AI and ML hold answers for Indian pharma’s logistics challenges

Rahul Vishwakarma, Co-founder and CEO, Mate Labs, asserts that deploying Artificial Intelligence (AI) and Machine Learning (ML) at scale can make the pharma industry more agile
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There hasn’t been a more complex challenge to the pharmaceutical industry in recent times as the COVID-19 pandemic. While concerns like batch losses, replenishment issues and lower yield plagued the pharma supply chain even pre-pandemic, the virus exacerbated the demand fluctuations in the sector. Thanks to the extreme challenges the industry has had to overcome during this pandemic, it has simply become impossible to rely upon traditional predictive systems. Ever since, Indian pharma has been increasingly turning to data, Artificial Intelligence (AI) and Machine Learning (ML) to improve its production efficiency, inventory management and the processes of replenishment.

Leveraging AI and ML to optimise capabilities

While the industry has begun leveraging AI and ML to optimise its capabilities, only when they are deployed at scale can they make the sector more agile. Data analytics is based on past events, but predictive analytics takes it notches ahead by drawing patterns, verifying assumptions and using ML algorithms to create and adapt to a model that yields the most accurate results. This method of predictive thinking allows the pharma industry’s supply chains to take a pro-active approach than a reactive one, allowing them to forecast demand more precisely. Even during unforeseen events and disruptions such as this pandemic, the industry can reduce its response time through accurate forecasting.

Owing to the gaps in infrastructure, logistics and cold chain management pose severe challenges to the pharma sector. That coupled with a lack of clear visibility and rising demands made it extremely challenging for the industry during the pandemic to forecast the demand accurately to a granular level. This resulted in inventory shortages and then manufacturing excess stock as a result of the bullwhip effect the supply chains were put through. This is where the companies felt the need for scalable AI solutions to optimise their supply chain operations, including forecasting demand, managing inventory, manufacturing, procurement and logistics.

Accurate demand forecasting using tech solutions

The predictive systems powered by AI and ML technologies can enable pharma companies to have a deep as well as narrow demand sending by tracking events and inputting them into reverse modelling. The technologies also allow pharma firms to run simulations with varied scenarios, enabling them to have a window into the demand fluctuations likely for each situation at each location, depending upon historical data and socio-economic conditions.

Systems driven by AI cannot just calculate buffers accurately, but also provide a clear view of safety stock by gaining visibility into inventory levels, real-time demand signals and supplier lead times. This kind of predictive system enables drugs to be tracked closely throughout the supply chain, allowing the pharma businesses to take pro-active measures to check and adjust inventory.

Smarter ways of procuring, manufacturing and delivering pharmaceuticals
Predictive AI can provide insights along all the stages of procuring, manufacturing and delivering pharmaceuticals. While procuring and manufacturing, eliminating raw material shortages is of utmost importance. Here’s how AI helps – by gaining visibility into lead times and precise inventory levels, it helps companies gain increased efficiency and faster drug production. Even suppliers can be identified by AI to provide additional raw materials. Production can be ramped up when the systems foresee a rise in demand, procure raw materials ahead of time and stay on track to meet the demand.

Most importantly, the predictive systems can help patients have steady access to medications, avoiding stock-out situations. During the first and second waves of the pandemic in our country, we have witnessed certain cities and towns being more affected by the virus than others. ML models can also accurately forecast demand across multiple locations. Based on real-time signals, the models can allow distribution centres with excess inventory to redistribute the stocks to a location where the demand is soaring. Furthermore, predictive AI can even identify the fastest routes while considering traffic congestion, weather conditions, delivery points and other factors like lockdown restrictions. As pharmaceuticals require temperature-controlled shipping, there is no room for errors or delays.

Artificial intelligenceMachine LearningMate Labspharma industrypharma logistic challengespharma manufacturingRahul Vishwakarma
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