Industrial manufacturing organizations have been challenged for many years in terms of how to operate more efficiently. Under attack from competitors with manufacturing capabilities in markets where labor costs are lower, organizations have been faced with the challenge of relocating or making substantial efficiency gains in their existing factories.
Generating savings in businesses that typically rely on a network of distributors to sell and maintain their products across the world and consequently have a disconnected relationship with their end users means opportunities to access new forms of revenue beyond the initial sale of a product are limited. A race to the bottom on price is of no attraction to shareholders because it offers no prospect of revenue growth as products become commoditized.
Therefore industrial manufacturing organizations must radically overhaul their cost bases and their strategies. IoT can help in both of these goals because of the capability it offers to collect and transmit data from across the manufacturing process. Sensors from the factory floor to the end product can feed vast volumes of data into a central point enabling manufacturing performance data to be analyzed and data from the product in deployment to be turned into actionable insights in near real-time.
However there are also many opportunities for IoT in the industrial world, stemming from a greater use of sensors and the data they capture.
Replacing Manual Processes : IoT can provide digitization allowing slow (and potentially error-introducing) manual processes to be replaced by automated digital processes. Shop floor operatives must no longer carry out monitoring via pen and paper, tools equipped with IoT modules and sensors can perform the role with greater accuracy. Sensors do not get tired like a worker after a long shift. They deal in actualities, not human estimates of how long a tool was used for, which can open the door to significant error in monitoring over time. Therefore resulting in tool usage beyond the recommended calibration cycle and risking damage to the tool and production of work outside of the required specification.
Predictive Maintenance : The fact is that machines fail and when they fail it doesn’t just impact the job they are working on, it impacts the jobs before and after in the production line or process sequence. Through predictive maintenance machines and other equipment are monitored to prevent them failing, materials are not spoiled, and secondary damage does not occur (or is minimized) to other components in the machine because an actual failure has been prevented.
Higher Yields : When implemented, predictive maintenance strategies can also allow the manufacturer to derive additional benefits such as obtaining higher yields (which we define as the percentage of QA passed goods out of the total number of goods produced). Being able to reduce spoiled finished goods, the yield increases as a result of the efficiency of the production line increasing. Therefore the manufacturing operation is spending less time and materials on the production of goods which will not pass QA (Quality Assurance) testing.
Digital Twins : Digital twins allow the modeling of a physical asset but without any risk to the physical asset. Being able to experiment and test on a virtual machine or system is a key benefit, as manufacturing firms very rarely have spare machines on which to test scenarios.
Asset Tracking : Manufacturing environments have little leeway in waiting for a component or raw material to become available for use in production. Being able to track the location and expected arrival of materials in the supply chain is an extremely useful application of IoT, such that downstream manufacturers have a greater knowledge of the supply chain and can react to potential problems that may occur in the logistics of their supply chain.