This construction equipment manufacturer provides innovative solutions for underground construction and trenching. They have eight manufacturing facilities and a robust dealer network selling in over 100 countries. The company prides itself on leadership in innovation, customer service, and a strong culture for continuous improvement.
In an ongoing effort to optimize manufacturing processes, the continuous improvement team identified the paint line as a potential bottleneck in the production process. There was excess build-up of work-in-process inventory in front of the paint line and a 5-7 month delay in part production. The team needed a data-driven approach to evaluate line performance, identify and eliminate waste.
Construction Equipment Manufacturer uses Analytics for Real-time Insight into Bottlenecks
At first, the continuous improvement team used a stopwatch and manual data collection to evaluate the paint lines’ efficiency. This process was error-prone and resource-intensive. Then, with VIMANA’s IoT Connectivity, IoT data was collected automatically from the conveyer monorail system in real-time from each point along the paint line by wiring the stop buttons to a Moxa I/O hardware data collector.
First, VIMANA’s industrial analytics provided insight into paint line’s performance, bottlenecks, and unplanned downtime. Second, the team identified the primary bottleneck areas to be the Topcoat Booth, Blow Off Booth, Load and Unload Station, Cure Oven, and Dry Off Oven. However, more in-depth knowledge was needed to find waste and lean processes. Third, VIMANA’s Operator Panel was customized to expand downtime classifications. As a result, the increased categorization provided more detailed insight into the causes, duration, and frequency of downtime. With this information they uncovered that machine and tooling failure, part inspection delays, and waiting for materials were significant causes of production line delays. Therefore, these value stream steps were a high priority for lean process improvement.
Over time, VIMANA’s historical reports provided the information to establish a 70%-line efficiency target. Actual line performance compared to this target is monitored and evaluated in real-time to ensure line productivity.
In addition, VIMANA’s analytics informed day-to-day decisions to help them optimize the process flow and cycle time. When stoppages exceed 30-minute thresholds, predictive alerts notified employees so that operators could quickly address line issues proactively.
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