The team designed an ML solution capable of accurately recommending the product inventory for the globally distributed sites.

• The first step was to eliminate the manual process of collating the information from multiple applications. The team chose an easy-to-scale data warehouse and chose to gather all the data from various sources into it as a single integrated database. The on-ground team saved considerable time which was otherwise spent collating data.
• Next, the team chose an AI platform that supported good MLOps practices and trained the ML models, allowing for seamless deployment, monitoring, and management of the machine learning projects. These models were trained to analyze historical data and make accurate product inventory predictions for the future.
• While predicting, the system was made agile enough to take into consideration complex scenarios which could affect inventory management.
• Data science experts at i2e also trained the model to recommend the best available routes and the time taken for the product to get shipped to the site. This helped the client team to make an informed decision as to which route would be most viable considering the product’s expiration date.
• The algorithm model was capable of analyzing and doing demand forecasting for interventions across the world.

• 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.

• 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.

Get in Touch