The client was looking for an automated solution to provide recommendations for replenishing their stores with various lines of products according to consumer’s buying behavior. A major cost-effective solution was achieved to eliminate the manual intervention in suggesting replenishment orders for their stores.
The challenge was addressed by following the below steps:
Understanding the data flow streams for various product lines and exploring the data statistically.
Analyzing the exploratory analysis for the data and bringing out meaningful insights to identify significant variables to show a considerable effect on the product lines.
Development of various statistical and machine learning models to understand the various transactions containing the products.
Shaping out the methodologies to sort out categories of product lines for the stores.
Generation of priority list of product lines to optimize the product distribution systems by understanding various evaluation metrics.
Improvement of efficiency in the process by reducing manual effort to track down customer behavior for product consumption.
The client was able to understand the ground-level metrics to optimize the process of product recommendations based on scoring techniques.
The insights from the analysis helped the client to troubleshoot specific blockers in their supply chain system.
The analysis helped to improve the inventory requirements and optimize the marketing campaigns for the featured products.
The automated product recommendation system showed 20% more efficiency in the process than the traditional manual system.
The solution helped to reduce the man hours in the distribution system by providing accurate recommendations for the products.
The client was able to optimize the availability of the products by carefully understanding the consumer’s buying behavior with an improvement of 7%.