With a strong, stable manufacturing environment expected for 2018, many companies will consider ways to enhance efficiency and productivity, boosting the popularity of Industry 4.0 – the German initiative to connect equipment with data networks to better track and monitor processes – also known as the Industrial Internet of Things (
However, the new world of online monitoring, Big Data analytics, and process optimization can be daunting, especially for smaller manufacturers that don’t have huge information technology (IT) and data departments willing to tackle the changes. Today’s Motor Vehicles recently sat down with Sascha Fischer, business manager for Siemens Industry lnc.’s machine tool business, to discuss how companies can start to take advantage of the wave of connectivity products hitting the manufacturing world.
“Everybody needs to start
First step – Examine production processes
“Identify where you spend most of your time,” Fischer says. “Start
He adds that some companies spend the most time in pre-production planning, others in production itself, and others in maintenance or post-production service. Digital technologies can improve any of those portions of the manufacturing process, so identifying where waste is occurring will tell managers where the focus needs to be.
“The first step is asking how to get transparency into a process,” Fischer says. “Connect two or three machine tools, then look for what kind of data you already have in your production process that you’re not utilizing.”
Comparing detailed, digital production data directly from machines to post-production data from measurement inspection stations or productivity counts can tell manufacturers how close their long-standing estimates are to real-world production. Such early steps will give companies a clear idea of how production processes are working, and
“Start small with one project – understand what you’re getting out of it, and expand from there,” Fischer says.
Identify what you need to know
Early data sets coming from connected machines will give manufacturers transparent processes, but they will also likely have gaps. Measuring feeds, speeds, and cycle times may not offer enough information, and engineers may want to link data to inspection reports or other systems.
“Once you have some data, you can ask, ‘Is this the right data?’ Or, ‘Am I using the data that I have properly?’” Fischer says.
This is the stage where many companies seek outside help from data experts, system integrators, or software providers. Most companies have at least one person who likes to think strategically about how to use and analyze data, Fischer says, so identifying that person and identifying him or her to be the change agent within the organization can help. Making that person the main contact for outside contractors can ensure that companies are getting the services they need, not the products a vendor wants to sell.
Automotive manufacturers and suppliers using Six Sigma or Lean manufacturing can look to those productivity champions to identify strategic thinkers, Fischer says, as those manufacturing concepts dovetail nicely with the rich, detailed data coming from connected machines.
“You need to have some dedicated resources,” Fischer explains. “Someone has to be studying this information constantly. If you have the pressure on one side to get parts out the door, you’re not spending the year you need to really understand your processes and how to improve them.”
Connected machines can produce massive amounts of data, and it’s going to take time for experienced manufacturers to understand what they’re seeing and how to use that information. The one-year timeframe Fischer mentions is how long most businesses take to see the full potential of an Industry 4.0, data-rich environment.
“We have examples of companies
A few easy-to-fix issues will likely crop up right away – machines that are more productive on one shift than another, parts that have a higher scrap rate on one machine than another, parts that burn through consumable tools faster than nearly identical parts. Those insights are helpful, but they won’t produce the big productivity gains that most manufacturers want, Fischer says.
“Grow your knowledge base every day. Use the data to identify every limiting factor,” Fischer says. “If you have a failure, is it coming from the machine? Is it coming from the program? Is it coming from a flaw in the initial design? These are the sorts of insights that you get once you move past the initial learnings.”Siemens Industry Inc.