ارزیابی عملکرد نیروی انسانی با روش فرآیند تحلیل سلسله مراتبی پوشش داده ها (DEAHP)

ABSTRACT DEAHP Approach for Manpower Performance Evaluation

Workforce constitutes a vital and valuable organizational asset as they are the source of creativity. Implicit knowledge assets of the employees in the organization constitute one of the most crucial elements that affect the work performance of the company. Companies compete for business through the people they employ. It is therefore an aim of any organization to ensure that their greatest asset, albeit the most elusive one, the human capital—the human resources, is utilized to its best and highest use. Performance management is a critical aspect of organizational effectiveness [1]. Because it is the key process through which work is accomplished, it is considered the ‘‘Achilles Heel’’ of managing human capital [2] and should therefore be a top priority of managers [3].

Evaluation of an employee’s performance against organizational goals is crucial to understand/determine whether an organization meets its goal satisfactory. Formal performance evaluation or appraisals are required to take human resource decisions related to pay raises, promotions, demotions, terminations etc. It is also necessary to assess an employee’s training needs. Manpower performance evaluation through quantitative and qualitative methods is being done by many organizations across the globe. Longenecker and Fink [4] stated the reasons for which performance evaluation is important to organizations. Wu [5] describes performance evaluation as a complex problem, and it involves various kind of judgments about which performance measures to use. Identification of the relevant and important criteria is the critical aspect of employee’s performance evaluation [6]. Suwignjo et al. [7] have developed quantitative models for performance measurement systems, a model measuring the performance with respect to a factor. Meyer [8] described the employee evaluation procedure adopted by a nursing home. Chandra [9] cited several problems in employee evaluation, which in his opinion can be easily overcome by following his prescribed guidelines. Taylor III et al. [10] have described a case study where AHP has been used to evaluate personnel for selection. Chan and Lynn [11] have used AHP to propose a model for evaluation of several branches of a firm. Islam and Rasad [12] have done a case study using AHP for employee performance evaluation.

The decision to hire a manger who will be able to ‘‘translate business strategy into action’’ requires clear identification of the criteria (attributes) that distinguish successful from unsuccessful performance and use only predictive measures of success, which are reliable and valid [13]. The choice of selection criteria (performance attributes) should be consistent with the organization’s strategic direction and culture. Performance attributes are defined as those qualities which all seniors and program managers are expected to develop and demonstrate in the performance of their responsibilities. Demonstration of these attributes will help to promote a culture of continuous administrative improvement where all are working toward bettering our services and environment. Some of the common performance attributes include leadership skills, strategic thinking, outstanding team and individual performance, responsible risk taking, innovation, learning from experience, identifying and correcting organizational limitations, ability to find root causes, accountability, authority, etc.

At a practical level, mathematical programming under multiple objectives has emerged as a powerful tool to assist in the process of searching for decisions which best satisfy a multitude of conflicting objectives, and there are a number of distinct methodologies for multi-criteria decision-making problems that exist. These methodologies can be categorized in a variety of ways, such as form of model (e.g., linear, non-linear, and stochastic), characteristics of the decision space (e.g., finite or infinite), or solution process (e.g., prior specification of preferences or interactive). Also for such studies, Saaty [14] suggested a technique called AHP. This management technique has been developed to handle multiple criteria decision-making (MCDM) problems using qualitative evaluation of the systems by experts from relevant disciplines and profession. DEA is one of the most popular non-parametric mathematical programming techniques for performance measurement and benchmarking [15]. The goal of DEA is to determine the productive efficiency of a system or decision-making unit (DMU) by comparing how well the DMU converts inputs into outputs, while the goal of MCDM is to rank and select from a set of alternatives that have conflicting criteria [16]. It has been recognized for more than a decade that the MCDM and DEA formulations coincide if inputs and outputs can be viewed as criteria for performance evaluation, with minimization of inputs and/or maximization of outputs as associated objectives.

In this paper, attributes measuring the performance of employees at different levels of managerial hierarchy have been quantified using AHP technique, and pairwise comparison judgment matrices are then checked for consistency. Thereafter, DEA has been used to generate local weights of alternatives from these judgment matrices. The underlying assumption behind the approach is explained, and some salient features are explored. It is proved that DEA correctly estimates the true weights when applied to a consistent matrix formed using a known set of weights. DEA is further proposed to aggregate the local weights of alternatives in terms of different criteria to compute final weights. Because of the synthesis, the proposed approach is named ‘‘Data Envelopment Analytic Hierarchy Process (DEAHP).’’ Thus, DEAHP proposed by Ramanathan [16] has been used in this paper as an alternative to the traditional methods of weight derivation in AHP.

This paper is organized as follows: Sect. 2 deals with the details of the techniques used i.e., AHP, DEA, and DEAHP. Empirical illustration of the problem, selected to justify the practicality of the approach, is presented in Sect. 3. In Sect. 4, we perform the analysis and present the results using the proposed DEAHP approach. Finally, in Sect. 5, we give the conclusions.