In the world of manufacturing, there are seemingly endless strategies from which to choose. Will you employ a lean mindset and work relentlessly to eliminate waste? Or will you apply the Six Sigma philosophy and use data to eliminate defects and errors? How about embedding maintenance into your everyday operations with a total productive maintenance (TPM) strategy?
No matter the approach you choose, any strategy will take careful thought and consideration from key decision makers within the organization. When a new strategy is decided upon, extensive planning and cross-functional support are required, and so is a project plan with frequent check-ins.
One common thread that runs through all manufacturing strategies is reliability. That is, any good manufacturing strategy will aim to enable the long-term health and performance of the assets you use. But where does IIoT come in, and where can it reinforce your strategy? Firstly, let’s define IIoT in the context of manufacturing.
What Is IIoT?
IIoT, or the “Industrial Internet of Things” (also known as Industry 4.0), refers to the usage of data and connectivity-focused technologies to enhance day-to-day manufacturing operations. Over the last few years, trends in digital transformation such as cloud computing, big data, sensors, automation, and artificial intelligence (AI) have completely changed what is possible in the manufacturing sector. A majority of companies are shifting toward embracing digitization as an element of their manufacturing strategy. Let’s briefly look at the benefits each of these IIoT subcomponents can offer to manufacturers.
Cloud-hosted applications have revolutionized the manufacturing landscape in recent years. Whereas in decades past organizations would have to invest in costly on-premises software, cloud-based software allows organizations quickly and inexpensively to set up software across multiple facilities with scalable, pay-as-you-go cloud services.
Sensors make it possible to connect and record data from devices in a facility. A piece of equipment that has an embedded sensor can be integrated with whatever compatible software platform you’re leveraging to monitor or maintain equipment.
En masse, connected devices output reams of data, which in turn you can use to analyze and improve processes, as well as to predict when equipment maintenance should occur based on super-human sensing capabilities.
Connected devices that are linked to an enterprise IoT platform allow you to automate processes—generally menial, repetitive ones. Automation relieves people of simple decision-making and repetitive tasks so that they can focus their efforts on higher-level, more complex tasks. An example of this would be using data to optimize and automate equipment maintenance.
AI—and its subset, machine learning— empowers connected equipment to use data to make decisions and improve processes. Basically, imagine the power of your equipment “learning” from and optimizing the work that it does every day with minimal human oversight.
IIoT Meets Manufacturing Strategy
Removing Waste with Lean Manufacturing
The purpose of a lean manufacturing strategy is to remove production waste (materials, time, or resources). Put simply, lean manufacturing aims to remove any part of the production process that isn’t adding value. There are a number of IIoT applications that align with this strategy.
Sensors have been one of the most transformative additions to the IIoT landscape in recent years. Often inexpensive and relatively easy to add to machinery, they can measure everything from flow to temperature to the status of valves. This allows them to provide constant feedback in the form of data so that organizations are aware of where problems and waste arise so that the business can materialize a lean strategy. Sensors can even help organizations move into a predictive maintenance strategy by using the data to predict when breakdowns will occur—and to respond to them preemptively—ensuring that time
Cloud-based maintenance management solutions are another IIoT offering that allows you to track inventory, work orders, and asset history. The data provided by CMMS and EAM solutions allow you to gather data on KPIs, e.g. time between breakdowns and scheduled maintenance compliance, which can help you determine where problems (e.g. waste) are occurring. Having identified the problems with superhuman, IIoT-driven insight, you can then develop tactics for overcoming those challenges in order to achieve your lean manufacturing strategy.
Minimizing Defects with Six Sigma
Where a lean manufacturing strategy aims to eliminate waste, Six Sigma aims to eliminate defects, using the statistical analysis method of “Define, Measure, Analyze, Improve, Control” or “DMAIC.” Since this strategy relies more heavily on data analysis than lean manufacturing does, this strategy could perhaps benefit even more from IIoT applications.
Sensors and associated maintenance management software are again very important for providing useful information to support a Six Sigma strategy. Another IIoT trend that will help with this strategy is artificial intelligence (AI). In manufacturing, AI can help organizations achieve a predictive maintenance strategy by taking data provided by sensors and using it to make decisions based on the patterns that it sees. From there, work can be tracked in a CMMS.
Getting Everyone Involved With TPM
While it has the word “maintenance” in its title, Total Productive M
When all is said and done, not machines but people are are vital to putting the right strategy in place and seeing it through. IIoT can nonetheless help immensely with how effectively a given strategy is executed. No matter which strategy you decide is right for your facility, IIoT solutions will help you build a strong, data-driven foundation on which to build the right strategy for your operations. IIoT applications that are components of a broader, human-centric manufacturing strategy will usher in truly smart manufacturing plants.