Seneviratne says many composite components carry the potential for defects, but if they fall within an acceptable defect limit there is not necessarily a need to repair the component. However, there are many unknowns inherent within this allowable potential for defects. With a digital manufacturing twin, companies not only know exactly where those defects are but can begin to collect data that can improve manufacturing parameters and drive down these potential defects without the need for destructive testing.
Seneviratne’s team begins by simulating the manufacturing process before making any parts. “We can virtually simulate the process on multiple different machines, because each machine has different capabilities, and then select which machine works best for a particular part without touching any composites,” he explains.
The challenge, Seneviratne says, has been programming a machine learning algorithm that allows for defects. “It’s very hard to make the robot make mistakes. But how are you going to create a massive database of defects when the robot doesn’t want to make defects? For that we are looking at different strategies to inflate the amount of limited data that we have on defects – to expand that set of experimental data and then manipulate the data and see whether we can simulate some of those other conditions,” he says.
Scaling Up to a Smart Factory
Building upon these individual strategies is the next step toward creating a large-scale smart factory. That’s how Pactiv Evergreen, a Lake Forest, Ill.-based manufacturer of food and beverage packaging, began its smart factory transition. While the company doesn’t use composites, it works with 14 different materials, including plastic, so it can serve as a beacon for composites manufacturers.
Evergreen Pactiv set a goal of transforming its manufacturing capabilities through a factory asset intelligence (FAI) program that would improve throughput on constrained assets across the operation. While the company had previously worked through several lean-driven, kaizen events to achieve progress, the management team routinely saw its experienced production workforce return to more familiar workflows. Company leaders recognized they would need to make a fundamental process change to secure the desired step-change process improvements.
The company began its Industry 4.0 journey with a pilot project that Christine DeHaven, senior director of operations, says was selected based upon the product mix in the facility, the leadership team within the facility and the potential value of capacity the company expected to unlock within that facility. The FAI program blended Internet of Things (IoT) technology, artificial intelligence and advanced analytics solutions to collect a wide range of data that the company could use to predict material flow and track equipment maintenance needs. Among other projects, the company developed a machine learning algorithm that could predict grinder blade wear and notify production maintenance teams when to replace blades in order to reduce unexpected downtime.
The initial project taught the team the importance of determining what data was necessary to achieve desired project outcomes. “During our first launch, we connected to thousands of data points across the plant and learned that, while all data is important, not all data has the same return on investment,” DeHaven says. After completing the pilot project, the leadership team reflected on what worked and what needed to improve going forward. “We modified our launches to ensure we could define and succeed with the small, sustainable wins and then build from there,” she says.
By starting small and addressing specific problems, Pactiv Evergreen quickly scaled up until each of its 11 production lines were sensorized. From there, equipment was connected to dynamic scheduling solutions. The associated mobile applications gave supervisors real-time insight into constraints, allowing them to make changes to equipment or material flow before a problem occured.
“We measured success by the increase in throughput across the assets,” DeHaven says. “The increased throughput may have come from increased line rate, reduced scrap or reduced mechanical downtime. We continue to drive to reduce the scrap that is generated on our lines, maximizing the quality of our products for our customers.”
The result has been an 11% increase in overall equipment effectiveness that translated to $20 million in annual EBITDA savings (earnings before interest, taxes, depreciation and amortization) for the company.
However, DeHaven cautions that technology tools alone cannot drive this level of savings. “Smart factory technology is reliant on people. We need to review the data, have confidence in the information being shared and use that information to make good process decisions to increase the performance of our equipment,” she says. “It is critical to have senior leadership support across all areas of the business to ensure the digital transition is seen as a business need.”
Working Toward Systemic Change
Structured change management is imperative to the long-term success of any new project, says DeHaven. To achieve systemic change, it may be necessary to bring in outside consultants to help implement specialized processes to achieve higher levels of automation, such as machine learning algorithms.
“A data scientist has a different skillset than most traditional manufacturing companies, especially smaller ones, would normally have,” says Librandi. “That’s not something you find in your typical IT or engineering department.”
In addition, maximizing outcomes depends on considering all areas of your operation. Librandi recalls working with an aerospace and defense company with a longstanding lean manufacturing culture. The company was “very good in crafting metrics and getting the data to feed their specific departments,” he says. “However, no one was integrated. They could optimize engineering – they thought – but that did nothing to help their next [step] downstream.”
Once a pilot project is perfected, it’s time to consider how the material and information that comes from that line is used by other lines, departments and potentially other partners. Integrating data across the company is a critical first step toward providing visibility into processes for supply chain partners and moving to a smarter manufacturing ecosystem.
Megan Headley is a freelance writer in Fredericksburg, Va. She can be reached at firstname.lastname@example.org.