Overall Equipment Effectiveness. Robert Hansen C.
projects at a time and complete them as quickly as possible. Do not let your resources be diffused on a multitude of jobs. Good progress will occur if you select and eliminate the right limiters.
In general, the loss analysis step is a point where synergy between OEE and other key parameters occurs. During this step, the detailed equipment performance records will help identify significant root cause limiters. Cross-functional teams properly trained in objective problem solving and focused on the areas of large losses often make breakthrough gains in OEE improvement. Detailed observations that are provided by an effective equipment performance system database will be of assistance. Once the key root cause limiters are identified and eliminated, significant improvement in performance will occur.
Section 2.1 identified several types of loss that together equal total loss. These are waste loss, speed loss, ST (stop time) operational loss, ST induced loss, and DT (downtime) loss. Examine the example that has been used throughout this section. Note that 4680 units were produced and 4362 units were good units. The difference in these numbers (318 units) is the quality loss and the theoretical factory time to produce these units is the lost time due to quality. Also, 340 minutes were used in operating at 1/2 rate (2 units/min) which results in 1/2 of this time (170 minutes) as 100 percent speed loss. Therefore the losses are as follows:
Total Loss = (4.3 + 9.3 + 9.3 + 3.3 + 14.2) = 40.4 percent
Recall from the previous section that OEE = 59.6 percent.
Therefore,
Total Loss + OEE = 40.4 percent + 59.6 percent = 100 percent
The reconciliation is complete.
This reconciliation step should be completed on a routine basis. If the OEE values do not correlate with factory output, then the lowest value should be assumed until the discrepancy is resolved. It takes discipline to correctly collect data and to confirm that the database is correct. But this discipline is necessary to be sure everyone is working with good information.
A sample report follows (see figure 2-5). The values are filled in for the results of the practice example. This type of form is useful when you are looking at similar process systems and developing areas for best practices. It is also useful for demonstrating improvement over time for the same equipment system.
The report displays various losses and OEE, showing that they can be reconciled to 100 percent. Also, the input for simple computation of OEE is available and can be used to confirm that true OEE is provided. If this format is used for monthly reports, the various OEE values can then be properly weighted relative to Scheduled Time to determine OEE for the quarter or year. You may also want to incorporate the number of frequencies of each category into the report. This information is necessary and valuable in computing reliability parameters.
With the Loss categories in mind, figure 2-6 Visualizing OEE Formulas, page 46 will help to understand OEE and TEEP relative to theoretical factory or process time.
Reference:
1. Nakajima, Seiichi. Introduction to TPM: Total Productive Maintenance. Cambridge, Massachusetts: Productivity Press, 1988.
Figure 2-5 Sample Report Form
Figure 2-6 Visualizing OEE Formulas
This visual graph of the OEE and TEEP formulas can be laid out over any timeframe that you want to investigate. The overall length A becomes the calendar time of the time period you are looking at.
B is the amount of production schedule time within A.
C is the amount of actual equipment uptime or runtime.
D is the amount of good production time. This should reconcile to your computed Theoretical factory time from the amount of good product transferred.
If you only look at a planned production time then TEEP = OEE.
Many manufacturing organizations have trouble accurately measuring the financial benefits of proposed improvement projects. Important projects are often overlooked or not properly prioritized relative to average projects. As a result, a manufacturing area can get mired in driving false improvement projects, never making significant gains in reliability and productivity. Furthermore, financial accounting methods used by factories vary widely leading to more confusion in identifying meaningful projects.
Financial statements are the scorecards used to communicate and benchmark a company’s business. Understanding the link between OEE and financial statements is of paramount importance in ranking reliability and improvement projects.
It is beyond the scope of this book to cover financial accounting methods. However, the book uses a generic simplified approach that can fit most specific cases and give you a relevant first look at financial benefits. In the end, you should enlist the assistance of your company’s financial analyst to help you measure your OEE improvement projects using a method similar to the one presented here. With your analyst’s help, you will know the financial impact of the best projects and can convey the information to your company’s decision-makers in terms they understand and fully support.
Remember that OEE strategy is to be applied to your factory’s bottlenecks as well as other key areas that are either high cost or critical to the factory operation. By focusing on bottlenecks at key stages in the factory, OEE is a true measure of factory output. After identifying constraints, the next step is to exploit the resource. Your test question should be, “If good improvement occurred in this area, will a major impact occur with the factory’s overall improvement goals?” If your answer is yes, then you are working on the right areas. In turn, if you are successful, you will significantly impact the bottom line.
To help understand how OEE impacts manufacturing’s contribution to the bottom line (operating income before interest expenses and taxes), we can look at a hypothetical factory in three different situations. In section 3.1, a base case establishes the current situation. Section 3.2 considers the same factory with an improved OEE, making the same amount of product. Section 3.3 then looks at the same factory with an improved OEE, but selling all it can produce. This analysis will demonstrate how even small increases in OEE can leverage into big increases in the bottom line. In sections 3.4 – 3.6, we will use a similar approach to review the impact on another key financial parameter, Return On Assets (ROA) sometimes called the ‘productivity ratio’.
3.1 Case A: Base Case
Cost is often the major focus of manufacturing operations, and bottom line income is the key measure for factories. You would generally think that these two factors would always be in line with each other. However, I have seen situations in which factory costs were reduced, yet operating income went down. Cost optimization of a local area was detrimental to the factory as a whole. Maintenance budgets were slashed and mechanic overtime was eliminated. When problems occurred on off shifts, equipment stayed down until regular day shift. Maintenance labor expense was reduced but the factory bottleneck was starved as a result.
In most cases, however, factory effectiveness correlates very well with operating income. The following example fits the majority of cases where the financial aspects of a company are major critical goals.