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CASE STUDY

Using AI, a pharma client contains inventory cost overruns by achieving 95% accuracy in drug dosage predictions

industry-iconCLIENT :Confidential
industry-iconINDUSTRY :Pharmaceutical
industry-iconDURATION :60 Days
CLIENT :
Confidential
INDUSTRY :
Pharmaceutical
DURATION :
60 Days

Business case

The client was struggling with manual processes to predict the product inventory needed for their supply chain workflows. Each site had a team of experts who were responsible for the asset inventory management. The team used to manually determine and order the next product SKUs which then needed to be shipped from the nearest depot. The team was spending considerable time navigating through multiple databases and dashboards to monitor the inventory and then manually calculating the inventory of SKUs. i2e was mandated to digitalize the entire workflow, cut down manual work and use AI/ ML to optimize the inventory workflow.

Challenges overcome

  • Lack of large amount of historical data to train the ML model.
  • Investigating and cleaning mismatches in the historical data
  • Training the algorithm to make accurate predictions amidst many influencing factors.
  • Collaborating with the stakeholders of multiple applications to understand the previous process.

Benefits

  • Accurate predictions eliminated the risk of excess order inventory.
  • The onsite team had a central system to get a bird’s eye view through a dashboard.
  • The best available transportation routes helped the on-site experts to take an informed decision.
  • Predictions were now based on the transit time vs the product expiry dates.

Results

Results