocking as a solution to improve on-shelf-availability resulting in higher inventory carrying costs and space requirements.
Phantom Inventory, also known as Ghost Inventory, is an occurrence of unknown stockout due to inaccurate inventory information in operational systems. Phantom inventory can "cost" 1%-3% of revenue depending on the retailer. These unknown or virtual stockouts are detrimental for fast moving consumer goods retailers who have brick-and-mortar stores and/or offer buy-online-pick-in-store (BOPIS).
Systems showing inventory while it physically doesn’t exist in store, causes revenue impact, poor customer experience and inaccurate forecasting. There are several approaches to identifying phantom inventory issue. Stores can increase inventory cycle frequency and scope, implement technology such as RFID tags or they can deploy machine learning models.
Ideal solution for larger FMCG retailers is AI and machine learning models due to their immense dataset making it difficult to manage in traditional analytics approaches. Ensure that your stores have good On the Shelf Availability (OSA) to maximize revenue.
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There are three major areas of impact due to stockouts caused by phantom inventory:
When products are not on the shelf they cannot be bought. Simple as that. Phantom inventory causes on average 4% loss in revenue and in some cases even more.
On average a consumer facing phantom inventory driven stockouts spends 20% more time taking valuable staff focus as well. Ifthe retailer has buy-online-pick/deliver-in-store model, having inaccurate inventory information can lead toexpensive substitutions.
When inventory is misplaced, it carries cost and cannot be sold. Investigating phantom inventory root cause can help discovery store operations issues.
Kloud9 Artificial Inventory Intelligence has been developed with leading retailers to help organizations identify location/SKU combinations that are candidates for stock-outs. Machine learning models are optimal for resolving this challenge due to large data volumes with high number of SKU/location pairs.
Our models predict both instock as well as stockouts leveraging historical sales data. The goal of the model is to be as accurate as possible predicting stockouts for the distribution center fulfillment cycle.
Retailers can drive business benefit from addressing the predicted stockouts and instructing fulfillment to deliver additional inventory and start root cause process.
