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After completion of the case study, students will be able to understand the concept of clustering and identify clusters for improving capacity utilization, analyse transport routes to optimize logistics resources, analyse the impact of a full truckload on resource optimization, evaluate unused capacity and ascertain the impact of reverse milk run to reduce the same and apply clustering and reverse milk run to optimize the logistics resources.

The case study is about a freight forwarding company that offered end-to-end logistics solutions for the exporters based in India. Within a short time span, the company became one of the sought-after service providers for its clients. However, when the company planned to expand its business by expanding its client base, the efficiencies reduced and hurt the profitability of the company. It was all excellent with the limited number of clients, but as the number of distantly located clients surged, the operating costs increased. Trucks were running with partial loads, thus reducing efficiency. The rate of increase in cost surpassed the rate of revenue every time. The cost per mile of transportation was on the rise. The surging fuel prices were adding to the heat. In spite of being one of the first choices for clients, the company could not generate good profit margins. If they chose to increase prices, the company would have lost customers to the cheaper unorganized players in the market. It was time to choose between growth and survival. The company could not sustain itself without devising a mechanism to reduce costs. The company would not have sustained itself without devising a mechanism to reduce costs. To sustain in the business, the company had to device a mechanism to reduce costs. Whether to continue operating the conventional way or to transform? Was there a logistics strategy that would have improved transportation efficiency and reduced the costs for the company?

The case study is suitable for teaching post-graduate management courses in operations and logistics, supply chain management and supply chain analytics, as well as entrepreneurship-related courses.

Teaching notes are available for educators only.

CCS 9: Operations and logistics.

Global trade depends on more complex, prolonged and larger port systems, where port logistics is a backbone for such operations. Ports are responsible for transferring more than 80 percent of the global trade. Port logistics are prone to being risk-oriented. The study proposes a model to study various port logistics barriers and their associated risks for emerging economies in the Industry 4.0 era.

The study develops a framework by integrating the fuzzy set theory, the evidential reasoning approach and the expected utility theorem for identifying the severity value of port logistics barriers under the Industry 4.0 era for emerging economies and prioritize them based on various perspectives. The study identifies multiple risks associated with the barriers, and intensity-based categorization of the risks is performed for risk profiling.

The study reveals that poor infrastructure, nonsupportive policy ecosystem, and lack of research and development are the top barriers that need immediate attention. A new approach has been proposed that changes the importance of perspectives, and 192 analytical experiments were done to study the changing behavior of barriers. The study also presents various types of risks associated with the selected barriers.

In future studies, other barriers can be discovered and studied to develop such models. To cover the entire spectrum of possibilities, belief degrees of the barriers could be used to study the barriers instead of changing the weights.

This study presents a quantification model to prioritize the barriers based on environmental, economic and operational perspectives. Further, the model helps create scenarios for decision-makers to improve port logistics performance and achieve sustainability. The study identifies various risks associated with port logistics barriers and allows decision-makers to take proactive actions.

This study contributes significantly to the literature on port logistics by developing a framework for determining the severity of the barriers in the Industry 4.0 era for emerging economies. Further, the study pinpoints various risks associated with port logistics, and risk profiling is carried out.

Supply chain finance (SCF) has become a vital ingredient that fosters growth and provides flexibility to the global supply chain. Thus, it becomes essential to understand the factors that contribute to the success of the supply chain finance ecosystem (SCFE). This study aims to identify the critical success factors (CSFs) for the development of an efficient and effective SCFE. Based on their characteristics, the study intends to classify the factors into constructs and further establish a hierarchical relationship among the CSFs.

The study is based on empirical data collected from 221 respondents based on administered questionnaires. Exploratory factor analysis (EFA) is carried out on 16 selected factors (out of 21 proposed factors) based on the feedback of the experts and the factors were classified into four constructs. The total interpretive structural modeling (TISM) model was developed by identifying and finalizing CSFs of the SCFE. The model developed a hierarchical relationship between the various factors.

The study identified significant CSFs for the efficient and effective SCF ecosystem. Four constructs were developed by analyzing CSFs using the EFA. The finalized 16 CSFs modeled through the TISM and further hierarchical relationship established between the CSFs concludes that governmental policies and sectoral growth are the strongest driving forces and financial attractiveness is the weakest driving force. Based on the CSFs and the constructs identified, it was found that for the success of the SCF ecosystem, the existence of an economic ecosystem provides a facilitating framework for the overall development of the SCFE. Also, the trustworthiness among the partners fosters better relationships and results in financial feasibility and offers business opportunities for all the stakeholders.

