Less diversity but higher satisfaction: An intelligent product configuration method for type-decreased mass customization

Highlights

• A type-decreased mass customized product configuration method is proposed.

• Fuzzy clustering is improved combining the Kano’s model and social inertia.

• Social inertia is introduced to satisfy customers and to gain better configuration.

Abstract

In the industrial 4.0 era, the development of an intelligent manufacturing business model is driven by satisfying customer needs and intelligent algorithms. Enterprises are desirous of adapting a product to the personalized needs of customers to the maximum possible extent. Thus, they increase production configuration diversity, and this leads to increased cost and less efficiency. In order to improve the economy and efficiency of production, this paper proposes an intelligent type-decreased mass customized product configuration method with an improved fuzzy clustering algorithm that combines the Kano’s model and social inertia. The Kano’s model is improved, and social inertia is introduced into the product configuration to adapt to customer psychology and gain better configuration results. We apply this intelligent proposed algorithm to the design of a tablet PC and verify that the improved algorithm is operable and effective in dealing with customer demands and decreases in the variety of products.

Introduction

With the progress in science and technology and the improvement of people’s living standards, the environment of modern enterprises is subject to tremendous changes. In the action outline of Sino–German cooperation, an item clearly states that the digitization of industrial production indicates that “Industry 4.0” is extremely important to the future economic development of China and Germany. In the industrial 4.0 era, product life cycles become shorter, competition increases, and the customer is the most influential factor (Wang et al., 2015, Pacaux-Lemoine et al., 2017). This indicates that the development of the manufacturing business is driven by solving and satisfying customer needs. The diversification of customer demands is a characteristic of the market, and immense importance for all the global manufacturing industries should be attached to the same. The diversification and personalization of customer demands drives enterprises to use a few competitive strategies and computational intelligence for smart manufacturing, such as mass customization (Salvador and Rungtusanatham, 2008, Murray et al., 2018) and this could achieve increases in both benefits and customization.

In order to achieve maximum benefits and to obtain a market competitive advantage, several enterprises adopt intelligent mass customization strategy to balance personalized customization. Intelligent configuring production processes is an effective means of dealing with product variety (Zhang, 2014, Kang et al., 2017). Although personal customization is applied to product configuration while purchasing configurable products, such as computers, financial services portfolios, smart watches, and smart glasses and automobiles (Murray et al., 2018), it can constitute a high-cost choice. Intelligent mass production can invest in more flexible technology because it can reduce the cost of variety and provide a larger set of products (Yuan et al., 2015, Dey et al., 2017). Thus, several successful enterprises use mass customization to satisfy customer demands and configure the production. For example, Dell allows users to select the configuration of the computer and then performs production based on different settings. Additionally, a few car manufacturer websites, such as Audi online, allow users to select different configurations such as engines, exterior colors, and rims. An increase in the level of configuration increases the cost and price. It requires more modules to be produced and prepared to satisfy the different needs of customers.

The two main problems in the industrial mass production involve a contradiction between economy and efficiency of production process and various customer personality needs. The mass customization method designs and produces customized products with the efficiency of mass production and lower costs and also achieves diversification to satisfy customer demands for rapid response. However, to meet the diversity needs of its users, companies produce more models, which increases costs and is not in line with economies of scale. Selecting and producing several products of the greatest value can help companies control costs, which may lead to economies of scale and increased user awareness. One example is that Xiaomi doesn't make many models for mobile phones. Research and development costs of Xiaomi are brought under control. Several major mobile phones bring a lot of purchases because they left a high degree of awareness in customers’ mind. A few studies used the Kano’s model to recognize and analyze customers’ demands in mass customization (Tang & Long, 2012). Selecting the important variables with sufficient information for robust clustering(Lee & Tsai, 2019) and identifying the most cost-effective predictors(Lee & Chen, 2018) can also be used to reduce the variety of products. However, they neglected the advantage of decreases in diversity and the economies of scale and they simply used Kano’s model to describe the customer group. In production purchase and configuration, decisions are influenced by the choices of other individuals. The introduction of a psychological terminology, such as social inertia, into product configuration can reduce the variability among customers’ decisions, reduce the types of products, and achieve the economies of scale.

Thus, we propose an intelligent type-decreased mass customized product configuration method that combines the Kano’s model and social inertia to improve the process of fuzzy clustering algorithm. The innovation of the study is as follows. First, the Kano’s model is improved to consider individual differences, and thus individual differences are considered in the study. A new form of scoring is used. Second, we propose a calculation method on social inertia and use it as a weight. This is the first time that social inertia is introduced in the production configuration. Third, we combine Kano’s model, social inertia, and fuzzy clustering to decrease the variety of products and the cost of enterprises. This improved model can improve economy and efficiency of production of enterprises based on the economies of scale.

This paper is organized in five sections. The first section is the introduction. The second part of the paper is the literature review. The third section involves related basic knowledge and the detailed description of the proposed model. The fourth section presents a case study. Finally, the conclusions of the study are presented at the end.