Advanced Quality Function Deployment

Franceschini, Fiorenzo

doi:10.1201/9781420025439

Cited by 87 publications

(109 citation statements)

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“…Fidelity is often difficult to define and measure because it is subject to judgments and perceptions. Nevertheless, the fidelity of workflows should be predicted whenever feasible and carefully controlled when needed (Kolarik 1995;Franceschini 2002).…”

Section: Task Fidelitymentioning

confidence: 99%

Quality of Service for Workflows and Web Service Processes

et al. 2004

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Workflow management systems (WfMSs) have been used to support various types of business processes for more than a decade now. In workflows for e-commerce and Web-services applications, suppliers and customers define a binding agreement or contract between the two parties, specifying Quality of Service (QoS) items such as products or services to be delivered, deadlines, quality of products, and cost of services. The management of QoS metrics directly impacts the success of organizations participating in e-commerce. Therefore, when services or products are created or managed using workflows, the underlying workflow system must accept the specifications and be able to estimate, monitor, and control the QoS rendered to customers. In this paper, we present a predictive QoS model that makes it possible to compute the quality of service for workflows automatically based on atomic task QoS attributes. To this end, we present a model that specifies QoS and describe an algorithm and a simulation system in order to compute, analyze and monitor workflow QoS metrics.

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Section: Task Fidelitymentioning

confidence: 99%

Quality of Service for Workflows and Web Service Processes

et al. 2004

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“…For example, in this study, Tier 2 companies could provide automotive However, there is the risk of inconsistent judgments with pairwise comparisons [13,18,20]. Therefore, one must look at the consistency ratio [18]:…”

Section: Tiermentioning

confidence: 99%

“…E-mail and web-based surveys offer low cost and short response time due to the electronic format of the data by eliminating the geographical distances [17]. The Analytic Hierarchy Process (AHP) developed by Saaty in the 1970s provides a more sufficient ratio-scale importance ranking approach to prioritize the customer needs [18,19]. This is a pairwise comparison process, where customers are asked to compare two customer needs utilizing the evaluation scale of 1-9, which continues until all of the needs are evaluated according to each other [18,20].…”

Section: Introductionmentioning

confidence: 99%

“…The Analytic Hierarchy Process (AHP) developed by Saaty in the 1970s provides a more sufficient ratio-scale importance ranking approach to prioritize the customer needs [18,19]. This is a pairwise comparison process, where customers are asked to compare two customer needs utilizing the evaluation scale of 1-9, which continues until all of the needs are evaluated according to each other [18,20]. The scale of 1 to 9 ranges from equally important to extremely important as follows.…”

Section: Introductionmentioning

confidence: 99%

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Prioritizing Sensor Performance Characteristics for Automotive Seat Weight Sensors in Quality Function Deployment (QFD)

Haroğlu¹,

Powell²,

Seyam³

2016

Adv Automob Eng

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Quality function deployment (QFD), a key tool to convert the customer needs into product features, is generally integrated into the New Product Development (NPD) process at the design stage. Prioritizing customer needs in a QFD process leads to using the resources (time, money, and staffing) effectively by eliminating the unimportant customer needs. The overall goal of the research was to develop a textile-based optical fiber sensor for automotive seat occupancy. The findings of this paper were focused on the design of experiments in our previous publication. In this paper, a research study was conducted to better understand market demands in terms of sensor performance characteristics for automotive seat weight sensors, as a part of the QFD House of Quality (HOQ) analysis. A survey was sent to more than 20 companies operating in the field of automotive seat weight sensors, and Original Equipment Manufacturers (OEM) via e-mail. Only five companies participated in this study due to competitive concerns and confidentiality reasons. However, the companies responded to the survey were of quality relevant to the research and could be perceived as representative of the group of experts. All 5 companies participated in the survey agreed on the first 5 most important sensor characteristics: reproducibility, accuracy, selectivity, aging, and resolution, where The Analytic Hierarchy Process (AHP) was applied to prioritize the sensor characteristics.

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“…On the other side, the quantitative methods aims to generate new ideas based on the gathered information from customers or users. Quality Function Deployment (QFD) is a well-known technique in this group (Franceschini, 2016). Given the customers' spoken requirements, as the qualitative input, QFD tries to map such information to particular plans, as the quantitative output.…”

Section: Related Workmentioning

confidence: 99%

Extracting new ideas from the behavior of social network users

Karimi-Majd¹,

Fathian²

2017

10.5267/j.dsl

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Online social networks (OSNs) provide services targeting multifarious types of users in order to attract and retain them. For this purpose, developing new services according to user preferences has recently been under focused by various researchers. Most of present studies focus only on extracting the behavioral patterns of users, and neglect users' interactions, which is the main part of the social activities in OSNs. To cope with this issue, this paper proposes a new methodology to bring both dimensions of data, the extracted behavioral patterns of users and their social interactions, in order to reach a better analysis. Moreover, the idea provides a basis for considering other dimensions efficiently. In order to evaluate the performance of the methodology, this paper performs a case study, and conducts a set of experiments on the computer-generated datasets. The results indicates the great performance of the methodology.Growing Science Ltd. All rights reserved. 7

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Advanced Quality Function Deployment

Cited by 87 publications

References 0 publications

Quality of Service for Workflows and Web Service Processes

Quality of Service for Workflows and Web Service Processes

Prioritizing Sensor Performance Characteristics for Automotive Seat Weight Sensors in Quality Function Deployment (QFD)

Extracting new ideas from the behavior of social network users

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