The time and effort required for maintenance of an automobile system are highly dependent on its disassemblability, which is one of the most important attributes of its maintainability. To evaluate the disassemblability index, i.e. to measure the ease of disassembly, the disassemblability factors (both the design factors and the contextual factors) of an automobile system are identified. These and their interrelations are modelled by considering their structure using the graph theory. The directed graph (digraph) of the disassemblability of the automobile system is defined; the nodes of this represent its disassemblability factors, while the edges represent their degrees of influence. An equivalent matrix of the digraph establishes the system’s disassemblability function which characterizes the disassemblability of the system, leading to development of the disassemblability index. A high value of the disassemblability index indicates that it is very easy to remove or replace parts. The disassemblability index ratio is used to compare the actual conditions of disassembly with the ideal conditions of disassembly. A case study of an automobile gearbox is illustrated using the step-by-step procedure of the proposed methodology of disassemblability. The proposed methodology is helpful to evaluate and compare various alternative designs of the automobile system and, therefore, can aid the design and development of automobile systems from the disassembly viewpoint.

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Purpose The purpose of this paper is to develop an ontological model of failure knowledge of automobile systems that will enhance the knowledge management of automobile system failures, which will help for design and maintenance of automobiles. Failure knowledge of automobile systems and components gained through maintenance and repair can mitigate future failures, if integrated in the design. This is an outcome of this paper. Design/methodology/approach A failure coding scheme is developed for assimilating various entities of automobile failure knowledge and an ontological model is developed for its systematic structuring and representation. The developed failure code is a combination of alphanumeric and numeric code that incorporates ingredients of the failure knowledge, which will help database management, with reduced data entry time and storage space. Findings The maintenance of automobiles not only brings back the systems into operating conditions but also convey a lot of information regarding the failures. This is a useful input to the designers in development of reliable and maintainable automobile systems. A knowledge base can be created for automobile systems/components failures from their maintenance and service experience. Research limitations/implications Developed ontological model of automobile failure knowledge gained through maintenance experience can be shared across automobile manufacturers and service providers. This would help in design improvements, with ease and efficient undertaking of maintenance activities. This paper proposes the conceptual ontology structure, which is populated with three cases of automobile maintenance. Originality/value This research work is a first attempt to develop an ontological model for automobile failures from their maintenance and service experience. The novelty of the work is in its explicit consideration of all knowledge related to failures and maintenance of automobile systems, with their coding and structuring.

Material selection is considered an important step in the design and manufacturing of a part/product. Wrong selection of material may cause poor quality, increase in cost, early failure of the product, or any combination of them. It may also create difficulties in the manufacturing process leading to increases in manufacturing time and cost. The general rule in design is to select materials based on their properties that match with the product requirements. Hence, decision making for material selection is a crucial process, and several mathematical tools and techniques have been presented in the literature to aid this process. Those techniques have their own advantages and limitations. This paper proposes the application of the grey-based fuzzy logic approach to solve three real time material selection problems. For all the examples, the first and the last ranks of materials derived using the adopted method and those obtained by past researchers are exactly the same. This indicates that when uncertainties are involved, grey-based fuzzy logic can be employed as an efficient tool for solving complex material selection problems.

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PurposeThe purpose of this paper is to develop a framework for benchmarking the service quality of amusement parks.Design/methodology/approachA hybrid approach, which is a combination of AHP (analytic hierarchy process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), is applied for service quality benchmarking.FindingsAmusement parks are centers of attraction at various tourist destinations across the world. Their service quality is constituted by the attainment of certain quality attributes that varies with different parks. For sustaining in the industry, the managers of the parks need to have a good overview of the practices followed by them and their competitors that necessitate benchmarking of the service quality.Practical implicationsThe developed framework using the hybrid methodology of AHP and TOPSIS can be applied for comparing different amusement parks based on quality attributes, which will help the organizers in improving their service quality.Originality/valueThe paper identifies various service quality attributes of amusement parks and an evaluation scheme for those attributes had been developed. Based on these, a framework had been developed for benchmarking of service quality of different amusement parks.