Many companies suffer from inefficiencies with their manufacturing control systems because they do not have adequate production information about their process from which to improve operations. They do not know where process changes need to be made to positively impact production efficiencies and product quality. Therefore, detailed information about their production process, how their control system is functioning, how asset health and human resources are impacting production efficiencies, and product quality are complete unknowns. MES (manufacturing execution system) level applications can address these issues, leading to higher production efficiencies, with less downtime, while improving product quality. All of these factors impact the bottom line performance of a company.

Quite a few companies are doing a good job of collecting manufacturing data and archiving it into a database; however, they are not analyzing the data properly to extract the useful information contained in it. Analysis of the data is the key!

Sometimes this is as simple as a trend chart to monitor a critical process parameter (e.g. temperature), and watch for deviations from the expected norm. Often, analysis is much more demanding. You may need to look at the same variable collected during different production runs. Here you want to move away from the traditional time based trend and overlay the same variable from different production runs on the same trend chart. Then we can begin to focus on consistent production performance and product quality.

Information extracted from analysis of the data can be distilled down to key performance indicators (KPIs). KPIs are the critical process/business parameters your operations team needs to focus on to improve operating performance. The key is to identify the proper KPIs for your situation, collect and analyze the necessary data, and generate the KPI values to share throughout the organization. This allows everyone to focus on improving their performance using these real-time metrics.

Complex calculations are often required on the available data in order to produce meaningful KPIs. Data may reside in many locations (DCS, maintenance systems, ODBC databases, ERP systems, etc.). You must be able to retrieve the data from all sources to perform the KPI calculations. Some examples of KPIs that can be utilized to drive bottom line performance are: actual Yield per Batch or production run; daily yield; total energy used per batch; energy used per shift, day, week, month; percent of first time quality; Overall Equipment Effectiveness (OEE); downtime summaries with reason codes; operator performance; percent of idle time and reason codes; and setup change times.

Here is an outline for improving your business performance by leveraging process data collected from your production operations:

1. Identify required KPIs to drive bottom line performance.
2. Collect the required raw data from various data sources around your plant (DCS, ODBC databases, ERP, etc.).
3. Perform analyses identifying problem areas on which personnel can focus.
4. Calculate KPIs from all data sources.
5. Present KPIs to personnel who can drive operational performance.
6. Make it easy for all personnel to generate reports based on their own informational needs.
7. Take action on the results.
8. Measure results of your actions.

The key is not just to collect data. The key is to extract useful information contained in the data to improve your business and operations profitability.

Bob Vavra, writer for MESA International. Siemens Energy & Automation contributed to this report, which can be found at the MESA Website, www.mesa.org.