Production performance ratings can provide crucial insights

Key performance indicators may lead the way to cost savings and capacity increases without large capital expenditures.

By David Emerson

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Many different metrics or key performance indicators (KPIs) can be used to measure the effectiveness of batch production. These include: batch cycle times; actual results versus expectations; capacity utilization; batch exceptions; conformance to schedule; and attainment to standard [1,2]. Some of these are very general categories — an item like “attainment” can include various criteria, such as critical process-measurement trends, laboratory results for a recipe, and product and labor costs, with the standard defined by an exceptional or “gold” batch.

Of course the ability to repeatedly produce on-specification and saleable product is absolutely essential. Once this is accomplished, however, many opportunities remain to improve production and appropriate KPIs may lead the way to cost savings and capacity increases without large capital expenditures.

With today’s increasing interest in real-time performance management, higher levels of management are accessing batch production KPIs in real-time on dashboards, web pages and in e-mails. One difficult aspect of using multiple KPIs is that they usually do not have an equal importance with regard to overall production efficiency and costs. Compounding this, individual KPIs can give conflicting indications, making it difficult to judge overall performance.

One method to make handling multiple KPIs per batch easier is to calculate a composite KPI that provides a single top-level metric for rating batches. The composite KPI can be refined by giving weights to each input KPI, measuring the variability of input KPIs between batches and normalizing the KPI value from 0 to 100%. This refined composite KPI is called the Production Performance Rating (PPR). While one normalized value cannot express all the subtleties of a batch’s production, it can act as a quick measurement of production performance and serve as a filter for finding top- and bottom-performing batches and products. PPRs for batches can be rolled up to master recipe versions, master recipes and products to enable comparisons regarding their production performance.

Basis for a PPR
The rating’s inputs are individual KPIs based upon meeting targets and specifications as well as KPIs that measure production performance against a batch’s peers, i.e., the other batches based upon the same master recipe version. This combination is weighted to meet the needs of each application and yields an overall percentage that can be used to quickly identify the best- and worst-performing batches.
While the criteria and calculation for the rating will vary with application, the ISA-88 standard provides a basis for a set of application-independent KPIs that are a good starting point for most batch-processing facilities. This set of KPIs can be refined with application-specific criteria and customized calculations can be developed to provide more meaningful results. Some of the standard ISA-88 derived KPIs are:
• cycle time;
• number of times a batch was held;
• percent of time a batch was in hold; and
• number of events associated with a batch.
By themselves these measurements have no context, so they must be compared against a target or their peers.

In some cases, comparison to a target makes sense — for example, specific lab results or process measurements may be required before a product can be released. These KPIs provide a base level for defining an acceptable batch.

However, such KPIs do not give adequate insight into the batch’s production performance. For instance, a set of batches may satisfy requirements for release, but some of the batches may have been produced with much shorter cycle times and less operator involvement than others. These batches should be considered higher performing ones because they met the release requirements at a lower cost (less asset utilization and labor cost). Pure target-based KPIs do not differentiate between the higher- and lower-cost batches. That’s where peer-based KPIs can play a role.

A potent combination
Target-based and peer-based KPIs can be used together as inputs to the PPR calculation (Figure 1).  The inputs should be weighted to reflect each application’s needs. For example, when meeting release targets is critical, target-based KPIs can be weighted to force a very low rating, even 0, when any release criterion is not met. When all the release criteria are met, the peer-based KPIs can be used to rate the batch’s production performance against other batches of the same master recipe, or even master recipe version. This provides finer resolution that will enable identification of good and poor production characteristics.

PPRs are normalized values from 0 to 100% to provide an easy-to-use scale. This normalization can require tuning the weighting factors and calculation to achieve meaningful results. Once tuned, the normalized scale allows quick identification of top performing batches, which may be those above 95%, 90%, 80% or other level depending upon the application.

Many operating companies traditionally have used an individual batch as the gold standard. Some have identified one golden batch for each unit recipe and built up a composite golden batch by assembling unit recipes from different batches. PPRs enable the rapid identification of the top performing batches and, if carried down to the unit recipe level, the top performing unit recipes. This permits engineers to move beyond the single golden batch concept to look at what production characteristics the top performing batches have in common — for instance, was it the equipment used, time of day, personnel, or ingredients?

Perhaps more important and offering faster results than chasing the golden batches is focusing on the bottom performing or “brown” batches. Not all of these batches necessarily yielded un-releasable or off-specification product. They may have made acceptable quality product, but perhaps required more re-work, resulting in longer cycle times and more operator involvement. Understanding the characteristics that lead to brown batches may enable corrective actions to decrease production variability and costs.

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