Customer Requirement Analysis Research Articles

An Investigation of Representative Customer Load Collectives in the Development of Electric Vehicle Drivetrain Durability

To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs.

The Customer Requirements Analysis Method of Engineering Products Based on Multiple Preference Information

To effectively evaluate the fuzziness of the market environment in product planning, a customer requirements analysis method based on multiple preference information is proposed. Firstly, decision-makers use a preferred information form to evaluate the importance of each customer requirement. Secondly, a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix. The ranking vector is then calculated using row and normalization methods, and the initial importance of customer requirements is obtained by aggregating the weights of decision members. Finally, the correction coefficients of initial importance and each demand are synthesized, and the importance of customer requirements is determined through normalization. The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.

Concept for the Customer Perspective of the Balanced Scorecard (BSC) System in Bus Transport Companies in the Slovak Republic

Customer awareness and customer satisfaction are generally important attributes for the success and competitiveness of any business. In some sectors, they are studied very intensively; in others, they are neglected. In the field of transport services, it is important that the attitudes of customers are sufficiently monitored because public passenger transport has a lot of individual competition. Only satisfied customers can prefer public transportation over their own. In this context, this contribution analyses customer requirements and their satisfaction with bus public transport in the Slovak Republic. The opinions of customers are supplemented in this article with those of strategic managers. We ascertained customer requirements through questionnaire surveys. Using theoretical knowledge, we established hypotheses, which were subsequently verified using the selected statistical methods. Based on the findings, we proposed the concept of the target perspective of the BSC system for bus transport companies in the Slovak Republic in an effort to increase their competitiveness. Our procedure was also based on knowledge that is the output of various research studies in this area. Their results show that marketing strategies are considered the most important part of strategic management. Today, it is common in many industries that all business entities offer the same product at the same price under approximately the same technological conditions and with the assistance of equally well-equipped personnel. Therefore, a correctly set marketing strategy can also be the key to business success in transport services. The methods used in this paper were analysis, synthesis, questionnaire surveys, and the selected managerial and statistical methods. Our goal was to propose a concept of the customer perspective of the BSC system based on the analysis of customer requirements. We are convinced that the results of our survey can contribute to increasing the competitiveness of not only bus carriers in Slovakia, but also in other EU countries.

A customer requirements analysis method of considering product scenarios for improving product design

With increasing concerns on customer requirements (CRs) in product improvement, the research of integrating product scenarios into CRs analysis has attracted the attention of designers. Importance-performance-analysis (IPA), which can be used to represent the relationship between CR importance and product performance, is a well-known model for analyzing CRs. However, a major challenge of the IPA model is the quantification of the importance of CRs accurately. Network analysis (NA) is an appropriate method to understand and calculate the importance of CRs in different usage scenarios. This research presents a Network Analysis-Importance Performance Analysis (NA-IPA) model that integrates NA with the IPA model to improve product design through mapping CRs to configuration plans. Accordingly, two mechanisms are integrated to support product design improvement, namely, (1) the classifier of the NA-IPA model is used to categorise CRs, and (2) the performance indicator is introduced as an evaluation metric of product configuration plans. A case study on product improvement was carried out for the interior space of automotive, to validate the feasibility and advantages of the proposed method. This proposed NA-IPA model provides designers with an efficient method to improve product design and customer satisfaction in response to market competition.

A QFD‐based approach for the development of smart product‐service systems

AbstractThe paper proposes a novel method for the development of smart product‐service systems (Smart‐PSSs) by focusing on the analysis of customer requirements and the selection of those features that can practically enhance the smart capabilities of the offering. To achieve such a goal, a quality function deployment (QFD) based method is presented to specifically address the development of Smart‐PSS solutions. The novel approach, named quality function deployment for smart‐product service systems (QFD for Smart‐PSS), relies on the deployment of smart characteristics and components besides the conventional product and service features. Such an approach allows a better granularity of the assessment and deployment of the system features by well defining the smart components in PSS. To preliminarily verify the applicability of the method, a case study concerning the development of a Smart‐PSS solution for the elevators of a building is proposed. The results achieved show the potential benefits that the proposed approach can have both in the case of novel solutions and when an existing system has to be augmented. The method is at the initial stage of development and while this study can augment knowledge on the development of Smart‐PSS, further research work is needed to extend its validation.

