ITW Hi-Cone, a leading supplier of plastic based multi-packaging systems for the worldwide beer, soft drink and general products industries, sought to improve wide variations in the throughput, quality, and overall efficiency of machine stations along production lines and across multiple plants. Initial analysis could not uncover the cause for such variations in what were otherwise identical processes in different plants.
As a result, Hi-Cone experienced higher-than-forecast production costs, quality problems, production schedule disruptions, changeover delays, and increased scrap. A smart IT solution was needed.
Higher-than-forecast production costs, quality issues, production schedule disruptions, changeover delays, and increased scrap.
Woodmark created a system to capture information about production in a number of different ways.
Using an custom database solution, plant production data is entered by employees per shift.
Using OLE for Process Control (OPC) standards, data is pulled from plant floor machinery to record real-time information about the production environment.
Using standard integration tools, data is pulled from OPC plant floor machinery to record real-time information about the production environment.
This data is then networked and made available for comparative analysis providing the following capabilities:
- Reporting of efficiency by plant, employee, shift, machine, product, and manufacturing step
- Historical analysis by user selected criteria
- Detailed reporting of machine-captured information for researching product issues and/or machine performance
- Integration with the accounting system
- Comparison of actual production throughput compared with production standards for measuring actual vs. expected results.
Return on Investment
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- Ability to pinpoint and drill down on trouble spots in the production line
- Ability to determine whether lagging performance is related to the operator, product, equipment, or upstream quality
- Increased resources devoted to correcting both production issues and updating production forecast assumptions where they were found to be unrealistic
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