A resilience model for cold chain logistics of perishable products

Abstract

Purpose Most of the extant literature on resilience builds on normative, conceptual or silo approaches, thereby lacking an integrative approach to cold chain logistics risks (CCLRs) and resilience. The purpose of this paper is to bridge the current research gap by developing a model, based on broad empirical evidence, of the interplay between CCLRs, resilience and firm performance (FP) in perishable product supply chains (PPSCs). Design/methodology/approach A mixed method approach is used with qualitative data from interviews and quantitative data from a survey across the supply chain. The analysis is framed by contingency theory and resource-based theory. Findings Four significant sources of CCLRs and six resources used to build resilience are identified. Then, supply chain resilience (SCR) as a moderator of the negative relationship between CCLRs and FP is corroborated. Practical implications The findings will help improve managerial understandings of critical sources of risks in cold chain logistics and resources indispensable to build resilience. The scope of the research is cold chain logistics for PPSCs, which has relevance to other cold supply chains as well. Originality/value While some theoretical frameworks suggest resilience being a moderator in the negative relationship between cold chain risks and a firm’s performance, this study empirically tests this relationship using the survey across the entire supply chain. A new empirically and theoretically driven definition of SCR is also developed.

Where is supply chain resilience research heading? A systematic and co-occurrence analysis

“…Cluster 5 revealed four methodological perspectives from our sampled articles (Table I), including: conceptual (33 studies e.g., Rice and Caniato, 2003;Ponomarov and Holcomb, 2009;Ivanov et al, 2018), simulation/mathematical modeling (48 studies e.g, Datta et al, 2007;Cardoso et al, 2015;Beheshtian et al, 2018), qualitative case studies (39 studies e.g., Blackhurst et al, 2005;Gunasekaran et al, 2011;Scheibe and Blackhurst, 2018) and surveybased methods (35)-single-method (31 studies e.g., Braunscheidel and Suresh, 2009;Mandal, 2012;Zhang et al, 2018), and mixed-methods (4 studies e.g., Pettit et al, 2013;Treiblmaier, 2018;Ali et al, 2018).…”

“…A theory-based study can confirm or extend an existing theory, and build a new theory(Ketchen and Hult, 201;Dubey et al, 2015), adding additional knowledge to the literature. Among 155 articles, 61 applied 11 theories, constituting: resource-based view (23 studies e.g.,Ponomarov and Holcomb, 2009;Ali et al, 2018;Yang and Hsu, 2018), relational view (7 studies e.g.,Wieland and Wallenburg, 2013;Gabler et al, 2017;Mandal, 2017), dynamic capability (7 studies e.g.,Golgeci and Ponomarov, 2013;Dabhilkar et al, 2016;Chowdhury and Quaddus, 2017), control theory (5 studies e.g.,Ivanov and Sokolov, 2013;Ivanov et al, 2016;Dolgui et al, 2018), complexity theory (6 studies e.g.,Day, 2014;Gunasekaran et al, 2015;Papadopoulos et al, 2017), contingency theory (3 studies e.g.,Brandon-Jones et al, 2014;Ali et al, 2018),Treiblmaier, 2018), normal accident theory (3 studies e.g.,Marley et al, 2014;Bode and Macdonald, 2017;Scheibe and Blackhurst, 2018), social capital theory (3 studies e.g.,Stewart et al, 2009;Akgün and Keskin, 2014;Bhattacharjya, 2018), game theory (2 studies e.g., Bakshi…”

Managing operational risks through knowledge sharing infoodsupply chains

“…In addition to the basic conceptualization of supply chain risk, unprecedented growth in literature on the performance impact of supply chain risk is evident. However, despite considerable advances most of the studies are either conceptual, non‐industry specific, normative, anecdotal or offer projection based mathematical models (Ali et al, 2018; Ali & Golgeci, 2019; Ho, Zheng, Yildiz, & Talluri, 2015). Although previous research has repeatedly highlighted the criticality of operational risks such as supply and demand mismatch, forecasting error, machine breakdown and high cost of business (Jiang et al, 2009; Kleindorfer & Saad, 2005; Lockamy & McCormack, 2010; Manuj & Mentzer, 2008; Sodhi, Son, & Tang, 2012; Wagner, Mizgier, & Papageorgiou, 2017), there is a lack of broad empirical evidence on the extent to which these risks undermine SCP, particularly in the context of FSCs.…”

“…The assessment of risks and risk mitigation initiatives work in tandem (Kleindorfer & Van Wassenhove, 2004). However, most studies discuss risk assessment and risk mitigation in isolation mostly through descriptive or conceptual frameworks (Ali, Nagalingam, & Gurd, 2018; Wagner & Bode, 2008). Drawing upon knowledge management literature, this study then presents knowledge sharing as a novel risk mitigation strategy—moderator on operational risk‐performance nexus.…”

Reliability Analysis and Optimization of Cold Chain Distribution System for Fresh Agricultural Products

“…Many scholars proposed principles or theories such as "3T principle" for frozen products [8], "3P theory" and "3C principle" for cooling process [9], "3M principle" and "3Q principle" for cold chain logistics management [10], which lay the foundation for the research of cold chain logistics [11]. Research on the distribution of fresh agricultural products mainly focuses on vehicle routing problem [12][13][14][15][16], location selection of logistics center [17][18][19][20], factor analysis and performance evaluation [21][22][23][24]. Only a few studies on the reliability of the cold chain logistics system.…”