The speaker had presented a method, based on international standards, to connect data among his brewing company’s SAP enterprise planning system and batch control systems. The real-time link improved the accuracy of the company’s production performance reports, and ultimately, its KPI information.
So just what are KPIs, and why should automation professionals be concerned about them? KPIs, or Key Performance Indicators, are data items that indicate how well the manufacturing process is performing. Examples of KPIs include cycle time, costs, throughput, productivity, yield, profits, quality, rework, inventory levels and asset utilization.
A recent survey of Automation World subscribers found that more than half of manufacturers use KPIs in their daily operations. As well, about 52 percent of respondents report that a portion of their compensation (salary, bonus, benefits) is tied to KPI metrics.
KPIs are an integral part of Overall Equipment Effectiveness, or OEE.
OEE is a method for monitoring equipment asset utilization and controlling production downtime. It looks at the cumulative impact of three factors: Availability Rate, Performance Rate and Quality Rate.
OEE = Availability Rate X Performance Rate X Quality Rate
Availability Rate is the time for which production equipment was available for operation; the Performance Rate is the rate of production divided by the capacity of the machine; and the Quality Rate is the percentage of high-grade material produced divided by total production. Typically, OEE is presented in terms of a percentage.
When 2 plus 2 equals 5
Many manufacturers monitor single KPIs, and present this information to various levels of plant personnel, from operators to business managers. Things become significantly more complex when plant personnel are asked to monitor multiple KPIs, simultaneously.
Decisions need to be made as to which KPIs are the most important. Asking personnel to pay attention to multiple factors, without prioritization, only causes confusion.
At the World Batch Forum (www.wbf.org), Dave Emerson, systems architect with the Yokogawa U.S. Development Center, in Carrollton, Tex., advised attendees on how to calculate a composite KPI, in order to improve batch production performance and eliminate variability from batch to batch.
“Variability is the enemy,” says Emerson. “It’s necessary to use peer-based KPIs in order to find and eliminate variability in processes, recipes, operations, materials and equipment.” By peer-based, Emerson is referring to a peer group that shares, at the minimum, the same product or recipe group. Peer groups can also be comprised of those batches that share the same recipe and, better yet, the same recipe revision.
Emerson’s technique is a production performance rating tool that compares batches by identifying the characteristics of the top and bottom performers. It uses a composite KPI that is calculated from peer- and target-based KPIs, which are weighted according to their relative importance, and normalized from 0 percent to 100 percent.
This is illustrated in the diagram “KPI Aggregation
Formula.” A KPI is compared to a target or a peer group and is assigned a weighting factor. The sum of these weighted factors comprise the Production Performance Rating.
This Production Performance Rating, which is a composite KPI, can be used to make peer comparisons, where low variability is rewarded and high variability is punished. As well, a mean variability can be calculated, with rewards put in place for significant and beneficial movement of the mean toward less variability.
To gage overall production effectiveness, the Production Performance Ratings can be rolled up by recipe version, recipe, recipe group, product and over different products. With this information, batch manufacturers can compare production performance among different recipes and among various products.
This method could work well in any manufacturing environment in which multiple products are produced. The most difficult part of the process is to build the aggregation formula, and get consensus on the weighting factors and normalization.
Jane Gerold, [email protected]
