Leslie Monplaisir

Matching product architecture with supply chain design

Product architecture is typically established in the early stages of the product development (PD) cycle. Depending on the type of architecture selected, product design, manufacturing processes, and ultimately supply chain configuration are all significantly affected. Therefore, it is important to integrate product architecture decisions with manufacturing and supply chain decisions during the early stage of the product development. In this paper, we present a multi-objective optimization framework for matching product architecture strategy to supply chain design. In contrast to the existing operations management literature, we incorporate the compatibility between the supply chain partners into our model to ensure the long term viability of the supply chain. Since much of the supplier related information may be very subjective in nature during the early stages of PD, we use fuzzy logic to compute the compatibility index of a supplier. The optimization model is formulated as a weighted goal programming (GP) model with two objectives: minimization of total supply chain costs, and maximization of total supply chain compatibility index. The GP model is solved by using genetic algorithm. We present case examples for two different products to demonstrate the model’s efficacy, and present several managerial implications that evolved from this study.

An integrated CSCW architecture for integrated product/process design and development

Abstract Computer Supported Collaborative Work (CSCW) is increasingly being used by engineering design teams to reach a consensus on a range of design issues. CSCW systems are designed to increase the effectiveness of decision-makers by facilitating information exchange, retrieval, sharing and use. They encourage interactive information exchange and have the potential to reduce diseconomies associated with design activities, member dominance, social pressure, inhibition of expression, and other difficulties encountered by project teams. The use of CSCW is expected to have a favorable impact on the group decision-making process and the quality of the resulting decision. In this paper, a general CSCW architecture has been developed to support integrated product/process design and development will be presented. The architecture has been tested extensively on a representative industrial problem. The case study and evaluation of the architecture will also be discussed in this paper. The integrated architecture will facilitate information access, sharing, and analysis among design teams members using the open World Wide Web platforms and resources to make product/process decisions.

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.

A methodology for integrating design for quality in modular product design

With inspection-based quality control techniques, the quality of a product remains undetermined until the product is built and tested, an expensive process that also delays the release of new products to the market. This paper brings the quality issues at early stages of product development, and enhances the existing work on design for quality by integrating with modular design concepts. Conceptually, modular design theory optimizes product quality at the conceptual phase by considering the underlying principles of axiomatic design and robust design along with the perceived quality of the product. Fuzzy logic is employed to estimate cost and quality performance indices of the candidate modules by analysing ambiguous product information at the conceptual stage. We consider two objectives for product modularization: minimization of modularization costs, and maximization of overall product quality. The Chebychevs goal programming model is used to solve the multi-objective optimization problem. The methodology is demonstrated using an example of a coffeemaker. The results of the case study identify the optimal number of modules, which are intuitive and also offer more design resolution for forming the product development teams.

A Framework for Capturing and Analyzing the Failures Due to System/Component Interactions

To keep up with the speed of globalization and growing customer demands for more technology-oriented products, modern systems are becoming increasingly more complex. This complexity gives rise to unpredictable failure patterns. While there are a number of well-established failure analysis (physics-of-failure) models for individual components, these models do not hold good for complex systems as their failure behaviors may be totally different. Failure analysis of individual components does consider the environmental interactions but is unable to capture the system interaction effects on failure behavior. These models are based on the assumption of independent failure mechanisms. Dependency relationships and interactions of components in a complex system might give rise to some new types of failures that are not considered during the individual failure analysis of that component. This paper presents a general framework for failure modes and effects analysis (FMEA) to capture and analyze component interaction failures. The advantage of the proposed methodology is that it identifies and analyzes the system failure modes due to the interaction between the components. An example is presented to demonstrate the application of the proposed framework for a specific product architecture (PA) that captures interaction failures between different modules. However, the proposed framework is generic and can also be used in other types of PA. Copyright

A multi-objective supply chain configuration model for new products

Configuring a supply chain for new products involves selecting how to source each stage in the supply chain given several alternatives that vary in cost, lead time, and other measures. One must also determine the best overall strategy for deploying safety stocks across the supply chain so as to buffer against demand uncertainty. Traditionally, this has been done based on costs (inventory cost, procurement cost, or a combination of both). This article introduces the use of a multi-objective optimisation model in configuring the supply chain during product development. In addition to using various production and inventory costs, the model makes use of subjective criteria such as alignment of business practices and financial objectives of member companies in configuring the supply chain. Fuzzy logic is used to analyse the subjective or qualitative variables, such as alignment of business cultures and practices. A genetic algorithm is used to solve the optimisation model. A bulldozer case study is then presented to benchmark and demonstrate the benefits of the proposed methodology.

A framework to integrate design for reliability and maintainability in modular product design

This paper presents a framework to optimise reliability and maintainability (R&M) of a product through modular design. A structured process is developed for incorporating three types of R&M metrics into the modular design. These metrics are failure potential and simplicity of module architecture, maintainability and maintenance commonality between the components, and serviceability of the module. In addition to reliability and maintainability, cost of modularisation is also taken into account for modules selection purpose. The metrics of modularisation costs are cost of interface to join the components, assembly resources requirements, and cost of reusability of modules. We use fuzzy logic and the goal programming models for developing the modular design. An example is presented to demonstrate the application of the proposed methodology.

Collaboration planning framework (cpf) to support distributed product development

Current marketplace is undergoing major changes that will affect the way organizations conduct their business. Organizations need to respond to a geographically dispersed marketplace. This can be achieved by leveraging globally distributed resources to fully understand and interpret individual customer needs. That is, organizations need to integrate their operations (product development) in a way that will allow dynamic response to market changes. Computer-supported collaborative engineering could provide the integrating mechanisms needed to integrate distributed operations. The change to collaborative engineering should be based on sound and comprehensive methodologies that can analyze current practices, assess their ability to be performed collaboratively, restructure organizational practices to enhance their performance in a collaborative environment, select appropriate tools to support practices, and provide an implementation plan. This paper presents a framework to build computer-supported collaborative product design and development operating in a distributed environment. The framework is composed of six modules that provide a systematic procedure to plan for computer supported collaborative engineering.

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.