Skip to main content

Supply Chain Evaluation and Methodologies

  • Chapter
  • First Online:
  • 1276 Accesses

Part of the book series: Management and Industrial Engineering ((MINEN))

Abstract

This chapter discusses some of the most common supply chain evaluation methodologies in the industrial context. The chapter first addresses the use of multivariate techniques; then, we discuss the goal and the characteristics of the methods employed in this research, which include linear regression and factor analysis. The goal of this chapter is to better understand Chap. 9, which provides a comprehensive description of the methodology followed in this book to explore the impact of a series of critical success factors on supply chain performance, namely supply chain competitiveness.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Akkermans HA, Bogerd P, Yücesan E, van Wassenhove LN (2003) The impact of ERP on supply chain management: exploratory findings from a European Delphi study. Eur J Oper Res 146(2):284–301. https://doi.org/10.1016/S0377-2217(02)00550-7

    Article  MATH  Google Scholar 

  • Arbuckle J (1997) Amos users’ guide, version 3.6. Marketing Division, SPSS Incorporated.

    Google Scholar 

  • Autry CW, Grawe SJ, Daugherty PJ, Richey RG (2010) The effects of technological turbulence and breadth on supply chain technology acceptance and adoption. J Oper Manag 28(6):522–536. https://doi.org/10.1016/j.jom.2010.03.001

    Article  Google Scholar 

  • Avelar-Sosa L, García-Alcaraz JL, Cedillo-Campos MG (2014) Techniques and attributes used in the supply chain performance measurement: tendencies. In: García-Alcaraz JL, Maldonado-Macías AA, Cortes-Robles G (eds) Lean manufacturing in the developing world: methodology, case studies and trends from Latin America. Springer, Cham, pp 517–541. https://doi.org/10.1007/978-3-319-04951-9_25

    Chapter  Google Scholar 

  • Barclay D, Higgins C, Thompson R (1995) The partial least squares (pls) approach to casual modeling: personal computer adoption ans use as an illustration

    Google Scholar 

  • Batista JM, Coenders G (2000) Modelos de ecuaciones estructurales: modelos para el análisis de relaciones causales.

    Google Scholar 

  • Bentler PM (1985) Theory and implementation of EQS: a structural equations program. BMDP Statistical Software

    Google Scholar 

  • Blalock H (1964) Causal inferences in nonexperimental research, University of North Carolina Press. citeulike-article-id:106824.

    Google Scholar 

  • Blome C, Paulraj A, Schuetz K (2014) Supply chain collaboration and sustainability: a profile deviation analysis. Int J Oper Prod Manag 34(5):639–663. https://doi.org/10.1108/IJOPM-11-2012-0515

    Article  Google Scholar 

  • Bollen KA (1989) A new incremental fit index for general structural equation models. Sociol Methods Res 17(3):303–316. https://doi.org/10.1177/0049124189017003004

    Article  MathSciNet  Google Scholar 

  • Boudon R (1965) A method of linear causal analysis: dependence analysis. Am Sociol Rev 30(3):365–374. https://doi.org/10.2307/2090717

    Article  Google Scholar 

  • Casas M (2002) Los modelos de ecuaciones estructurales y su aplicación en el Índice Europeo de Satisfacción del Cliente. In X Jornadas Madrid 2002-ASEPUMA. Madrid, España, pp 1–11

    Google Scholar 

  • Chan ATL, Ngai EWT, Moon KKL (2017) The effects of strategic and manufacturing flexibilities and supply chain agility on firm performance in the fashion industry. Eur J Oper Res 259(2):486–499. https://doi.org/10.1016/j.ejor.2016.11.006

    Article  MathSciNet  MATH  Google Scholar 

  • Chin WW (1998) The partial least squares approach to structural equation modeling. Modern Methods bus Res 295(2):295–336

    Google Scholar 

  • Chin WW (2010) How to write up and report PLS analyses. Handbook of partial least squares, pp 655–690

    Google Scholar 

  • Chin WW, Marcolin BL, Newsted PR (2003) A partial least squares latent variable modeling approach for measuring interaction effects: results from a monte carlo simulation study and an electronic-mail emotion/adoption study. Inf Syst Res 14(2):189–217. https://doi.org/10.1287/isre.14.2.189.16018

