Production, Manufacturing and Logistics
Performance evaluation of purchasing and supply management using value chain DEA approach

https://doi.org/10.1016/j.ejor.2010.04.023Get rights and content

Abstract

Purchasing and Supply Management (PSM) today is increasingly becoming more important to senior management due to its potential to strategically influence both operational performance as well as financial performance outcomes. However the cross-functional nature of many PSM activities has led to inadequate data collection and performance measurement resulting in weak performance evaluation methodologies and mixed results. We address this gap in the current study, firstly by using an external assessment survey methodology that complements the internal perceptional measures of PSM performance, to collect data for a sample of over 120 firms across the globe with more than 3 billion US dollar turnover, representing seven industry sectors. Next, we develop a comprehensive performance measurement framework using the classical and two-stage Value Chain Data Envelopment Analysis models, which make use of multiple PSM measures at various stages and provide a single efficiency measure that estimates the all-round performance of a PSM function and its contribution to the long term corporate performance in each of these seven industry sectors. The relevance of this measurement methodology is demonstrated through an in-depth analysis of the distribution of efficiencies within and across industry sectors and through the estimation of target PSM performance levels.

Introduction

The ever increasing competitive pressures across the globe are forcing corporations to look internally and cut costs to survive the downturns through operational excellence. Today, one of the major components of cost is the purchasing spend, which on an average accounts for 40–70% of a firm’s sales volume (depending upon the degree of vertical integration in the industry), and hence offers large scope for the creation of competitive advantages (CAPS, 2009). Global corporations like Wal-Mart, Dell, HP, Nokia and Zara have demonstrated that it is possible to achieve industry leadership through the efficient and effective management of purchasing and supply practices, irrespective of the nature of the industry. Consequently, many companies are sourcing raw materials and other supplies in recent times from across the globe in pursuit of lower costs. As a result, the role of the Purchasing and Supply Management (PSM) function has been widened significantly and its impact on corporate performance began to receive considerable attention from senior management as well as academia over the last few decades (Das and Narasimhan, 2000, Ellram et al., 2002, van Weele, 1984).

The role of today’s PSM function has therefore transformed from a mere clerical activity to a competence with the capability to structure, develop and manage the supply base in alignment with corporate objectives (Das and Narasimhan, 2000). In order to develop these capabilities, organizations are following best practices in recruiting and training employees in the PSM function, are establishing processes that enable cross-functional collaboration and are developing systems for supplier collaboration. These PSM activities drive the performance of the PSM function in terms of cost savings, better quality of products, or co-innovations with suppliers (Das and Narasimhan, 2000). However, the ultimate goal of a PSM function from the senior management perspective is the role it plays in improving the financial performance at the corporate level. Therefore, the need for an alignment between purchasing strategies and corporate strategies cannot be overemphasized in the current economic scenario where firms are plagued by price pressures and margins are driven primarily through cost savings.

However, as the PSM function plays more of a supporting role rather than directly adding value to products and services offered by the firm, measurement of the direct value addition made by this function to corporate performance has become a major challenge (Nollet et al., 2008). There is also the need to create a measurement system and an appropriate incentive structure that can motivate PSM employees towards corporate goals considering the indirect nature of their activities.

Although PSM performance evaluation seems to be still in its infancy (Easton et al., 2002), there is a general agreement on categorizing performance measures into performance drivers and outcome measures (Kaplan and Norton, 1992). PSM performance drivers are measures concerning PSM activities such as global sourcing, demand and specification management, supplier development, procure-to-pay management etc. that facilitate outcomes, while PSM performance outcome measures refer to the PSM results such as cost savings, quality improvements, supplier integration etc. achieved through deployment of PSM drivers. One can address the PSM staff motivation part by linking the incentive system with efficient utilization of performance drivers to achieve PSM performance outcomes (Wagner and Kaufmann, 2004). However, this is too simplistic and may not necessarily result in better financial performance either because the PSM objectives are not aligned with corporate objectives or due to the inefficient conversion of PSM outcomes such as cost savings into corporate financial outcomes (e.g. profit margins). On the other hand, the conversion efficiency of PSM drivers into corporate financial performance without taking into consideration the intermediate outputs (PSM performance outcomes) seems also not the right approach because these PSM outcomes have a significant impact on financial performance and one may end up with spurious results that do not adequately represent the contribution of PSM (Ellram et al., 2002). Finally, the changing role and broadening scope of PSM activities have also magnified the difficulties of measuring the overall impact of PSM on corporate success. Especially the increased cross-functional nature of PSM activities, the lack of aggregate performance data and the intangible nature of some of the performance outcomes, have all contributed to PSM performance evaluation problems in academia (Ellram et al., 2002) and industry (van Weele, 1984).

