I interviewed Viliam Kovac who discussed Digital Supply Chain During the Last Mile Distribution.

 

 

 

 

 

 

My first question is can you talk about what is the process involved with this digital supply chain in the last mile distribution?

 

Thank you very much. I'm happy to have the chance to talk about such an exciting topic like the digital supply chain within the last distribution. Yes, of course. When I speak about a process, I'm talking about visibility and traceability during the last mile distribution. And I'm talking about the data, which we're using for compliance, and for efficiency purposes same way.

So usually, the process starts with a product categorisation using the fixed data, like item number, storage conditions or product name etc. With these set data we can build a category — how we transport the product.  Then, using the historical forecasting data for planning,  we define what quantity to be carried to our customers.

 

After that, we add flexible product data from product Serialisation or UDI like batch or lot numbers. Combining this data is going to be a legal requirement in the nearly future as well.

 

Last but not least, we from  Globalworx GmbH, support this process with an Internet of Things, like Intelligent Box, equipped with sensors, and Bluetooth monitors. On this way  — monitors collecting not only the data like temperature but also box sensors gathering data like the pressure, humidity, speed, location, etc..

 

While combining all these data in clouds — fixed data, flexible data and additional data we can use them for further supply chain process management to increase visibility and efficiency.   That means we are more productive from both sides — from compliance as well as from an efficiency point of view. That's the process [inaudible 00:02:11].

 

What about the bottlenecks? Can you talk about bottlenecks that might be faced?

 

Of course. Bottlenecks — what does it mean? For example, If we're handling the data, it means that the data might be wrong, the data might be false or straightforward we do not have enough data to perform the analysis. The bottleneck is also hardware as an interface, e.g. if UDI data are not transmitted correctly, so the conclusion is wrong. That's very critical, and it might cause losing the data, creating a gap, and finally making a different conclusion and data interpretation. The additional bottleneck might be humanity himself or herself. If a man does not understand how to deal with the data and how to interpret the collected data, it is very critical. So that's probably the two-three essential bottlenecks which we're facing too.

 

Can you talk about the opportunities?

 

That's probably the fascinating piece in the discussion. As we are collecting additional data which are available in the process but never have been obtained before with available monitoring tools, now we can move preventive actions in front instead of being only reactive. Now we can be more deterrent regarding product quality as well as process efficiency. We can be more customer-oriented due to be able to better control the process limits and process conditions like speed, weather, location,

 

So I do believe that that's the future, and we need to learn how to use the data collected in the past much more efficient than we're doing now in the future.

 

Can you provide us with a brief background of yourself?

 

Of course. I grew up in Germany. I  have graduated from two universities after then I worked for big multinational companies like Henkel, GSK, Roche. Now I'm running with my partners Globalworx GmbH to help companies to explore digital supply chain compliance and efficiency solutions within the Pharma and Diagnosis industry.

 

 

 

About Viliam Kovac

 

 

 

 

 

Viliam Kovac

 

Global Supply Chain Compliance

 

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