1.Fewer suppliers


In *World Class Manufacturing* [3], Schonberger claimed production management in the US had become overly focused on "managing by the numbers" by which he meant measuring plant performance at too high a level (revenue, fixed and variable costs, profit) to really uncover hidden efficiency and quality effects; whereas in

**53**

*A Service Management Metric with Origin in Plant Management*

contrast, WCM mandates simplification and direct action—do it, judge it, measure it, diagnose it, fix it, manage it, on the factory floor. He provided a variety of examples with diagrams/photographs in [4], *World Class Manufacturing Casebook.* In [3] Schonberger observed that in 1980, the first US WCM thrusts followed two parallel paths: a quality path with a goal of zero defects known in Japan at Total Quality Control (TQC) and in the US as Total Quality Management (TQM); a justin-time (JIT) productivity path, as a means of coping with high-variety, small lot, short lead-time production. JIT aims to have every operation make the needed items at the right time in the right quantities, at low cost. JIT pursues a goal of one-piece flow, small-lot production, with minimal inventory throughout the system. A third WCM practice which supports both TQC and JIT is known as Total Productive Maintenance (TPM) and will be described below when we introduce the metric Overall Equipment Effectiveness (OEE). The three practices are synergistic—for instance, JIT will fail if incoming part quality is not (nearly) perfect and processing

Most US industrial engineers first learned details of TPM through the 1988 English version of Nakajima's *TPM: Introduction to Total Productive Maintenance* [5]. According to Nakajima "TPM is an innovative approach to maintenance that optimizes equipment effectiveness, eliminates breakdowns, and promotes autonomous operator maintenance through day-to-day activities involving the total workforce." Seven years later, two North American practitioners Charles Robinson and Andrew Ginder produced the well-known *Implementing TPM: The North American Experience* [6] and therein defined TPM to be "a plant improvement methodology which enables continuous and rapid improvement of the manufacturing process the use of employee involvement, employee empowerment, and closed-loop measurement of results." In the decade following Robinson and Ginder [6], publications on "TPM Practices and Cases" [7] and "Lean TPM" [8] appeared in the US and Great Britain. In the recent past, Ortiz contributed *The TPM Playbook: A Step-by-Step Guideline for the Lean Practitioner* [9] and Peng, in response with the digital revolution in production, published *Equipment Maintenance in the Post-Maintenance Era: A New Alternative to Total Productive Maintenance (TPM)* [10]. All of these references include a section or entire chapter explaining the measurement of Overall Equipment Effectiveness; furthermore, all suggest an engineer or improvement team could focus on OEE for a single machine (workstation), OEE for a production line or process, and OEE for the overall plant. Comparisons across shifts, days, months, etc. would detect improving or deteriorating performance. Furthermore, comparisons across similar machines, processes, or plants could be very useful to department, process, or plant managers. OEE for a given machine, line, or plant is the product of availability, performance efficiency (processing rate ratioed with the design "ideal"), and quality rate (proportion of good products produced—the yield). Because each of these inputs is measured as a percentage, the closer OEE is to 100%, the better; world-class OEE is considered 85% or higher (some authors say 90% or higher). OEE is formally calculated using the following expressions, each expressed as a percentage:

Availability = loading time – downtime / loadi ( ) ( ng time) (1)

( ) Performance Efficiency = theoretical cycle time× processed amount /

Quality Rate = processed amount – defect amo ( unt / processed amount ) (3)

OEE = Availability ×Performance Efficiency ×Quality Rate. (4)

( )

operating time (2)

*DOI: http://dx.doi.org/10.5772/intechopen.93139*

equipment does not have OEE near 100%.

#### *A Service Management Metric with Origin in Plant Management DOI: http://dx.doi.org/10.5772/intechopen.93139*

*Concepts, Applications and Emerging Opportunities in Industrial Engineering*

broadening to the work processes and the overall work system.

