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This paper aims at understanding the firm‐specific determinants of logistics outsourcing in a transitional economy. Some hypotheses are made and tested based on a survey conducted in 2002. The results show that factors such as financial strength, production and industrial type, management level at which the logistics decision is made can significantly affect outsourcing decisions. Other factors such as number of employees and ownership type are considered as well. Some of our findings are different from previous studies that target at developed countries. Implications for practitioners and areas for future research are considered at the end.

The learning outcomes of this paper are as follows: to understand the context of seaport logistics and supply chain design structure, to apply Monte Carlo simulation in the interface of the supply chain and to analyze the Monte Carlo simulation algorithm and statistical techniques for identifying the key seaport logistics factors.

It was 9:00 p.m. on November 10, 2020, and Nishadh Amonkar, the CEO of OCTO supply chain management (SCM) was glued to the television watching the final cricket match of the Indian Premier League, 2020. Amonkar’s mobile phone rang and it was a call from Vinod Nair, a member Logistics Panel of Ranji Industries Federation. Nair informed Amonkar that it was related to the rejection of several export consignments of agricultural products from Ranji (in the western part of India). The rejection was due to the deterioration in the quality of the exported agricultural products during transit from Ranji to various locations in Europe.

This course is suitable at the MBA level for the following courses: Operations research (Focus/Session: Applications on Monte Carlo Simulation). SCM (Focus/Session: Global SCM, Logistics Planning, Distribution Network). Logistics management (Focus/Session: Transportation Planning). Business statistics (Focus/Session: Application of Hypothesis Testing).

Teaching Notes are available for educators only.

CSS 9: Operations and logistics.

Asia has been transformed into a well-regulated dynamic platform for trade and is today world’s fastest-developing economic region. However, the increasing cross-border economic activities create new opportunities for corruption. The purpose of this paper is to assess the impact of corruption on trade facilitation using logistics performance index (LPI). This paper also examines the moderating effect of governance or government effectiveness (GE) on the relationship between corruption and LPI within Asian countries.

A panel of time-series data from year 2007 to 2014 of 26 Asian countries was collected for analysis. Static linear panel models which comprised of pooled ordinary least squares, fixed-effect model and random-effect model were utilised to analyse the panel data.

The findings show that corruption significantly affects LPI and each of the six dimensions in LPI. The results also show that governance or GE has a moderating effect on the relationship between corruption and LPI.

This study benefits Asian governments to gain a better understanding on influences of corruption on trade facilitation and triggering suggestions of a government role in the relationship. Practically, the results could be used as a guideline in improving national LPI. Besides, the findings could be used to support policy decision to modify corruption regulations at the national and regional levels.

This study reveals that the optimistic view of sands in the wheel overcomes the dark side of the grease in the wheel practices. To be corrupt free or less corrupt is a rare and inimitable resource capability that makes nations logistically competitive.

Disruptive technologies in the global logistics industry are often regarded as a threat to the existing business models of incumbents’ companies. Existing research, however, focuses mainly on whether technologies have disruptive potential, thereby neglecting when such disruptive transitions occur. To understand the timing of potential disruptive technological change, this paper aims to investigate the elements of the underlying ecosystem shaping these transitions.

Building on the established ecosystem framework from Adner and Kapoor (2016a), this paper constructs four categories of technology substitution to assess how quickly disruptive change may occur in the global logistics industry and defines key technology substitution determinants in logistics to emphasize the role of ecosystems for further consideration into disruptive innovation theory.

Based on the key determinants, this paper proposes first definitions of distinctive ecosystems elements linked to the three types of innovations, namely, sustaining innovations, low-end disruptions and new-market disruptions, thereby integrating ecosystems into Christensen’s (1997) disruptive innovation theory.

By developing a framework that conceptualizes the pace of technology substitution, this paper contributes to a more nuanced understanding of how logistics managers and academics can better predict disruptive transitions and develop strategies to allocate resources.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

The purpose of this paper is to present a decision‐making framework for outsourcing levels of logistics activities. These are: execution level of basic activities (such as transportation, warehousing); value‐added activities; planning and control level of activities (such as transportation and inventory management); and strategic decision‐making level of activities (distribution network design).

The research design comprises three stages. Literature review was undertaken to study outsourcing theories. Successively, case studies on three food manufacturers were conducted resulting in a framework for make‐or‐buy decision. Finally, an exploratory survey was undertaken to examine the determinant factors for outsourcing the different activities.

