Using Measurement and Management to Track Aggregates Performance.
By Alan R. Bessen and Sam Sawant
“Management by numerical goal is an attempt to manage without knowledge of what to do.”
– W. Edwards Deming
In this article, sustainability, which often refers only to energy and environmental concerns, is expanded to include efforts to increase yield, reduce waste and increase profit. Common programs focused on Performance Sustainability include Statistical Process Control (SPC), Total Quality Management (TQM), Six Sigma, and Lean programs along with Benchmarking and Best Practices initiatives.
Each of these programs relies on analysis of historical data as the primary information source for establishing the performance capabilities of the production process. Most often the data used is provided through a corporate accounting system which rarely includes current performance data.
Key Performance Indicators (KPIs) developed from these initiatives are generally derived using daily, monthly or annual performance statistics to establish measureable targets required to achieve daily, monthly or annual corporate financial objectives.
Daily or monthly KPIs may provide useful accounting measures but have little or no impact on actions resulting in managing or improving performance. Even a daily report arrives too late to make changes in what happened at the beginning of a shift much less the previous day.
In fact, it is often too late to even identify the cause of yesterday’s performance failure. Establishing targets and measuring performance this way has little value. Actually, reliance on accounting measures alone often results in a reactive approach to management often limited to explaining actions that occurred, or did not occur, days or weeks in the past. The most common accounting based measurement responsible for this is the variance report.
Variance reports basically compare cost per unit produced over the past month with a previously predicted target value. Target values are generally derived from budgets based on months-old “best guess” or historical statistics without adequate consideration for current operational realities such as equipment condition, design limitations, market variations, raw material quality, sourcing options, inventory imbalance, etc.
Information in the variance reports is both too little (no process specific detail) and too late (the month is over) to be of real value to the people actually working to keep the plant operating.
To supplement accounting measures and effectively support Performance Sustainability, KPIs must be carefully selected, specifically developed and effectively utilized to provide valid measurement of both current and expected performance in each operating mode.
Not everything measured is a KPI. Within the production process a KPI represents a performance target used to compare actual vs. expected results for a designated portion of the processing system.
For maximum effect, KPIs must focus on specific elements within the process that offer actionable feedback to frontline personnel, preferably in real-time, enabling changes to be made within the daily on-the-job time frame.
Real-Time Operator Interface
The example interface in Fig. 1 provides the plant operator continuous real-time belt scale feedback on key process measures including daily production tons and yield percentage as well as instantaneous indication of rate changes. When combined with other process measures such as crusher amperage, bin levels and feeder settings the operator has continuous and comprehensive feedback on system performance enabling improvement adjustments to be made with confidence.
KPIs based on real-time performance data are referred to as Dynamic KPIs. Dynamic KPIs utilize recorded statistics to establish and update targets for key elements of the production process in each operating mode. They should be limited only to those items within direct control of the plant operator and include only those items where real-time data is available for feed back as adjustments are made.
Real-Time Performance Benchmarks
Non-KPI measures monitored by the control system are used to assist the operator in maintaining optimal performance. These measures represent discrete details within the process that often combine to provide the actual value to be compared to the target KPI. Their purpose is to provide current data to the plant operator to assist in adjusting process variables to improve performance. (Fig. 2)
Key Product Yield Improvement
Real-time product yield data as seen in Fig. 3, gives the plant operator an ability to assess the effect of various plant configurations on key product and by-product yield. Inadequate yield data often results in unnecessary operating hours, added operating cost and lost profit as the plant operates blindly accepting whatever is produced without regard for current inventory or sales demand.
System limiting, but commonly unseen, process variability caused by such things as improper crusher feed distribution, crusher liner wear, inconsistent feed rate control and performance limiting flow restrictions are also identifiable by observing variations in product yield.
Performance Benchmarks and KPIs derived from real-time historical data are substantially more realistic than even the short-duration measures used to create the initial process flow model. They provide current, statistically relevant measures of maximum achieved rate in each mode that can be used to provide a valid assessment of whether or not current performance is acceptable.
A Real-time Measurement Interface (RMI) linked to key performance details as in Fig. 4 will provide the plant operator continuous performance feedback in either statistical or graphic display. The same data can be used to provide management reports and can be configured for real-time remote display or email alarm notification over a corporate network or as a web-based interface.
- Optimizing Performance comes from measuring, managing and controlling your process. Field measurement and flow validation will identify process capability and identify improvement opportunities. Valid data representing current performance is necessary to establish or support operational realities such as maintenance/overhaul intervals, current process limits, inventory issues and capital improvement justifications.
- Optimizing Profit requires knowledge of both process capabilities and projected sales requirements for each inventoried product.
- Maximum Profit comes from balancing plant performance and capacity with sales demand. A comprehensive production model enables production, sales, inventory and cost balancing. Without acceptable sales and inventory forecasts the plant is driven blindly by short-term sales demand without realistic regard for managing production yield or operating costs. Without an effective means of modeling sales requirements and production capacity there is no practical means of creating a common understanding of capabilities and limits among sales and operations personnel and therefore no hope of optimizing profit.
- KPI measures produced in the accounting system are inadequate for optimizing or sustaining performance and profit. They have little or no impact on actions resulting in managing or improving performance on a daily basis. Accurate evaluation of the present state of a process is necessary to achieve optimal performance. The more accurate and timely data is, the more likely intended targets will be valid and desired results will be achieved.
- A Real-time Measurement Interface provides performance statistics necessary to establish valid process-based Dynamic KPIs. It also supplies current process data to the plant operator enabling improvement adjustments to be made with confidence during the production shift.
- Performance Management requires a corporate cultural commitment to measuring and managing the production process.
- Technology makes it possible to simplify critical aspects of process measurement. However, data alone provides little or no value.
Reduced operating cost and increased profit relies totally on effective and Sustainable Performance Management.
- You cannot manage what you cannot control!
- You cannot control what you do not measure!
- If you don’t measure it, you are not managing it!