A Manpower Allocation Model for Service Jobs

A Manpower Allocation Model for Service Jobs

Isaac Balaila (Technion – Israel Institute of Technology, Israel) and Issachar Gilad (Technion – Israel Institute of Technology, Israel)
DOI: 10.4018/jssmet.2012040102
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Abstract

While the work measurement concept has evolved from the manufacturing world, it has not been fully adopted yet to the global shift into the service sector. Certain factors create inherent difficulties in determining time standards for manpower allocation in service jobs: (a) wide variation in Time Between Arrivals and Service Performance Time and (b) the difficulty of assessing the damage done to the organization by long customer Waiting Times (WT) for service. This difficulty makes it hard to calculate the Break-Even Point (BEP) between raising worker output, which minimizes labor costs but increases customer WT, and improving service quality by lowering customer WT. The model proposed overcomes most of the difficulties by taking a multi-domain approach to the problem: 1) The model deploys a series of indicators for a correlation between output and WT. The indicator values are affected by service level of urgency and the initial number of service workers and 2) Cost-Benefit – finding the best BEP by comparing the operational cost of an additional worker with the economical benefit caused by the decrease in WT at the margin. Thus, the model finds the best balance between worker output and service quality.
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1. Introduction

1.1 Manpower Standards

The Process of Determining Manpower Standards (PDMS) is the procedure for evaluating the number of employees to be allocated to perform defined tasks for a given quality of service or product. The Manpower Allocation Process (MAP) is based on these manpower standards, taking into consideration environmental and financial constraints.

Labor costs are usually the highest operational cost for most organizations. Therefore, accuracy in the MAP has a critical importance for the survival of any organization and naturally each organization aspires to minimize labor costs.

PDMS’s basic stance is analytical and is usually derived from two complementary disciplines:

  • a)

    Work measurement (Time Study) for determining the standard time required to perform each task. This concept (the scope of this study) is output oriented and concentrates on the individual worker but it does not deal with the qualitative aspects of system functioning, such as the harm caused the organization by customers' or products Waiting Time (WT).

  • b)

    Queuing theory is more service-quality oriented, and complements Work Measurement’s lack of information about queue length (Schultz, 2005).

Both disciplines deal with a narrow aspect of PDMS. Neither of them solve the economic conflict that is significant for MAP, namely how to balance the Tayloristic attitude, which aspires to raise worker output and so minimize labor costs, (sometimes even at the price of lowering product or service quality), with the Total Quality Management (TQM) concept, which aspires to improve service quality, usually at the price of lowering productivity (Oswald, 1997). This lack of balance between the two concepts is one of the causes of the decline in status of TQM: it underrated the importance of output and sometimes brought firms into deficit (Sarin, 1993; Flynn, 1998; McClure, 1998).

Dossett (1995) states that when dealing with PDMS in the service sector, which is characterized by vast variation in environmental conditions, there is no such thing as an “accurate” stable manpower standard time, and PDMS is more a state-of-the-art than a precise science. It seems that one way to improve accuracy in PDMS is to combine the application of several analytical tools. This gives better results than applying a narrow and focused model (Gowan, 1999).

However, the more we consider different aspects and conditions, the more complicated the manpower allocation models become, but not necessarily the more accurate.

All the above difficulties with PDMS and MAP are much greater when dealing with white-collar jobs and knowledge workers in the service sector than when dealing with blue-collar jobs in the industrial sector.

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