In the current state of machining and manufacturing technology, constant technological innovation puts pressure on operations to be relentlessly reactive, to adopt what’s new, now, in order to remain competitive. In short, to implement Industry 4.0 concepts in their organizations. This is a daunting task, especially for smaller machining operations. In 2022, how can machine shops start to create smarter organizations? What do they have to gain by doing so?
Contributing to their already busy conditions, machine shops often have plenty of work and problems to solve, so the prospect of adding more technologies and possibly overwhelming data to the ever-changing industry can seem intimidating.
As a start, it’s important to define what it actually means for a machine shop to be smart. “Utilizing technology on the basic level to make decisions based on facts,” said Josh Davids, President and CEO of the software development and consulting company Scytec Consulting Inc. Davids said that he always emphasizes the notion of working smarter, not harder. Formed in 2001, Scytec emphasizes the use of Industry 4.0 technologies to create productive systems and improve the everyday use of manufacturing equipment. According to their website, “our mission is to provide a quality OEE manufacturing data collection and process control system that is simple, powerful, affordable and scalable.”
The Industrial Internet of Things refers to interconnected devices and instruments that can collect, store, and share data and information; it is able to enhance system visibility and enable in-depth analysis. No matter how small, any kind of IIoT integration into machine shops will drive efficiency.
Regarding the specific data needed by machining operations, “basic running and not running for the machines is a great starting point to get people used to utilizing data,” he said. “The operator is going to report, ‘Well, I ran for six hours today.’ They’re not going to tell you about the small stops through the gauging that took 15, 20 minutes longer than they thought, right? Because they were busy. They were doing their job. So, from their standpoint, everything was being utilized.”
Davids places importance on the simple things, such as using live data to define planned and unplanned downtime for tasks like setting up a machine or blowing off chips. “A lot of things that are not necessarily so obvious in these little gaps of time, that could be improved by some very basic procedural changes, or identifying some processes that can be updated … having visibility to what these machines are actually doing when they’re running versus when you’re really stopped, and those time frames in between, and understanding why that is happening allows you to make improvements.”
For a machine shop of any size, identifying opportunities to be competitive and to grow doesn’t have to mean jumping in all at once; it can start with a simple daily approach. “Smart manufacturing isn’t necessarily full-blown analytics and a lot of money … it’s the culture of how you run your shop floor. And that starts with the very basics of utilizing even the simplest of data.”
So, if getting started is really as straightforward as that, what is holding back operations from implementing these IIoT strategies? Davids said it is the anticipation of barriers that do not actually exist. It is a misconception that things such as personnel or capital equipment are holding companies back. “The most common misconception is that they need a lot of hardware, they need a lot of resources, and a lot of time to be able to do this, and that is not accurate. Not to get started,” he said.
In reality, the barriers to entry for all machining operations are low, and the only thing holding back these companies is the perceived expectation of complexity, the need for education, and not having reliable information about what it would really take to get started, Davids said.
All of these points may sound plausible on paper, but what does it take to actually make them function and create tangible value for individual operations of different scales? “It doesn’t matter whether you’ve got four plants or whether you’ve got four machines, the concepts are exactly the same … getting the data, making the data meaningful to you based on your process, and exposing that data,” Davids said.
Although, even with a single approach, there must be some differentiation based on what kinds of machines and operational environments the users are trying to pull data from. For example, Davids said low operator interaction with specific machines would warrant more automatic data, while environments with a lot of collaboration between people and machines might require different types of data, such as set-up times.
He reinforces the point that data on running and not running is by far the most important data to collect when first trying to create value. “What that would usually do is open people up by (demonstrating) how much machines are revealing,” he said. Instead of just estimating how productive a machine may be, he said that finding out the true running percentage then allows shops to start asking themselves why the machines are performing as they are. “Let the data drive those questions.”
To make sure the entire team is excited about using these new technologies, Davids said that the process of getting everyone on board must be driven through the ranks of the machine shop from the top down. “If it’s just one person that’s in charge of running reports and disseminating that information, the system will fail.” He also emphasizes the value of a live dashboard. “Have a TV on the wall that is showing what took place over the last few hours,” encouraging the data to be the center of the IIoT operation.
Moreover, additional reasons for failure include jumping in too fast. Initially, collecting too much data can actually hinder the ability to innovate in ways that are meaningful. “That’s how projects fail: too much data overload, and people don’t really know what it means to have different definitions of things,” he said. “There’s no right or wrong … the key is everybody’s on the same page.” To avoid information overload and possibly subsequent failure, Davids said to initially avoid collecting data that is complex or difficult to coordinate, such as tracking parts.
Diving deeper into the feasibility of implementing IIoT, what should operations be expecting to pay to get started in this Industry 4.0 space? It can vary, Davids said. For older equipment that does not have Ethernet accessibility, “you could plan on $500 to $1000 per machine for some hardware.” For newer equipment, there is a bit more variability; the cost for exposing and pulling data from this equipment can depend on a variety of features. Additionally, the software itself can be around $50 to $100 per month per machine; startup costs, including things like initial onboarding, set-up, and training, can be expected to be around a few thousand dollars. Although the numbers may seem intimidating, “it is not 10s of 1000s or hundreds of 1000s of dollars,” Davids said.
Once machine shops have entered the IIoT space with beginner running and not running data, what comes next? And how do you know when you should take those next steps? Davids said that once machine shop operators are comfortable, and once they are effectively managing by the data, it is time to take a step forward.
To enhance their established processes, Davids said that more complex IIoT data strategies can be implemented to push operations to the next stage of using Industry 4.0 to create value in their manufacturing technology operations. He brings up specific examples of more advanced IIoT ventures to consider, such as ERP integration, a richer set a of data by tracking parts, or even a Kaizen project to understand specifics about certain tools on certain machines.
In addition to a competitive advantage, machine shop operators that put effort into Industry 4.0 technologies can give their operations an edge. “They can have lower tool costs, a higher quality percentage, and better trained staffing.”
Davids said that these technologies are very quickly turning into something that is required. Anything that manufacturing technology operators can do to begin simple integration of IIoT technology will drive efficiency. “The most common reason that we hear when people don’t start is that they don’t think they’re ready for it. They don’t think it’s something for them.”
“There are no more excuses or large upfront costs,” he said. “They can either hop on the bandwagon or get run over by it.”
Anna Smith is a staff writer for IndustryWeek and New Equipment Digest. Contact her at [email protected]
