Delivery Optimization for Pharma Distributor

By leveraging historical transactional data, the distributor aimed to identify insights related to product ordering patterns and subsequently implement cost-effective measures. Therefore, the client sought out AI consulting services to help with their challenges.

Determining the products that were ordered most frequently in a week. Identifying and addressing sloppy or inefficient ordering patterns.

Data Mining: The distributor effectively employed data mining techniques, which are advanced analytical methods, to thoroughly analyze the provided dataset. By leveraging these powerful techniques, they were able to extract valuable and meaningful insights from the data.

By utilizing data analysis techniques, businesses can generate valuable insights. Typically, we present these insights in the form of easily understandable and visually appealing reports. This makes it simpler for stakeholders to interpret and make informed decisions based on the data.

Product and Customer Analytics: Implementing RFM (Recency, Frequency, Monetary) analyzing, the distributor was able to gain valuable insights into their top customers. As a result, we identified and addressed any inconsistent or careless ordering patterns more effectively.

Supply Chain Analytics: When incorporating analytics into their supply chain process, the distributor was able to gain valuable insights. This allowed them to optimize their delivery operations and effectively manage costs by analyzing customer buying patterns.

The implementation of these solutions yielded the following results:

Derived insights related to top customers with sloppy ordering patterns, enabling targeted improvements.

Achieved a significant 30% reduction in delivery costs because of the insights provided by the analytics approach.

This pharma distributor improved their delivery processes and became more efficient. They achieved this by using our partner's AI consultant and data-driven methods. As a result, they were able to save money.