A New Quantitative‐Based Performance Management Framework for Service Operations

• Date: 01 October 2016
• DOI: http://doi.org/10.1002/kpm.1524
• Author: Arsalan Safari
• Published date: 01 October 2016

■Research Article

A New Quantitative-Based Performance

Management Framework for Service

Operations

Arsalan Safari*

College of Business and Economics, Qatar University, Qatar

Performance measurement system for individuals and teams is still qualitative and compliance oriented in many or-

ganizations. Therefore, most performance evaluations are considered overstated and unreliable by senior manage-

ment. A new performance management method, discussed in this study, has been designed and developed in a

service operation environment to measure and enhance the efficiency and effectiveness of employees and teams based

on a new quantitative approach. In this data-driven approach, all work efforts are evaluated and converted to a com-

prehensive credit system, allowing aggregation of individual outcomes using a few metrics. All employees need to

pass a minimum threshold to start earning benefits. Other key metrics, including quality and risk, can be added to

the system to ensure that employees follow standards, restrictions, and other expectations. This model directly aligns

performance-based payouts and rewards to performance outcomes, and therefore improves employees’efficiency

and effectiveness. The model has been tested and implemented in several operational units of a large financial insti-

tution in North America with outstanding impacts and outcomes. Copyright © 2016 John Wiley & Sons, Ltd.

INTRODUCTION

Performance management is a systematic process to

ensure that mission and goals of an organization are

consistently met in an efficient and effective man-

ner, and it can be used to measure the performance

of different business units, teams, employees, or

even processes. Performance measurement systems

for individuals and teams are still qualitative and

compliance oriented in many service organizations.

Therefore, most performance evaluations are con-

sidered problematic and unreliable by senior man-

agement. In addition, the current models do not

provide a bottom line score or a unified view along

with clear recommendations (e.g. Jensen, 2001; and

Brignall, 2002). To address these shortcomings, this

paper provides a new data-driven and target-

oriented approach that changes performance

management from a subjective exercise to a more

quantitative and target-oriented model in service

operations that consequently supports institutional

leadership in decision-making process and achiev-

ing key business objectives.

Performance management is a factor supporting

the management of an organization, as it

measures and aligns the organization’s outputs

with its short-term and long-term goals. Thus,

performance management and productivity have

been of increasing both researchers and practi-

tioners in recent years (Tangen, 2004; Katic et al.,

2011), with many authors adding to the rapidly

developing literature. Neely (1999) provides seven

reasons for implementing a performance measure-

ment system in an organization: increasing com-

petition, the changing nature of work, business

improvement initiatives, local and international

awards, changes in organizational rules, changes

in external demands on the organization, and

the availability and power of information technol-

ogy (Neely, 1999). Performance measurement

helps managers to understand where they

currently are, and to identify what they want or

need to improve (Beatham et al., 2004). The per-

formance measurement system is traditionally

viewed as an integral part of planning and con-

trol and is assumed to provide performance data

for decision making. This mechanistic approach is

complementary to the general idea that perfor-

mance measurement affects organizational behav-

ior (Katic et al., 2011).

*Correspondence to: Arsalan Safari, College of Business and Eco-

nomics, Qatar University, 2713, Al Tarfa, Doha, Qatar, P.O.

Box 2713

E-mail: asafari@qu.edu.qa or asafari@mit.edu

Knowledge and Process Management

Volume 23 Number 4 pp 307–319 (2016)

Published online in Wiley Online Library

(www.wileyonlinelibrary.com) DOI: 10.1002/kpm.1524

Copyright © 2016 John Wiley & Sons, Ltd.