Is Your Supply Chain Stuck in the Bermuda Triangle?
Thanks to data and the IoT, the industrial shipping journey needn't be fraught with uncharted waters and black holes.
It’s 2018. You can track your pets, your car, and even your pizza as it’s delivered from the oven to your doorstep. And yet, most Fortune 500 companies have no idea where their valuable shipments of pharmaceutical, electronic, or industrial products are as they travel through the supply chain.
So why do we have real-time visibility into our pizza deliveries while multi-million-dollar shipments drop from sight as soon as they leave the loading dock? It turns out that there are a handful of barriers that have historically stood in the way of the industrial supply chain achieving anything close to the level of visibility that’s become commonplace in the consumer world.
Supply Chain Tracking: Harder Than It Looks
First, there’s timing. Industrial supply chains in pharmaceuticals, electronics and other big manufacturing industries generally last several weeks or even months, as goods are transported between manufacturing facilities, testing facilities and distribution centers. The long time scale of most commercial supply chains can pose a challenge for effectively keeping track of in-transit goods.
Furthermore, industrial manufacturing is an international, multi-modal endeavor, as supply chains can stretch across oceans and include segments on air, ship, truck or rail carriers. Tracking goods as they cross borders and change carriers becomes more and more challenging as new layers of complexity are added.
In addition, manufacturing supply chains often encounter specific bottlenecks, such as ports or trucking terminals, that can create blind spots for supply chain managers. With no insight into location or condition, shipments can essentially disappear—sometimes for weeks or months on end—while stuck in a shipping bottleneck.
Because of these additional complexities in the industrial supply chain, tracking systems that are commonplace in the consumer world are generally less effective in supply chain. Many parcel tracking systems rely on manual scanning of each box as it passes through certain gateways, such as arrival at an airport or post office. For a shipment that lasts a few days and is limited to a single carrier, that can provide a pretty comprehensive picture. But for a months-long international commercial shipment, these sorts of manual check-ins are woefully inadequate.
A New Level of Visibility
Fortunately, new technologies are enabling a new level of visibility into the supply chain, bringing it up to par with—and even beyond—the world of consumer tracking. Specifically, the Internet of Things (IoT) is transforming “black hole”-riddled delivery systems into data-driven supply chains where both the shipper and receiver have real-time visibility into the location and condition of shipments throughout the supply chain journey.
So what does IoT look like in supply chain? In general, supply chain IoT entails constantly connected trackers that monitor the location and condition of in-transit goods, and send that data to users in real time. A handful of recent enabling factors have made these IoT tools possible: on the one hand, advances in battery and sensor technology have made it possible for tracking devices to monitor everything from location to temperature, humidity, shock, orientation, and other key metrics, all with a battery lifetime of six months or more. The other key piece of the puzzle is the communications network. Today, IoT devices can rely on the global cellular network to transmit data in real time all around the world, enabling reliable, low-cost global coverage for supply chain trackers.
Operational Impact
These global, real-time tracking and monitoring solutions are already having a huge impact on supply chain. First, access to location and condition data makes it possible for managers to react faster when problems occur. For example, recently an international pharmaceutical company used IoT trackers to monitor the temperature of sensitive products travelling from a European manufacturing facility to North America for distribution. A tracker detected that the shipment was set to the wrong temperature and sent an alert to the manufacturer. Armed with that real-time visibility, the supply chain manager called the shipper and had them reset the container to the correct temperature, thus saving the product.
In addition, access to contextual data can enable root-cause analysis of enduring issues in the supply chain. When managers gain insight into exactly when and where a problem occurred, they can eliminate the source of the issue and prevent re-occurance. For instance, if a certain route frequently experiences harmful shock events, the supply chain manager can use IoT data to pinpoint exactly when and where the damages are occurring, and reroute future shipments to avoid the problem area.
These IoT tools can have a huge impact on the day-to-day operations of the modern supply chain, as they enable manufacturers to respond proactively to real-time problems. But more than that, they are also giving companies access to new data sources that make it possible to bring data-driven manufacturing strategies and big data analytics to supply chain. These data-driven strategies are helping companies to optimize their supply chains on a macro level, reducing waste and increasing efficiency across the board.
The Data-Driven Supply Chain
For example, manufacturers can use data analysis tools like SPC (Statistical Process Control) to identify outliers and improve the quality levels of their supply chain processes. With SPC, each incoming data point (be it delivery time, damage rates, or any other relevant metric accessible with an IoT tracking system) is immediately compared to historical average, high, and low levels. In this manner, problems can be easily quantified and addressed, instead of relying on guesswork or gut instinct to determine when and how to take action.
In addition, access to comprehensive, contextual data makes it possible to apply all sorts of tools from the lean and Six Sigma frameworks to the industrial supply chain. At a high level, lean and Six Sigma refer to methodologies that use a quantitative, data-driven approach to reduce waste and improve quality. Tactically, this can mean anything from using real-time location data to reduce safety stock levels (if you know in advance when shipments will be late, you don’t have to hold as much buffer stock), to using temperature data to compare quality levels for different carriers and enforce clear, quantifiable quality standards for each step of the supply chain.
When it comes to big data analytics, the possibilities are endless. We are just scratching the surface as manufacturers begin to realize the potential of real-time IoT tracking in the supply chain.
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