Inventory Management As a Service

There is nothing more frustrating for customers than to walk into a supermarket or a retail store and find space in the place of the item/items that they were planning to buy. Out-of-stock (OOS) products result in a disparity between the shopping lists of the customers and the availability of products in a particular store. The constant inability to restock products can lead to customer dissatisfaction, a shift in customer loyalty and finally, loss of sales.

What are the Key Factors Leading to an OOS situation?

Lack of Store-level Operational Compliance

The following distortions of crucial aspects can result in a lack of operational compliance at the store level:

• Improper balance on hand (BOH).
• Human interference in the system suggested purchase orders.
• Gaps in DSD (direct store delivery) process.
• Negative inventory.
• Manual purchase orders.
• Wrong safety stock.
• Improper minimum display quantity, etc.

These can also be referred to as human failures. 

Distribution centre-based replenishment (supplier-to-DC-to-store) according to static distribution centre allocation algorithms can result in more OOS situations.

Lack of Retailer-Supplier Coordination at Functional and Strategic Levels

The deployment of planning and demand forecasting systems for calculating aggregate demand is often done on a cumulative level regarding key suppliers without building it from the ground up. Even when the forecast model takes store complexities into account, it neither caters to the actual replenishment system with relevant input nor takes the daily output from the sales system.

Furthermore, stores often have minimal visibility of incoming inventory until inventory from the supplier arrives at the store’s distribution centre and a discovery session is done. This means that any inventory misbalance is not known to the retailer before the arrival of the inventory. Inventory visibility gaps result in functional inefficiency for retailers and distribution centre managers.

Incompetent Set of Algorithms

Many retailers still use outdated packaged software for tracking replenishments. The algorithms of such old packaged software fail to efficiently deal with the flexible and ever-evolving market and multichannel presence. Such software updates are costly, and hence, the retailers are often forced to work by implementing custom improvements to the existing software.

Lack of Machine Learning Input

Many retailers are reluctant to introduce machine learning input into their static algorithms, fearing the cost and time required by the process. The static algorithms used by businesses fail to match up with the changing market requirements.

How Will Inventory Management As a Service Help in Removing the Inefficiencies?

The existing enterprise and merchandising systems used by retailers fail to keep up with the automated replenishment quantities generated by IAAS by combining the robustness of modern quantitative modelling and machine learning. Retailers can continue using ERP/packaged software for workflow, transaction management, etc., as IAAS only deals with calculating accurate replenishment quantities across multiple channels. 
Retailers can perceive this as an investment made towards complementing the existing packaged software using the robust algorithm of IAAS. Inventory management as a service is undoubtedly the best way of ensuring customer satisfaction and increasing sales.