Manufacturing Success: Transforming the Manufacturing Industry through IIoT


Sean Riley, vice president of global industry solutions at Software AG, discusses how IIoT empowers smart manufacturers to operate smarter, stay ahead of the competition and improve the CX. 

The Industrial Internet of Things (IIoT) provides manufacturers with the opportunity to implement solutions empowering them to operate smarter through connected assets, real-time data analytics and monitoring tools and predictive maintenance. These solutions help manufacturers keep agile, informed, in control and smart.  

Equipment & Automation Customers are looking for smart, connected equipment – largely because it helps them cut maintenance costs and reduces downtime. Remote asset condition monitoring can reduce maintenance costs by 30% and cut machine downtime by 50%, according to McKinsey. This significant reduction not only makes work more efficient, but it leaves equipment customers with a higher value product which increases your pricing ability. But with ongoing supply chain disruptions, rising inflation and chip shortages, the ongoing volatility in the space even further proves the necessity of IIoT investments. 

An essential stepping stone in this transformation is investing in smart factory operations. A successful smart factory can enable organizations to aggregate big data covering production, energy costs, materials and other essential factors for sharing with partner companies to inform capacity planning, create faster response times between partners to disrupt events and, most importantly, allow for unprecedented visibility in interdependent processes throughout the supply chain. Ultimately, by utilizing IIoT, manufacturers not only develop smart factories, but keep themselves in control of the supply chain. 

Driving Transformation with Data

Manufacturing companies have vast amounts of data generated by equipment and sensors on their sites, so the potential to become a data-driven or a ‘smart’ plant is already in place thanks to IIoT technology. However, these same companies also have vast amounts of data going unused. To become a fully data-driven, smart organization, manufacturers must be able to access equipment and sensor data easily and in real-time. Having a piece of equipment that stores data on itself and requires manual collection or distribution of that data is almost the same as not having the data. This has been proven time and again by manufacturers. 

For example, CodeWrights turns data into information and the company’s deep expertise in writing device drivers now lets its customers in industrial manufacturing connect sensors and actuators to pull off incredible feats of automated manufacturing efficiency. Once these smart sensors get working, it’s time to define operation efficiency. 

Smart factory organizations have a different approach to analytics, as they define operational efficiency use cases based on desired business outcomes rather than implementing a library of tools first. The adage, “rules before tools” has proven to ring true. One of the reasons for following this mantra is understanding who needs to use it, usually engineers and not data scientists. They create condition-monitoring and predictive analytics, usually a job relegated to IT and data scientists.

Data-driven organizations understand this conundrum and, when selecting “tools” focus on empowering individual departments or teams with specific expertise to utilize analytics directly, without depending on data scientists. This self-service approach to industrial analytics can enable smart factories to avoid the delays and costs of data science projects, achieve higher ROI and accelerate time-to-value and time-to-impact of projects. This is where predictive analytics comes into play. 

See More: The Big Data-IoT Relationship: How They Help Each Other

How Predictive Analytics and Digital Twins Benefit Manufacturing

By using predictive analytics with an IIoT solution, factories can reduce downtime by anticipating maintenance needs. Once data is collected, it can be accessed and analyzed, leveraging predictive models to forecast the need for repairs. Here, there is a definite increase in customer lifetime value and first-call repair rates on a platform for usage- or outcome-based services. 

Having a solution that can process behaviors and present them as business process models will allow manufacturers to analyze differences between as-is and to-be, to analyze the fulfillment of business goals and to identify potential optimizations of the business processes. Companies can also create a “digital twin”—a digital representation of a physical asset—to remotely monitor a machine’s status at any time. Ultimately manufacturers will be able to quickly identify bottlenecks and generate improved processes.

This can be done more easily with digital twin applications at the enterprise level – the next step of real-world applications for digital twins – which moves beyond simulations of singular pieces of equipment. Instead, manufacturers can model the entire processes and connections of an enterprise. After analyzing the process with process mining, organizations can then improve the processes and implement improvements into process execution.  

Because uptime equals revenue for your customers, you have every incentive to make smarter products that monitor equipment health and catch problems before they arise. Monitoring also drives efficiency: acting on real-time data enables you to be more proactive and efficient with service calls. Remote monitoring and predictive maintenance are just the start of the shift to smart manufacturing. There are specific ways that organizations can move towards this shift. 

How can Manufacturing Companies Transform Business Processes?

The manufacturing organizations that create smart, connected products can use equipment as a platform to transform their business in four ways:

  1. New revenue streams: You can grow revenue by increasing sales of services, consumables and components. Services revenue tends to be more repeatable and resilient than hardware sales, and McKinsey has found that margins for services can be up to four times higher than equipment sales.
  2. Faster product development: When you know exactly how and when your products are used, you can accelerate the launch of new valuable features and retire unused features just as fast.
  3. Increase customer satisfaction: You can improve the customer experience using insights on usage and performance from your connected products. Additionally, you have the ability for immediate issue resolution.
  4. Shift to equipment-as-a-service business models: Just as cloud and software-as-a-service (SaaS) help customers shift from capital expenditure (CapEx) to operating expense (OpEx), EaaS helps customers keep expenses in line with revenue and better manage costs. At the same time, transitioning to an EaaS business model helps you generate subscription revenue, which is more consistent than equipment sales.

Smart Manufacturing and these same principles extend outside of your four walls to your customers as well. They are also seeking the same benefits. If you’re not yet offering connected products, trust that your competitors will. If manufacturers are looking to stay competitive in the industry, they must invest in the industrial internet of things (IIoT) in order to operate their data using connected assets. IIoT solutions work continuously to empower manufacturers to operate smarter using predictive maintenance and real-time analytics – always expanding the company’s bottom line. 

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