In order to address these issues, we propose a performance evaluation methodology based on Data Envelopment Analysis (DEA), which can incorporate multiple inputs and outputs in multiple stages and results in a single relative efficiency measure. Since the conventional DEA models are found to be ineffective in measuring the performance of various supply chain related functions, many multi-stage DEA models have been developed to accommodate various indirect processes and their contribution to corporate performance (Chen and Zhu, 2004, Liang et al., 2006, Golany et al., 2006, Kao and Hwang, 2007, Kao, 2009). In the current paper, we use the two-stage value chain DEA model developed by Chen and Zhu (2004) to incorporate the effect of mediating and moderating variables, such as IT investments, on firm performance. This two-stage value chain DEA model is capable of accommodating intermediary PSM outputs such as cost savings or level of cross-functional collaboration along with initial PSM drivers and ultimate corporate financial performance, and hence is comprehensive enough to meet the needs of both PSM staff and the Chief Purchasing Officer (CPO) as well as senior management executives. Therefore, we strongly feel, this two-stage DEA model can address the existing gaps in the performance evaluation of PSM functions and can also make a significant contribution in terms of highlighting the criticality and contribution of the PSM function in an organization.

Besides the need for a multi-stage performance evaluation model, the typical data collection approaches in PSM literature, which are based on perceptional views of purchasing managers, pose another set of challenges for its performance measurement. It has been found that traditional questionnaire-based surveys alone may not provide valid and reliable measurement of the spirit of the overall system with which PSM best practices are implemented. Pagell (2004) attributes these measurement challenges to (1) issues of social desirability and to (2) overly negative impressions of respondents’ own practices.

In this paper we make two contributions to the PSM performance evaluation literature. Firstly, we apply a diversified data collection approach including the external assessment survey methodology, following Pagell, 2004, Bloom and van Reenen, 2007 to measure PSM performance drivers and performance outcomes. We integrate information from a variety of sources such as public databases, questionnaire-based survey data (evaluating facts, not perceptions) and interviewer-rated data to supplement the deficiencies of different survey based methodologies as well as to avoid problems with common method bias (Podsakoff et al., 2003). Secondly, we present a PSM performance evaluation methodology that incorporates intermediary outcomes and evaluates the PSM performance of an organization by measuring the relative efficiency of its PSM function vis-à-vis PSM functions of other organizations in the same industry. This approach also enables one to benchmark a PSM function against industry best practices and learn from the frontier firms. We thus analyze the efficient transformation of PSM performance drivers into PSM and corporate financial performance for seven industries, each industry consisting of a set of sample firms, using both conventional as well as multi-stage DEA methodologies, which enable the integration of precise as well as imprecise data and quantify the performance gaps of inefficient PSM functions.

The remainder of the paper is organized as follows. In Section 2, we briefly discuss the related PSM literature and develop the proposed evaluation framework. The data collection methodology is presented in Section 3. Section 4 narrates the various DEA models used for PSM performance evaluation in the current study. The results from the application of the DEA models for seven industry sectors are presented in Section 5. Finally, we conclude with a discussion on the managerial relevance of our study in Section 6.

Section snippets

Purchasing and supply management performance evaluation

Early conceptual developments of performance evaluation in PSM focused on cost issues only, which resulted in the emergence of two major concepts (Easton et al., 2002). The first is concerned with the proper utilization of purchasing personnel, and considers lower overhead cost being equivalent to better PSM performance. The second deals with end product cost as the ultimate measure of PSM performance. These traditional measures, which mainly consisted of costs and profits, continued until the

Data collection

For this study, we chose all firms with global revenues above US $ 3 billion as the target population across industry sectors around the world. This revenue threshold had been chosen to ensure sufficient complexity of the organization for questions regarding cross-functional collaboration or supplier performance management, in addition to ensuring that companies have similar sizes. Out of the initial sampling frame of 2251 firms from the OneSource data base, a stratified random sample of 1000

