International Journal of Physical Distribution & Logistics Management

Rahman, Shams; Ahsan, Kamrul; Sohal, Amrik; Oloruntoba, Richard

2022 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-08-2022-518

Acar, Mehmet Fatih; Özer Torgalöz, Alev; Eryarsoy, Enes; Zaim, Selim

2022 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-05-2021-0204

This paper aims to clarify the effects of learning culture and trust on supply chain resilience (SCR) and to investigate their role specifically during COVID-19 pandemic to aid decision-makers. For this, a conceptual model proposing relations between variables was developed. The focal point of this research is to investigate the relationship between organizational learning culture (OLC) and SCR, and the mediating effect of supplier trust (ST) in the relationship before and during a pandemic.Design/methodology/approach This study relies on a unique dataset collected through two separate cross-sectional surveys corresponding to pre- and during-pandemic times that were conducted at the same time. The questionnaire was collected from 245 medium- to senior-level managers, to ensure a thorough understanding about the company’s inner workings and supply chain (SC). To test the proposed research model, the authors processed their data and model using lavaan package in R.FindingsThe findings show that OLC and ST have positive and significant effects on SCR. Furthermore, learning culture also triggers ST. Thus, it is ST that explained, as a mediator, the positive effects of OLC on SCR. All these findings are similar for both before and after the pandemic. A critical finding is about the effect of size (small vs. large) and ownership (local vs. multinational). The analysis suggests that during pandemic multinational companies and larger organizations exhibit higher SCR than their counterparts.Research limitations/implications First, responses to the questionnaire were collected from only one country. Cross-cultural comparisons can be made by collecting data from different countries in future research. Second, the data were obtained from companies operating in different sectors, with a majority in manufacturing. It is possible to obtain more specific findings by analyzing responses from a specific industry. Third, results of this study reflect responses of only SC and manufacturing managers, but other departments such as marketing or finance can also complement the findings. Finally, several other organizational variables may be factored in as moderators to enrich the conceptual model.Practical implicationsThe authors believe that findings of this research will guide shareholders and managers to develop effective strategies in order to prevent SC disruptions during similar risk/shock scenarios.Originality/value Similar to earlier research, this study considers the importance of ST on SCR. But this study differs in analyzing the effects of OLC on SCR directly and in taking the mediating effect of ST into account. The authors test the strengths of these relationships individually before and during COVID-19 pandemic. Under pandemic conditions, the authors present empirical evidence on the effects of organizational learning and ST on SCR. In contrast to previous research on SCR, this study connotes the importance of an organization’s internal dynamic capabilities in developing resilience.

Mariappan, Mahesh Babu; Devi, Kanniga; Venkataraman, Yegnanarayanan; Fosso Wamba, Samuel

2022 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-05-2021-0192

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.Design/methodology/approachThe researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.FindingsThe proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.Research limitations/implicationsThe implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.Practical implicationsE-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.Originality/valueThe present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

Nikookar, Ethan; Yanadori, Yoshio

2022 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-05-2021-0167

Rethinking how to build resilience in supply chains is once again highlighted by COVID-19. Research on supply chain resilience has established flexibility as a firm-level antecedent that contributes to supply chain resilience. However, the authors know little about how supply chain flexibility is developed within a firm. Drawing on social capital theory, the authors claim that the way supply chain managers are embedded in their social networks plays a critical role in developing this antecedent. Specifically, the authors hypothesize that supply chain managers' structural and relational embeddedness in their reference network, comprised of individuals from whom they seek advice, is instrumental to developing supply chain flexibility, which subsequently enhances the firm's supply chain resilience.Design/methodology/approachSurvey data collected from 485 manufacturing firms in Australia and Hayes and Preacher's (2014) parallel multiple mediator model were employed to empirically test the hypotheses.FindingsThe findings of the study establish that supply chain managers' structural and relational embeddedness in their reference network indeed have implications for developing supply chain resilience. Furthermore, the mediator through which managers' social embeddedness influences supply chain resilience is identified in the current study.Originality/valueThe study contributes to the extant literature on supply chain resilience, investigating the role that supply chain managers' social capital play in developing the resilience of their firm.

Spieske, Alexander; Gebhardt, Maximilian; Kopyto, Matthias; Birkel, Hendrik; Hartmann, Evi

2022 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-06-2021-0231

The coronavirus disease 2019 (COVID-19) pandemic unveiled resilience deficits in supply chains. Scholars and practitioners aim to identify supply chain resilience (SCRES) measures suitable for this unique disruption; however, empirical evidence on a pandemic's specific characteristics, resulting challenges, and suitable countermeasures is scarce.Design/methodology/approachA single-case study on an automotive supply chain network (ASCN), including eight nodes, was conducted. Based on current research and interviews with 35 experts, characteristic pandemic challenges for the ASCN were identified. Moreover, promising SCRES measures were determined along the most prominent SCRES levers. The findings lead to five central propositions and advance organizational information processing theory in the context of SCRES.FindingsThis study’s results confirm unique pandemic characteristics along the supply chain disruption's duration, severity, propagation, and volatility. The resulting unprecedented challenges made the ASCN apply novel SCRES measures, particularly regarding collaboration and risk management culture. However, well-known visibility and flexibility strategies were also suitable. Overall, agility and collaboration measures showed the highest capacity to address characteristic pandemic challenges. A lack of preparedness impeded some measures' application, calling for enhanced proactive risk management following the pandemic.Originality/valueThis paper addresses several research calls by providing in-depth empirical evidence on hitherto conceptually researched pandemic characteristics, challenges, and suitable SCRES measures from a network perspective. The study uncovers the different perceptions of individual tiers, emphasizing the need to analyze supply chain disruptions from multiple angles.