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Digital Transformation Begins With Real-Time Monitoring


Date:2017-10-20


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An industrial packaging company invests in real-time KPI software to push its overall equipment effectiveness levels for its Wisconsin plant.

By Grant Gerke , Automation World Contributing Writer

For years, multinational food, beverage and pharmaceutical manufacturers led the way in real-time data analytics with standards-based manufacturing execution system (MES) implementations to better understand overall equipment effectiveness (OEE) or cycle times. Many MESs were proprietary and bulky, though.

Now, a wide range of industrial manufacturers are getting more comfortable with lighter, real-time data software analytic solutions. Rockline Industries, which makes coffee filters and wet wipes, recently introduced an OEE pilot project at its facility in Sheboygan, Wis., that leveraged the packager’s Rockwell Automation platform. Driving forces behind the change included increased product demand and Rockline’s continuous improvement initiative for its existing packaging lines.

“Some would call the wet wipes market hyper-competitive, so our business has been one to find and drive incremental capacity,” says Frank Hacker, general manager of Rockline’s Wisconsin operations. The company enlisted Rockwell Automation’s FactoryTalk Metrics software to provide real-time data analytics to help meet increased product demand.

“It became obvious that the only way to sustain growth was to find a set of tools that would allow us to deliver year-on-year continuous improvement,” says Pat Rusch, operations manager at Rockline’s Wisconsin plant.

The packaging plant includes five work cells, which each produce a specific size canister and amount of wet wipes. The packaging process begins with slitting large rolls and then feeding this raw material to the different work cells. These lines include slitting, filling, capping and labeling machines, along with conveyors to move product along to different stations. Maintaining the right balance and speed on the line is critical to avoid common bottlenecks, such as jams with a capper, labeler or filler, which disrupt the flow and trigger unplanned downtime.

Before the pilot project, OEE for many of the manufacturing cells was below 50 percent, and operators entered handwritten metrics to flip charts, whiteboards and Excel spreadsheets.

Rockline hired system integrator Stone Technologies to implement the pilot program and the FactoryTalk Metrics software for one work cell. “Previously, Rockline was catching big downtime, but missing the micro stops and how they were disrupting continuous flow,” says Dan Engelhard, a director at Stone Technologies.

Micro stops or minor stops are becoming more visible to industrial manufacturers as OEE and other key performance indicators (KPIs) become more prevalent in the digital factory. A 2017 OEE Benchmark Study from Sage Clarity and Epicor shows that minor stops are a chronic problem in the food and beverage industry. “Companies could be ignoring up to 60 percent of production problems by missing these downtime metrics,” the study details.

“Identifying micro stops has helped us drive incremental throughput with a limited capital expense,” Hacker says. “By addressing micro stop and throughput constraints within a specific piece of equipment or process, we have been able to avoid excess costs.”

Rockline focused on one work cell to prove out the system to management and help ensure return on investment (ROI). “The pilot ran for six months, in total, with three months of data collected to support improvement claims and return on investment,” Hacker says.

The analytical package uses the ISA-95 plant model and offers structured data, such as site/location, production area, line and, of course, work cells. Also, the “good part counter” KPI prescribes an accumulated value that does not reverse or go negative, but can be reset by an operator.

“OEE is measured by work cells and each has a target OEE,” Hacker says. “OEE is based on the design speed of the pace setter operation in each machine.”

The company measures speed, uptime and scrap for all machines within a work cell, but OEE is calculated by grouping machines together. Rockline computes the quality component of OEE based on product rejected at various points throughout the process.

Operators at each work cell have two 70-in. displays on the plant floor. The LED displays provide work cell run-state or real-time default codes to help operators identify sources of downtime or process interruption.

“The LED screens cycle through four screens: overall cell OEE performance with top downtime causes; an hourly production scorecard updated in real time with red and green condition based on performance to target; scrap/waste in real time; and a screen that displays downtime by cause,” Rockline says.

“Leadership also has browser access to real-time dashboards and pre-configured reports for each work center, as well as a composite dashboard for all,” Hacker says. Rockline is also investigating the use of tables to support short-interval control, a Kata-like lean maufacturing tool that will use FactoryTalk data to drive team decision-making.

Once the ROI proved out, Stone Technologies implemented the analytics to the other packaging lines. With the integration complete, plant utilization is up and OEE exceeds 70 percent. The digital transformation is underway at this Wisconsin plant.

Source :automationworld.com