**3. Overall service effectiveness**

*Concepts, Applications and Emerging Opportunities in Industrial Engineering*

ent customer base

the industrial engineer:

enrollment

satisfaction (dis-satisfaction)

deliver quality to the customer.

• A college instructor depends on:

provided in the lecture hall.

computer access

computer access

vaccinations, etc.

payments, etc.

depends on:

report final grades to the registrar

class materials in the correct quantities, on time

○ The patient appointment scheduling system

including start-up and service end dates

• A medical doctor seeing patients in his/her clinic depends on:

○ Equipment he/she may use during the patient encounter.

• Each happy customer tells five others, who may become future customers.

• The best opportunity to increase sales and market share is through your pres-

• Customer perception of quality of service depends heavily on employee's job

• Service personnel are *critically dependent on systems* (often computer-based) to

Let us consider several examples of the last point, which is very important for

○ The student registration system to provide accurate class rolls and a means to

○ The classroom assignment system to provide a lecture hall to match the

○ The textbook ordering system to order, receive, and distribute the correct

○ The classroom audio-visual system and computer software/internet access

○ The measurement of patient vital signs by nurses as the visit begins, with

○ Blood testing machines and/or radiological scans done prior to the visit, with

○ Computer access to records of previous ailments and treatments, surgeries,

• A service representative at a cable television/internet provider depends on:

○ An information system showing the customer's current service details,

○ An information system showing additional or alternative services available to the customer based on location, with costs and time frame for change in service

○ A billing system should balances in accounts, due dates, penalties for late

• A bank teller for customers who walk in the branch and queue for service,

**58**

Overall Service Effectiveness (OSE) was first described in Berhan [24], and is the focus of the remainder of this chapter. The OSE metric for services extends the OEE production metric developed along with TPM as described earlier in the chapter. For the reader's convenience, we shall demonstrate how OSE is a simple rewrite of the formulas for OEE and its three input components in a manner that fits service transactions an industrial engineer might be challenged to design or improve, using OSE as a guide.

In the equations below, the term "units" could be units of a manufactured good or quantities of a service completed. Examples of the latter might be: queries to an information systems; patients seen by a doctor or dentist; customer transactions at a bank—in person or electronic; riders transported by a bus or aircraft, or by the bus line or airline. Note these are all situations which an industrial engineer might encounter, and traditional IE tools such as queuing theory or system simulation might be in use. Agreeing with the OSE equations of Berhan [24], we shall use:


OSE = Availability ×Performance×Quality. (8)

An example for an urban transportation system described in Berhan [24] adapted the equations for the three inputs above to the specifics of the service operation as follows:


$$\begin{aligned} \text{Performance} &= \left( \text{Number of Possible Transports} \right) / \\ &\quad \left( \text{Target Number of Possible Transportable} \right) \end{aligned} \tag{10}$$


Note that the bus service, like many encountered in modern society, is a "knowledge embedded service" [25] which are services which embed the customer value in a system that provides the service, so human-machine system reliability is a key component of the availability input to OSE for such services. Here, the driver's knowledge of the route and how to operate the bus matters as much as the bus reliability.

Using real data from the public transport (bus) system in Addis Ababa, Ethiopia: Berhan first computed planned downtime (lunch breaks and shift changes), downtime and speed losses, performance efficiency losses, and finally quality and yield losses; Berhan then computed:

$$\text{Availability} = 78.85\% \tag{12}$$

$$\text{Performanceance} = 74.89\% \tag{13}$$

$$\text{Quality} = 70.8 \,\text{\AA} \,\text{\AA} \tag{14}$$

**61**

**Author details**

Robert G. Batson

The University of Alabama, Tuscaloosa, Alabama, USA

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: rbatson@eng.ua.edu

provided the original work is properly cited.

*A Service Management Metric with Origin in Plant Management*

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

which yielded a system - wide effectiveness measure of

$$\text{OSE} = 7\ $.8\$ \text{\ $6} \times 7\$ .8\ $\text{\$ 6} \times 7\ $.8\$ \text{\ $6} = \text{41.83\$ }\ $\text{\$ 6} \tag{15}$$

showing this service system needs significant improvement in order to be rated "world-class".

Just like in manufacturing, the OSE could also have been calculated for each bus individually, or for groups of busses that act together to cover a given route or sector within the city. Hence, OSE would be useful for service performance improvement at the bus, route, or (as demonstrated) system level.

### **4. Conclusions**

This chapter provided background on the application of work measurement to services, starting with Taylor and his associates, and tracing the evolution from the plant performance metric Overall Equipment Effectiveness to an innovative service performance metric Overall Service Effectiveness (OSE). As illustrated in the analysis of an existing city bus system, the details used to compute the OSE inputs (availability, performance efficiency, quality rate) point toward actions that would improve OSE toward 100%. When designing a new service system (e.g., bus line, bank layout, fast food restaurant) the OSE metric can be used along with other industrial engineering tools (e.g., classic queuing formulas, systems simulation, engineering economy) to arrive at the most cost-effective layout, equipment/ software, and staffing to handle forecast service demands.

*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*

bus reliability.

"world-class".

**4. Conclusions**

losses; Berhan then computed:

at the bus, route, or (as demonstrated) system level.

software, and staffing to handle forecast service demands.

value in a system that provides the service, so human-machine system reliability is a key component of the availability input to OSE for such services. Here, the driver's knowledge of the route and how to operate the bus matters as much as the

Using real data from the public transport (bus) system in Addis Ababa, Ethiopia: Berhan first computed planned downtime (lunch breaks and shift changes), downtime and speed losses, performance efficiency losses, and finally quality and yield

which yielded a system - wide effectiveness measure of

showing this service system needs significant improvement in order to be rated

Just like in manufacturing, the OSE could also have been calculated for each bus individually, or for groups of busses that act together to cover a given route or sector within the city. Hence, OSE would be useful for service performance improvement

This chapter provided background on the application of work measurement to services, starting with Taylor and his associates, and tracing the evolution from the plant performance metric Overall Equipment Effectiveness to an innovative service performance metric Overall Service Effectiveness (OSE). As illustrated in the analysis of an existing city bus system, the details used to compute the OSE inputs (availability, performance efficiency, quality rate) point toward actions that would improve OSE toward 100%. When designing a new service system (e.g., bus line, bank layout, fast food restaurant) the OSE metric can be used along with other industrial engineering tools (e.g., classic queuing formulas, systems simulation, engineering economy) to arrive at the most cost-effective layout, equipment/

Availability = 78.85% (12) Performance = 74.89% (13) Quality = 70.83% (14)

OSE = 78.85% ×74.89% ×70.83% = 41.83% (15)

**60**