3.Focused factories (focus on a narrow line of products)

4.Scheduling to a rate, instead of scheduling by lots

became an independent consultant and ultimately lectured on IE at four US universities. Another early practitioner of IE was Morris L. Cooke, whom Taylor funded to work on efficiency and effectiveness of the ASME, the Carnegie Foundation, and the municipal government of Philadelphia. Frank B. Gilbreth originated the practice of work measurement in the construction trades, though he never attended college. His approach came to be known as time and motion study, which he first applied to bricklaying (a trade he learned as an apprentice). He insisted on division of labor between the brick mason (skilled labor) and the unskilled workers who "set up" the mason with bricks and fresh mortar; the specific location of the bricks and mortar relative to the mason, and even the consistency of the mortar, could be planned to make the mason as productive as possible. Furthermore, with appropriate design of the motions the mason should use, he demonstrated that the mason could increase the number of bricks laid in a given time by a factor of three. At age 27, Gilbreth founded (1895) a highly successful construction firm wherein all work was designed using time and motion study, but gave it up at age 44 to become a full-time management consultant. Frank's wife, Lillian M. Gilbreth, was a PhD psychologist who assisted Frank in the preparation of six books between 1908 and 1917 to disseminate what he had learned about the broad topic of performance measurement, starting with the worker and

As Japan began to recover from destruction of its industrial base during WWII, and to transition from essentially an agrarian society to an economic powerhouse, their industrial/production engineers originated many practices now considered part of modern industrial engineering. Starting in the 1970s and intensifying in the 1980s, there was significant debate in the US and other advanced economies in the West concerning what was enabling the Japanese to capture larger and larger market share in technological products such as automobiles, televisions, and copy machines. There was a US IE professor, Richard J. Schonberger, who spend a significant amount of time in Japan and authored several books [2–4] detailing his interviews and observations from visiting top-performing Japanese manufacturing firms. In *Japanese Manufacturing Techniques* [2], he revealed the following nine "hidden lessons in simplicity":

6.More frequent deliveries (in-plant moves, as well as deliveries from suppliers)

In *World Class Manufacturing* [3], Schonberger claimed production management in the US had become overly focused on "managing by the numbers" by which he meant measuring plant performance at too high a level (revenue, fixed and variable costs, profit) to really uncover hidden efficiency and quality effects; whereas in

8.Shorter distances, less reporting, less inspectors, less buffer stock

**52**

1.Fewer suppliers

7.Smaller plants

2.Reduced part counts

5.Fewer racks on the plant floor

9.Fewer job classifications.

contrast, WCM mandates simplification and direct action—do it, judge it, measure it, diagnose it, fix it, manage it, on the factory floor. He provided a variety of examples with diagrams/photographs in [4], *World Class Manufacturing Casebook.* In [3] Schonberger observed that in 1980, the first US WCM thrusts followed two parallel paths: a quality path with a goal of zero defects known in Japan at Total Quality Control (TQC) and in the US as Total Quality Management (TQM); a justin-time (JIT) productivity path, as a means of coping with high-variety, small lot, short lead-time production. JIT aims to have every operation make the needed items at the right time in the right quantities, at low cost. JIT pursues a goal of one-piece flow, small-lot production, with minimal inventory throughout the system. A third WCM practice which supports both TQC and JIT is known as Total Productive Maintenance (TPM) and will be described below when we introduce the metric Overall Equipment Effectiveness (OEE). The three practices are synergistic—for instance, JIT will fail if incoming part quality is not (nearly) perfect and processing equipment does not have OEE near 100%.

Most US industrial engineers first learned details of TPM through the 1988 English version of Nakajima's *TPM: Introduction to Total Productive Maintenance* [5]. According to Nakajima "TPM is an innovative approach to maintenance that optimizes equipment effectiveness, eliminates breakdowns, and promotes autonomous operator maintenance through day-to-day activities involving the total workforce." Seven years later, two North American practitioners Charles Robinson and Andrew Ginder produced the well-known *Implementing TPM: The North American Experience* [6] and therein defined TPM to be "a plant improvement methodology which enables continuous and rapid improvement of the manufacturing process the use of employee involvement, employee empowerment, and closed-loop measurement of results." In the decade following Robinson and Ginder [6], publications on "TPM Practices and Cases" [7] and "Lean TPM" [8] appeared in the US and Great Britain. In the recent past, Ortiz contributed *The TPM Playbook: A Step-by-Step Guideline for the Lean Practitioner* [9] and Peng, in response with the digital revolution in production, published *Equipment Maintenance in the Post-Maintenance Era: A New Alternative to Total Productive Maintenance (TPM)* [10]. All of these references include a section or entire chapter explaining the measurement of Overall Equipment Effectiveness; furthermore, all suggest an engineer or improvement team could focus on OEE for a single machine (workstation), OEE for a production line or process, and OEE for the overall plant. Comparisons across shifts, days, months, etc. would detect improving or deteriorating performance. Furthermore, comparisons across similar machines, processes, or plants could be very useful to department, process, or plant managers.

