High-precision machining company Exacto Inc. tracked scrap data in their ERP (Enterprise Resource Planning) system to monitor production and inventory. Once a month, Exacto leadership extracted scrap data into an Excel report for quality analysis.
Besides being cumbersome to extract and analyze, the scrap data was often four-to-six weeks old, making it almost useless for timely problem solving and process improvement. Leadership wanted the ability to do more with the ERP system data, and they wanted timely access to the data.
GainSeeker® and Exacto Inc.
View a short video featuring Al Swain and Andy Jordan of Exacto Inc. as they demonstrate how they use GainSeeker to drive real-time decision making. The integration between GainSeeker and their ERP system provides Exacto with the “best of both worlds,” Jordan says.
Exacto was a longtime GainSeeker user for real-time shop floor SPC that had never implemented its powerful defect management system—labor costs to have operators enter scrap data into two systems were too high. Manufacturing operations needed scrap data entered in the company’s ERP system to track WIP, inventory, and costing. The quality manager’s need for timely access to scrap data was left on a back burner and never moved forward.
Hertzler Systems Provided
Hertzler Systems created an integration process between GainSeeker and the ERP system. The process automatically extracts scrap data from the ERP system and loads it into GainSeeker. Because the integration is automated, there is no additional labor cost to collect the data. The current practice is to extract the data daily, although it could be done more frequently.
The quality manager, engineers, and other manufacturing leaders now have daily access to scrap counts, defect reasons, and scrap costs. They can track costs for the same product at different points in the process, and for different job runs. Exacto leadership can now quickly spot trends in the data, and drill down to the root cause of scrap problems. These capabilities helped the team reduce scrap, shift the culture to rely on data, and uncover previously unknown situations in the shop.
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