Customer buying behaviour analysis in mass customization

2005

Mass customization results in proliferation of products and the corresponding routings. It thus necessitates a coordination issue regarding product and process variety. In addition to coping with product and process variety individually, the linchpin of variety coordination lies in the mapping relationships between the product and process domains. Due to the unstructured nature inherent in product and process variety mapping, it is difficult to coordinate product differentiation and process variation while leveraging product families upon consistent process platforms. Taking advantage of knowledge discovery from historical data, this paper proposes a systematic data mining approach to coordinating product and process variety. In the approach, product and process variety are handled in their individual domains by clustering products and routings into families. The mapping relationships in between are addressed through association rule mining. The potential of the proposed approach is illustrated through a case study of vibration motor mass customization. The performance of the data mining approach is evaluated based on sensitivity analysis.

Leveraging upon product and process families has been well recognized as an important area for manufacturers to exploit mass production efficiency, wherein the linchpin of managing variety propagation from design to production lies in the mapping relationships between product differentiation and process variation. Taking advantage of knowledge discovery from historical data, this paper applies an association rule mining technique to deal with product and process variety mapping. A case study of mass customization of vibration motors is presented to demonstrate how association rule mining mechanism helps maintain the coherence between product and process variety.

Journal of Intelligent Manufacturing, 2018

Product design is greatly influenced by product configuration processes and can be suspended or result in failure if the configuration process consumes too much time, cost, or resources; such results can also occur if the end products manufactured based on configurations failed to satisfy customers. Therefore, a configuration approach that saves time, cost, and resources, as well as highly satisfies customers, is necessary and significant. Against the background, this study proposes a configuration approach that uses online data to map customer requirements into product configurations, including the product transaction data and customer review data. The approach generates feasible configurations initially by using transaction data. Next, the approach produces training samples based on positive customer review data. Lastly, the intelligent classifier is trained by the training samples and is utilized to select final configurations from feasible configurations to satisfy customer requirements. A real-world design case of smartphones is used to illustrate the proposed approach, and the results indicate that this approach saves time, cost, and resources and is competitive compared with other product configuration methods. This novel configuration approach provides designers and companies with a superior and efficient method to complete configuration tasks with competiveness and low risk and adds value to the usability and analysis of online data.

Routledge eBooks, 2022

The ability to customize a mass product is one solution for the survival of the industry in today's global competition. Hence, understanding what users want becomes a very important aspect of the customization ability of a mass product. The emotional side of the user is often used as an approach to understand and evaluate the needs and wants of the user. Thus, the emotional satisfaction of users is a determining factor for the success of a product in the market. This paper proposes the Association Rule Mining (ARM) method to map the emotional side of the users from a product design in which the basic requirements that the user needs and wants can be identified. Using the "rule mining" method to evaluate users and product specifications (in the form of a motorcycle as a case example), this study was conducted in three phases: data preprocessing, extracting association rules, and rule evaluation. This approach can be useful for designers and the industry to develop a mass product that can satisfy the emotional side of users so that mass-produced products can be more personally accepted by users.

Proceedings of The Institution of Mechanical Engineers Part B-journal of Engineering Manufacture, 2006

Modern manufacturing systems equipped with computerized data logging systems collect large volumes of data in real time. The data may contain valuable information for operation and control strategies as well as providing knowledge of normal and abnormal operational patterns. Knowledge discovery in databases can be applied to these data to unearth hidden, unknown, representable, and ultimately useful knowledge. Data mining offers tools for discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data. Extraction of previously unknown, meaningful information from manufacturing databases provides knowledge that may benefit many application areas within the enterprise, for example improving design or fine tuning production processes. This paper examines the application of association rules to manufacturing databases to extract useful information about a manufacturing system's capabilities and its constraints. The quality of each identified rule is tested and, from numerous rules, only those that are statistically very strong and contain substantial design information are selected. The final set of extracted rules contains very interesting information relating to the geometry of the product and also indicates where limitations exist for improvement of the manufacturing processes involved in the production of complex geometric shapes.

2000

Evolution of products is inevitable throughout their life cycle - driven by advancing technology, increasing customer requirements or bug fixes. Therefore, the set of components as well as the dependencies between those components are getting more complex and only few experts are able to configure products. But in mass customization scenarios it is desired to generate products specific to customer

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

Because of the fierce competition in the market, everyone is busy with getting the maximum attention of people. For that producer must have products which satisfies the needs of customers. Huge scale research is going in this field. In such situations, customer requirements are very important. The value of a production plan can be modeled as a function that reflects the communication of the company with different agents, for example, customers and competitors. The issue concentrated in this system is to recognize the production plan with the maximum utility for a company, where expected number of the customers for the chosen products assesses the utility of a production plan in the plan. The solution is achieved using Apriory Algorithm in Data Mining.

European Journal of Operational Research, 2019

Remanufacturing has received increasing attention from researchers over the last decade. While many associated operational issues have been extensively studied, research into the prediction customer demand for, and the market development of, remanufactured products is still in its infancy. The majority of the existing research into remanufactured product demand is largely based on conventional statistical models that fail to capture the non-linear behaviour of customer demand and market factors in real-world business environments, in particular e-marketplaces. Therefore, this paper aims to develop a comprehensible data-mining prediction approach, in order to achieve two objectives: (1) to provide a highly accurate and robust demand prediction model of remanufactured products; and (2) to shed light on the non-linear effect of online market factors as predictors of customer demand. Based on the real-world Amazon dataset, the results suggest that predicting remanufactured product demand is a complex, non-linear problem, and that, by using advanced machine-learning techniques, our proposed approach can predict the product demand with high accuracy. In terms of practical implications, the importance of market factors is ranked according to their predictive powers of demand, while their effects on demand are analysed through their partial dependence plots. Several insights for management are revealed by a thorough comparison of the sales impact of these market factors on remanufactured and new products.

Journée SPL, Lignes de …, 2009

A very promising approach to increase productivity, quality and competitiveness approach of information systems development is the reuse and development of a family of systems guided by Product Line (PL) practices. One of the main goals of PL engineering is to develop a model that represents the family of products (product line model PLM), which is then customized to configure individual products.

2014

E-commerce makes a wide use of recommendation techniques to help customers identify relevant products or services in large collections of offers. Customers' requirements are seamlessly elicited by observing purchase habits, information requests, features of products formerly acquired, etc. A trend in the industry is to go a step further, beyond the selection of pre-defined products from a catalogue by handling products customization. The systems engineering community has shown that, based on product line engineering methods, techniques and tools, it is possible to produce customized products (such as software, cars, machine tools, etc.) efficiently and at low cost. A Product Line (PL) is a family of products that share common characteristics and satisfy the needs of a particular mission [POH 05].The products of a PL have many common features, but also many features that vary from one product to another. The problem is that there are usually so many products in a PL that it is impossible to specify all of them explicitly, and therefore traditional recommendation techniques cannot be simply applied. Scoping on a subset of Triki Raouia, Mazo Raúl, Salinesi Camille. Combining configuration and recommendation to enable an interactive guidance of product line configuration. Les systèmes de recommandation.