The Value of Looking Within

There is a wealth of useful information that can be gleaned by connecting a manufacturing execution system with a local system from within the production facility.

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A manufacturing execution system (MES) can be a powerful tool in your organization’s quest to follow the continuous improvement trail. It can help expose gaps in your process, lower costs, improve quality and reduce data errors.

A lot of focus and effort are put into the integration of an MES with an enterprise resource planning (ERP) system, but there is also value in looking the other way to focus on integration at the process level. By adding process data to your company’s analytics, it is possible to open paths into the process that you might have never known existed.

When you connect process data to in-process quality results, the exact reason for out-of-spec material can become crystal clear. Not only that, but you can also gain the ability to look back in time and determine what variables caused the out-of-spec episode. This principal can also be applied to alarm conditions, overuse of raw materials, startup/change over events, etc.

Eventually, it is possible to reach that state of nirvana where you can begin practicing the dark arts of machine learning and predictive manufacturing.

Imagine getting to the point where you can run a report that flags variables like motor runtime, maintenance records and current quality trends to indicate an impending out-of-spec period. When that happens, you will be able to make informed decisions. You will know immediately, for example, if you will be able to fill orders for lower-grade material or if it will be necessary to take a downtime hit to correct the issues and get back on track making A-grade materials.

In addition to those benefits, there is a wealth of useful information that can be gleaned by connecting an MES with a local system from within the production facility and gathering additional data that might not be pertinent in a corporate environment.

For example, I had the opportunity to work with a solar panel manufacturing client’s R&D department, where we used a mixture of quality data, process data and MES data to refine the process trimming materials and develop a solution that not only saved money but also resulted in a faster manufacturing approach.

That same result was also achieved at a building materials company, where we implemented an updated system across several oriented strand board (OSB) plants. The company used a combination of Wonderware’s QI Analyst, InSQL and bill of materials (BOM) data—all combined to track several variables, including line speeds, press temperatures, quality results, wax and resin flow rates, and the depth of wood wafers. With the resulting data, the client was able to reduce raw material usage and change process variables, and still conform to APA industry standards.

A lot of enhancements can be revealed by narrowing the focus onto specific functions on the plant floor. Once there are systems in place integrating those functions together, finding the resulting opportunities becomes a matter of prioritizing.

One area that is important to consider is the final product release. In many industries, the time gap between final product production and the resulting release to the customer can vary depending on in-process testing requirements, process reconciliation and other variables. By implementing solutions that remove the human element, reduce the steps required, and/or increase the speed at which these requirements can be met, the product can go out the door and into your customers’ hands that much more quickly.

Not all analytics belong at the corporate level. The previous examples would have been either impossible or very difficult and time-consuming if the associated data had been transmitted to a centralized data warehouse or to an MES data store.

To be successful, it is necessary to spend the time to determine what improvements to chase at what level, since this can result in multiple dividends. Some items to consider:

  • Process orders
  • Bill of materials
  • Material transfer
  • Material status (quarantine, expiry)
  • Operator training status
  • Equipment preventive maintenance status
  • Equipment calibration status
  • Process order status
  • Material consumption
  • Material produced
  • Final product release

Trying to implement a system for tracking orders, production levels and order planning at the production facility level is much more complicated and cost-prohibitive than moving a select number of data points to a centralized data store and comparing them plant by plant. In this case, the corporate solution gives a better overall view and greater control.

It is possible to track machine-level overall equipment effectiveness (OEE) at the corporate level. But it is important to ask the question, “Will we get better results from having this at the local level?” In many cases, a “yes” answer is obvious. The operator, supervisor and possibly the plant manager need to know the OEE on a device-by-device basis in order to make split-second decisions and to identify bottlenecks and opportunities. The corporate level can also benefit from seeing a dashboard that lays out a plant or line OEE and performs analytics to determine higher-level bottlenecks and opportunities.

In all of these cases, the true value of looking within can be realized when the resulting MES data is used to improve processes.

Brian Briggs is a consultant with Avid Solutions Information Solutions. For more information about Avid Solutions, a certified member of the Control System Integrators Association (CSIA), visit its profile on the Industrial Automation Exchange.

 

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