    Article  Google Scholar 

  • Chin WW, Newsted PR (1999) Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research, 1(1):307–341

    Google Scholar 

  • Diamantopoulos A (2008) Formative indicators: introduction to the special issue. J Bus Res 61(12):1201–1202

    Article  Google Scholar 

  • Duncan OD (1966) Path analysis: sociological examples. Am J Sociol 72(1):1–16. https://doi.org/10.1086/224256

    Article  Google Scholar 

  • Fornell C (1982) A second generation of multivariate analysis. 2. Measurement and evaluation, vol 2. Praeger

    Google Scholar 

  • Fornell C (1983) Issues in the application of covariance structure analysis: a comment. J Consum Res 9(4):443–448

    Article  Google Scholar 

  • Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Market Res 18(1):39–50. https://doi.org/10.2307/3151312

    Article  Google Scholar 

  • García Ochoa JJ, León Lara JdD, Nuño de la Parra JP (2017) Propuesta de un modelo de medición de la competitividad mediante análisis factorial. Contaduría y Administración 62(3):775–791. https://doi.org/10.1016/j.cya.2017.04.003

    Article  Google Scholar 

  • Gefen D, Straub D, Boudreau M-C (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst 4(1):7

    Google Scholar 

  • Govindan K, Azevedo SG, Carvalho H, Cruz-Machado V (2015) Lean, green and resilient practices influence on supply chain performance: interpretive structural modeling approach. Int J Environ Sci Technol 12(1):15–34. https://doi.org/10.1007/s13762-013-0409-7

    Article  Google Scholar 

  • Haenlein M, Kaplan AM (2004) A beginner’s guide to partial least squares analysis. Underst Stat 3(4):283–297. https://doi.org/10.1207/s15328031us0304_4

    Article  Google Scholar 

  • Hair JF, Anderson RE, Hair JF, Tatham R (2009) Análisis multivariante, texto en línea, consultado el 4

    Google Scholar 

  • Hair JF, Anderson RE, Tatham RL, Black WC (1999) Análisis multivariante, vol 491. Prentice Hall Madrid

    Google Scholar 

  • Hair JF, Ringle CM, Sarstedt M (2013) Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance.

    Article  Google Scholar 

  • Hair JF, Sarstedt M, Ringle CM, Mena JA (2012) An assessment of the use of partial least squares structural equation modeling in marketing research. J Acad Market Sci 40(3):414–433. https://doi.org/10.1007/s11747-011-0261-6

    Article  Google Scholar 

  • Jin Y, Vonderembse M, Ragu-Nathan TS, Smith JT (2014) Exploring relationships among IT-enabled sharing capability, supply chain flexibility, and competitive performance. International Journal of Production Economics 153 (Supplement C):24–34. https://doi.org/10.1016/j.ijpe.2014.03.016

    Article  Google Scholar 

  • Jöreskog KG (1988) Analysis of covariance structures. In: Nesselroade JR, Cattell RB (eds) Handbook of multivariate experimental psychology. Springer US, Boston, MA, pp 207–230. https://doi.org/10.1007/978-1-4613-0893-5_5

    Chapter  Google Scholar 

  • Jöreskog KG, Sörbom D (1986) LISREL VI: analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods. Scientific Software

    Google Scholar 

  • Jöreskog KG, van Thillo M (1973) Lisrel: a general computer program for estimating a linear structural equation system.