OEE for a given machine, line, or plant is the product of availability, performance efficiency (processing rate ratioed with the design "ideal"), and quality rate (proportion of good products produced—the yield). Because each of these inputs is measured as a percentage, the closer OEE is to 100%, the better; world-class OEE is considered 85% or higher (some authors say 90% or higher). OEE is formally calculated using the following expressions, each expressed as a percentage:


	- OEE = Availability ×Performance Efficiency ×Quality Rate. (4)

The calculation of each of these quantities is illustrated by example in the references by Nakajima [5], and Robinson and Ginder [6]. The example in Nakajima further illustrates why the three input quantities to OEE are seldom calculated to be 100%. Essentially, the use of OEE in TPM uncovers the "Six Big Losses" which become the focus of improvement efforts by an individual engineer, or a team. The Six Big Losses are grouped as follows:

Losses that determine equipment availability


Losses that determine performance efficiency


Losses that determine rate of quality products


Some examples of how to improve OEE above status quo, based on the six big losses as numbered above, would be:


**55**

it changes hands.

Dept. of Labor:

*A Service Management Metric with Origin in Plant Management*

5.Defects and rework must be recorded and carefully examined to determine root causes, and then immediate corrective actions taken (standards modified or adjusted) to hopefully prevent the same problems in the future. Follow-up is critical by the engineer, manager, or team to verify the action installed is work-

6.Start-up losses may be unavoidable with the materials, machine, and set-up required; or, they may indicate a company policy that is outdated (current machine *can now* produce acceptable quality with first unit produced, or *could* with appropriate attention from engineering, maintenance, and

For more details on data collection and the calculation/application of OEE, see three references focused specifically on OEE: Muchiri and Pintelon's 2008 article "Literature Review and Practical Application Discussion" [11]; Hansen's *Overall Equipment Effectiveness* book [12]; and *The OEE Primer: Understanding Overall Equipment Effectiveness, Reliability, and Maintainability* [13] by Stamatis. As with the world-wide spread of Scientific Management, the use of OEE by industrial engineers has spread to practitioners in Europe (e.g., see an application in France to large-scale production of ductile iron pipe [14], and in Asia, for example, applica-

A simple definition of service is "work performed for someone else." In other words, services are all those economic activities in which the primary output is neither a good nor a construct, so services are for the most part intangible. Of course, services may occur *internal* to a company, educational institution, medical facility, or government agency, or in service encounters between individuals, or exchanges between larger-scale entities—two companies, or perhaps a university and a government agency. In general, services cannot be inventoried. When considering services in context of the overall economy, a big distinction is that product-oriented sectors of an economy always produce a tangible product; a service may or may not terminate in a tangible product. Also, services are rendered on demand—either instant demand or scheduled demand—often with the customer present and involved in the service. So, the reliability characteristic "ready on demand" is critical to high quality service. Once service begins, uninterrupted service may be another customer expectation. Variability of service in response to specific customer requests may be intentional—information gets transformed into customized action in an attempt to satisfy the requests. It may also be unclear which party "owns" the service. In contrast, a manufactured product is tangible, produced and consumed at different locations at different times, expected to be of consistent quality, to be inventoried with like items, and to have clear ownership as

The Service Sector of the US economy in 2019 accounted for 79% of US employment. The percentage of service workers in other advanced economies are approximately 75% in Great Britain, 65% in France, and 60% in Germany and Japan. Service is considered a tertiary sector following the Primary Sector "Extractive" industries (Fishing, Agriculture, Mining, Oil and Gas, etc.) and the Secondary Sector "Transformative" industries (Manufacturing and Construction). The service sector is highly diverse; see the following groupings used by the US

*DOI: http://dx.doi.org/10.5772/intechopen.93139*

ing and has become the standard.

tions to sugar mills in India [15, 16]).

**2. Service management**

production).

#### *A Service Management Metric with Origin in Plant Management DOI: http://dx.doi.org/10.5772/intechopen.93139*


For more details on data collection and the calculation/application of OEE, see three references focused specifically on OEE: Muchiri and Pintelon's 2008 article "Literature Review and Practical Application Discussion" [11]; Hansen's *Overall Equipment Effectiveness* book [12]; and *The OEE Primer: Understanding Overall Equipment Effectiveness, Reliability, and Maintainability* [13] by Stamatis. As with the world-wide spread of Scientific Management, the use of OEE by industrial engineers has spread to practitioners in Europe (e.g., see an application in France to large-scale production of ductile iron pipe [14], and in Asia, for example, applications to sugar mills in India [15, 16]).