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.

Integrating Industry 4.0 and agile new product development practices to evaluate the penetration of sustainable development goals in manufacturing industries

PurposeThe United Nations member countries adopted a set of 17 sustainable development goals (SDGs) to achieve a better and more sustainable future for all. It encourages the use of sustainable practices during new product development (NPD). Competitiveness has put pressure on organizations to maintain their market share and look for new approaches related to NPD. The current study aims to focus on creating a framework that can help to achieve the SDGs by adopting agile new product development (ANPD) practices and Industry 4.0 technologies.Design/methodology/approachFrom the literature, various ANPD practices, Industry 4.0 technologies, performance metrics, their interconnection and their contribution toward achieving SDGs are extracted. The weights of selected Industry 4.0–ANPD practices are computed by robust best worst method (RBWM), and the Fuzzy-VIKOR method is used to rank the selected performance metrics. To test the robustness of the developed framework, sensitivity analysis is also performed.FindingsThe results show that among the various Industry 4.0–ANPD practices “Multi-skilled employees” have the highest weight followed by “Customer requirement analysis and prioritization.” Whereas for performance metrics, “The number of innovative products launched per year” is ranked first, with the “Average time between two launches” at second place.Practical implicationsThis research contributes to the adoption of ANPD practices and Industry 4.0 technologies for the achievement of the business SDGs. The shortlisted Industry 4.0–ANPD practices will help in resolving the social and environmental issues. The set of performance metrics will help practitioners and managers to evaluate the performance of ANPD in the context of business SDGs.Originality/valueThis study adds to the understanding related to Industry 4.0–ANPD practices adoption. And to the best of the authors’ knowledge, it is believed that no similar work has been done previously and by using industry insights into technology components, this work contributes to valuable insights into the subject.

A semantic analysis-driven customer requirements mining method for product conceptual design

Precise customer requirements acquisition is the primary stage of product conceptual design, which plays a decisive role in product quality and innovation. However, existing customer requirements mining approaches pay attention to the offline or online customer comment feedback and there has been little quantitative analysis of customer requirements in the analogical reasoning environment. Latent and innovative customer requirements can be expressed by analogical inspiration distinctly. In response, this paper proposes a semantic analysis-driven customer requirements mining method for product conceptual design based on deep transfer learning and improved latent Dirichlet allocation (ILDA). Initially, an analogy-inspired verbal protocol analysis experiment is implemented to obtain detailed customer requirements descriptions of elevator. Then, full connection layers and a softmax layer are added to the output-end of Chinese bidirectional encoder representations from Transformers (BERT) pre-training language model. The above deep transfer model is utilized to realize the customer requirements classification among functional domain, behavioral domain and structural domain in the customer requirement descriptions of elevator by fine-tuning training. Moreover, the ILDA is adopted to mine the functional customer requirements that can represent customer intention maximally. Finally, an effective accuracy of customer requirements classification is acquired by using the BERT deep transfer model. Meanwhile, five kinds of customer requirements of elevator and corresponding keywords as well as their weight coefficients in the topic-word distribution are extracted. This work can provide a novel research perspective on customer requirements mining for product conceptual design through natural language processing.

Application of risk-based fuzzy decision support systems in new product development: An R-VIKOR approach

Innovative manufacturing firms strive to sustain and enhance their competitive advantages by running a range of new product development (NPD) projects in a consistent manner. The capital and time required to execute the NPD projects have substantially increased over the past years. This magnified the risk-aversion behavior of R&D managers and has increased their sensitivity towards the underlying risk of NPD projects. In particular, the R&D departments have recently started to proactively assess the accuracy of ambiguous information that is extensively used in preliminary market study and customer requirements analysis. Thanks to its high performance in dynamic environments, the R-numbers method can be employed to capture and analyze the risk of fuzzy numbers in a variety of decision making models. To tackle the complexity of such analysis, this paper proposes a novel risk-based fuzzy VIKOR (R-VIKOR) methodology. Using the interpretive structural modeling, the risk factors are first classified to identify and rank the existing critical risk factors of NPD projects. The ultimate goal of this study is to develop a practical yet simple decision support system tool that enables the R&D managers to effectively examine the riskiness of fuzzy information and assess the relevant risk factors. A real-world case study is presented to test and examine the accuracy and effectiveness of the proposed risk management method.

