Measuring the Performance of Irregular Manufacturing

by Scott Whitbread

One of my favorite stories about the history of industry is that of Charles Schwab and a piece of chalk, as told by Dale Carnegie in his classic book: How to Win Friends and Influence People. Schwab, a steel magnate, came upon an innovative way to improve the productivity in one of his steel mills: he asked the day shift crew how many heats of steel they had produced that day, was told six, and proceeded to write a large “6” on the floor with a piece of chalk. When the night shift crew came in, they asked what the number was and were informed that it was the number of heats of steel produced that day. When the day shift crew returned the next morning, they found the number 6 had been erased and replaced with a 7. Then, when the night shift crew returned to begin their shift, their number 7 had been erased and replaced with a 10. Within 24 hours, productivity had been improved by two thirds!

This story highlights the power of providing capable, motivated people with a meaningful scorecard and enabling them to healthily compete with themselves and others. By helping them to view their work as a productive “game”, Schwab was able to unleash their ingenuity and competitive spirit to become more productive. This sentiment is well captured by Peter Drucker’s quote: “what gets measured, gets managed.”

However, for many manufacturing and industrial processes, the work is not as uniform as producing heats of steel and so is more difficult to measure. Some production processes require workers to constantly produce new, and meaningfully different, products. For example, made-to-order component manufacturers in the auto or aerospace industry may routinely be approached with different design specifications for future orders. Each product will typically require some combination of casting activities, machining activities, assembly, and so on, but the actual work to create each one is different.

Others’ production processes may have a consistent unit of output but require different combinations of tasks to keep producing them. In a shift of underground mining, for example, mining crews will perform some combination of drilling rock, loading and detonating explosives, mucking away the blasted material and securing the ground to prevent cave-ins. However, on any given day, these activities could be performed in different rock types with different technical specifications in different locations within a labyrinth of underground tunnels.

For such processes, inspiring productivity through performance measurement is much more difficult that simply writing a “6” on the floor because a single, meaningful unit of output is much more challenging to devise. As the combination of production activities constantly changes, no consistent baseline emerges against which improvements can be measured. As such, the full creative potential of workers may be left untapped.

How can you measure performance when production activities are irregular?

To accommodate the complexity associated with an irregular manufacturing process, businesses typically devise an equally complicated metric. In the case of the made-to-order component manufacturer, they may create a different labor and material standard for each new stock keeping unit (SKU), of which there may be thousands, and measure an aggregated variance of performance to these standards. In the case of the underground mine, they might create a detailed mine plan of exactly which areas to mine, in which order, and by what methods, and then measure overall adherence to this mine plan over time. Such metrics, based on aggregated variance to standard, are helpful for planning production but very poor at creating the “Schwabian” motivation that breeds excellence.

In an attempt to be completely comprehensive, these metrics lack meaning for production crew members who are simply looking to understand, “did we do a good job today or not?” The standards themselves are always open to debate; good or bad performance can be attributed to a favorable, or unfavorable, mix of activities on that particular shift. The creative energy of the crews and their leadership is squandered in such debates, rather than being invested to increase productivity.

To create a better outcome, irregular manufacturing businesses should consider adding a “credit-based” scoring metric as a complement their variance to standard metric. A credit-based metric measures the contribution of the various tasks that the production crews perform, regardless of the precise combination of these tasks in the production plan. Think of it as similar to the way you keep scope is a sport or a board game. Take American football for example - there are number of ways to score points: six for a touchdown, three for a field goal, one for an extra point conversion, etc. The points themselves are all of a common currency, each contributing equally to the overall score. It doesn’t matter how they are scored, only how many are scored. As such, the team will adapt to the conditions to become ever more effective at scoring points, similar to the effect Schwab created in his steel mill. This effect can also be created in an irregular manufacturing environment by using a credit-based scoring system.

Steps to Creating a Credit-Based Scoring Metric

Step 1: Break down the work into a handful of routine task groups. These are the groups of tasks that are combined (and recombined) in various ways to create the finished output, whether it be a made-to-order component or a ton of mine ore. To keep it simple, the tasks should be grouped at a level that results in a total number somewhere between 5 and 12 groupings, where each group represents a building block of the overall process.

Step 2: Assign a point value to each task grouping based on its relative value to producing finished products. Start by estimating how many labor hours it should take to complete the task grouping, and assign a point value in proportion to this. Points can also be assigned to alter the desirability of certain tasks groups. Imagine, for example, making a 3-point shot in basketball was suddenly worth 4 points instead. Overnight, you would see the number of players working on their outside shooting significantly increase. These point values can also be honed over time as the process matures and conditions change.

Step 3: Set an expectation for the total number of points the crew should be scoring in a shift, and explain the point values of all task groups to the production crew members.

Step 4: Begin measuring and displaying the credit-based scoring metric to the production crews and their leadership (in addition to the aggregated variance to standard metric). As the creative potential of the crews is unleashed by the credit-based metric, productivity will steadily rise, making the variance to standard metric progressively easy to achieve and to exceed.


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