Making Cold Chain Predictive

Cold Chain is gaining much more attention lately, even the Bill Gates Foundation emphasizes on the importance of cold chain as billions of euro’s are lost and lifesaving medicines aren’t effective anymore.

Looking at the pharmaceutical industry, Cold Chain isn’t something new (since 2011 US and 2013 EU a regulatory requirement). It’s a difficult to control and manage process, that sadly often goes wrong outside the notion of pharmaceutical manufacturers or logistical service providers, with potentially big impact on the product and eventually patients.

Like all complex processes we are likely to turn to technology to see where it could help us. Within the cold chain domain, there are a lot of developments over the years, like coloring stickers that turn red when a temperature deviation has occurred, however good old’ data loggers are still abundantly used within our industry.

These technologies are focused on what has been, it reports on data that has been gathered. What is there to change with things that happened…. Well… Nothing.

We can do better!

With a small team, we tried to improve real-time visibility into the cold-chain and make this process more predictive. By making use of available technology which can trigger us with potential cold chain issues, it should give the possibility to quickly respond when products (threaten to) offend temperature requirements.

But let’s first discuss some requirements from different perspectives and roles within the company. An obvious department is quality, but also customer service are very keen on cold chain management. So we identified multiple basic requirements and just started from there. Examples are:

  • Show temperature over time
  • Current and history of location
  • Temperature deviations
  • Active sensors
  • Batch information
  • Delivery information

Luckily we have access to all the required technology and could start quickly. We got ourselves some IoT devices, hooked up an ERP system with a pharmaceutical demo company, connected everything to a development platform and off we go!

The Development Process

During an iterative development process, we’ve actually learned that tons of data can be used, not only for cold chain, but also from a supply chain perspective.

One of our objectives was that we wanted to know how IoT sensors would survive a real world test. We send some sensors across the globe to see how well they performed (some better than others, to be honest). But still, it gave us great insights in the maturity of the technology and the hurdles that still need to be taken.

So, with not so much effort we developed a monitoring platform with live data, where we could add other data sources like weather forecast, flight and carrier information. The combination of all these data sources provides us with meaningful insights about the influence of various factors along the supply chain of temperature-sensitive products and also enables us to perform predictive analytics.

The latter ultimately allows us to predict when and where the temperature limit will be reached. This gives companies the possibility to respond adequately and in time.

From my point of view, these types of Cold Chain initiatives are a great way to gain insight and making a big step in a more safe and efficient way of handling temperature sensitive medicines.

Curious and want to know more about this initiative or Cold Chain in general? Please fill in the form below and I’ll be happy to tell you all about it.




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