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QAD 2019 Predictions: Optimizing the Integrated Life Science Supply Chain Ecosystem with Machine Learning

Industry executives and experts share their predictions for 2019.  Read them in this 11th annual VMblog.com series exclusive.

Contributed by Mike Kolias, Director, Life Sciences at QAD

Optimizing the Integrated Life Science Supply Chain Ecosystem with Machine Learning

For life sciences companies, machine learning will have more impact than any other advanced technology in 2019.

Machine learning is the ability of systems to analyze and learn from vast amounts of complex supply chain data, identify patterns within the data, and make decisions based on those patterns. For many years, the discussion surrounding machine learning has focused on the unlimited opportunities but relative immaturity of the technology. However in 2019, technological advancements in machine learning, in conjunction with the integrated supply chain ecosystem, will make decision making performance increases possible for the life science industry by generating more models and continuing to optimize algorithms. This development is ideal for the life sciences industry, which has notoriously failed in the area of forecast accuracy, due to a tendency to make decisions based on incomplete data and speculation. This lack of precision has resulted in companies inflating inventories to satisfy unforeseen demand and avoid the specter of stockouts. Machine learning, using real-time data, has the potential to significantly optimize inventory management and increase the operating efficiency of many other areas across the life sciences enterprise.

Machine learning will increase the ability of life sciences organizations to navigate their complex operating environments while taking into consideration factors that have historically been difficult to track or quantify. General supply chain optimizations resulting from machine learning include reduced freight costs, quicker customer response times and more accurate predictions and forecasting.

Another benefit of machine learning is that it will help life sciences companies minimize the impact of regulatory and compliance bottlenecks. Although regulatory and compliance requirements are not technically a part of supply chain processes, they can severely impact process efficiency. Aspects such as track and trace, critical to compliance within the pharmaceutical industry, can be optimized through machine learning by creating data hierarchies for each supplier through finding patterns within supplier quality levels.

Raw material handling and inspection are critical to all segments of life sciences. Much of what is performed during this process can be managed more effectively through machine learning. Machine learning can also perform visual pattern recognition, allow life sciences organizations to automate inbound inspection and recommend the best course of action for handling raw materials.

These are only a few examples where life sciences companies will utilize machine learning in 2019 and beyond. With the advance of this technology, the life sciences industry is primed to take advantage of tremendous opportunities for optimization and cost reduction. We are entering an exciting time in supply chain and the life sciences industry. Advanced technologies like machine learning are evolving and offering new breakthroughs that companies are utilizing to make their products better, their processes more streamlined and their business goals more obtainable.

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About the Author

 

Michael Kolias is QAD's Director for Life Sciences industry. He has 10 years of experience implementing ERP solutions and delivering strategies that enable organizations to maximize their manufacturing and supply chain processes.

Published Wednesday, January 23, 2019 7:32 AM by David Marshall
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