Manufacturing companies have to constantly keep up with evolving technology as tried-and-true processes give way to more efficient and agile solutions. Embracing the industrial internet of things (IIoT) is a key to this transition. IIOT devices push intelligence out further into the factory floor and supply chain, giving manufacturers more visibility into their operations. Employees can use this data to streamline processes, enhance productivity, and increase profits. However, change can be challenging. To ease the transition, it’s important to define the use case and determine the approach to be taken before choosing a system.
Industrial IIoT devices include hardware like sensors and other special purpose microprocessors that are coupled with AI-enabled software to perform various functions. They are generic, horizontal technology that can be applied anywhere and complete just about any task in any manufacturing phase, including the supply chain, manufacturing runs, and product distribution.
Examples of IIOT systems include human-to-machine interfaces (HMIs) and data collection modules (DCMs). HMIs enable plant personnel to understand how equipment is functioning. With such solutions, employees can communicate and control devices like plant floor IIoT sensors, robots, programmable logic controllers, and other special purpose devices that produce goods. DCMs monitor machine performance and collect metrics like temperature, cycle time, and reject rates. This kind of information lets manufacturers to see how well systems are performing in real time, so they can make adjustments as needed.
Define the IIoT Use Case
Because the possible use cases are so wide and varied, the first step that a company needs to take in selecting its IIoT hardware is identifying the scope of its project. Plants have many autonomous processes, some of which run well and others that need tuning. Only by defining its objectives can a manufacturer identify the appropriate IIoT use case. When you understand your goals, you can set quantifiable objectives. From there, select the IIoT hardware, software, and metrics that will help determine what impact the deployment has on the operation.
Take a Singular or a Holistic Approach?
Manufacturers have options in how they deploy IIoT solutions. In certain cases, enterprises focus on a single device and a well-defined objective, such as predictive maintenance for factory conveyor belts. In other cases, they prefer a holistic view that touches upon a number of devices, applications, and processes. For example, they may deploy IIoT sensors in all plant floor equipment in a project to revamp their maintenance processes. The former is simpler and quicker to enact than the latter. However at some point, businesses need to step back from ad hoc deployments and create a factory-wide IIoT architecture. Because different elements collectively contribute to the success or failure of each manufacturing run, and integrated approach is going to be most effective.
Interoperability Ties the Pieces Together
The reality is that manufacturers want to optimize functions across the entire plant. However, manufacturing is a collection of isolated tasks, and little islands of automation may sprout at different times to support various objectives. To fully realize IIoT’s potential, manufacturers must break down the barriers between systems, so the plant operates as a cohesive unit. An important step in this direction is to collect information in a centralized industrial data platform. A manufacturing execution system (MES) fills that role. Manufacturers have a hodgepodge of communication protocols, like MQTT and CoAP, but common protocols and standards are needed to connect systems. As a result, device interoperability becomes crucial as companies begin to link systems.
What to Do with the Data?
Factory DCMs consolidate information about many variables. However, simply collecting data does not solve any business problem. McKinsey determined that most of IIoT data collected lies dormant, and even when the data is used, it is only marginally leveraged. As companies gain access to real-time production monitoring, they need to build reports that illustrate trends. Analytics enable manufacturers to create and monitor key performance indicators (KPIs), actionable insights that illuminate steps that could enhance plant performance.
The IIoT Business Case
Implementing an IIoT solution requires a significant investment in time, money, and labor. However, it can significantly improve manufacturing operations. In a 2022 survey, companies that made the move reported their deployments are going as well as or better than expected, delivering cost reductions and revenue increases of 30% to 40%. Furthermore, usage is growing at a healthy clip: the number of organizations implementing IIoT proof of concept projects grew nearly 20% from 2018 to 2022 and is expected to grow another 20% by 2026. As a result, businesses that do not make the investment will find themselves at a competitive disadvantage.
IIoT hardware allows manufacturers to centralize operational data and gain real-time visibility and insights, and automated alerts and notifications allow companies to make agile responses to events as they happen. SYSCON’s PlantStar MES accommodates an unlimited number of process variables for plant-wide production and process monitoring. To learn more, explore our MES hardware or subscribe to the SYSCON PlantStar blog.
