How The Industrial IoT and the Generational Shift to Big Data Analytics Will Radically Improve Supply Chains

April 23, 2020

We’ve already witnessed decades of improvement made possible by instrumenting factories, job sites, warehouses, public spaces, transportation systems and more. In its current state, Industrial IoT solutions have led to natural benefits and big wins, especially in “Industry 4.0” applications.

That said, we’ve only just begun to experience the full potential of connected manufacturing and the supply chain that comes before products are built – and the supply chain that comes after those products are built and brought to market.

The key to unlocking massive gains, whether in productivity and operational cost control, or in creating and selling more competitive products, is data. Big data. Big data that can be pumped into existing ERP and other systems and can be analyzed in real time and in the aggregate for longer views.

Here are five ways IIoT can dramatically improve performance and business results in supply chains and logistics supporting those supply chains, and how the data generated today and tomorrow can lead to clear and immediate ROI.

Asset Management

Sensors improve the performance and maintenance of industrial machines, which are often expensive and complex, difficult to replace, and often have a lifespan of 10-20 or more years. By equipping industrial machinery with sensors and connectivity, companies can manage those machines using software platforms, gathering real time data to schedule maintenance and to avoid costly downtime, to schedule workloads as more and more factories are working 24/7, and to assess output and identify ways of improving that output.

By harnessing machine data, operators have insights into equipment that is breaking down, underperforming, causing a possible safety risk and other patterns. When data from the IIoT platform can be easily shipped into existing systems, including factory automation systems, even greater value can be created.

Inventory Levels

Not having a replacement part available for a piece of critical factory equipment can bring entire production lines to complete halts. By instrumenting the replacement part supply inventory, operators understand gaps in replacement parts, or overstocks, which can help also control the costs of procurement and support “just-in-time” planning. In the most advanced factories of the future, operators have integrated their parts and supplies inventory tracking systems with robotics, so no human has to find then carry the correct part to the correct machine – this is done automatically in a type of “co-botting” between the machine operator and parts robot.

By harnessing inventory data, the inventory required can be more intelligently managed, and the movements of the parts inventory measured and analyzed for important patterns (for example, a higher than usual need for part replacement in a certain machine). Data generated by IIoT logistics solutions allow manufacturers to understand all the variables, across multitude equipment in many factories, leading to intelligent decisions put to work to continually improve operations over time.

Advanced Manufacturing Automation

The growth of fully automated manufacturing and supply chain processes, driven by real time data analytics is emerging as one of the ultimate opportunities made possible by IIoT. In this scenario, information flows both ways, not just from the device to the application in the cloud, but to and from applications that are increasingly being run at the edge. Edge computing and IIoT work hand-in-glove to allow for the automatic adjustments required to ensure machines are operating a peak performance, without the need for human intervention. Today IIoT devices aren’t simply sources of information, but interconnected, remotely adjustable means to manage manufacturing plants more efficiently and often more safely.

By harnessing manufacturing data, we can support complete, end-to-end feedback between machines and analytics, sensing and responding to conditions using data streams – data being pumped into systems, and back out to adjust how the machines are working.

In the end, the holy grail of industrial manufacturing is to have a complete feedback loop between real-time information, analytics, command and control—sensing and responding all via interconnected data streams.

Big data analytics can measure workloads, stress on systems, capacity of equipment, and be connected to the shipment of products to customers.  With a holistic approach, manufacturers can extend the use of big data analytics within the factories themselves into the outbound supply chain, helping to determine the when and how to ship goods in such a way that customers are served and operations are more profitable.

Many manufacturers have deployed IIoT solutions, but to reach this level vision, big data analytics enhance basic systems with information sharing methods and infrastructure – locally at the edge, and in a more distributed way via the cloud.

Originally posted by Eric Simone on LinkedIn Pulse

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