Results indicate that logistics activities at different levels are outsourced for different reasons. Three main determinant factors are identified: asset specificity, core closeness and supply chain complexity. This implies that the evaluation of outsourcing different activities requires insights of three theories, namely transaction cost, resource‐based and supply chain management theory.

The research and resulting framework are based on three small cases. Furthermore, there are few companies that outsource higher levels of activities, which limits the statistical assessment of the survey results.

The framework can support the decision‐making process for outsourcing different logistics activities in food industry.

The key contribution of this paper is that it creates a comprehensive framework for outsourcing of both basic and advanced logistics activities specifically for the food industry.

Using China's burgeoning logistics industry as a backdrop, the present study focused on how to build trust between logistics users and third‐party logistics (3PL) providers, and the antecedents and consequences of trust.

A questionnaire‐based mail survey was conducted in mainland China. The conceptual model was tested using structural equation modeling.

The findings indicate that logistics users' satisfaction with prior interactions with logistics providers, 3PL provider's relationship‐specific investment, 3PL provider's information sharing, and 3PL provider's reputation are key determinants of logistics users' level of trust towards 3PL providers. Additionally, logistics users' trust may facilitate their loyalty behavior towards 3PL providers.

The findings were drawn from a Chinese setting in which logistics outsourcing is in a relatively early developmental stage. The uniqueness of Chinese culture may also limit the findings' generalizability. China's transitional economy was not considered in the study. Different company ownership may significantly influence relationship formulation, maintenance and consequences.

The findings demonstrated how logistics providers can improve customers' trust in logistics outsourcing relationships. The study also revealed how logistics providers can improve customers' loyalty.

This paper was a pioneering study that investigates the logistics outsourcing relationship in China, a rapidly growing economy. The results offered valuable managerial insights regarding how to cultivate trust and loyalty in logistics outsourcing relationships.

The authors extend the World Bank's Logistics Performance Index (LPI) for 30 additional countries and 13 additional years. The authors develop an inexpensive method for extending survey data when frequent, universal surveys are unavailable. The authors identify groups of country characteristics that influence LPI scores.

Using data from the World Development Indicators—the broadest global dataset of country socioeconomic features—the authors test machine learning algorithms for their ability to predict the LPI. The authors examine importance scores to identify factors that influence LPI scores.

The best performing algorithm produces predictions on unseen data that account for nearly 90% of observed variation, and are accurate to within 6%. It performs twice as well as an OLS model with per capita income as the only predictor. Explanatory factors are business environment, economic structure, finance, environment, human development, and institutional quality.

Machine learning offers a simple, inexpensive way of extending the coverage of survey data. This dataset provides a richer picture of logistics performance around the world. The factors the authors identify as predicting higher LPI scores can help policymakers and practitioners target interventions.

This paper is one of the first applications of machine learning to extend coverage of an index based on an international survey. The authors use the new data to provide the most wide-ranging analysis of logistics performance across countries and over time. The output is an important resource for policymakers tracking performance, and researchers particularly in smaller and lower income countries. The authors also examine a wider range of explanatory factors for LPI scores than previous work.

This study aims to empirically identify investment incentive preference segments for international logistics zones from the manufacturer's perspective.

Eight critical investment incentives were identified, based on the following factors: cost, agglomeration, resource, port, policy, political stability, location and transport, and economic. Cluster analysis was subsequently performed to group respondents on the basis of their factor scores. Three groups or segments were identified: firms that preferred political stability and location factors; those which preferred low‐cost and port‐related factors; and those which preferred agglomeration effect and resource factors. Six factors, i.e. cost, agglomeration effect, resource, port, policy, and political stability, differed significantly across the three segments.

Results suggest that political stability is the most important incentive, followed by corporate tax incentives, government administration efficiency, labor cost, and energy cost.

This study is a first attempt to understand investment incentive preferences for an international logistics zone from the manufacturers' perspective and to segment investors into different groups.

For its sustainable growth, an organization should drive customers from the role of consumers of products or services to value co-creators. Logistics performance, which produces value for customers, may activate value co-creation behavior among them. The purpose of this paper is to investigate entrepreneurial orientation (EO) as the determinant and customer value co-creation behavior as the outcome of logistics performance.

The data for this research came from 328 dyads of logistics managers of chemical manufacturers and purchase managers of their customer companies in Vietnam context. The data were analyzed using structural equation modeling approach.

The research results confirmed the role of EO in predicting logistics performance. Logistics performance was also found to positively influence customer-organization identification, which, in turn promoted customer value co-creation behavior.

Entrepreneurship, logistics, and marketing research streams converge through the research model of the relationship between EO, logistics performance, and customer value co-creation behavior.