This study will help the SCF partners across the globe understand the CSFs that ensure development of mutually beneficial SCF ecosystems and provide flexibility to the supply chain partners. The CSFs would provide insights to the policymakers and the financial intermediaries for providing a conducive environment for the development of a better SCF ecosystem. Also, the buyers and sellers would understand the CSFs that would develop better relationships among them and ultimately help in development of business across the globe.

The study identifies the CSFs for the SCF ecosystem. The study ascertains the significant factors and classifies them into clusters using EFA. Unlike the literature available, the paper develops the hierarchical relationship between the CSFs and develops a model for an efficient and effective SCF ecosystem.

The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.

This research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.

This study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.

In future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.

Russia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.

Presently, Indian sectors are manifesting a higher level of interdependency and making the economy more vulnerable to human-caused and natural disasters. COVID-19 pandemic creates a devastating effect on the world economy. The Indian economy was expected to lose around ₹ 32,000 crores every day during the first 21 days of complete lockdown. This motivates to conduct the research on how the COVID-19 pandemic affects the port logistics sector and how the effects of COVID-19 on port logistics propagate to other sectors owing to its interconnectedness and affect the economy of the country.

The purpose of the study is analyze how perturbation in one sector can affect the system of interdependent sectors and it is done with interdependency analysis. It uses Wassily Leontief’s inoperability input-output model (IIM) and interval programming (IP) to develop a framework. IP is used to address situations where assumptions are not valid because of uncertainties associated with disruptive events.

The model helps in describing how the effect of the COVID-19 pandemic in port logistics can propagate owing to the interconnectedness across other sectors. The model uses the latest five-year data available on the Organisation for Economic Co-operation and Development database. It uses metrics like inoperability and economic loss to study the consequences of COVID-19 pandemic on various sectors. This study also presents the ranking of the affected sectors based on their inoperability and economic loss

In the future study, other techniques like dynamic evolution, multiplex network analysis, analytical hierarchy process, pinch analysis, stochastic evolution and pinch graph could be integrated with input-output (I-O) modelling. Integrated stochastic evolution with an I-O model allows capturing the likelihood of the events; it includes probability distributions instead of point estimates for scenario parameters. Methods like dynamic evolution and multiplex network analysis can be introduced in future work to shed lights on interdependency among the sector, which could potentially provide additional insights for transport policy formulations.

This study discusses the theory, methodology and application of the IIM-IP model in the domain of port logistics. The developed IIM-IP model helps decision-makers to manage risk in port logistics. Firstly, it studies how different sectors are interconnected with each other. Secondly, it helps in identifying the most vulnerable sectors based on economic loss and inoperability. Thirdly, it provides the ranking of the sectors based on their economic losses.

In Industry 4.0 era, many existing port logistics systems are inconsistent, old and ineffective and it restricts the effective operations of port logistics. The study aims to understand the issues faced by the players/actors of port logistics in the Industry 4.0 era for emerging economies and to develop a conceptual framework for managing the port logistics issues associated with it and by providing their possible solutions.

The study is divided into two parts, first part deals with identifying the major port logistics issues in Industry 4.0 era for emerging economies. It is achieved by conducting a semi-structured interview during the field visit to one of the major container handling ports in India. Second, the study adopts Soft System Methodology (SSM) to understand the issues and challenges faced by various actors of port logistics in the Industry 4.0 era and uses CATWOE analysis to identify the root causes.

Issues related to loading/unloading, transit, storage (warehouse), customs clearance, regulatory authorities, port management unit and inland transport connection providers are considered in the study and using SSM a final implementable model has been developed. This study focuses on analyzing and understanding the complete communication and organization structure of the port logistics system. The study identifies the major issues, various inefficiencies and root causes faced by various actors of port logistics during information sharing, cargo movement, the arrangement of the cargo shipments, etc. Further, the study develops a final implementable model by combining the delivery system, criteria system and Industry 4.0-enabled system.

The study enables concerned authorities like state government, central government and policymakers to have a profound understanding of the issues faced by the actors of the port logistics system. The study brings out valuable insights that help managers and stakeholders to make informed decisions for managing the port logistics issues and develop necessary policies aimed to deliver the cargoes in right place at right time. The current study also has some limitations because of sensitivity associated with concerned areas, due to its confidentiality, lack of availability of complete data and the nonsharing attitude of respondents. Further, the study was conducted only for private container shipping terminals and public container terminals were not included.

This research analyzes the port logistics sector as a whole system through SSM to identify issues and challenges faced by various actors of port logistics for emerging economies in the Industry 4.0 era. The study develops a comprehensive and integrated framework for reducing the unpredictability of costs and time for key processes. Further, the framework creates a transparent platform and helps in bringing standardization to ports.