    Google Scholar 

  • Kim D, Cavusgil ST, Cavusgil E (2013) Does IT alignment between supply chain partners enhance customer value creation? An empirical investigation. Indust Market Manag 42(6):880–889. https://doi.org/10.1016/j.indmarman.2013.05.021

    Article  Google Scholar 

  • Kline RB (2005) Methodology in the social sciences. Principles and practice of structural equation modeling. Guilford Press, New York

    Google Scholar 

  • Lévy-Mangin JP, Varela J (2006) Modelización con estructuras de covarianzas en ciencias sociales. Temas esenciales, avanzados y aportaciones especiales. A Coruña, Netbiblo

    Book  Google Scholar 

  • Lévy J-P, Varela J (2003) Análisis multivariable para las ciencias sociales. Editorial Pearson Educación, Madrid

    Google Scholar 

  • Loehlin JC (1998) Latent variable models: an introduction to factor, path, and structural analysis. Lawrence Erlbaum Associates Publishers

    Google Scholar 

  • Loehlin JC, Beaujean AA (2016) Latent variable models: an introduction to factor, path, and structural equation analysis. Taylor & Francis

    Google Scholar 

  • Lomax RG, Schumacker RE (2012) A beginner’s guide to structural equation modeling. Routledge Academic New York, NY

    MATH  Google Scholar 

  • Lu C-S, K-h Lai, Cheng TCE (2007) Application of structural equation modeling to evaluate the intention of shippers to use Internet services in liner shipping. Eur J Oper Res 180(2):845–867. https://doi.org/10.1016/j.ejor.2006.05.001

    Article  MATH  Google Scholar 

  • Lu X-H, Huang L-H, Heng MSH (2006) Critical success factors of inter-organizational information systems—a case study of Cisco and Xiao Tong in China. Inf Manag 43(3):395–408. https://doi.org/10.1016/j.im.2005.06.007

    Article  Google Scholar 

  • Mangla S, Madaan J, Sarma PRS, Gupta MP (2014) Multi-objective decision modelling using interpretive structural modelling for green supply chains. Int J Logistics Syst Manag 17(2):125–142

    Article  Google Scholar 

  • Montgomery DC, Peck EA, Vining G (2006) Introducción al análisis de regresión lineal.

    Google Scholar 

  • Pérez E, Medrano LA, Sánchez Rosas J (2013) El path analysis: conceptos básicos y ejemplos de aplicación. Revista Argentina de Ciencias del Comportamiento 5(1):52–66

    Google Scholar 

  • Prajogo D, Oke A, Olhager J (2016) Supply chain processes: linking supply logistics integration, supply performance, lean processes and competitive performance. Int J Oper Prod Manag 36(2):220–238. https://doi.org/10.1108/IJOPM-03-2014-0129

    Article  Google Scholar 

  • Qrunfleh S, Tarafdar M (2014) Supply chain information systems strategy: impacts on supply chain performance and firm performance. International Journal of Production Economics 147 (Part B):340–350. https://doi.org/10.1016/j.ijpe.2012.09.018

    Article  Google Scholar 

  • Ramanathan U, Gunasekaran A (2014) Supply chain collaboration: impact of success in long-term partnerships. International Journal of Production Economics 147 (Part B):252–259. https://doi.org/10.1016/j.ijpe.2012.06.002

    Article  Google Scholar 

  • Ranganathan C, Teo TSH, Dhaliwal J (2011) Web-enabled supply chain management: Key antecedents and performance impacts. Int J Inf Manag 31(6):533–545. https://doi.org/10.1016/j.ijinfomgt.2011.02.004

    Article  Google Scholar 

  • Reinartz W, Haenlein M, Henseler J (2009) An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int J Res Market 26(4):332–344. https://doi.org/10.1016/j.ijresmar.2009.08.001

    Article  Google Scholar 

  • Ringle CM, Götz O, Wetzels M, Wilson B (2009a) On the use of formative measurement specifications in structural equation modeling: A Monte Carlo simulation study to compare covariance-based and partial least squares model estimation methodologies. http://dx.doi.org/10.2139/ssrn.2394054

  • Ringle CM, Sinkovics RR, Henseler J (2009b) The use of partial least squares path modeling in international marketing. In: New Challenges to International Marketing, vol 20. Advances in International Marketing, vol 20. Emerald Group Publishing Limited, pp 277–319. https://doi.org/10.1108/s1474-7979(2009)0000020014

  • Roldán JL, Cepeda G (2013) Modelos de Ecuaciones Estructurales basados en la Varianza: Partial Least Squares (PLS) para investigadores en Ciencias Sociales. Universidad de Sevilla, Curso del ICE

