Archive for the ‘ Toolbox ’ Category
Similar to the generic job description for a maintenance engineer, here is a template for a reliability engineer (or manager) paraphrased from Making Common Sense Common Practice: Models for Manufacturing Excellence. In MBM terms, these would be the responsibilities and expectations for the role of reliability engineer.
- Loss Accounting [Use existing databases to perform Pareto analysis and follow up to improve results. Since everything can't be done at once, this item is the foundation for prioritizing all others.]
- Root Cause Failure Analysis (RCFA) [Perform and/or facilitate RCFA and prospective solutions based on RCFA.]
- Managing the Results of Condition Monitoring Functions [Ensure quality data is being collected and that condition-monitoring technology is applied and used in an integrated way.]
- Overhaul/Shutdown Support [Review equipment condition to ensure the right work, and only the right work, is performed. Commission equipment during startup using applicable technologies.]
- Proactive Support [Working with all other departments to ensure good practices in design, purchasing, maintenance, stores, and operation.]
- Facilitator/Communicator [Deal with gray areas where responsibilities are shared by two or more groups. Find and implement solutions while avoiding finger-pointing.]
There is an absolute minimum cost associated with any unit. This cost is equal to the per-unit sum of:
Fixed costs (overhead) is not considered for this analysis: only variable costs.
Your total unit cost is equal to the minimum cost plus all associated waste:
If a manufacturing facility has immature processes and systems, one tends to see a lot of emphasis on total production or equipment uptime. More mature facilities will have a broader perspective including safety, reliability, quality, etc. along with production.
One best-practice metric for manufacturing lines is Overall Equipment Effectiveness (OEE), which is the product of
One advantage (depending on your perspective) of OEE is that problems are tough to hide. You can’t slow the machine down to improve uptime and expect to get away with it. You can’t slack on quality in order to improve rates. Any production problem will ultimately show up in OEE numbers.
There are many variations on operations metrics, but if you have the capability to measure OEE, you can learn from looking at other factors as well.
OEE can be seen in the context of many operational metrics in the diagram below. Definitions follow.
Widget, Inc.’s B line produced 1274 widgets in one day. Due to a lack of demand B line was only scheduled for one 12-hour shift. A lot of 75 widgets was found to be defective. Ideally, 200 widgets per hour are produced.
The shift log shows that 2.55 hours were down for scheduled breaks and a planned repair. 45 minutes down was caused by an unexpected actuator jam. 1.12 hours were used to change the size of the widgets being produced.
In the following table standard Excel cell formats are used:
for input cells and
for output cells. Calculated values are explained below.
Operations metrics are calculated as described above using the given numbers.
The losses in the example can also be plotted to show the relative impact of each loss type. A waterfall chart based on both 24 hours and demand hours would look like this:
This figure shows one strategy for improving operations and maintenance reliability: first, solve obvious problems, then proceed to standardization, and lastly look to continuous improvement methods.

Ordering improvements in this manner has several advantages:
One method I’ve seen employed successfully is after a morning safety meeting, the supervisor hands a procedure to a millwright to read aloud. Then the supervisor asks “what’s missing?” As millwrights give feedback about the procedure, the supervisor writes it down and passes it along to the planners. The planner then adds missing steps and specifications and updates the plan in the system.
Emissivity is the relative ability of a surface to emit energy by radiation and varies in value between 0 and 1.
In general, the duller and blacker the surface, the closer its emissivity is to 1.
Different surfaces have different emissivity values, and these values impact the instruments we use to take temperatures. If the emissivity value is not set correctly on infrared thermometers for the surface being measured, then they will display incorrect temperatures.
If you take a temperature that seems high while using an infrared thermometer, check the emissivity setting on the thermometer as shown in the figure below. The value should be set to 0.95 for most normal rounds.
To adjust emissivity, repeatedly press the MODE button while the display is on HOLD until the emissivity symbol blinks. Then use the arrow keys to set the value to 0.95.
Typical values for selected surfaces are:
Clean Snow: 0.83
Soil: 0.92 to 0.96
Brick: 0.93 to 0.96
Black rubber: 0.94
Powdered Charcoal: 0.96
Human Skin: 0.98
If you must use an infrared thermometer to measure the temperature of a low-emissivity surface (such as clean copper piping or a stainless tank), cover the surface with a flat black paint beforehand. Raising the emissivity of the surface in this manner is generally easier than precisely measuring the original emissivity.
However, the best tool for low-emissivity surfaces is a contact probe.