Application Of Money Based Overall Equipment Effectiveness

The case for translating inefficiencies into economic terms is an easy one to explain. Such a metric can be a powerful tool in helping organizations make better decisions on where to invest efforts and resources.

By Aitor Goti, Gorka Unzueta, Irati Salaberria and Iñaki Badiola, Mondragon University (Spain)

Readers of this magazine are no doubt familiar with the concept of Overall Equipment Effectiveness (OEE). As first proposed by Nakajima in 1988, this metric sorts inefficiencies of equipment into productivity losses (Fig. 1).

Fig 1. Original OEE and the “six big losses” (Click to enlarge)

In his original methodology, Nakajima classified the inefficiencies into three main groups and six big losses:

• Equipment failure/breakdown losses (a) and setup/adjustment time losses (b) are categorized as inefficiencies that reduce Availability.
• Reduced speed losses (c) and idling and minor stop losses (d) are considered as wastes that reduce Performance Rate.
• Quality losses (e) and reduced yield occurring during the early stages of a manufacturing process (f) are identified as wastes that reduce product Quality.

Thus, OEE is traditionally measured in the following terms:

OEE = Availability x Performance Rate x Quality

As shown in Fig. 1, OEE takes loading time as a measurement basis. This loading time—something that a productive plant is determined to achieve—can be defined as the total length of the shift after any deductions for planned downtime. Planned downtime can typically include the following activities: waiting due to completion of current orders; operator breaks; planned maintenance activities, equipment trials and process improvement activities; machine cleaning and general operator maintenance; and operator training.

Once the loading time is calculated, operating time can be evaluated by excluding the time losses due to equipment failures (a) and setup and adjustment (b) from the loading time. The net operating time is determined by excluding the time losses owing to reduced speed (c) and idling and minor stoppages (d) from the operating time. Finally, valuable operating time is obtained by reducing the time losses due to defects in process (e) and reduced yield (f) from the net operating time.

Limitations of OEE’s original methodology
It is worth noting that OEE is not usually implemented as it was first defined. Accordingly, when used as classification criteria, the typical “six big losses” are transformed into the inefficiency types illustrated in Fig. 2. This example comes from Kide S. Coop. (“Kide”), a Spanish manufacturer of industrial cold rooms, doors and cooling equipment. The application involves a 12-meter press where panels used in a final product are injected. (Kide began calculating OEE of this process in January 2010. )

Although industry considers the original OEE calculation—defined and understood as a combination of Availability, Performance Rate and Quality offered by equipment—to be a key metric in the area of productivity growth, it comes with certain limitations:

• It does not consider time for preventive maintenance and scheduled downtime.
• It focuses on a machine, but not on a system.
• It does not express inefficiencies in economic terms.

The evolution of OEE
In light of inherent limitations to the original OEE approach, different versions of the methodology have been developed over time—each one adapted to a specific management requirement. Some evolutions have been oriented to measure equipment’s effectiveness based on whole calendar time. Others have sought to measure the overall effectiveness of a plant as opposed to that of its equipment. There also have been several proposed evolutions that quantify inefficiencies from an economic point of view.

As the title of this article makes clear, the focus here is on an OEE evolution known as Money Based Overall Equipment Effectiveness (MBOEE). Although it was developed by Juric, Sánchez and Goti, in 2006 the concept was initially presented by Goti, Sánchez and Fernández Pérez in 2005. By the time the technical paper on which this magazine article is based was submitted, the referenced MBOEE metric had been implemented in three manufacturing operations.

The remainder of this article discusses the results of its application, compared with the use of original OEE methodology (as proposed by Nakajima).

Determining the true value of MBOEE
OEE monitors inefficiencies, but how much do these inefficiencies cost? The MBOEE model establishes some ratios that answer this question. To do so, it reclassifies OEE’s six big losses into the following three types:

1. Losses related to stopped equipment.
2. Losses related to equipment operating slower than its nominal production speed.
3. Losses related to the manufacture of defective products.

Table I reflects the relationship between OEE’s six big losses and MBOEE’s three loss types.

As illustrated in Fig. 3, the main costs for each inefficiency type associated with the studied equipment—i.e., unavailability, bad performance and non-quality costs and sales of poor-quality products—must all be considered in calculating this economic-based version of OEE.

Fig 3. MBOEE visualization and calculation (click to enlarge)

Thus, the MBOEE is defined as:

This allows comparison of the cost of each group of inefficiencies with sales generated by each product type (or reference). The lower the MBOEE achieved, the better.

It is also worth studying the effect of an inefficiency value improvement in the MBOEE—the ratio of which will be sensitive to every inefficiency value reduction (or increment) in two ways. On one hand, an inefficiency value reduction will be directly related to an inefficiency cost reduction. Conversely, an inefficiency value reduction will also improve the sales of the company (assuming that the market is not the bottleneck), because the efficiency improvement will be reflected in an availability increase.

Real-world application…
As discussed, MBOEE was implemented as a way to manage inefficiencies in three companies. All part of the Mondragon Corporation (www.mondragon-corporation.com) business group, they include:

1. A manufacturing facility that produces plastic components for the automotive industry.
2. Fagor Electrodomésticos S. Coop. that manufactures home appliances.
3. Kide S. Coop., the previously referenced manufacturer of industrial cold rooms, doors and cooling equipment.

As in Fig. 2, the following figures come from Kide’s implementation. For comparison purposes, Kide chose to implement both OEE and MBOEE. Figures 4, 5 and 6 show the original OEE and the MBOEE evolution (also broken down into percentages of Unavailability, Bad Performance and Non-Quality costs).

(Click to enlarge)

MBOEE, as shown in Figs. 4, 5 and 6, offers information about money the equipment is losing due to each inefficiency group. This information allows managers to focus on improvement initiatives related to the most expensive inefficiency types.

Regarding the comparison between Nakajima’s OEE model and the economic version proposed in this article, the original OEE methodology identifies unavailability as the principal inefficiency group. MBOEE points to the quality-cost group as the most important—and goes beyond mere productivity considerations to support effective decision-making based on economic justification.

Conclusions
The evaluation of equipment effectiveness is one of the most interesting topics for plant managers in that it identifies the inefficiencies determining where to focus improvement actions. This effectiveness is typically measured by using Nakajima’s original OEE methodologies, which allows the combining of operation, maintenance and management of manufacturing equipment and resources.

The OEE evolution presented in this article—that of Money Based Overall Equipment Effectiveness—has proven to be a straightforward way of gaining useful management-oriented plant information. Showing how much each type of equipment inefficiency costs, this means of comparative measurement offers a simple, yet powerful tool for monitoring the costs of inefficiencies and generating valuable prioritization insight.

One last observation: The larger the company, the harder it is to obtain information. It has been much easier to manage data in Kide’s operations (a small production operation) than in the automotive-components manufacturing plant (a 700-employee facility) and the Fagor operations (with almost 4000 employees). MT

Acknowledgement
This research initiative was supported under the project “IMBOEE: Development and application of a Continuous Improvement program based on the Money Based Overall Equipment Effectiveness,” funded by the Department of Education of the Basque Government (UE09+/122 code).

References

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At the time the technical paper on which this article is based was written, all four authors were researchers in the Mechanical and Manufacturing Department of Spain’s Mondragon University. Since that time, lead author Aitor Goti has left his position at the university to become operations manager of the Natra group (www.natra.es), a leader in the production and commercialization of cocoa derivatives and chocolates. For more information on the MBOEE methodology discussed in this article, email Dr. Goti at: aitor.goti@natra.es.