Arash Shahin and Ali Nourmohammadi
This paper aims to revise the ideal ratio for selecting new products based on their qualitative analysis of desired/undesired functions.
Abstract
Purpose
This paper aims to revise the ideal ratio for selecting new products based on their qualitative analysis of desired/undesired functions.
Design/methodology/approach
The Kano model has been integrated with the ideal ratio to select and prioritize the design of new products. First, the functional analysis method in value engineering was used to determine the attributes and functions of each product design. Then, the Kano model was used to determine the type of each product attribute and to use the desirable functions of attractive attributes, one-dimensional, must-be and undesirable functions of reverse attributes in the ideal ratio to select and prioritize the design of the product. To examine the application of the proposed approach, a gas instruments manufacturing company was investigated, and five new products were selected for the study.
Findings
Based on the results, the product design of industrial regulator GS 77/22 was selected as the superior product and the digital diaphragm gas meter, ultrasonic gas meter, axial regulator and turbine gas meter had the second to fifth priority, respectively.
Practical implications
The proposed method can help product designers determine product designs suitable for customers' expectations and provide a desirable prioritization of the product design in terms of their ideal ratio according to the customers.
Originality/value
The proposed approach provides a more desirable prioritization compared with other prioritization methods based on customers' viewpoints. In the proposed method, the Kano model results in respecting customers, understanding community needs, respecting consumers' rights and increasing the organization's social responsibility, which will significantly increase the chance of product success in the market.
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Keywords
The purpose of this paper is to explore how GenAI can help companies achieve a higher level of hyper-segmentation and hyper-personalization in the tourism industry, as well as…
Abstract
Purpose
The purpose of this paper is to explore how GenAI can help companies achieve a higher level of hyper-segmentation and hyper-personalization in the tourism industry, as well as show the importance of this disruptive tool for tourism marketing.
Design/methodology/approach
This paper used the Web of Science and Google Scholar databases to provide updated studies and expert authors to explore GenAI in the tourism industry. Analysing hyper-segmentation and hyper-personalization modalities through GenAI and their new challenges for tourists, tourism cities and companies.
Findings
Findings reveal that GenAI technology exponentially improves consumers’ segmentation and personalization of products and services, allowing tourism cities and organizations to create tailored content in real-time. That is why the concept of hyper-segmentation is substantially focused on the customer (understood as a segment of one) and his or her preferences, needs, personal motivations and purchase antecedents, and it encourages companies to design tailored products and services with a high level of individual scalability and performance called hyper-personalization, never before seen in the tourism industry. Indeed, contextualizing the experience through GenAI is an important way to enhance personalization.
Originality/value
This paper also contributes to enhancing and bootstrapping the literature on GenAI in the tourism industry because it is a new field of study, and its functional operability is in an incubation stage. Moreover, this viewpoint can facilitate researchers and companies to successfully integrate GenAI into different tourism and travel activities without expecting utopian results. Recently, there have been no studies that tackle hyper-segmentation and hyper-personalization methodologies through GenAI in the tourism industry.
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Hui Zhang, Xiyang Li, Za Kan, Xiaohai Zhang and Zhiyong Li
Reducing production auxiliary time is the key to improve the efficiency of the existing mixed-flow assembly line. This paper proposes a method combining improved genetic algorithm…
Abstract
Purpose
Reducing production auxiliary time is the key to improve the efficiency of the existing mixed-flow assembly line. This paper proposes a method combining improved genetic algorithm (GA) and Flexsim software. It also investigates mixed-flow assembly line scheduling and just-in-time (JIT) parts feeding scheme to reduce waste in production while taking the existing hill-drop mixed-flow assembly line as an example to verify the effectiveness of the method.
Design/methodology/approach
In this research, a method is presented to optimize the efficiency of the present assembly line. The multi-objective mathematical model is established based on the objective function of the minimum production cycle and part consumption balance, and the solution model is developed using multi-objective GA to obtain the mixed flow scheduling scheme of the hill-drop planter. Furthermore, modeling and simulation with Flexsim software are investigated along with the contents of line inventory, parts transportation means, daily feeding times and time points.
Findings
Theoretical analysis and simulation experiments are carried out in this paper while taking an example of a hill-drop planter mixed-flow assembly line. The results indicate that the method can effectively reduce the idle and overload of the assembly line, use the transportation resources rationally and decrease the accumulation of the line inventory.
Originality/value
The method of combining improved GA and Flexsim software was used here for the first time intuitively and efficiently to study the balance of existing production lines and JIT feeding of parts. Investigating the production scheduling scheme provides a reference for the enterprise production line accompanied by the quantity allocation of transportation tools, the inventory consumption of the spare parts along the line and the utilization rate of each station to reduce the auxiliary time and apply practically.
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Derya Deliktaş and Dogan Aydin
Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the…
Abstract
Purpose
Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.
Design/methodology/approach
This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.
Findings
The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.
Originality/value
This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.