It’s the too common plant management problem: too much data too poorly defined and improperly collected to be usable for process management and continuous improvement.
Automation and manufacturing system expert Charlie Gifford took on these issues in our recent webinar, “Designing Data Collection for Consistency that Improves Process Management”. One conclusion: since no quality system can be better than the data entering the system, management needs to step back, look at the process and determine:
- What is the data we really need to monitor and improve the process?
- How do we properly measure it?
- How do we design and implement a data collection system that enables our operators and technicians to collect high quality data that seamlessly flows into our quality system to deliver manufacturing intelligence?
Building a successful quality system requires that we:
- Define and maintain consistent data formats
- Design for compliance with industry standards and best practices
- Design value-add data collection for actionableprocess control
- Design data collection methods that deliver high integrity data
These systems use a rich interface such as Quality Monitor to guide the SOP based workflow and error proof the data collection process. The interface needs to give operators and managers feedback as quickly as possible, no later than the end of the shift.