Why does industrial manufacturing need IoT?
Industrial manufacturing organisations have been challenged for many years in terms of how to operate more efficiently. Under attack from competitors with manufacturing capabilities in markets where labour costs are lower, organisations 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 commoditised.
Therefore industrial manufacturing organisations 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 analysed and data from the product in deployment to be turned into actionable insights in near real-time.
This means that IoT can offer more than traditional process efficiency improvements on the factory floor. These are important but insights provided by IoT deployments also enable more in-depth efficiency to be achieved by addressing personnel optimisation, environmental and fuel efficiency and increased automation.
At Siemens’ electronics manufacturing plant in Amberg, Germany, for example, machines and computers handle 75% of the value chain autonomously, with approximately 1,000 automation controllers in operation from one end of the production line to the other. Parts being produced communicate with machines by means of a product code, which tells the machines their production requirements and which steps need to be taken next. All processes are optimised for IT control, resulting in a minimal failure rate. Employees are essentially supervising production and technology assets, including handling unexpected incidents.
From product to service
The efficiencies offered by IoT deployments are helping industrial manufacturing organisations in their wider business transformations. The more accurate, highly detailed performance insights IoT is providing give organisations a clearer picture of the strengths and weaknesses of their businesses and enable the identification of areas for improvement. At the same time organisations are embarking upon substantial digital transformations to prepare their business for the digital, highly automated economy being ushered in by Industry 4.0 and smart manufacturing.
These transformations are at an early stage but manufacturer commitment to digital transformation is strong, reports research firm, Aberdeen Group. Analysis by the firm of recent research has found that 35% of manufacturers plan to achieve digital transformation, with more than 91% of survey respondents seeing manufacturing benefits in deploying IoT.
For industrial manufacturing companies, the next generation of IoT technology will take them far beyond real-time monitoring of machines on the factory floor. The next generation will involve connected information platforms that use data and advanced analytics to create higher-quality, more durable and reliable products. An example of this is with wind turbines manufactured by General Electric. PwC analysis reports that this equipment contains 20,000 sensors that produce 400 data points per second. Immediate, ongoing analysis of this data allows GE and its customers to optimise turbine performance and proactively make decisions about maintenance and parts replacement.
However, the valuable data must be identified in order for useful insights to be generated. Simply collecting data in great volumes doesn’t generate value so industrial manufacturing companies must determine precisely what data is most valuable to collect and how it can be analysed effectively…
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