Manufacturers that are watching the Industry 4.0 movement from the sidelines should take note: the time to transform is now. Companies of all sizes can benefit by integrating digital information from a range of sources within their manufacturing operation and supply chain ecosystem to drive improvements across their businesses.

While Industry 4.0 – also called the Fourth Industrial Revolution – has been moving into manufacturing operations for several years, many companies are discovering that the gap between action and stasis is now critical. The COVID-19 pandemic, coupled with material supply shortages, have altered the manufacturing landscape. Companies with insight into their processes and supply chain partners are rebounding more quickly.

In a 2020 survey of manufacturers by Deloitte and the Manufacturers Alliance for Productivity and Innovation, 85% of respondents indicated that smart factory initiatives are going to be a main driver of competitiveness over the next five years. Investing in an Industry 4.0 transformation will provide companies with the processes and tools necessary to take advantage of emerging opportunities.

Prioritizing Process Changes

Louis Librandi, principal of supply chain and manufacturing operations for Deloitte, describes digitalization as an ongoing cycle, known as the physical-digital-physical loop. First, information is captured in the physical world, creating a digital record of a physical manufacturing operation and supply ecosystem. Next, machines share information, driving advanced analytics of real-time data. Finally, by applying algorithms and automation, this data drives decision-making and action in the physical world.

Because Industry 4.0 emphasizes the digitalization of processes, it’s easy to think that converting your manufacturing operation into a smart factory is as simple as implementing technology solutions. However, Industry 4.0 strives to meet the broader goal of organizational transformation. Technology supports this change, but process improvements must serve as the foundation of smart factories.

This is particularly good news for small- and medium-sized manufacturers that may not be able to fund investments in leading-edge technology solutions. While digitalization of processes is central to driving the significant productivity leaps characterized by Industry 4.0, all companies can take steps to leverage the smart factory trend.

“Technology can be the change agent to start changing some of the cultural norms companies have been battling for years,” Librandi says. “The technology is always interesting, but you need to start by addressing underlying systemic issues.”

By identifying pain points in their workflow, companies can better determine where a technology investment will make the biggest impact.

Identifying Smart Opportunities

Because each company’s pain points are likely to be different, solutions will vary. However, there are a few key areas where technology is being applied in composites manufacturing facilities to improve production workflow, including the following:

  • Smart asset management – By digitalizing asset management, composites manufacturers can maximize the throughput of materials to gain cost savings. For many manufacturers this is a particularly simple step, as radio frequency identification (RFID) tags for tracking material are already widely available and relatively inexpensive. RFID tags create a data trail that manufacturers can incorporate with software systems to make numerous process upgrades.

    Librandi says that prepreg materials offer a strong example of the benefit of smart asset management. Given the tight constraints around prepreg storage, real-time asset visibility can maximize use of the material within its shelf life. By adding RFID tags to the prepreg material, manufacturers can precisely track the time the material leaves the freezer and how long it has to move through production processes before it must be cured within an autoclave. Real-time alerts can notify material handlers when time is running out.

    Manufacturers can also use RFID to track the progress of a specific manufactured component during production to help reduce downtime for production lines. Moving material more efficiently through an expensive fixed asset like an autoclave can translate into significant cost savings. When an RFID tag identifies that a part has finished curing, an automatic alert could be sent to quality inspectors or to material handlers who can then lay up the next stack.

    This location information is also valuable in identifying potential bottlenecks and targeting production investments that can drive step improvements in operational efficiency.

  • Workforce scheduling – Smart factories can also use data to adapt the production schedule to match what’s happening in real time on the factory floor. This allows manufacturers to respond with greater flexibility to unplanned downtime or new orders and better manage their workforce.

    For example, integrating RFID data about materials into scheduling software allows a manufacturer to identify where adjustments may be needed to daily production schedules to accommodate material constraints. If data indicates that prepreg material is reaching its usable limits, manufacturers can reroute capacity to use more material and reduce waste.

  • Predictive maintenance – Factories typically avert equipment problems with routine preventive maintenance, whether or not that maintenance is needed. By adding sensors to equipment to track and measure material throughput, equipment vibration and other data points, manufacturers can precisely predict when maintenance is required and reduce unnecessary downtime.

    For example, as resin transfer systems age, equipment tends to become clogged. Resin backs up, which can impact efficiency. However, cleaning equipment more frequently than required is a waste of labor and production time that can be reclaimed with better data. With analysis of data gathered by smart sensors, composites manufacturers can shut equipment down for cleaning at the first indication of slowing throughput. Librandi says this simple step can increase overall equipment effectiveness between 8% to 15%.

  • Optimized manufacturing quality –Too many manufacturers base their material curing times on trial and error or “what we’ve always done.” However, this approach can lead to under- or over-curing. It’s a problem that manufacturers tend to solve by buffering with additional material, a costly solution. By using machine learning algorithms, manufacturers can mine historic machine interactions to identify optimal cure times, among other factors. This can prevent costly scrap and rework, which leads to significant savings.

    Waruna Seneviratne, director of the Advanced Technologies Lab for Aerospace Systems (ATLAS) at Wichita State University’s National Institute for Aviation Research (NIAR), is using machine learning and artificial intelligence solutions to address the issue of part quality with an in-process inspection system.

    “With the automated fiber placement machines that we use, efficiency is low because every time we lay up something we have to stop and send a bunch of people to inspect the part. We have to do this with every layer,” Seneviratne says. Between 30% to 70% of manufacturing time may be spent inspecting a single part, he says. Then, if a defect is identified, the production team must try to manually repair the part, with potentially mixed results.

    To eliminate this downtime, ATLAS’ system will inspect the part, as well as create its digital manufacturing twin – the data-filled virtual representation of a physical product. This digital twin can help reduce the possibility of defects by pinpointing where and why they occur.