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.