Dead Stock Reduction using Association Rule Mining
P Kumar, P Shivabharathi. · 2022 7th International Conference on Communication and Electronics Systems (ICCES) · 2022
In serious conditions such as corona and omicrons, how long the lockdown and restrictions for selling time and regular lifespan may last is uncertain. Many, or practically all, small shopkeepers to industry-level merchants were impacted because of these factors, and their stocks could not be sold. It remains a shortfall of principal amount as they invested previously, putting them in a bind. Items in the grocery and medical departments that have not sold before their expiration date become “dead stock”. These dead stocks must be detected before they are close to expiration and classified as sluggish moving products, with the goal of selling them as soon as feasible. This is the issue that must be addressed right away. To devise an acceptable strategy for selling these stocks before they expire. Another major issue is that the salesperson does not identify the things purchased by the consumers together, which is difficult to find manually to recognize common patterns. This study uses the Frequent Pattern Growth method. The dilemma of stocks that must be sold before they expire via discount sales has been consdered. People of all age like purchasing discounted items at both in retail and online stores. Some association rules using the Frequent Pattern Growth algorithm, which is superior to the Apriori method are obtained, and effective frequent patterns are selected and the discount offer on these are formed. The most effective technique to eliminate dead stock is to provide a discount.