When companies leverage IoT and engineering analytics, they can create immense value by eliminating waste, conserving energy and optimizing the supply chain.
By Sampath Kumar, Partner, ISG
The Internet of things (IoT) is enabling a new brand of engineering analytics that will directly contribute to a sustainable world. IoT-powered engineering will lead to a better and a more affordable product, higher productivity in the manufacturing process and increased profitability.
Much has been said about how the Industrial Age brought immense benefits to humankind and—at the same time—immense dangers to the environment. Peter Senge’s 2008 book, Necessary Revolution, stresses the need for individuals, organizations and nations to work together to create a sustainable world, specifically pointing out the role that private enterprise can play in identifying responsible environmental and social practices that make smart business sense as well. The potential of IoT offers just that solution.
When manufacturing companies leverage IoT and its associated engineering analytics, they can create immense value by maximizing utilization, eliminating waste, conserving energy and optimizing the supply chain. The potential of IoT is in its ability to generate and streamline data at every stage of a product lifecycle, from development to manufacturing to distribution and usage. By subsequently processing, analyzing and interpreting that data, engineering analytics can convert it into information, intelligence and actionable insights.
In 1908, when Henry Ford introduced the assembly line, the manufacturing industry was transformed forever by mass production. IoT is transforming it once again by enabling mass customization. There are three ways this will happen.
Creating Products to Meet the Exact Needs of the Consumer
Traditionally, there has been no way to capture and analyze the usage of a product once it leaves the manufacturing floor. However, the connectivity of IoT establishes a continuous feedback loop between the user, the product development team and the manufacturing stakeholders. Over time, as intelligence is gathered on product usage patterns—the frequency of use per feature, for example, or the downtime experienced due to system errors—engineering and product development teams will have vital inputs for redesigning a better and more highly customized product.
A connected car, for example, is continuously collecting data about the driving habits of its operator. A usage pattern analysis can enable development teams to optimize the driving experience by eliminating those features that certain users never use and enhancing other features that improve driver and vehicle safety. Engineering analytics powered by IoT can, in this way, develop a car that intelligently adapts to the needs of its driver.
Eliminating Waste Along the Manufacturing Process and Supply Chain
Real-time multivariate data and analytics can be used to study the behaviors, usage and seasonality of buyers to better predict quantity and variants of sales. When this feedback is fed to manufacturing facilities to produce the right quantity of the right variants at the right time, the facility can minimize its inventory and optimize its supply chains. Ford’s Smart Inventory Management Systems (SIMS) illustrates how the auto giant and its dealer network leveraged data analytics to match up to 98 percent demand on a predictive basis and saved $90 per vehicle on more than 50,000 vehicles manufactured every week in North America alone.
Enhancing Efficiency Through Smart Manufacturing
The Industrial Internet and Industry 4.0 digitizes manufacturing based on cyber-physical systems that collect virtual and real-time data from processes at various stages of execution. These systems and the resulting data offer huge opportunities for automation as a means for increasing the Overall Equipment Effectiveness (OEE) in terms of efficiency, utilization and yield. Smart manufacturing has proven to increase efficiency across multiple dimensions, including machine efficiency (creating higher throughput), labor efficiency (managing more machines), material efficiency (reducing scrap) and energy efficiency (optimizing consumption).
Improving Runtime Performance and Reducing Equipment Downtime
The placement of sensors in products and their ecosystems is generating a wide range of data linked to the products’ physical state, including temperature, speed, pressure, etc. Real-time monitoring and predictive analytics can identify the initial signs of abnormal performance and can then trigger the appropriate preventive maintenance. The product, therefore, can predict its own maintenance schedule based on wear and tear and can communicate that to the stakeholders for preventive measures.
In the case of an airplane, for example, engineers are using condition-based monitoring of data from engines, avionics systems, fuel sensors, weather data, etc. to enhance flight performance and avoid accidents. GE Aviation is increasing its investment in big data analytics so that it can have the ability to flag potential engine performance trouble spots. Some of this learning is already contributing to the revamp of its engine-support portal.
As more and more enterprises adopt IoT and establish mechanisms to appropriately measure value, they will discover countless use cases to employ engineering analytics to drive better productivity, design more useful products, lower costs and contribute to the making of a greener world. As this happens, solution providers and service providers will benefit from a community of practitioners that leverage each other to drive sustainable business growth.