Strengthening the supply chain with IIoT technology

The supply chain has been continually evolving over the years with the rise of advanced technologies like sensors and artificial intelligence (AI), with the goal of keeping the supply chain lean. Lessening the amount of inventory in the pipeline meant using components and raw materials in the most efficient and cost-effective ways, but when the COVID-19 pandemic hit, it caused global supply chain disruptions the like we have never seen before.

An initial dramatic drop in end product demand sent a shock through the system, and later on in the pandemic we experienced the exact opposite issue amidst a surge of demand for products and a delivery and inventory storage shortage. This revealed major shortcomings in the supply chain – mainly, a lack of robustness. The process had become so centralized that when an event affected even just one manufacturer, it had a broad impact around the world. When demand came back rapidly, the supply chain was not robust or diverse enough to bring enough raw material into factories, and many of them shut down entirely. The labour shortage only added to the disruption – with closures or staff shortages causing delays in steps along the entire supply chain.

Technology like smart sensors and AI can help create a more robust system and fix many of the cracks in the supply chain revealed by the pandemic. The impact the Industrial Internet of Things (IIoT) has already had on the manufacturing industry and the supply chain as a whole cannot be understated –  it enables new functionalities and more intelligent, predictive, and proactive processes that lead to enhanced efficiencies.

Improved asset tracking

IIoT is bringing sensor usage to a new level, primarily when it comes to asset tracking for assessing where assets are in the supply chain at any given time. For example, if a container has an asset tracker, a customer can see if that container is still on a ship or if it has arrived at a port. But, only knowing the location of one particular asset is not sufficient when it takes, say, 20 components, before actually starting to produce a product.

Since manufacturers are often managing multiple products, components and raw materials, IoT sensors allow a unified view of all of the data coming from each of these different containers. Aggregating all of this data so it is more meaningful to both the business, and often the consumer who is tracking their order from home, is also crucial.

Beyond location tracking, many different sensors are used for a variety of products to provide more data throughout the supply chain and improve asset tracking – from environmental sensors that track temperature, humidity and pressure, to positioning sensors like accelerometers and gyroscope sensors that track a product’s position and orientation. With this kind of data, you can pinpoint the time and place a product may have been damaged on route.

Asset trackers and sensors are becoming less expensive, so there is more widespread usage. With that increase in the number of sensors, data management becomes more important.

Data management using AI

If an operations manager needs to determine whether a factory can run production at a certain time, they need to know if all the necessary components will be at the factory in time. In the past, this has been something of a guessing game with little visibility into accurate, real-time information needed to plan production runs.

The good news is it is now possible to utilize AI to easily aggregate and analyze all of the asset tracking data available for the different components needed for production. An AI system can use data from the sensors while pulling in third-party data like weather information and port information to predict delays and when all of the necessary components will arrive. Edge computing – processing that data collected by sensors closer to the source rather than communicating it back to the cloud – also greatly increases data efficiency.

Because there are so many data points from the sensors and outside parties, AI can usually do this analysis quicker and more accurately than a human, and this intelligent, automated system allows workers to spend less time on the mundane, manual tasks. Instead, they are freed up to do more creative, skilled work like managing the system and their internal customers and providing human judgment calls when necessary.

It is important to note that this data management system is not taking the place of workers. Rather, it is feeding more meaningful information to the individual managing the system. Ultimately, data management using AI brings new value through increased efficiency and accuracy across the board.

What’s ahead for the supply chain?

The intersection of real-time data and AI technologies, including the application of new sensors, has created numerous new avenues for insight into the supply chain, and will continue to fuel the future of how manufacturing works.

The IIoT has offered so much new functionality and much of it is becoming relatively inexpensive to implement – such as low-cost processors. From a cost perspective, it is easier now than it has ever been to integrate sensors into different types of products to monitor data that was not financially feasible in the past. For example, vision sensors, which are essentially cameras, are advancing rapidly and AI can analyze all of the data coming out of each pixel in the camera with improved software.

All of the devices in our lives – from cars to health trackers to industrial automation – have become smarter and more connected with the use of sensors used in conjunction with AI, machine learning and edge computing. As more and more data is collected from sensors, systems will become more complex and the use of AI and machine learning will be critical to performing higher-level analyses of all of the information being gathered. These innovations will continue to give us a more holistic view and control of the supply chain, and hopefully help prevent disruptions in the global supply chain like those we have experienced in the last few years.

– This originally appeared on Control Engineering Europe’s website. Edited by Chris Vavra, web content manager, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.

Original content can be found at Control Engineering.

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