Research on Data Mining and Investment Recommendation of Individual Users Based on Financial Time Series Analysis

With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence.

Data-driven product design toward intelligent manufacturing: A review

With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design is an effective and popular design method, which can provide sufficient support for designers to make smart decisions. This article focuses on a comprehensive review of the existing research in data-driven product design. Based on the product design process, this article summarizes the data-driven design methods into the following aspects: customer requirement analysis, conceptual design, detailed design, and design knowledge support tools. In the customer requirement analysis stage, through data mining and transformation methods, customer requirements are predicted and then mapped to obtain accurate requirement expressions for aiding designers to explore the design space. In the conceptual design stage, the intelligent algorithms and data warehouse technologies are discussed in detail for function reasoning and scheme decision-making to achieve the iterative mapping from customer space to solution space. In the detailed design stage, data modeling languages and methods are introduced to support the simulation verification of the design process. For the design knowledge support tools, the methods of extracting knowledge from product data are discussed in detail, and the realization of computer-aided conceptual design is assisted through the development of knowledge-oriented design tools. Finally, this article summarizes the key points of data-driven product design research and provides an outlook for future research directions.

Competitive Force Model for Indopipe Industry with Analysis of Customer Requirements

The pipeline industry is one of the manufacturing industries that support national development infrastructure. Increased efficiency and expanded brand market share with optimizing the opportunities available on the market is one of the efforts in increasing competitive advantage. The design of this study used interviews and data collection through questionnaires. The analytical unit is the engineering expertise of the Indopipe company at all manager levels. The result of this analysis is the determination of competitive strength strategies with Porter Five Forces in the pipe industry based on criteria of quality, cost, delivery, safety, and morale. The results of the analysis of the study show that strategies to address the threat of new entrants are a priority for managers in this industry. In addition, by improving the quality of pipe products and making this product brand as a market leader, it is the main strategy in increasing competitiveness.

An integrated approach to identify function components for product redesign based on analysis of customer requirements and failure risk

An integrated approach to identify function components for product redesign based on analysis of customer requirements and failure risk

Research on Wind Power Equipment Selection System Based on Axiomatic and Knowledge

With the changes of product market requirement and fierce competition, there are kinds of wind power equipments. Combined with the current situation and development trend of wind power enterprise, the establishment of wind power equipment selection system for wind farm design would be proposed and researched. Axiomatic design method is adopted to realize express of customer requirement analysis, and mapping relationship between function demands and design parameters are researched. Wind power equipment selection model based on knowledge is adopted and studied, the reuse of resource is actualized mostly by management of model information and knowledge. Axiomatic design method is adopted to design the platform system, and wind power equipment selection system structure would be established by the method, knowledge of related field and wind power equipment selection process are embed in the system. The reusability method for wind power equipment selection is researched, which realized the reuse of model, information, data and knowledge for wind power equipment selection process. The system structure of wind power equipment selection for wind farm design will be established by the above theories and methods, which would improve wind power equipment selection effectively.

Integrating QFD for product-service systems with the Kano model and fuzzy AHP

The paper proposes a systematic procedure for the development of Product-Service Systems (PSSs) by focusing on the analysis of customer requirements, and the selection of those that can practically enhance the offerings’ value. With this goal in mind, the Quality Function Deployment for Product Service Systems (QFDforPSS) method was augmented by means of the Kano model to filter the customers’ needs and transform the attractive ones into Receiver State Parameters (RSPs), as the cornerstone of QFDforPSS. Then, to properly assess these parameters and their inherent uncertainty, the Fuzzy Analytical Hierarchy Process (FAHP) method was also integrated into the procedure. To validate the proposed procedure, it was implemented in a case study in the medical devices sector, in collaboration with a haemodialysis equipment manufacturer, which operates in a regulated market of product-oriented services.

