A DEA-based approach for competitive environment analysis in global operations strategies
Introduction
Competitive environment analysis is important for different levels of strategy management, including corporate strategy, business strategy, global strategy (Birkinshaw et al., 1995), and functional strategies, including IT strategy and marketing strategy (Pang et al., 2014). In a global operations strategy (GOS), competitive environment analysis is critical for the decision-making of global retailing organizations (see its position in the strategy structure in (Ward and Duray, 2000; Gong, 2013)). Previous competitive environment analysis has applied a number of research methods, including qualitative analysis and quantitative methods, to address the influence of environmental factors on an operations strategy. Some influential methods such as partial least squares (PLS) models (Birkinshaw et al., 1995), spatial lag models (Geys, 2006), taxonomy, and configurations (Morrison and Roth, 1992) were applied in building theories, analyzing the operational structure, and investigating the environmental determinants for GOS. We summarize previous research in competitive environment analysis of GOS (see Table EC.1 of Appendix) and find there exist the following three research gaps in competitive environment analysis of a retailing global operations strategy.
The strategy management literature examines a diverse array of objectives and actions regarding the achievement of competitive advantages (Dyer, 1996; Hillman and Keim, 2001; Douglas and Judge, 2001). While current literature relevant to competitive environment analysis has considered measures like sales, market value, returns on investments (ROIs) (see Table EC.1) in other industries, these research objectives may not fully address the challenges in competitive environment analysis of the retailing industry, and new guidelines for performance measure selection relating to retailing operations are needed (Dess and Robinson, 1984; Waddock and Graves, 1997; Godfrey et al., 2009; Combs et al., 2005). An important operational performance is operational efficiency, considering industrial characteristics of low profit ratio in the retailing industry. For example, even for the largest retailer Walmart, its profit ratio (group net profit/net sales) is only 3.4% (based on data in Planet Retail, see https://www.planetretailnetgroup.com). Low profit ratio may imply low output-to-input ratio. Therefore, an optimal output-to-input ratio is an interesting measure for retailing practitioners.
Operational efficiency in this paper refers to a well-built definition in the literature of data envelopment analysis (DEA) (Farrell, 1957; Zhu, 2014). Generally, a decision-making unit (DMU) is regarded as the entity, which is a firm in this paper, that has multiple performance measures (classified as DEA inputs and outputs) and its performance is to be evaluated based upon the selected measures. In DEA, a group of DMUs is used to evaluate each other with each DMU having a certain degree of managerial freedom in decision-making. Fare et al. (1985) proposed to measure efficiency as the distance between an observation and an estimated ideal referred to as an efficient frontier. The efficient frontier guarantees at least the outputs of firms in all components while reducing the inputs proportionally to a value as small as possible. Although previous studies in DEA have focused on the production efficiency, DEA is more than an efficiency measure under the notion of a conventional production process (Cook et al., 2014; Liu et al., 2013). DEA can be a type of “balanced benchmarking” (Sherman and Zhu, 2013) that examines performance in operational processes and helps organizations to test their assumptions about performance, productivity, and efficiency in operation decisions. Thus, understanding the impact of the environment on retailing GOS requires an appropriate definition of the concept of operational efficiency.
We therefore identify the following research gap: operational efficiency, a critical indicator measured with output-to-input ratio, has not been fully studied integrated with competitive environment analysis for global retailing with industrial characteristics of low profit ratio.
Today's retailing organizations are adopting different GOSs to improve operational performance considering industrial environments (Nair, 2005). In different industrial environments, retailing firms utilize various facilities such as warehouses, outlets, and the supplier resources to improve operational efficiency and increase the productivity of operational systems (Newman and Cullen, 2002). Taking advantage of the industrial environment as an opportunity for development, retailers build a global operations strategy with alignment to the industrial environment, make use of local environmental factors, and achieve competitive advantages. Evaluating efficiency without considering the environmental impacts of different regions may not accurately reflect the relative performance of firms in retailing. Therefore, while there are a number of studies assessing the performance of an operations strategy, we will further consider environmental factors in the assessment of retailing GOS (Newman and Cullen, 2002).
Current environment-relevant measures and constructs (Ward and Duray, 2000) are not regular measures used in retailing practices and cannot catch practical features in the retailing industry, which creates difficulty in the understanding of retailing practitioners and the implementation of a retailing operations strategy. We resort to PlanetRetail, a major database used by retailing practices and retailing consulting, for indicators and measures of an industrial environment. Therefore, we consider new practice-based environment variables from PlanetRetail and identify a research gap: a set of practice-based measure systems about industrial environment indicators, consistent with retailing practices to facilitate the understanding of retailing and the easy implementation, is missed.
