Celestine Aguwa

Voice of the customer: Customer satisfaction ratio based analysis

Voice of the customer (VOC) is a critical analysis procedure that provides precise information regarding customer input requirements for a product/service output. The ability to conduct a voice of the customer analysis, which could be gained through direct and indirect questioning, will enable engineers and other decision makers to successfully understand customer needs, wants, perceptions, and preferences. The information obtained from the customers is then translated into critical targets that will be used to ultimately satisfy the customer requirements. During this research project, different forms of customer input, including qualitative and quantitative data, were transformed to a common data format to develop a correlation between design input requirements and product/service outputs. We have developed a new method for measuring customer satisfaction ratio (CSR) by considering the following: mining both textual and quantitative data, multiple design parameters, mapping output on a scale of 0-1, and a decision template for means of measure. Previous measures of CSR fail to incorporate the cost implication of fixing customer complaints/issues; however, we include this important and unique measure in our research. The implication of this research will reduce Things Gone Wrong (TGWs) and engineering development time and will achieve improvements in JD Power ratings, quality perception, marketing tools, and customer satisfaction.

Integrated Fuzzy-Based Modular Architecture for Medical Device Design and Development

In this paper, we present an integrated collaborative modular architecture method for medical device design and development. The methodology is focused on analyzing the input of stakeholder data from existing products and components to achieve an optimal number of modules. The methodology starts by defining a product’s functional and physical decompositions. Product parameters are selected such as quality, reliability, ease of development, and cost. These are prioritized using analytical hierarchy process (AHP) to determine the medical device manufacturers’ focus area. The parameters’ subsequent metrics are selected for performance requirements. Next, we evaluate the candidate modules by acquiring stakeholder data and converting them to crisp values by applying the Sugeno fuzzy-based method. Finally, we determine the subsequent optimal module values using a multi-optimization goal programming model. We present here a proof of concept using a typical glucometer. The implication of this work is the determination of the optimal number of product modules based on stakeholder constraints. Hence, an original equipment manufacturer (OEM) can work on fewer components per module without adversely affecting the integrity, quality, and reliability of the final product. Next is the improved quality of patient care by enabling cost reductions in product design and development, thereby improving patient safety. This methodology helps reduce product cycle time, thereby improving market competitiveness among other factors.

Rules modification on a Fuzzy-based modular architecture for medical device design and development

Medical devices have a very high failure rate in their first prototype tests. According to the international testing body Intertek, out of every ten medical devices, nine fail in their first prototype tests—a 90% failure rate. In addition to the cost implication, quality is a key issue. To address this, we present an integrated, collaborative modular architecture method for medical device design and development. The methodology focuses on analyzing the input of stakeholder data from existing products and components to achieve an optimal number of modules. The objective of this research is to investigate the effect of rules modification on the final number of product modules. The methodology starts by defining a products functional and physical decompositions. Next, product parameters are selected and prioritized using an analytical hierarchy process (AHP) to determine the medical device manufacturers’ focus area(s). Candidate modules are evaluated by acquiring stakeholder data and converting them to crisp values by applying the fuzzy-based Sugeno method. Optimal module values are then determined using a multi-optimization goal programming model. Finally, we analyse the effect of changing the number of fuzzy rules on the optimal number of modules and minimum deviation, ‘d’. A typical glucometer is used for a proof of concept. The implication of this work is the determination that the optimal number of product modules is affected by the rules changes.

Collaborative architecture framework for the design & manufacturing of medical devices

The purpose of this project is to develop a modular architecture framework for the design and manufacture of medical devices. This modular framework aims to incorporate design variables and criteria that are unique to the medical domain to facilitate reliable operation, easier maintenance, and faster product development time. Central to this research effort is the need for inputs from range of stakeholders. The specific goals for this effort are: to determine design criteria by collaborating with users and manufacturers of medical equipment and literature search; to translate user inputs to specific design targets; to develop a preliminary modular design framework using multi criteria optimization methods; to test preliminary modular architecture using a simple medical device such as a glucometer. The importance of the research with respect to its application in the medical arena can be very significant. With the product interaction with humans, both on the manufacturing level, and the user level, the issue of safety is paramount. Some of the other significant contributions are in the improvement of the following: product quality and reliability; product life cycle issues; an enabler for the medical community.

Modeling of fuzzy-based voice of customer for business decision analytics

Identification, interpretation and response to customer requirements are the key success factors for companies, regardless of their industry. Failing to satisfy customer requirements can damage a companys reputation and cause heavy losses. In this study, we have developed a new approach for properly interpreting and analyzing the fuzzy voice of the customer using association rule learning and text mining. This unique methodology converts textual and qualitative data into a common quantitative format which is then used to develop a mapped Integrated Customer Satisfaction Index (ICSI). ICSI is a framework for measuring customer satisfaction. Previous measures of customer satisfaction ratio failed to incorporate the cost implications of resolving customer complaints/issues and the fuzzy impact of those complaints/issues on the system. In addition to including these important and unique factors in the present study, we have also introduced a dynamic Critical to Quality (CTQ) concept, a novel method that provides a real-time system to monitor the CTQ list through an updated CTQ library. Finally, a procedure for customer feedback mining and sentiment analysis is proposed that handles typographical errors, which are unavoidable in every real database. The results of this study suggest that incorporating the fuzzy level of negativity and positivity of comments into the model instead of treating negative and positive comments as binary variables, leads to more reasonable outcomes. In addition, this study provides a more structured framework for understanding customer requirements.

Effect of rating modification on a fuzzy-based modular architecture for medical device design and development

The goal of this research is to determine the effect of customer ratings on the optimal number of modules for medical device design. Medical devices have a 90% failure rate in their first prototype tests according to the international testing body, Intertek. To address this key issue of quality, we present an integrated, collaborative, modular architecture method for medical device design and development. A typical glucometer is used as proof of concept to demonstrate the methodology and analyze the impact of changing the customer ratings on the optimal number of modules and minimum deviation. The implication of this research is to generate scholarly work and to reduce the number of potential failure points in medical devices by determining the optimal number of modules.