High-end equipment customer requirement analysis based on opinion extraction

High-end equipment customer requirement analysis based on opinion extraction

A Research Review on the Key Technologies of Intelligent Design for Customized Products

Abstract The development of technologies such as big data and cyber-physical systems (CPSs) has increased the demand for product design. Product digital design involves completing the product design process using advanced digital technologies such as geometry modeling, kinematic and dynamic simulation, multi-disciplinary coupling, virtual assembly, virtual reality (VR), multi-objective optimization (MOO), and human-computer interaction. The key technologies of intelligent design for customized products include: a description and analysis of customer requirements (CRs), product family design (PFD) for the customer base, configuration and modular design for customized products, variant design for customized products, and a knowledge push for product intelligent design. The development trends in intelligent design for customized products include big-data-driven intelligent design technology for customized products and customized design tools and applications. The proposed method is verified by the design of precision computer numerical control (CNC) machine tools.

Understanding customer requirements through quantitative analysis of an improved fuzzy Kano’s model

Customer requirement analysis has become a primary concern for companies who compete in the global market. Kano’s model, as a customer-driven tool, has been widely used for customer requirement analysis in product improvement. Although a number of authors have improved the traditional Kano’s model, there has been a limitation of dealing with the fuzzy and uncertainty of human thought under multi-granularity linguistic environment. Furthermore, the traditional Kano’s model faces problems regarding quantitative data computation and customer requirements importance assessment. In this article, an improved fuzzy Kano’s model is proposed to analyze customer requirements under uncertain environment. A 2-tuple linguistic fuzzy Kano’s questionnaire is developed to model the uncertainty and diversity of customers’ assessments using 2-tuple linguistic variables under multi-granularity linguistic environment. Then, a comprehensive and systematic methodology is presented to prioritize customer requirements through quantitative analysis of improved fuzzy Kano’s model. This method integrates subjective judgments assigned by decision maker, objective weights based on maximizing deviation method and customer satisfaction contribution to determine the priority ratings of customer requirements. A case study of combine harvester development is presented to evaluate the proposed model.

A system framework for OKP product planning in a cloud-based design environment

Nowadays, one-of-a-kind (OKP) companies, which generally operate in an 'engineer-to-order' business mode, strive to deliver individualized products with quality to achieve customer satisfaction. Thus, an accurate and prompt analysis of customer requirements (CRs) in the early design stage is critical to its success. However, most OKP companies are small or medium-sized enterprises (SMEs). Due to the limited resources and low product planning budget, they often cannot obtain abundant CR information nor can they afford the expense of complicated planning process. To address these issues, a system framework is proposed in support of OKP product planning process in a cloud-based design (CBD) environment. The challenges and future market niches of OKP companies are presented. The comparison of typical distributed systems shows that CBD, which utilizes advanced information technologies and business model, has advantages in providing sufficient resources, decreasing product development time span for OKP companies in a cost-efficient way. This article describes the proposed system architecture, the business interaction process and the information communication among customers, designers and marketing analysts at the product planning stage. To validate the proposed framework, a prototype system module MyProduct is under development in the CBD environment with an illustrative example.

Analysis of the quality of the manufacture of cuffs rubber reinforced with the use of the QFD method

The use of cuffs rubber reinforced diverse, from pipe fittings to domestic plumbing. In this regard, relevant is the development of high-quality cuffs rubber reinforced on the basis of various fillers in order to prevent leakage of the working fluid and improve the reliability of cuffs in a wide range of loads. To improve the quality of cuffs rubber reinforced and meet the requirements of consumers a fundamentally new way to strengthen the rubber, which will improve the qualitative characteristics of cuffs rubber reinforced, consequently, increase their resistance to solvents is added to a polymer filler on the basis of nanocarbon. The purpose of this study is to plan quality cuffs rubber reinforced with the use of tolerancerange filler using tools monitoring and quality management the methodology of Quality Function Deployment (QFD). The relevance of QFD method is to determine the most important issues and related priorities, especially in the choice of positions that should be solved and improved when developing new products. To achieve this goal the following tasks: conducted market analysis of rubber products; carried out a consumer benchmarking and ranking of customer requirements; based on the analysis of customer requirements to cuffs rubber reinforced identified the most important requirements and via the SFC installed priority technical characteristics for further improvements; designed new product using the QFD. QFD method allowed a large amount of information in a compressed form and identify areas to improve specific technical specifications based on customer requirements.