We compare studies of competitive environment analysis (some of them are listed in Appendix EC.1) and find that the most competitive environment analyses in an operations strategy use environmental factors as control variables, moderate variables, or elements in configurations (Pang et al., 2014; Birkinshaw et al., 1995; Geys, 2006; Grossman et al., 1999; Morrison and Roth, 1992). We would like to introduce a new analytical perspective of nondiscretionary factors. “Nondiscretionary,” which describes the external constraints from the environment in this research, is defined as environmental factors in which the DMU in an organization may not have the ability to decide or adjust according to its own discretion or judgment. The nondiscretionary factor is a concept rooted in economics and widely used in the field of DEA, productivity analysis, and performance evaluation (Cooper et al., 2011; Gorman and Ruggiero, 2008; Pendharkar, 2013). To conduct a competitive environment analysis, we use nondiscretionary models to deal with these environmental factors in the operational process of retailing firms. The “nondiscretionary” inputs such as inhabitants’ population, market concentration, and total consumer spending per capita in different regions, which are not subject to management control in retailing, can influence the business performance of retailers. Even though nondiscretionary, it is important to take account of such inputs in a manner that is reflected in the measures of operational efficiency. Only in two special environments, including markets with high monopoly and economies where a state-owned company or a large organization can influence some environmental factors such as consumer price, may environmental factors be discretionary. However, the retailing industry is not in these special cases, and environmental factors are nondiscretionary. Thus, we consider a retailing industry where environmental factors can be nondiscretionary in a market with full competition. For example, the market share of total grocery spending is just 2.54% from top 5 grocery retailers (China Resources Enterprise, Auchan, Walmart, Lianhua, Carrefour) in China and just 22.40% from top 5 grocery retailers (Walmart, Kroger, Walgreens, CVS, Costco) in the USA (Country reports, PlanetRetail, 2013). It is difficult even for the largest retailer CRE (with 0.73% market share) in China and Walmart (with 9.58% market share) in the USA to significantly influence environmental factors.
Nondiscretionary analysis in the DEA model can be an appropriate tool and enable an accurate understanding of firm performance. Nondiscretionary analysis, as an appropriate business measurement tool, is a new method for environment analysis in a GOS study. Compared with the traditional PESTLE (political, economic, social, technological, legal, and environmental) analysis, nondiscretionary analysis provides an accurate quantitative framework of competitive environmental factors used in the environmental components of GOS. We therefore identify the following gap: the perspective of nondiscretionary factors has not been applied in the competitive environment analysis of a global retailing operations strategy.
Considering the three research gaps, our research question therefore is, “Can we find an accurate quantitative approach to conduct a competitive environment analysis for a global operations strategy considering multiple inputs, multiple outputs, and optimal operational efficiency in the retailing industry?”
To answer this research question and understand the competitive environment in retailing clearly, it is important to build an accurate framework to measure the efficiency of supply chains in different environments when considering the multiple inputs from both internal operations and external environments in retailers. DEA, particularly suited to conceptualize and measure firm-specific capabilities (Dutta et al., 2005; Zhu, 2014), can be used by researchers to conduct environment analysis in GOS, although DEA methods are never used in a competitive analysis of GOS as far as we know.
Using data from 124 organizations in the retailing industry, we develop a nondiscretionary operational evaluation framework in retailing, integrating DEA and econometric methods in efficiency measuring, to study the relationships between facility inputs of the supply chain, environmental inputs of environment analysis, and business performance in retailing. We consider the direct impact of facility and environmental inputs on business performance and build a nondiscretionary DEA model to assess how facility and environmental inputs can influence business performance. We adopt a multinomial logit (MNL) to conduct an empirical analysis in values of operational efficiencies provided by DEA and use hierarchical regression analysis to test the results correlated with the DEA model. Our nondiscretionary DEA model, which is a nonparametric approach, provides a new perspective to understanding the performance of retailing using supply chain facilities in different competitive environments and test the influence of competitive environmental factors on supply chain efficiency. Our model captures the relationship of facility inputs and the productivity of different DMUs based on multiple input-output data, considering nondiscretionary environmental inputs.
