On the manufacturing plant floor, IoT is more of an evolution than a revolution. Plants have been highly instrumented for decades, with distributed control systems reading sensor data and controlling the machines, with SCADA systems layered on top providing plant-wide or multi-plant supervisory and control functions. IoT adds connectivity via the internet, bringing in massive scalability potential of cloud computing, but also creating many security threats not anticipated in the original design of underlying plant systems. Beyond very real security concerns, other issues need to be considered: which analytics should run locally in the plant and which run in the cloud, which data should stay in the plant and which should go over the internet, policies for historical data retention, and so forth. There are a number of start-ups focused on lowering the cost of adding additional sensors to a brownfield plant, rather than relying solely on traditional plant equipment vendors. Others are developing machine learning tools to do predictive maintenance, plant optimization (optimizing throughput, quality, energy use, material use, etc.), and other tools.
In supply chain, IoT can be used in both warehouse and transportation logistics. The warehouse use cases are analogous to the plant floor; i.e., instrumenting and automating the warehouse and its various material-handling equipment. In the supply chain, the use cases are largely around getting much more granular visibility and real-time tracking of goods as well as enabling autonomous vehicles. IoT data is being combined with other data (e.g., weather, port congestion, traffic) and fed into geospatial-aware complex event-processing engines to do precise ETA predictions and early warning of delays.
Service is an area of huge potential impact for IoT. Instrumented products provide the basis for predictive maintenance — the product out in the field telling the service organization exactly which part is about to fail and when. This enables efficient planning ahead for repairs, and a big increase in customer satisfaction and reduction in cost and time compared with the traditional approach of waiting until a machine fails, then the customer calling in to complain, the technician coming out to diagnose, then coming yet again once they get the part. Alternatively, companies replace parts on a conservative maintenance schedule designed to cover perhaps 90 percent of failures, resulting in most parts being replaced long before needed, while others still fail.
Changes to the product and what a company sells are perhaps the most revolutionary changes. IoT enables selling product-as-a-service, such as charging by the scan for an MRI machine or by the lumen-hour for light bulbs. IoT enables all kinds of value-add capabilities and services to be layered on top of the equipment. For example, the intelligence in a truck could be used to help reduce its fuel consumption, increase its safety, and provide real-time visibility about the disposition of its content. IoT also provides “x-ray vision” into exactly how products are actually being used, delivering valuable information and insights to product designers, quality and manufacturing engineers, marketers and the service organization.
Manufacturers are unlikely to have a single IoT strategy for their entire organization. Rather, they will have dovetailing and integrated strategies covering production, supply chain, service, and product/business model. Some of these can be done as discrete, contained projects, especially on the plant floor and in the supply chain. Others, such as changing to a product-as-a-service business model, are major strategic decisions impacting the future of the entire business.
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