Indian Pharma speeds up steps for production efficiency & inventory management

Logistics and cold chain management are huge challenges because it is fragmented with huge infrastructure gaps

Medicine Factory
Picture: Pixabay

Last Updated on July 19, 2021 by The Health Master

Indian pharma industry has accelerated wide-scale AI and machine learning (ML) to improve decision-making to optimize production efficiency, inventory management, and replenishment processes.

Only if AI and ML are deployed at scale, it can bring in agility to the industry. Replenishment issues, batch losses, and lower yield have historically been common challenges in pharmaceutical supply chain. The C-19 has exacerbated demand fluctuations, making it difficult to predict using traditional predictive systems, said Rahul Vishwakarma, co-founder and CEO, Mate Labs.

While data analytics is based on past events, predictive analytics identifies patterns, tests assumptions, and employs ML algorithms to re-evaluate and adapt a model for the most accurate results. This enables supply chains to adopt a proactive approach, allowing companies to forecast demand more accurately even during major disruptions and reduce response time, he added.

Picture: Pixabay

Logistics and cold chain management are huge challenges because it is fragmented with huge infrastructure gaps. There is lack of visibility and demand spikes made it difficult for companies to forecast accurately at granular levels, resulting in inventory shortages and the manufacturing of excess stock due to the bullwhip effect. Therefore, pharma companies need to use scalable AI solutions to optimize their supply chain operations, including demand forecasting, inventory management, logistics, procurement, and manufacturing.

With predictive solutions built with AI and ML technologies, pharma companies can learn how deep and narrow demand sensing is by tracking events and feeding them into reverse modelling. Planning enables pharmaceutical companies to run simulations with various ‘what-if ‘scenarios, allowing to view demand fluctuations for each SKU at each location based on historical data and various socioeconomic factors, said Vishwakarma.

AI-powered solutions can accurately calculate buffer or safety stock by gaining visibility into inventory levels, supplier lead times, and real-time demand signals. Using predictive analytics, drugs can be tracked throughout the supply chain and proactive measures can be taken to adjust inventory levels, allowing patients to have consistent access to their medications and companies to avoid stock-outs, he said.

The implementation of predictive analytics is critical to be highly responsive to changing customer demand across multiple locations. It can adjust forecasts based on real-time demand signals, allowing distribution centres with excess inventory to redistribute it to a region where demand is high. ML models accurately predict whether a specific drug order will be consolidated with another upcoming order to the same location.

Further, predictive AI determines the fastest routes while accounting for traffic congestion, distance, weather, delivery points, and other factors such as lockdown restrictions. Since pharmaceuticals require temperature-controlled shipping, there is no room for transportation delays.

In the area of procurement and manufacturing, avoiding raw material shortages is a top priority. Predictive AI provides visibility into lead times and accurate inventory quantities, resulting in increased productivity, improved efficiency, and faster drug production. Companies can use AI to identify suppliers to provide additional materials. With predictive AI, they can ramp up production when the forecast indicates an increase in demand, procure raw materials ahead of time, and be ready to meet demand, stated the Mate Labs chief.

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