Supply Chain Track & Trace Initiatives in the Era of Digital Transformation

The importance of data and technology for process manufacturers

Supply chain visibility (SCV) has been around for a long time. Most SCV solutions have failed due to poor data quality. New technologies have the potential to fix this, but there is no magic bullet.

What you should know before embarking on a track & trace project.

The Supply Chain Event Management solutions of the early 2000s worked by importing pertinent transactional data and comparing status updates with expected outcomes. External supply chain partner data was accumulated and then “lifted and shifted” to the solution. The result? Data latency that caused either too many false errors or missed alerts.

Across the process-industry landscape, supply chain innovators can agree that digital technologies will propel supply chain visibility to a new era. Combining mature automation and integration technologies with new approaches and data sources (think IoT) has the proven potential to provide more supply chain transparency and exception management than ever before. Companies who adopt digital supply chain solutions are expected to remove operating costs in the range of 4% to 8% and increase revenue as much as 5%.

But while many companies are investing in digitization, it is estimated that more than ¾ of them lack the skills within the organization to execute. Of the small percent who manage to conduct a successful Proof of Concept, most of these fail to scale to a meaningful level. Why? Because of data availability and data quality problems. In other words, while we have access to more data sources and technology solutions than ever before, we are still plagued by the same issues that caused us to turn off our previous event management solutions.

So what is the answer?

Supply Chain track & trace solutions are only as good as the data. Real-time transaction data is important. This implies connectivity to and participation from all external supply chain partners.

Supply Chain transparency is achieved when you can see transactions connected together in a process. Data from these transactions constitute requested, estimated and actual values – comparing these values fuels management by exception.

Additionally, streaming (and often unstructured) data enhances the transactional data, offering predictions which, if applied correctly, improve management by exception.

Finally, the mass of historical data can be used as another source of predictive analytics. If the historical data extends beyond one shippers’ network, predictive analytics are even more useful because they are supported by more data.

These are the elements of Elemica’s Digital Supply Network. Automation with external trading partners, process automation and collaboration to collect the most pertinent data, the addition of new IoT data sources to complement transactional data, and the use of network content to provide further insights and improvement opportunities.

To find out more about Elemica’s transformative solutions please visit our solutions page.