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Measuring uncertainty to identify missing customer information relevant to the design process
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0001-7918-003X
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The advances in customer information gathering techniques are constantly increasing. However, the tools used today to translate such information into product specifications provide lack of emphasis on communicating insights to engineering teams. In addition, little investigation on how the gathered customer information is helpful to product designers is rarely explored in the literature. At the end, this situation results in a still uncertain target setting process that increases the risk to set wrong product specifications due to the lack of customer information insightful to the designers. In order to quantify such, todays’ risk assessment methodologies cannot be used. The reason is that they use a set of undesirable events as a starting point without ensuring that all possible undesirable events are considered. Thus, uncertainty cannot be estimated without knowing what customer information is relevant to designers. By means of the p-diagram and Analytical Hierarchy Process this paper proposes a novel way to identify what customer information is relevant to the design process and calculates uncertainty as the risk of designers’ decisions to deviate from the customer picture due to the lack of relevant customer information. To do so, existing customer information from the company database is taken as basis. To show the validity of the proposed methodology, a case study regarding the balancing of electric consumption of an electric vehicle is proposed. Results show that the risk indicator helps the team members to identify what customer information is uncertain and therefore relevant to the design process as well as to establish a more customer-focused and context-specific information gathering strategy.

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 273-279
Keywords [en]
uncertainty, risk, customer information, design process, P-diagram, Analytical Hierarchy Process
National Category
Other Engineering and Technologies not elsewhere specified Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
URN: urn:nbn:se:ltu:diva-66593DOI: 10.1109/ICDIM.2017.8244651ISI: 000428615600047Scopus ID: 2-s2.0-85049381539ISBN: 9781538606643 (electronic)OAI: oai:DiVA.org:ltu-66593DiVA, id: diva2:1157419
Conference
12th International Conference on Digital Information Management, Fukuoka, Japan, September 12 -14, 2017
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2019-09-13Bibliographically approved
In thesis
1. Customer-focused data-driven target setting
Open this publication in new window or tab >>Customer-focused data-driven target setting
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

To develop products through a customer-centric strategy, early stages of product development such as target setting play an important role. In the target setting stage Customer Needs (CN) are gathered and translated into Design Requirements (DR) in order to subsequently set product targets that fit cost constraints and at the same time result in high Customer Satisfaction (CS). Continuous advances in information technology create new opportunities for companies to gather information about the customer, for example, for marketing purposes, or to assess customer reactions after the launch of new products. In addition, products are becoming complex systems that are successively equipped with more software and sensors offering opportunities for collecting data on how they are used. Knowing how customers use the product enhances a company’s ability to segment customers and customize products.

Despite customer information availability from different sources (sensors, social media, etc.), surveys and focus groups are considered today as the main data source to derive the set of CN statements during target setting. Further, the team’s interpretation of CNs, which are often described in abstract language, must be translated into DRs, which are described in a more technical language. Hence, the translation process of CNs into DRs is said to be subjective. To set product targets, CS sensitivity to changes in DR levels is also considered. Surveys and benchmarking data containing customer perceptions on competitors’ performance are often the main customer data input into the process. While insightful information may be obtained, surveys are costly and time consuming and only encompass a small part of the market population.

The research presented in this doctoral thesis explores how customer information obtained from sensors (e.g. product usage data) and text data (e.g. from websites, open-survey questionnaires) can be factored in the target setting process before concept generation to enhance customer focus without compromising product development time. The aim is to increase designers’ awareness of target population and in turn increase the quality of the design decisions on product targets. For this purpose, a customer-focused data-driven target setting methodology is proposed. The presented methodology changes the actual target setting methodology by means of indicators and autonomous activities on those parts of the process where marketing or design decisions are needed. The proposed methodology gives the incentive for a more integrated product development where marketing and designers need to work closely. This further allows a sustainable customer information gathering strategy that strives for missing customer information that is required for setting product targets. The indicators act as feedback channels for continuous product improvement. The use of such indicators and autonomous activities highlights the potential of a more efficient, less subjective and higher-quality target setting process.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2019
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-72655 (URN)978-91-7790-304-8 (ISBN)978-91-7790-305-5 (ISBN)
Public defence
2019-04-08, E632, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-03-19Bibliographically approved

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Marti Bigorra, Anna

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Citation style
  • apa
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