Reinventing life sciences supply chain and manufacturing capacity with digital innovation

by Gireesh Gaonkar

As a medicinal product moves from R&D into approval and manufacturing, it must deal with a complex and dispersed distribution network and a global network of manufacturing sites, some of which may be using old, noncomputer numerical control-enabled (non-CNC-enabled) machinery.

Advances in technologies and the digital value chain create new pathways for pharmaceutical companies to address these complexities and derive greater benefits from the manufacturing and supply chain.

In the pharmaceutical supply chain, the network of suppliers and consumers — the pharmaceutical management and pharmaceutical commercial organization that must be supplied with the final drug — needs to be connected and able to collaborate with the various dynamic elements of the supply chain.

This is made more complex because pharmaceutical companies typically have many partners and agencies globally from which they source their raw materials. Companies must therefore manage the ethical, manufacturing and risk challenges that arise. To do that, companies must keep a close eye on key performance indicators (KPIs) to ensure that the right ingredient is made available to the site at a competitive price point to enable effective and efficient manufacturing.

Having clear KPIs requires adopting a data-led focus on the risk profile of the raw materials that go into producing the end product. While various agencies can assist from a risk standpoint, companies still need to be able to address risk potentials, gain transparency into the data, and connect the dots with regard to the supply chain of raw materials required for their products.

Having these insights allows a brand or product manager in the value chain to assess how specific risks in a particular part of the distribution network will affect the estimated time of arrival (ETA) for a product in a given geography. With a clear view of the entire value chain, the brand manager can better mitigate risk by channeling resources appropriately.

For example, if two critical ingredients for a product are coming via the South Pacific Ocean — which has a cyclone warning leading to a three-week delay in delivery — the brand manager will need to know about this to determine the knock-on effects and costs. What does that delay mean in terms of impact on labor resources at the manufacturing site? Should the brand manager be looking to source ingredients from an alternative source?

The insights enabled by digitizing the value chain allow the brand manager and others to make important decisions about the products in the supply chain.

Dealing with old assets

Another challenge for companies, particularly those that have been through multiple rounds of mergers and acquisitions, is managing inherited assets. For example, a large European pharma company buys a company in the Far East and inherits a manufacturing site there. This site will quite probably have dated assets on the shop floor; therefore — unlike modern CNC-enabled machinery that uses numerical techniques for optimal yields — it might not be possible to connect a sensor to remotely control and monitor this old machinery. Additionally, prescribed values in the data sheets for parameters such as heat, temperature and speed parameters are less likely to generate optimum yields.

Not being able to monitor yields presents a challenge to the manufacturing site manager, who needs to be able make decisions based on the yield cycle in the manufacturing chain from each site and connect the dots across their various site networks. To do that, the manufacturing site manager needs to be able to see the entire manufacturing yield cycle in a simulated data network and use those findings to alert the brand manager about the estimated time of arrival (ETA) of a product.

How do you resolve that challenge when your inherited assets don’t enable remote monitoring? This is again where the digital value chain opens doors for the industry. A cyber sensor system underpins what is commonly called Industry 4.0, by simulating sensors on old equipment. This allows the data to be digitized and made available in a simulated mode, enabling the manufacturing site manager to make timely decisions.

For example, the equipment in a Far East manufacturing site functions best at a particular heat parameter, at a particular fire point, at a particular temperature cycle and at a particular speed. If the site manager can extract those parameters and simulate the data, it will be possible to change these parameters, test the effects and refine them to ensure that the machinery is working optimally to achieve the maximum yield.

Today’s cyber systems allow companies to extract these parameters and simulate the working of the machine digitally by using stochastic data modeling. This is done by creating a digital twin of the manufacturing network that allows the company to run what-if scenarios in a digital environment. And these what-if scenarios can enable companies to avoid costly product quality issues, speed time to market and increase the entire throughput.

Having a digital representation of your entire manufacturing network has far-reaching consequences in terms of predictability, speed and time to market. The opportunities for reinventing your entire manufacturing and supply chain network through the digital model have never been more exciting.


Gireesh Gaonkar is the account general manager for DXC Life Sciences in the United Kingdom and Ireland. He is an advocate and keen proponent of the convergence of pharma with payers and providers in the health outcomes marketplace. He has over 20 years of experience in the life sciences industry.

Comments

  1. Great content. Having digitized your entire supply chain, from manufacturing to the last mile delivery will help increase efficiency and will bring transparency in real time.

    Like

    • Gireesh says:

      Indeed. It also sets up a good foundation to to explore and evaluate technology innovations like blockchain with quick result data to establish RoI.

      Like

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: