Artificial Intelligence and Machine Learning Application for Pharma Supply Chains
It’s fair to say that the COVID-19 pandemic has exposed many of the weaknesses in our global supply chains. This is bad enough when supermarkets run out of toilet rolls or pasta due to poor supply but becomes a critical issue when lifesaving drugs and treatments are unable to reach their destinations.
The pharmaceutical supply chain business was already embracing digital transformation before COVID hit, but now adopting this technology has gone from being optional to essential and those brand without the resources or willingness to embrace it risk being left behind.
One of the most significant technological challenges facing pharmaceutical brands is the way they manage data, and this can lead to significant inefficiencies in supply chain operations.
Data
Like many others, the pharma industry has historically relied on siloed data structures which are managed by ancient legacy computer systems.
While these information structures are not completely without use, they prevent brands from gaining access to manufacturers' and distributors' data in real-time. Leading to inefficiencies in almost every corner of the business including:
- Lack of end-to-end visibility
- Malfunctions at the manufacturing level
- Tight deadlines and costly expedites
- Fragmented multimodal networks
- Cold chain - temperature control and strict handling along the entire process
- Issues related to drug counterfeiting
- Quality and repeatability of drug manufacturing
- Personalised treatment production for individual patients
This is in stark contrast to the drug discovery side of the industry which embraces and employs new and advanced technologies like few other sectors of business.
Artificial Intelligence and Machine Learning
AI and machine learning technology can address many of these issues by removing legacy data siloes and empower pharma supply chains to achieve complete visibility into their entire operations. Process visibility, inventory management, predictive maintenance, demand forecasting, logistics, automation, counterfeit resistant processes, and more become significantly easier to implement with AI algorithms sorting data and conducting analysis which transforms it into meaningful and actionable insights.
AI implementation is not something that can be achieved overnight and without investment, however. Brands need to identify the areas where AI can be of benefit, ensure data is in proper order to make the most of it, start small and be prepared for early failures, hire the right people to manage implementation and ongoing management, and bring in third-party help where appropriate.
“With the help of machine learning, deep learning, and neuronal networks, we will be able to use our data in an advanced manner to ultimately increase the accuracy of our forecasting and to enable real-time decision-making in order to react to last-minute changes in demands,” said Global Head of Digital and Data for Healthcare at Merck, Michelangelo Canzoneri. “A digital pharmaceutical supply chain provides real-time data to increase full transparency to visualize and analyse end-to-end performance along the entire supply value chain.”