A Triple-A supply chain measurement model: validation and analysis

"This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here https://doi.org/10.1108/IJPDLM-06-2018-0233. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited"[EN] Purpose The purpose of this paper is to establish definitions and dimensions of Triple-A supply chain (SC) variables based on a literature review and to validate a Triple-A SC measurement model using a worldwide multiple informant sample. Design/methodology/approach Following a literature review, Triple-A SC variables (agility, alignment and adaptability) are conceptualized and a list of possible items is created for their measurement. An international 309 plant sample is used to validate the convergent and criterion validities of the composites proposed to measure Triple-A SC. Findings Contributions to the literature: clarification of Triple-A SC variable concepts; identification of key dimensions of Triple-A SC variables; development of a validated Triple-A SC measurement scale for future empirical research and industrial applications. Research limitations/implications A rigorously validated instrument is needed to measure Triple-A SC variables and enable researchers to credibly test theories regarding causal links between capabilities, practices and performance. Practical implications Proposal of a scale for use by managers of different functions to analyze Triple-A SC deployment in the company. Originality/value The only Triple-A SC scale used in the previous literature has serious limitations: scales were not taken from an extended literature review; data were collected from single respondents in a single country. This is the first validated Triple-A SC measurement model to overcome these limitations.This study has been conducted within the frameworks of the following projects: 'Accion especial SGUIT-2015 (SBAPA2015-06) HPM-(Project 2015/148 U.S.)-Junta de Andalucia (Spain); PAIDI Excellence Project P08-SEJ-0384-Junta de Andalucia (Spain); and DPI2009-11148-Spanish National Program of Industrial Design and Production.Marin-Garcia, JA.; Alfalla-Luque, R.; Machuca, JA. (2018). A Triple-A supply chain measurement model: validation and analysis. International Journal of Physical Distribution and Logistics Management. 48(10):976-994. https://doi.org/10.1108/IJPDLM-06-2018-0233S9769944810Agarwal, A., Shankar, R., & Tiwari, M. K. (2007). Modeling agility of supply chain. Industrial Marketing Management, 36(4), 443-457. doi:10.1016/j.indmarman.2005.12.004Alfalla-Luque, R., & Medina-López, C. (2009). Supply Chain Management: Unheard of in the 1970s, core to today’s company. 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A Systematic Literature Review

Purpose – The purpose of this paper is to provide groundwork for an emerging theory of supply chain robustness – which has been conceptualized as a dimension of supply chain resilience – through reviewing and synthesizing related yet disconnected studies. The paper develops a formal definition of supply chain robustness to build a framework that captures the dimensions, antecedents and moderators of the construct as discussed in the literature. Design/methodology/approach – The authors apply a systematic literature review approach. In order to reduce researcher bias, they involve a team of academics, librarians and managers. Findings – The paper (1) provides a formal definition of supply chain robustness, (2) builds a theoretical framework of supply chain robustness that augments both causal and descriptive knowledge, (3) shows how findings in this review support practice and (4) reveals methodological insights on the use of journal rankings in reviews. Research limitations/implications – At this stage, managers may benefit from seeing these relationships as clues derived from the literature. The paper is fundamentally a call for researchers to conduct quantitative testing of such relationships to derive more reliable understanding and practical applications. Practical implications – Rather than presenting empirical findings, this paper reveals to managers that visibility, risk management orientation and reduced network complexity have been the main predictive antecedents of supply chain robustness (as discussed in the academic literature). This provides a potentially important signal as to where to invest resources. Originality/value – The study is the first to develop a formal definition of supply chain robustness and to establish a comprehensive theoretical framework for understanding the construct