    Google Scholar 

  • Romero SJ, Ponsoda V, Ximénez C (2006) Validación de la estructura cognitiva del test de signos mediante modelos de ecuaciones estructurales. Psicothema 18(4):835–840

    Google Scholar 

  • Ruiz MA, Pardo A, San Martín R (2010) Modelos de ecuaciones estructurales. Papeles del Psicólogo 31(1):34–45

    Google Scholar 

  • Schumacker RE, Lomax RG (2004) A beginner’s guide to structural equation modeling. Psychology Press

    Google Scholar 

  • Silva C, Schiattino I (2008) Modelos de ecuaciones estructurales¿ qué es eso? Cienc Trab 10(29):106–110

    Google Scholar 

  • Sosik J, Kahai S, Piovoso M (2009) Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group Organ Manag 34(1):5–36. https://doi.org/10.1177/1059601108329198

    Article  Google Scholar 

  • Su Y-f, Yang C (2010a) A structural equation model for analyzing the impact of ERP on SCM. Expert Syst Appl 37(1):456–469. https://doi.org/10.1016/j.eswa.2009.05.061

    Article  Google Scholar 

  • Su Y-f, Yang C (2010b) Why are enterprise resource planning systems indispensable to supply chain management? Eur J Oper Res 203(1):81–94. https://doi.org/10.1016/j.ejor.2009.07.003

    Article  Google Scholar 

  • Subramanian N, Gunasekaran A, Yu J, Cheng J, Ning K (2014) Customer satisfaction and competitiveness in the Chinese E-retailing: structural equation modeling (SEM) approach to identify the role of quality factors. Expert Syst Appl 41(1):69–80. https://doi.org/10.1016/j.eswa.2013.07.012

    Article  Google Scholar 

  • Swafford PM, Ghosh S, Murthy N (2006) The antecedents of supply chain agility of a firm: scale development and model testing. J Oper Manag 24(2):170–188. https://doi.org/10.1016/j.jom.2005.05.002

    Article  Google Scholar 

  • Swafford PM, Ghosh S, Murthy N (2008) Achieving supply chain agility through IT integration and flexibility. Int J Prod Econ 116(2):288–297. https://doi.org/10.1016/j.ijpe.2008.09.002

    Article  Google Scholar 

  • Tarafdar M, Qrunfleh S (2017) Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility. Int J Prod Res 55(4):925–938. https://doi.org/10.1080/00207543.2016.1203079

    Article  Google Scholar 

  • Valencia JB, Torres AIZ, Paniagua CFO (2017) Variables e Índices de Competitividad de las Empresas Exportadoras, utilizando el PLS. CIMEXUS 10(2):13–32

    Google Scholar 

  • Weston R, Gore JPA (2006) A brief guide to structural equation modeling. Couns Psychol 34(5):719–751. https://doi.org/10.1177/0011000006286345

    Article  Google Scholar 

  • Williams LJ, Vandenberg RJ, Edwards JR (2009) Structural equation modeling in management research: a guide for improved analysis. Acad Manag Annals 3(1):543–604

    Article  Google Scholar 

  • Wold H (1985) Systems analysis by partial least squares. In Nijkamp P, Leitner H, Wrigley N (eds) Measuring the unmeasurable. Dordrecht, the Netherlands: Martinus Nijhoff, pp 221–252

    Chapter  Google Scholar 

  • Wright S (1932) The roles of mutation, inbreeding, crossbreeding, and selection in evolution, vol 1. na,

    Google Scholar 

  • Wright S (1971) Path coefficients and path regressions: alternative or complementary concepts. Causal models in the social sciences 101–114

    Google Scholar 

  • Zhang C, Dhaliwal J (2009) An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management. Int J Prod Econ 120(1):252–269. https://doi.org/10.1016/j.ijpe.2008.07.023

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liliana Avelar-Sosa .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Avelar-Sosa, L., García-Alcaraz, J.L., Maldonado-Macías, A.A. (2019). Supply Chain Evaluation and Methodologies. In: Evaluation of Supply Chain Performance. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-93876-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93876-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93875-2

  • Online ISBN: 978-3-319-93876-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics