Not one supply chain conference or event goes by without some random discussions on big data.

Presenter after presenter make grandiose statements about how big data will change the way supply chains operate and how everything will be incredibly different. But when you ask what is so disruptive about all that “big” supply chain data, everything you hear is RFID, GPS, last mile of “same day delivery”, and shipment track and trace. Whoa! Is this disruptive and game changing? Please, call me when we can find all our shipments every minute, make that every second, of the day and that we can actually teleport them back onto the right track, or a trade lane in the clouds, if they veer off course. Better yet, the shipment teleports itself and sends the signal home. That’s game changing.

OK, I got carried away a bit. So, let’s think what is possible, what is relevant, and what is useful for supply chain people in all this noise about big data in the clouds. In my previous blog, I discussed what big data means to manufacturers operating in the B2B market. In this blog, I will look at supply chain logistics and how big data could potentially help us make supply chains robust, responsive, and flexible enough to sustain business operations.

There is no question that we now have a plethora of electronic devices that enable us to establish geo-location of the container in which our shipment presumably travels. When I say container, I don’t just mean a standard 20’ steel box that you can see every day on your commute to work. No. A container can be anything that encapsulates your shipment, even that wooden pallet covered over with a tarp and some safety netting.

While the RFID tags are easy to slap on just about everything, the GPS device is still too costly to attach to every type of a container traveling around the world. The obvious disadvantage of an RFID tag is that is must be in close proximity to the reader, therefore a GPS reporting device acting as a reader of RFID tags can easily report on individual shipments stowed in its proximity. Mental note – an obvious technological gap in the Internet of Things – GPS devices capable of scanning the environment for RFID tags, recognizing the shipments, and reporting them back to the tracking system along with longitude/latitude coordinates.

Think bigger

The RFID tag is meaningful for most shipments, but meaningless for the ones that really need to be tracked. No, I’m not referring to Johnny’s pair of designer shoes, but temperature-sensitive shipments like food, liquids, and pharmaceuticals. All of the refrigerated containers today carry one or another type of a sensor. The problem with those sensors is that we usually read them only upon arrival at the designated FOB. Any temperature variation dangerous to the shipment cannot be assessed and changed, in real time, during the voyage itself. This is an expensive technological difficulty resulting in unrecoverable spoilage. So, we have plenty of useful supply chain “big” data, just no means to get at it quickly and then act upon it.

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And bigger….

Another type of useful “big” data would be a match between the shipment tracking device and the container tracking device. Why? Well, I have a few customers who use third parties to load their containers and they rely on those loaders to correctly assign the shipment tag to the container tag. That is the reason I earlier used the expression “in which our shipment presumably travels”. In many cases, while you are happily thinking that your shipment is traveling in container “X” and you can see that container in the tracking portal of your shipping provider, your parcel is in fact in container “Z” traveling on a completely different routing. Because you don’t actually see your particular load, the third party (hopefully) has time to re-route it to the correct destination on their next consol/deconsol point. That type of error is not visible to you, unless that alternate routing results in delay of your shipment.

And even bigger….

While we are at it, how about some “big” data that matches the intelligent sensor of the shipment with the intelligent sensor of the container and the intelligent sensor of the transport (think truck, vessel, or the plane). Now, that would be some useful information – big data or otherwise. From passive (sensor can report) or “active” (sensor can sense and change the transport environment), this type of data could really make a lot of difference to the shippers and to the transporters. Too bad we are not talking about data in that context, but merely in the context of where we can process it (in the cloud?), if we managed to get hold of that data in the first place.

Step one

When we give it some thought, we have a few things: some useful data, some devices that can deal with that data, and automated decision optimization tools that can use that data to make useful decisions. The next problem is that those useful elements are trapped in silos of the supply chain logistics participants. The data from the shipment tag is read and processed by the shipper, the data on the container is read and processed by the container owner, and the data of the transporting asset is read and processed by the transporter. If we could access and process all this data to make optimal decisions, we would enable sophistication that would focus on knowledge sharing and collaboration, and not on completing supply chain transactions. That type of networked supply chain knowledge would allow for the formation, growth and proliferation of new markets for suppliers and buyers alike. It could also result in significant reduction of supply chain and logistics costs.

Bringing it together

So far, just as discussed in my earlier blog, we have disconnected and ambiguous silos of data. Those silos together produce and hold significant amount of data useful to supply chain and logistics optimization, but, due to their separation, cannot be used to change plans and schedules across the entire chain(s), either by a machine running supply chain optimization software, or by a human (supported by the aforementioned machine). For that to happen, we need additional progress in technologies, but also the alignment between the supply chain theories, understanding of the value of all that additional data, and a holistic change in the way companies view supply chain logistics as mission critical undertaking.

Until such alignment materializes and the technology makes data access, transportation and unification for the common good of the supply chain participants manifestly easy, the supply chain “big data” concept in supply chain logistics will remain just that, a concept.

If you found this topic interesting, leave your comment or suggestion. I look forward to hearing from you.