Analytics & Marketing Metrics

4 Metrics Every Manufacturing BI Dashboard Needs

Maybe you are used to having to go to different places to check on different numbers. It might be such an ingrained ritual that you think nothing of it. But dashboards with centralized data can save you vital time. David Gillman writes for Toolbox for IT with four metrics that should go on your manufacturing business intelligence (BI) dashboard:

  1. Machine utilization
  2. Scrap or reject rate
  3. Labor hours per production unit
  4. Production days of raw material on hand

Dashing to the Data

Manufacturing involves sophisticated machines that are often highly specialized and proportionally expensive. These machines need to see as much use as possible in order for the expense of purchasing and maintaining them to be worth it. Gillman says measuring the use of individual machines and then aggregating amongst related machines is a metric with practical use to multiple layers of management.

Scrap rate is another metric with obvious and direct uses. When you know how much product is unusable, you know whether or not defects are a real problem, and if they are, you can then begin the investigation into root causes. As for labor hours per production unit, Gillman says:

Labor hours needed to produce a certain amount of product should go down over time. Some companies measure production by unit count, while others use dollar value of items produced. Whichever is the case, seeing how the amount of labor needed changes over time is key to efficient manufacturing.

Lastly, regarding the final metric, you want to make the most efficient use of raw materials possible. You want to balance these materials in a way that you are prepared with a safety stock in the case of emergencies. Some analogous comparisons to a manufacturing dashboard could be made with a more IT project management-centric dashboard. For instance, instead of scrap rate, you could measure the rate at which incidents are reported and addressed. It is good to stop and think every now and then how one industry’s best practices might be of use to another.

You can read Gillman’s original article here:

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  1. The identification of gaps can be used to develop a set of metrics that will be used as the basis for the development of the dashboard.

  2. The team started to jump into developing the dashboard by defining the metrics, but quickly realized that the audience must be defined first. Who should be acting on the dashboard data? For Ball, it was determined that the biggest benefit of visible metrics would be on the manufacturing floor. The manufacturing floor employees would be able to use the dashboard data to drive and realize improvements on the production lines.

  3. Given that you manage to aggregate the entire dashboard into maybe 4-7 metrics, you end up with big 8-inch boxes which are either green or a problem for investigation .

  4. Operational, or KPI dashboards tell you if you’re on target today. Analytical dashboards set targets for tomorrow. Choose by knowing what problem you need to solve.

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