Cluster analysis in empirical OM research: survey and recommendations

Purpose The purpose of this paper is twofold. First, the authors provide a survey of operations management (OM) research applications of traditional hierarchical and nonhierarchical clustering methods with respect to key decisions that are central to a valid analysis. Second, the authors offer recommendations for practice with respect to these decisions. Design/methodology/approach A coding study was conducted for 97 cluster analyses reported in six OM journals during the period spanning 1994-2015. Data were collected with respect to: variable selection, variable standardization, method, selection of the number of clusters, consistency/stability of the clustering solution, and profiling of the clusters based on exogenous variables. Recommended practices for validation of clustering solutions are provided within the context of this framework. Findings There is considerable variability across clustering applications with respect to the components of validation, as well as a mix of productive and undesirable practices. This justifies the importance of the authors’ provision of a schema for conducting a cluster analysis. Research limitations/implications Certain aspects of the coding study required some degree of subjectivity with respect to interpretation or classification. However, in light of the sheer magnitude of the coding study (97 articles), the authors are confident that an accurate picture of empirical OM clustering applications has been presented. Practical implications The paper provides a critique and synthesis of the practice of cluster analysis in OM research. The coding study provides a thorough foundation for how the key decisions of a cluster analysis have been previously handled in the literature. Both researchers and practitioners are provided with guidelines for performing a valid cluster analysis. Originality/value To the best of the authors’ knowledge, no study of this type has been reported in the OM literature. The authors’ recommendations for cluster validation draw from recent studies in other disciplines that are apt to be unfamiliar to many OM researchers.

[1]  Varun Grover,et al.  Interorganizational System Characteristics and Supply Chain Integration: An Empirical Assessment , 2011, Decis. Sci..

[2]  Michael Y. Hu,et al.  The human factor in advanced manufacturing technology adoption , 1998 .

[3]  Soo Wook Kim,et al.  Disentangling leanness and agility: An empirical investigation , 2006 .

[4]  Karl T. Ulrich,et al.  Opportunity Spaces in Innovation: Empirical Analysis of Large Samples of Ideas , 2011, Manag. Sci..

[5]  L C Morey,et al.  A Comparison of Cluster Analysis Techniques Withing a Sequential Validation Framework. , 1983, Multivariate behavioral research.

[6]  Peter T. Ward,et al.  Business strategies and manufacturing decisions: An empirical examination of linkages , 2007 .

[7]  T. Jambulingam,et al.  Entrepreneurial orientation as a basis for classification within a service industry: the case of retail pharmacy industry , 2005 .

[8]  Douglas Steinley,et al.  A New Variable Weighting and Selection Procedure for K-means Cluster Analysis , 2008, Multivariate behavioral research.

[9]  Wei-Chien Chang.  On using Principal Components before Separating a Mixture of Two Multivariate Normal Distributions , 1983 .

[10]  A. Roth,et al.  A taxonomy of manufacturing strategies , 1994 .

[11]  Mohamed A. Youssef.  The Impact of the Intensity Level of Computer‐based Technologies on Quality , 1994 .

[12]  Tobias Schoenherr,et al.  Revisiting the arcs of integration: Cross-validations and extensions , 2012 .

[13]  Antti Tenhiälä,et al.  Order Management in the Customization-Responsiveness Squeeze , 2012, Decis. Sci..

[14]  Viswanath Venkatesh,et al.  Designing E-Government Services: Key Service Attributes and Citizens’ Preference Structures , 2012 .

[15]  M. Noble,et al.  Manufacturing Strategy: Testing the Cumulative Model in a Multiple Country Context* , 1995 .

[16]  Markham T. Frohlich,et al.  e-Integration in the Supply Chain: Barriers and Performance , 2002, Decis. Sci..

[17]  Serden Özcan,et al.  How Does Rivals' Presence Affect Firms' Decision to Enter New Markets? Economic and Sociological Explanations , 2013, Manag. Sci..

[18]  G. D. Silveira,et al.  Compensation‐based incentives, ERP and delivery performance: Analysis from production and improvement perspectives , 2013 .

[19]  S. Wathen.  Manufacturing strategy in business units , 1995 .

[20]  P. Jonsson.  An empirical taxonomy of advanced manufacturing technology , 2000 .

[21]  Maria Fernanda Hijjar,et al.  Logistics sophistication, manufacturing segments and the choice of logistics providers , 2007 .

[22]  L. Hubert,et al.  A general statistical framework for assessing categorical clustering in free recall. , 1976 .

[23]  Ravi Kathuria.  Competitive priorities and managerial performance: a taxonomy of small manufacturers , 2000 .

[24]  Chin-Shan Lu,et al.  Segmenting manufacturers' investment incentive preferences for international logistics zones , 2008 .

[25]  M. S. Díaz,et al.  A view of developing patterns of investment in AMT through empirical taxonomies: new evidence , 2003 .

[26]  M. Frohlich,et al.  Arcs of integration: an international study of supply chain strategies , 2001 .

[27]  Douglas Steinley,et al.  Standardizing Variables in K -means Clustering , 2004 .

[28]  Roger G. Schroeder,et al.  Refining the product‐process matrix , 2002 .

[29]  G. Stock,et al.  Enterprise logistics and supply chain structure: the role of fit , 2000 .

[30]  D. R. Dennis,et al.  An analysis of process industry production and inventory management systems , 2000 .

[31]  Andrea Masini,et al.  ERP Competence-Building Mechanisms: An Exploratory Investigation of Configurations of ERP Adopters in the European and U.S. Manufacturing Sectors , 2009, Manuf. Serv. Oper. Manag..

[32]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[33]  R. Cagliano,et al.  Evolutionary patterns in e‐business strategy , 2009 .

[34]  Mandar Dabhilkar,et al.  Converging production models: the STS versus lean production debate revisited , 2013 .

[35]  Chao-Hsien Chu.  Cluster analysis in manufacturing cellular formation , 1989 .

[36]  Mahmoud M. Yasin,et al.  Performance measurement practices in manufacturing firms revisited , 2011 .

[37]  Jan A. Van Mieghem,et al.  Clickstream Data and Inventory Management: Model and Empirical Analysis , 2014 .

[38]  D. Steinley.  Profiling local optima in K-means clustering: developing a diagnostic technique. , 2006, Psychological methods.

[39]  Chwen Sheu,et al.  The Impact of Competitive Strategy and Supply Chain Strategy on Business Performance: The Role of Environmental Uncertainty , 2011, Decis. Sci..

[40]  Andrea Bonaccorsi,et al.  Entry Strategies Under Competing Standards: Hybrid Business Models in the Open Source Software Industry , 2006, Manag. Sci..

[41]  P. Hines,et al.  Demand chain management: an integrative approach in automotive retailing , 2002 .

[42]  José Carlos Tiomatsu Oyadomari,et al.  Does the competitive orientation really lead to emphasis on different internal capabilities , 2015 .

[43]  Shawn P. Curley,et al.  Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions , 2012, Manag. Sci..

[44]  Brian Squire,et al.  An empirical taxonomy of purchasing functions , 2006 .

[45]  Mark Pagell,et al.  Are safety and operational effectiveness contradictory requirements: : The roles of routines and relational coordination , 2015 .

[46]  Robert D. Klassen,et al.  Socially Responsible Practices: An Exploratory Study on Scale Development using Stakeholder Theory , 2014, Decis. Sci..

[47]  A. Longoni,et al.  Environmental and social sustainability priorities: Their integration in operations strategies , 2015 .

[48]  Jan Olhager,et al.  Plant roles: Site competence bundles and their relationships with site location factors and performance , 2013 .

[49]  Chee-Chuong Sum,et al.  A taxonomy of manufacturing strategies in China , 2006 .

[50]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[51]  Steve Brown,et al.  The contribution of manufacturing strategy involvement and alignment to world-class manufacturing performance , 2007 .

[52]  Concepción Maroto,et al.  Operations strategy configurations in project process firms , 2005 .

[53]  Ke-Lin Du,et al.  Clustering: A neural network approach , 2010, Neural Networks.

[54]  Timothy M. Laseter,et al.  Through the service operations strategy looking glass: Influence of industrial sector, ownership, and service offerings on B2B e-marketplace failures , 2011 .

[55]  Larry P. Ritzman,et al.  REVISITING ALTERNATIVE THEORETICAL PARADIGMS IN MANUFACTURING STRATEGY , 2000 .

[56]  Michael J. Brusco,et al.  Emergent clustering methods for empirical OM research , 2012 .

[57]  D. Steinley.  Properties of the Hubert-Arabie adjusted Rand index. , 2004, Psychological methods.

[58]  B. Talbot,et al.  The internationalization process model through the lens of the global color picture tube industry , 1998 .

[59]  R. Narasimhan,et al.  Effect of supply chain integration on the relationship between diversification and performance: evidence from Japanese and Korean firms , 2002 .

[60]  G. W. Milligan,et al.  A study of standardization of variables in cluster analysis , 1988 .

[61]  Kenneth K. Boyer,et al.  Supply chain information flow strategies: an empirical taxonomy , 2009 .

[62]  Paul E. Green,et al.  A Computational Study of Replicated Clustering with an Application to Market Segmentation , 1991 .

[63]  Girish N. Punj,et al.  Cluster Analysis in Marketing Research: Review and Suggestions for Application , 1983 .

[64]  Ravi Kathuria,et al.  Leadership practices, competitive priorities, and manufacturing group performance , 2010 .

[65]  Thomas Langer,et al.  What are Investors Willing to Pay to Customize Their Investment Product? , 2009, Manag. Sci..

[66]  José Pinto Paixão,et al.  Using clustering analysis in a capacitated location-routing problem , 2007, Eur. J. Oper. Res..

[67]  J. Breckenridge.  Replicating Cluster Analysis: Method, Consistency, and Validity. , 1989, Multivariate behavioral research.

[68]  M. Brusco,et al.  A variable-selection heuristic for K-means clustering , 2001 .

[69]  Kirk R. Karwan,et al.  The Effects of Severity of Failure and Customer Loyalty on Service Recovery Strategies , 2004 .

[70]  Stefano Ronchi,et al.  A transaction costs approach to purchasing portfolio management , 2012 .

[71]  Gregory R. Heim,et al.  SERVICE PROCESS CONFIGURATIONS IN ELECTRONIC RETAILING: A TAXONOMIC ANALYSIS OF ELECTRONIC FOOD RETAILERS , 2002 .

[72]  Tobias Schoenherr,et al.  An Exploratory Study of Procurement Strategies for Multi‐Item RFQs in B2B Markets: Antecedents and Impact on Performance , 2011 .

[73]  Gianluca Spina,et al.  Advanced manufacturing technologies and strategically flexible production , 2000 .

[74]  Soo Wook Kim,et al.  An exploratory study of manufacturing practice and performance interrelationships: Implications for capability progression , 2005 .

[75]  Chee-Chuong Sum,et al.  A Taxonomy of Operations Strategies of High Performing Small and Medium Enterprises in Singapore , 2004 .

[76]  Srinivas Talluri,et al.  Faster, better, cheaper: A study of NPD project efficiency and performance tradeoffs , 2006 .

[77]  Kenneth K. Boyer,et al.  Analysis of Effects of Operational Execution on Repeat Purchasing for Heterogeneous Customer Segments , 2006 .

[78]  Peter T. Ward,et al.  A mapping of competitive priorities, manufacturing practices, and operational performance in groups of Danish manufacturing companies , 2003 .

[79]  Morgan Swink,et al.  Patterns of Advanced Manufacturing Technology Utilization and Manufacturing Capabilities , 2009 .

[80]  Ioannis Ioannou,et al.  The Impact of Corporate Sustainability on Organizational Processes and Performance , 2012, Manag. Sci..

[81]  Andy C.L. Yeung,et al.  An empirical taxonomy for quality management systems: A study of the Hong Kong electronics industry , 2003 .

[82]  Xiande Zhao,et al.  Quality management and organizational context in selected service industries of China , 2004 .

[83]  A. Roth,et al.  The effect of an ambidextrous supply chain strategy on combinative competitive capabilities and business performance , 2010 .

[84]  Robert D. Klassen,et al.  Environmental Management in Operations: The Selection of Environmental Technologies* , 1999 .

[85]  Michiya Morita,et al.  The linkage among management systems, practices and behaviour in successful manufacturing strategy , 1997 .

[86]  M. Noble,et al.  Manufacturing competitive priorities and productivity: an empirical study , 1997 .

[87]  Mohamed A. Youssef.  Design for manufacturability and time‐to‐market , 1995 .

[88]  David J. Ketchen,et al.  THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE , 1996 .

[89]  Varun Grover,et al.  Examining the Impact of Interorganizational Systems on Process Efficiency and Sourcing Leverage in Buyer-Supplier Dyads , 2005, Decis. Sci..

[90]  S. Moss,et al.  Comparing IT success in manufacturing and service industries , 2001 .

[91]  Karen E. Papke-Shields,et al.  Evolution in the strategic manufacturing planning process of organizations , 2006 .

[92]  M. Brusco,et al.  The p-median model as a tool for clustering psychological data. , 2010, Psychological methods.

[93]  R. Blashfield,et al.  A Nearest-Centroid Technique for Evaluating the Minimum-Variance Clustering Procedure. , 1980 .

[94]  R. Cagliano,et al.  E‐business strategy , 2003 .

[95]  María Luz Martín-Peña,et al.  A taxonomy of manufacturing strategies in Spanish companies , 2008 .

[96]  Phipps Arabie,et al.  Three-way scaling and clustering. , 1987 .

[97]  Kenneth K. Boyer,et al.  Factors influencing the utilization of Internet purchasing in small organizations , 2003 .

[98]  Ruggero Golini,et al.  The impact of country culture on the adoption of new forms of work organization , 2011 .

[99]  G. W. Milligan,et al.  An examination of procedures for determining the number of clusters in a data set , 1985 .

[100]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[101]  Rohit Verma,et al.  Configurations of low-contact services , 2000 .

[102]  P. Jonsson.  Towards an holistic understanding of disruptions in Operations Management , 2000 .

[103]  Mahmoud M. Yasin,et al.  An examination of manufacturing organizations' performance evaluation: Analysis, implications and a framework for future research , 2004 .

[104]  S. N. Wasti,et al.  Buyer‐supplier relationships in the Turkish automotive industry , 2006 .

[105]  Barbara B. Flynn,et al.  The impact of supply chain integration on performance: A contingency and configuration approach , 2010 .

[106]  M. Frohlich,et al.  Demand chain management in manufacturing and services: web-based integration, drivers and performance , 2002 .

[107]  Carsten Zimmermann,et al.  Linking strategic flexibility and operational efficiency: the mediating role of ambidextrous operational capabilities , 2014 .

[108]  M. Brusco,et al.  Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures , 2008 .

[109]  Zeinep Aksin Karaesman,et al.  Effective strategies for internal outsourcing and offshoring of business services: An empirical investigation , 2008 .

[110]  R. Schroeder,et al.  Strategic, structural contingency and institutional explanations in the adoption of innovative manufacturing practices , 2004 .

[111]  Yinggang Zhou,et al.  Credit Risk Spillovers among Financial Institutions around the Global Credit Crisis: Firm-Level Evidence , 2012 .

[112]  Elliot Bendoly,et al.  Performance Metric Portfolios: A Framework and Empirical Analysis , 2007 .

[113]  P. Hansen,et al.  Variable neighborhood search for the p-median , 1997 .

[114]  J. Breckenridge,et al.  Validating Cluster Analysis: Consistent Replication and Symmetry , 2000, Multivariate behavioral research.

[115]  Douglas Steinley,et al.  Local optima in K-means clustering: what you don't know may hurt you. , 2003, Psychological methods.

[116]  D. Lehmann.  Market research and analysis , 1979 .

[117]  J. R. Dixon,et al.  A taxonomy of manufacturing strategies revisited , 2001 .

[118]  Jack R. Meredith,et al.  An Empirical Analysis of Process Industry Transformation Systems , 2000 .

[119]  Arnoud De Meyer,et al.  A Typology of Plants in Global Manufacturing Networks , 2006, Manag. Sci..

[120]  M. Salanova,et al.  Information technology implementation styles and their relation with workers’ subjective well‐being , 2004 .

[121]  M. Brusco,et al.  Choosing the number of clusters in Κ-means clustering. , 2011, Psychological methods.

[122]  H. Boer,et al.  Patterns of change in manufacturing strategy configurations , 2005 .

[123]  Aleda V. Roth,et al.  Agility in Retail Banking: A Numerical Taxonomy of Strategic Service Groups , 2001, Manuf. Serv. Oper. Manag..

[124]  A. Bhalla,et al.  Is more IT offshoring better? An exploratory study of western companies offshoring to South East Asia , 2008 .

[125]  Xiande Zhao,et al.  Supply Chain Strategy, Product Characteristics, and Performance Impact: Evidence from Chinese Manufacturers , 2009, Decis. Sci..

[126]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[127]  Kenneth K. Boyer,et al.  Approaches to the factory of the future. An empirical taxonomy , 1996 .

[128]  Michael J. Brusco,et al.  Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques , 2007, J. Classif..

[129]  Yi Qian,et al.  Counterfeiters: Foes or Friends? How Counterfeits Affect Sales by Product Quality Tier , 2014, Manag. Sci..