Research on the Evaluation of Customer requirement Importance of Smart Products Based on Online Comments and Improved Quantitative Kano Model

With the development and popularization of smart products, the technological differences of products are decreasing, and the phenomenon of product homogeneity is becoming more and more obvious. It is necessary for the smart product manufacturing firms have the capability to analyze customer requirement deeply and adapt to the dynamically changing market quickly. Therefore, the traditional technology-oriented product development model is no longer suitable for manufacturers to obtain a competitive advantage. Based on this, this paper proposed a method to evaluate the importance of customer demands based on online comments and quantitative Kano model. First, the Python crawler tool is used to obtain online customer reviews of relevant products and the word segmentation processing is performed to obtain the product features and frequency that customers are mainly concerned about, and then the initial importance of demand can be calculated. Furthermore, use the quantitative Kano model to determine the customer satisfaction and revise the initial importance of the requirements to obtain a more reasonable ranking of the importance of user needs. Finally, a case study is carried out with the smart bracelet as an example to verify the effectiveness and feasibility of the model proposed in this paper.

Importance Calculation of Customer Requirements for Incremental Product Innovation

Incremental product innovation is achieved by finding and solving problems of existing products. The importance of customer requirements reflects the severity of existing product problems, which points out the direction for incremental product innovation. In this research, the calculation process of customer requirement importance mainly includes three steps. Firstly, from the perspective of customers, the improvement gap analytical method is used to obtain the improvement and original importance of customer requirements by measuring customer perceived satisfaction and dissatisfaction. Secondly, from the perspective of industry experts, an improved interval grey number ranking method is proposed to calculate the basic importance of customer requirements, which can deal with the inadequate problem of the data provided by experts due to the limited number of experts. Finally, a multi-dimensional vector cosine method, which avoids the interference of subjectivity of importance weight calculation to the final importance, is proposed to integrate the importance data provided by customers and experts. A case of a water purifier is considered to illustrate the validity of the proposed process. This research improves existing calculation methods and proposes an integrated calculation process from three dimensions to calculate the final importance of customer requirements effectively.

Uncertainty in Consumer Behaviour with Reference to Factors Influencing Consumer Purchase Decision of Smartphones in the City of Bengaluru

This article attempts to investigate and understand the factors that influence the purchase decision towards smartphones in Bangalore. Bangalore is a cosmopolitan and multilingual city with a mixed culture. It is also a famous city in India with the IT sector and educational institutions, which has grown in the recent past. The shopping pattern in Bangalore is found to be very interesting when compared to other south Indian cities because the spending pattern of people in Bangaloreprovokes the consumers to purchase. A survey was conducted to determine the factors influencing smartphone purchase. A simple random sampling technique has been used with a sample size of 190. Factor analysis was run to reduce the dimensions and find the aptest variables influencing the consumer purchase decision. From the analysis, the researchers have derived five iterations which are price and in-built features(Component 1), camera, battery backup and sound quality(Component 2), marketing strategy and social groups(Component 3), brand image and origin of the company (Component 4), EMI and replacement option(Component 5). This study will help the marketers in understanding the expectation of the consumer’s from the product and figure out the areas of improvement in smartphone features and other factors influencing the purchase so that they can tailor make the product as per customer requirement and understand the most crucial factor contributing to the sale. Uncertainty is to be understood by the marketers, as drastic changes in needs, wants, desire, and expectations are needed.

Integrating machine learning with QFD for selecting functional requirements in construction automation

An important aspect of the conceptual design is at the customer requirement definition stage, where an optimal number of functional requirements are specified with the application of quality function deployment. To facilitate a systematic specification of functional requirements, state-of-the-art unsupervised machine learning techniques will be introduced in the feature selection of functional requirements. However, the scarcity of references on unsupervised feature selection in the literature reflects the difficulty associated with this topic. At the customer requirement definition phase, three techniques will be proposed for selecting functional requirements, namely: (a) principal component analysis, (b) forward orthogonal search, and (c) Kohonen self-organizing map neural network. These machine learning feature selection techniques address the limitations of current approaches in systematically determining the minimum functional requirements from the mapping of customer requirements in quality function deployment. When applied to the conceptual design of the transportable automated wood wall framing machine that is under development at the University of Alberta, the proposed feature selection techniques have been observed to be: (i) fast, (ii) amenable to small quality function deployment dataset, and (iii) adequate in realizing design objectives. The results presented in this paper can be easily extended to online determination of customer requirements and functional requirements, project management, contract management, and marketing.

Early Prediction of Quality Issues in Automotive Modern Industry

Many industries today are struggling with early the identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with the customer requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the original equipment manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using machine learning (ML) to forecast the failures of a given component across the large population of units. In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage. We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently.