Manufacturing ERP Software
Manufacturing Processes—Production and Business: Measurements for Effective Decision Making, Part 7

Manufacturing Processes—Production and Business: Measurements for Effective Decision Making, Part 7

By Bob Sproull

Review of Measurements for Effective Decision Making, Part 6

In the last post in this series on measurements, I explained and provided samples for each of the following measurements: sales productivity, ratio of throughput booked to shipped, and trend line of sales backlog.

In today’s post, I will discuss the next three measurements: ratio of maintenance downtime to operating time on constrained resource, throughput of post-constraint resource, and constraint utilization. As I have throughout this series, I will borrow and excerpt from [1] Throughput Accounting—A Guide to Constraint Management by Steven M. Bragg. Let’s now move on to the next measurement.

How well is preventive maintenance working?

One of the best ways to measure the effectiveness of the maintenance group is by examining its ability to keep the constrained resource running for long periods. The ratio of maintenance downtime to operating time on constrained resource measurement grades the implementation of preventive maintenance. What this means is that the effectiveness of maintenance is more important than the efficiency of repairs or, more specifically, the breakdown rate of the resource constraint is more important than how quickly a breakdown can be repaired.

The best measurement for tracking the overall maintenance effectiveness over efficiency is to compare the total time required for maintenance to the total machine downtime for the constrained resource. The actual measurement is:

Total downtime for maintenance

Total operating time of the constrained resource

Let’s look at an example from Stephen Bragg. The Klaus Candy Company produces a signature line of hard candy in the shapes of various Christmas-themed figures. Its constrained resource is the bagging operation, which mixes the various hard candies on a vibrating metal tray and seals them into a standard eight-ounce bag. The bagging operation runs for two shifts per day for a total of 960 minutes. It has a daily output of 28,800 bags, or 30 bags per minute. Each bag has a throughput of $1.20, so the operation can potentially produce $34,560 per day of throughput, or $36.00 per minute. The maintenance staff conducts one hour of downtime per day to adjust the machine, as well as an additional 20 minutes to correct more critical issues, which reduces total operating time to 880 minutes (960 available minutes minus 80 maintenance minutes). This is a ratio of maintenance downtime to operating time of:

80 maintenance minutes

880 operating minutes

= 9%

This level of maintenance also represents a throughput loss of $2,880 per day ($36.00 throughput per minute x 80 maintenance minutes). Using this measurement, the maintenance manager wisely decides to pay overtime to his staff in order to shift the one hour of routine adjustment maintenance into the third shift, when the bagging machine is not operating. This decision increases the throughput by $2,160 ($36.00 throughput per minute x 60 maintenance minutes) and also shrinks the ratio of maintenance downtime to operating time to 2.1 percent (20 maintenance minutes divided by 940 operating minutes).

Account for scrap with throughput of the post-constraint resource

It’s important to avoid scrapping product after it passes through the constraint, as it negatively impacts bottleneck capacity that can never be recovered. So, it should make sense that one of the best throughput-related measurements is scrap occurring after the constrained resource. One of the best measurement methods is to determine the number of constraint hours spent producing scrap that occurred after it passed through the resource constraint. Then multiply it by the average throughput per hour generated by the constraint. The formula for this calculation is:

Constraint hour used to produce the scrap x throughput per hour

On the other hand, scrap which occurs before it passes through the constraint has no impact on constraint utilization. This is much less important from the throughput generation perspective. Since this is pretty straight forward, no example is necessary.

Constraint utilization

I have written many times about how the performance metric efficiency measured on non-constraints will lead you down the wrong path, but this is not the case with the constraint. The constraint resource should be operated at the highest level possible in order to maximize throughput. A good measurement for this essential operation is constraint utilization, which is the actual hours of constraint run time divided by the number of constraint hours available for use.

The calculation for constraint utilization is:

Constraint Utilization =

Actual production hours of constraint operation

Constraint hours available

Here is an example from Stephen Bragg:

The Blowhard Glass Works has determined that its annealing furnace, used to cool shaped glass slowly to room temperature, is its constrained resource. The furnace is operational 24 hours a day on a perpetual basis, so constraint utilization is always 100 percent. In this case, the proper method of measurement is to track the proportion of the annealing furnace that is filled during the cool-down process, since Blowhard can achieve a higher level of throughput if the furnace is fully loaded at all times. In the past month, the furnace was only 48 percent filled on average, so this would be a more acceptable measurement to use.

Coming in the next post

I will examine the next three of the performance measurements: constraint schedule attainment, manufacturing productivity, and manufacturing effectiveness. As always, if you have any questions or comments about any of my posts, leave a message and I will respond.

Until next time,

Bob Sproull


[1] Throughput Accounting – A Guide to Constraint Management, by Steven M. Bragg, John Wiley & Sons, Inc, 2007.

Bob Sproull

About the author

Bob Sproull has helped businesses across the manufacturing spectrum improve their operations for more than 40 years.

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