Section snippets
Competitive environmental analysis
Competitive environmental analysis is an integral part of systematic strategic planning (Ginter and Duncan, 1990). One of the major implications of the enacted environment concept for the strategic management theory is the prescription that organizations should adapt to their environments (Smircich and Stubbart, 1985). The relationship between strategy making and environment, besides the relationships between strategy and structure, and between environment and structure, tends to be stronger in
Inputs and outputs of DMU
In the operational decisions process in retailing, we consider three discretionary inputs : the number of outlets which is critical to downstream supply chain operations (Hollander, 1960; Chopra and Meindl, 2012), the number of warehouses which is critical to internal supply chain operations (Beamon, 1998; Gong and de Koster, 2011), and the number of suppliers which is important to upstream supply chain operations (Dedrick et al., 2008; Chopra and Meindl, 2012). By optimizing the number of
Data
We applied our nondiscretionary DEA model to a real multiple-source data set for 124 retailing DMUs. Data were collected from multiple sources, including first-hand and second-hand data. We obtained detailed information on output and input quantities from firms and were also able to obtain all the measures from managers.
The main data sources are from the second-hand data of the database “PlanetRetail”. We obtained detailed information and all the indicators on three outputs and six inputs from
Implications in methodologies
We proposed an integrated nondiscretionary approach for competitive environment analysis, including six steps: factor identification, data collection, model building and nonparametric methods, model analysis, result verification and exploration with parametric methods, and result explaining. We summarize the research steps in Table 6.
“In recent years, there is an emerging trend toward employing a multi-methodological approach (MMA) to address complex and intriguing OM issues.” (Choi et al., 2016
Concluding remarks
This paper studies the competitive environment analysis in a global operations strategy by integrating a DEA methodology and econometric analysis to explore the relationship between competitive environmental factors, inputs, and operational efficiency in different industrial environments. The main contribution is to develop a new six-step methodological framework based on nondiscretionary DEA and econometric models, considering multiple nondiscretionary inputs and multiple outputs, to study and
Acknowledgement
This work was supported by NSFC under Grant 71620107002, NSSFC under Grant 16ZDA013, and the Chutian Scholarship.
Jiawen Liu got a PhD at School of Management, Huazhong University of Science and Technology, and was a visiting PhD student at Worcester Polytechnic Institute. His research interest is DEA and Operations Strategy.
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2021, Computers and Industrial EngineeringCitation Excerpt :For instance, Sinuany-Stern and Friedman (1998) proposed a discriminant data envelopment analysis of ratios (DR/DEA) approach to rank all units on the same scale. Liu, Gong, Zhu and Zhang (2018) developed a nondiscretionary data envelopment analysis model to assess performance in retailing and integrated it with econometric analysis (e.g., hierarchical regression). Principal component analysis and factor analysis are usually employed for the purpose of classifying the variables and reducing the number of dimensions (Andrew, Andrea, Carmela, & Gerardo, 2021).
Jiawen Liu got a PhD at School of Management, Huazhong University of Science and Technology, and was a visiting PhD student at Worcester Polytechnic Institute. His research interest is DEA and Operations Strategy.
Yeming (Yale) Gong is a Professor of Management Science at emlyon business school. He holds a Ph.D. from Rotterdam School of Management, Erasmus University, Netherlands, and an MSc from INSEAD, France. He was a post-doc researcher at University of Chicago, USA. Prof. Yeming (Yale) Gong studies Operations Strategy, Logistics, and Supply Chain Management. He has published two books “Stochastic Modelling and Analysis of Warehouse Operations” in Erasmus and “Global Operations Strategy: Fundamentals and Practice” in Springer. He published articles in journals like Production and Operations Management, IIE Transaction, European Journal of Operational Research, International Journal of Production Economics, International Journal of Production Research, and IEEE Transactions on Engineering Management.
Joe Zhu is Professor of Operations Analytics in the Foisie Business School, Worcester Polytechnic Institute. He is an expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA), and his research interests are in the areas of operations and business analytics, productivity modeling, and performance evaluation and benchmarking. He has published and co-edited several books focusing on performance evaluation and benchmarking using DEA and developed the DEAFrontier software. He has published extensively on peer reviewed journals such as Management Science, Operations Research, European Journal of Operational Research, Journal of Operational Research Society, IIE Transactions, Sloan Management Review, OMEGA, and others.
Jinlong Zhang is a Senior Professor of Management Science and Chair of Research Center at the School of Management, Huazhong University of Science and Technology. He has published articles on peer reviewed journals such as European Journal of Operational Research, International Journal of Production Economics, Information Systems Journal, International Journal of Project Management, Expert Systems with Applications, Knowledge-Based Systems, and International Journal of Production Research.