September 07, 2017
Improving Life Sciences through Advanced Supply Chain Analytics
By: Oonagh O'Shea

Revolution is defined as a radical and pervasive change. It is the word that comes to mind when I think about analytics in Life Sciences.

Rapid advancements in technologies are shifting the Life Sciences analytics agenda from evolutionary to revolutionary. The Life Sciences industry now needs to stand up, recognise and embrace the opportunities presented by Advanced Analytics and move rapidly to implement Digital Supply Chains. Advanced Analytics will power the modern supply network and be embedded throughout highly connected, intelligent and automated Life Science operations.

Big Data, Cognitive Computing, the Internet of Things (IoT), and Robotics are all terms that we are familiar with, but what do they mean for Life Sciences and the Supply Chain? Let’s look at four key components of a Digital Supply Chain and the opportunities it presents:

Planning: Powered by machine learning and demand sensing, forecasting and inventory optimisation approaches can help prevent over- or under-stocking, achieve higher sales, and increase customer satisfaction. Implementing statistical forecasting by combining a variety of data sources can improve forecast accuracy, reduce stock outs and improve batch times.

Sourcing: The procurement process can be enhanced and costs significantly reduced by using sourcing analytics for commodity pricing, risk management, spend and supplier performance management. Applying these procurement analytics processes improves inventory turn, allows better adaptation to market fluctuations, reduces inventory and capital expenditures, and strengthens supplier relationships.

Production: Apply multivariate analytical techniques to optimise manufacturing cycle times, improve product quality and prevent down time. There is increasing ability to leverage large volumes of sensor data to power innovative approaches to factory operations. For example, the application of predictive analytics can reduce maintenance costs, reduce breakdowns and increase line efficiency.

Fulfilment: By streamlining network flows, companies can reduce costs to serve and improve flexibility. Leveraging emerging sources of data such as connected vehicles enables organisations to remain cutting edge in network and flow path optimisation, achieving the lowest cost network and meeting projected demands. Applying Advanced Analytics can reduce warehouse costs, reduce network wide inventory and reduce transportation costs.

Wrapping digital and analytics capabilities with virtual and augmented reality technologies will deliver an empowered supply chain for all supply chain partners. An end to end capability is key to translating insights into real action. Organisations should seek to show highlights effectively, so they leverage insights for decision making and simplify information by effectively representing Big Data and the Supply Chain through accessible interfaces.

The Life Sciences industry is becoming more patient-centric and the industry must fully embrace the Digital Supply Chain to keep up with ever changing customer demands. The new end-to-end supply chain strategy must ensure that products and services are delivered in a timely, safe and in a compliant manner.

Ultimately, the innovative use of data and technology can ensure improved patient outcomes and provide business profitability. Companies must leverage the opportunities presented by Advanced Analytics, Digitisation and Augmented Reality to enhance their supply chains. Implementing a truly integrated Digital Supply Chain is undoubtedly challenging but it also provides a platform to become industry leading, drive growth and, of course, improve health worldwide. The opportunity is there for those willing to lead in the new era of Life Sciences.

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