From Machinery Lubrication:

Tips for Reaching Contamination Targets

The Basics of Synthetic Oil Technology

From Reliable Plant:

Strategies for Overcoming Resistance to Change

6 Steps to Update Your Lubrication Program

From MindTools:

Role Playing: Preparing for Difficult Conversations and Situations

Thinking On Your Feet: Staying cool under pressure

Theory of Constraints: Strengthening Your “Weakest Link”

Swim Lane Diagrams: Mapping and Improving the Processes in Your Organization

Porter’s Value Chain: Understanding How Value is Created Within Organizations

Get Ready for Promotion: Showing what you can do

From Ryan Holiday:

I haven’t linked to this guy yet, but a lot of his posts seem modeled after Marcus Aurelius: reminders to the self in the stoic tradition.

Total Commitment

I apologize for missing weekend reading last week and for a lack of substantive updates.

A bug with a plug-in prevented me from making updates, so only previously-scheduled updates were occurring.

I still have to re-install some plug-ins, so you might temporarily notice some missing features. However, we should be back into the groove shortly.

2008 Explosion at Imperial Sugar in Port Wentworth, GA

Quoted in Making Common Sense Common Practice: Models for Manufacturing Excellence:

So long as they are focused on events, they are doomed to reactiveness. Generative learning cannot be sustained in an organization where event thinking predominates.

—Peter Senge

Quoted in Making Common Sense Common Practice: Models for Manufacturing Excellence:

Predestruction Authorization

I, (insert name), do hereby authorize premature destruction of the equipment being repaired under Work Order No. (insert number), because I have not allowed adequate time for the performance of certain maintenance and/or startup and commissioning tasks (insert task numbers). I understand that not doing these tasks is likely to reduce the life of this equipment and result in its premature failure. I also understand that it may also increase the overall maintenance costs and reduce the quality of the product being produced.

___________________
Signature

___________________
Title

In my opinion, the inclusion of the words “likely to” and “may” make this authorization too soft and squishy. However, it is a problem easily remedied for application in your facility.

Nevertheless, the creation and use of this form is rather ingenious. I can’t think of many operations supervisors who would sign.

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

• availability: uptime within the line’s control,
• quality: percent good product, and
• production rate as a percentage of ideal (or best proven) rate.

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.

OEE and Other Operating Metrics

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.

• Asset Utilization: % of ideal operating rate achieved. Would typically be used by business management, not operators, due to effect of market demand, which is mostly outside of their control.
• OEE: asset utilization plus market losses. By adding market losses back into utilization, certain forces beyond the control of operators and maintenance employees are backed out. Therefore, OEE will be meaningful to a wide variety of operations, maintenance, and planning employees.
• Quality Utilization: OEE plus changeover losses. Backing out changeover losses yields a metrics that considers downtime, production rate losses, and quality losses. Since retooling and changeovers are not considered, quality utilization is a measure of steady-state operations efficiency. If there are frequent changeovers, this will not impact quality utilization.
• Potential Rate Utilization: Quality utilization plus quality losses. Backing out quality losses measures only downtime and production rate losses. This metric has limited application, but might be used if systems to measure quality defects are not in place.
• Asset Availability: Potential rate utilization plus production rate losses. By subtracting only downtime (both scheduled and unscheduled), asset availability shows how much the equipment was available for production. This metric is commonly used as a measure of reliability.

Steps to Measure OEE

1. Select system boundaries. Managers need to clearly what lies within the responsibility of their areas.
2. Define the output. For the sake of calculating OEE, inputs do not need to be known. Therefore, wasted energy, materials, and labor do not factor into OEE. If there are multiple outputs that can not be consolidated, then there will be more than one OEE number.
3. Decide on loss subcategories. There are always three categories: downtime, rate reductions, and quality defects. In the example below, downtime is split into changeover time, scheduled downtime, and unscheduled downtime. Process rate and quality issues are not subdivided.  Depending upon the situation, management might want to split the “due to”s differently. Losses of any kind might be allocated to internal or external causes. Scheduled operator breaks might get its own category. It’s up to the management team to decide what is most useful and informative.
4. Record losses. Record and track the data.
5. Calculate metrics. As the data begins coming in, compile operational metrics on a daily, weekly, monthly, or other time basis.
6. Benchmark. A world-class facility is supposed to be around 95% OEE. Where do your assets stack up?
7. Improve. This is the most important step. Use the data to create a Pareto chart of operational losses and fix the problems.

Example Calculation

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.

Initial Calculations

•  Actual Production Rate = (Total Units Produced) / (Running Hours)
• Process Rate Loss = (Ideal – Actual Production Rates) / (Ideal Production Rate)
• Quality Losses = (Defective Units Produced) / (Total Units Produced)
• Nonrunning Hours = Sum of Downtime Losses
• Demand Hours = 24 – (No Demand Downtime)
• Running Hours = 24 – (Nonrunning Hours)

Calculated Losses

• Downtime Losses are calculated as hours down divided by the relevant time period, which is either 24 hours or Demand Hours.
• Rate and Quality Losses are equal to the given percentage times running hours divided by the relevant time period.

Calculated Operations Metrics

Operations metrics are calculated as described above using the given numbers.

Plotting Losses

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:

Challenges in Measuring OEE

1. Recordkeeping. OEE requires that all losses be recorded and correctly allocated. Depending on the complexity of the process, automated systems can help, but employees may need some convincing that correctly allocated losses will not simply become a stick to beat them with. The emphasis has to be on improving, not on placing blame. Once a system is in place, operators must be accountable for complete and accurate records.
2. Subdividing Categories. Too many loss subcategories creates confusion. Too few subcategories makes data interpretation difficult. Dividing the loss types into categories should be done based on need.
3. Analysis Not Performed. Data collection is a means, not an end. If the data is not used to set priorities and drive decisions, then it is wasted effort.