Data-driven manufacturing, Industry 4.0, and IIoT are discussed widely these days, and the hype is creating some confusion and misrepresentations in the market. Often discussed in all-or-nothing terms, and replete with generalizations, many companies are not clear about the value that digitalization will bring to their operations. Since every manufacturing company has different equipment, processes, and day-to-day challenges, some of the promises are difficult to evaluate. An investment in digital technology without a clear path to ROI is not strategic, and it is critical that you know what you are going to do with the data you collect before you start a project.
That said, no one questions that basing decisions on accurate and timely data—as opposed to best guesses or perceptions—is optimal in business. Actionable data leads to better decision-making and greater efficiency, ultimately increasing productivity and reducing costs.
Machine monitoring is a common entry point to data-driven manufacturing since operational data can be collected and analyzed from almost any kind of equipment, including legacy equipment, through sensors. A digital sensor can provide OEE metrics on machine availability, cycle times, production counts, program execution, and more, enabling manufacturers to see important production patterns. AMT’s MTConnect standard plays a key role by supporting interoperability and real-time data sharing between manufacturing devices and software, including legacy data systems. Most machine-monitoring software today comes with customizable dashboards and reporting features, making it easy to visualize the data you need.
“Most of our customers are looking for a solution to provide customizable dashboards and reports,” said Will Sobel, CEO of VIMANA, a provider of machine-monitoring software. “Our software collects data on operational performance, part quality, equipment health, and operator efficiency; contextualizes it; and prepares it for analysis. Real-time dashboards show shop floor operations and status of machines. More efficient operations tie directly to revenue and profits.”
Likewise, data can be collected on temperature, pressure, vibration levels, and other aspects of equipment function, providing information on degradation and warnings of impending failures. This area of data analysis enables more proactive, predictive maintenance and reduces unexpected downtime. Predictive maintenance is especially valuable in high-volume production environments.
Yet in low volume, high-value precision manufacturing environments such as aerospace and medical device markets, operations will not become more productive by measuring cycle time, OEE, and utilization metrics in the same way as in a high-volume production environment. Likewise, when manufacturing high-value products with long cycle times or with many manual steps, it is more strategic to focus on optimizing processes. In these environments, process efficiency is the goal, and if a problem in a process is detected early, it results in greater efficiency and cost savings.
In some manufacturing environments, the variability of each machine’s functionality makes it a harder process to measure and evaluate quantitatively. CNCs are highly complex, multi-process machines, and they produce a huge variety, variance, and volatility of data, which is a challenge for analytics programs. A huge number of rules must be customized given so many scenarios. Each process has its own set of analytics, and it is hard to measure the true productivity of a machine given this variability.
Another starting point in digitizing information can be making information and knowledge you already have more accessible by capturing and digitizing existing “shop knowledge.” Through intelligent tagging of this unstructured information, you are creating digital documentation that is easily accessible by everyone in your manufacturing operation and as easy as a Google search. A digital knowledge base of complex assembly processes or implementation of complex processes is a very valuable asset in many companies.
“Although I do believe that any company can benefit from digitizing information and knowledge and making it accessible, machine monitoring is not the starting point for everyone,” said John Murphy, CTO of True Analytics Manufacturing Solutions, a provider of machine monitoring and process documentation solutions. “Many companies with complex manufacturing processes and high-value products can benefit from documenting processes and making these readily searchable and accessible on the plant floor.”
When SMBs evaluate whether to digitalize equipment, they typically face more limited budget resources and therefore have more at stake if they make the wrong decisions. The cost of digitalizing goes beyond software and sensors and includes customization, communications infrastructure, and ongoing or in-house IT support.
“The best approach for SMBs is to be strategic; make small, incremental changes, and wait for ROI from the first project before embarking on the next,” advises Travis Egan, vice president – business development of AMT. “It is better to think in terms of continuous improvement instead of a one-time transformation. Collect data on a problematic part or piece of equipment, and apply lessons learned from the first project to your next.”
For companies with a limited budget, a cost-effective approach to getting started is to seek out the resources available at local universities and technical colleges. Engineering students are often looking for real-world opportunities for their required Capstone Projects, so checking in with the engineering departments is a good place to start. Most states also have both manufacturing and technology resources available to businesses.
Another strategic approach is to research and use open-source code to develop your own pilot applications. One such example is SIMBA Chain, which is a free, open-source blockchain code that companies can use to build apps to easily automate their quality control documentation requirements, saving time and money in their annual ISO audits. This is especially valuable to manufacturers in highly regulated industries such as aerospace and medical devices. SIMBA Chain Inc. was formed in 2017 as a spinoff from a grant awarded by the Defense Advanced Research Projects Agency (DARPA) to Indiana Technology and Manufacturing Companies (ITAMCO), and the Center for Research Computing at the University of Notre Dame.
“SIMBA Chain has a core underlying goal of providing tools for developers to make it easy to build and deploy blockchain systems,” said Joel Neidig, CEO of Simba Chain, and engineer and lead IT developer at ITAMCO. “With it, companies can create a ledger—an immutable historical record of assets, including machines and parts—that can all be backward-traceable to a NIST artifact.”