Application of DEA to measure PSM efficiency

The most popularly known PSM evaluation methodologies typically use single input–output ratios, which can only partially measure the performance, based on the specific set of inputs and outputs used. To address such issues, the non parametric DEA methodology is widely used in many different fields as a comprehensive measure of performance evaluation, especially in the presence of multiple input and output measures. DEA models are capable of incorporating maximum information about the system

Empirical results

Table 1 below reports the descriptive statistics for all the input/output parameters corresponding to the seven industry sectors in total. As discussed in Section 2, the individual analyses as shown in Fig. 1 refer to primary concerns of different senior executives. While the transformation efficiency of PSM activities into corporate financial performance (analysis 1) and into PSM performance outcomes (analysis 2) is the major focus of the CPO, the transformation efficiency of PSM performance

Conclusions and discussion

We have tried to address some of the challenges in the PSM performance evaluation literature by providing an alternative rigorous data collection methodology and an evaluation framework that takes into account multiple inputs and outputs at various levels of PSM activities and performance outcomes and provides a single efficiency measure that accurately captures the comprehensive performance of a PSM function and its ultimate contribution to corporate performance.

This paper contributes to the

Acknowledgements

This research project has been supported by the EADS-SMI Endowed Chair for Sourcing and Supply Management, IIM Bangalore.

References (26)

  • W.D. Cook et al.

    On the use of ordinal data in data envelopment analysis

    Journal of Operational Research Society

    (1993)
  • W.W. Cooper et al.

    IDEA and AR-IDEA: Models for dealing with imprecise data in DEA

    Management Science

    (1999)
  • A. Das et al.

    Purchasing competence and its relationship with manufacturing performance

    Journal of Supply Chain Management

    (2000)
  • Cited by (80)

    • Performance evaluation of two-stage network structures with fixed-sum outputs: An application to the 2018winter Olympic Games

      2021, Omega (United Kingdom)
      Citation Excerpt :

      Also, the two-stage structure, as the classical and most basic one in network structure, has attracted many scholars who have produced abundant relevant literature. It can be summarized as follows: using traditional DEA models (CCR or BCC) to access sub-stages efficiency (for example, Seiford and Zhu [40], Sexton and Lewis [41], Tsai and Wang [44], Tsolas [45], Wang et al. [46], Zhu [57]); the network DEA method of using sub-stage efficiency to describe the overall system efficiency (for example, Chen and Zhu [10], Chen et al. [11], Chen et al. [12], Lewis et al. [28], Lim and Zhu [33], Saranga and Moser [39], Yang et al. [50]); some sub-stage efficiency decomposition models (for example, Chen et al. [9], Chen et al. [12], Despotis et al. [18], Despotis et al. [19], Kao and Hwang [25], Kao and Hwang [27], Li et al. [29], Zhai et al. [55]); and the two-stage efficiency evaluation method based on the perspective of game theory (for example, Chu et al. [14], Du et al. [23], Liang et al. [32], Yin et al. [53], Zhou et al. [54]). Those network DEA researches focus on studying the DMUs’ component processes with the goal of identifying the cause of any inefficiencies in the system and making performance evaluation more accurate and meaningful (Ang et al. [3], Chen et al. [12], Kao [26], Li et al. [30], Liang et al. [32]).

    • Assessing the performance of tourism supply chains by using the hybrid network data envelopment analysis model

      2018, Tourism Management
      Citation Excerpt :

      Various calculation approaches are reported in the literature as to how this measurement can be calculated. For instance, overall efficiency has been calculated by summing the scores of divisional efficiency (Azadi, Jafarian, Saen, & Mirhedayatian, 2015), averaging the scores of divisional efficiency (Khodakarami, Shabani, Saen, & Azadi, 2015; Saranga & Moser, 2010), or by using a convex linear combination of divisional efficiencies to define the overall efficiency (Cook, Zhu, Bi, & Yang, 2010; Shafiee et al., 2014). However, because the aforementioned modes have computed overall efficiency mostly by using a sum or weighted average rather than structuring an index for all excess input utilizations and all output deficits in every division, the sum of divisional scores cannot represent the overall efficiency though a ratio.

    View all citing articles on Scopus
    View full text