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The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods. This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation. At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management.

Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method. Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods. Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring.

Fleet performance monitoring is done based on fuel consumption (FC), CO₂ emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature. MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan). FC and RG are the critical parameters for monitoring the fleet performance.

The geographical aspects have not been considered for this study.

A pilot run of 300 fleets shows saving of Rs. 2,611,013/- (US$36,264.065), which comprises total maintenance cost [Rs. 1,749,033/- (US$24,292.125)] and FC cost [Rs. 861,980/- (US$11,971.94)] annually.

Reduction in CE (4.83%) creates a positive impact on human health. The reduction in the breakdown maintenance of fleet improves the reliability of fleet services.

This study investigates the most useful parameters for fleet management are FC, CE, CT. Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring.

The purpose of this paper is to identify the risks involved in the construction project based on a literature survey (LS), to develop a project risk management (PRM) framework based on Industry 4.0 technologies and to demonstrate the developed framework using Internet of Things (IoT) technology.

A comprehensive LS was carried out to know the different risks involved in the construction project and developed a PRM framework based on Industry 4.0 technologies to increase the effectiveness and efficiency of PRM. Heavy equipment and parameters were identified to demonstrate the developed framework based on IoT technology of Industry 4.0.

This paper demonstrates Industry 4.0 in the various stages of PRM. LS has identified 21 risks for a construction project. The demonstration of the PRM framework has identified the sudden breakdown of equipment and uncertainty of equipment as one of the critical risks associated with heavy equipment of construction project.

The project complexity and features may add a few more risks in PRM.

The PRM framework based on Industry 4.0 technologies will increase the success rate of the project. It will enhance the efficiency and effectiveness of PRM.

The developed framework is helpful for the effective PRM of construction projects. The demonstration of PRM framework using IoT technology provides a logical way to manage risk involved in heavy equipment used in a construction project.

This paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality, software reliability and software development cost/effort.

The methodology developed by Kitchenham (2007) is followed in planning, conducting and reporting of the systematic review. Out of 625 research papers, nearly 100 primary studies related to our research domain are considered. The study attempted to find the various techniques, metrics, data sets and performance validation measures used by researchers.

The study revealed the need for integrating the four dimensions of defect management and studying its effect on software performance. This integrated approach can lead to optimal use of resources in software development process.

There are many dimensions in defect management studies. The authors have considered only vital few based on the practical experiences of software engineers. Most of the research work cited in this review used public data repositories to validate their methodology and there is a need to apply these research methods on real datasets from industry to realize the actual potential of these techniques.

The authors believe that this paper provides a comprehensive insight into the various aspects of state-of-the-art research in software defect management. The authors feel that this is the only research article that delves into the four facets namely software defect analysis, software quality, software reliability and software development cost/effort.

This case introduces the context of seaport logistics supply chain management with a focus on the issues of risk management in handling and transportation of dangerous goods (DG). The authors present the following learning objectives under the overarching framework of Bloom’s Taxonomy as follows: To understand the severity of handling and transportation of DG in the export supply chain context. To understand the relevance of multi-criteria decision-making in risk assessment. To apply Delphi Technique to appropriately explain the process of risk assessment in a supply-chain context.

It was midnight on December 21, 2020, and Nishadh Amonkar, Chief Executive Officer, Yorokobi, was still awake recollecting his telecon with Tushar Rane, the Head-Materials, Western Maharashtra site of Crop Life Pvt Ltd. The organization was developing and manufacturing pesticides and other specialty chemicals for its clients worldwide. As new and diverse products were being manufactured in the organization, transportation of the products was becoming challenging. The case highlights the need for a data driven risk assessment approach to manage supply chains that were prone to product driven risks such as the handling and transportation of DG.

This course is suitable at the Master of Business Administration level for the following courses: Supply Chain Management (Focus/Session: Supply Chain Risk Management), Logistics Management (Focus/Session: Risks in Logistics and Supply Chain), Research Methodology (Focus/Session: Application of Delphi Technique).

Teaching notes are available for educators only.

CSS 9: Operations and logistics.