Bimal Nepal

A fuzzy-AHP approach to prioritization of CS attributes in target planning for automotive product development

Understanding customer requirements and incorporating them into the conceptual vehicle design is the first step of automotive product development (PD). However, lack of quantitative data and undefined relationships between the attributes makes it difficult to develop a quantitative model for analyzing subjective customer satisfaction (CS) attributes. While researchers and practitioners have accomplished a significant success in terms of developing tool such as quality function deployment (QFD) to capture the voice of customers, and mathematical models for selecting engineering design alternatives, there is limited precedence in terms of prior works on customer satisfaction driven quality improvement target planning and prioritization of customer satisfaction attributes for target planning. This paper presents a fuzzy set theory based analytic hierarchy process (fuzzy-AHP) framework for prioritizing CS attributes in target planning. Furthermore, unlike prior QFD papers, we consider a broad range of strategic and tactical factors for determining the weights. These weights are then incorporated into target planning by identifying the gap in the current CS level. A case example from automotive industry is presented to demonstrate efficacy of the proposed methodology. The framework has been implemented on MS Excel(R) so that the industry can easily adopt it with limited amount of training and at no additional software cost.

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.

Improving the NPD Process by Applying Lean Principles: A Case Study

Abstract: This article extends the new product development (NPD) literature by presenting a case study of a lean product development (LPD) transformation framework implemented at a U.S. based manufacturing firm. In a departure from typical LPD methods, in this article the design structure matrix and the cause and effect matrix are integrated into the lean transformation framework, allowing analysis of the underlying complexity of a product development (PD) system, and thus facilitating determination of the root causes of wasteful reworks. Several strategies to transform the current PD process into a lean process are discussed. Besides the two-phase improvement plan, a new organizational structure roadmap and a human resources plan are also suggested to support the recommended changes in the NPD process. The results of the first phase show a 32% reduction in PD cycle time due to the proposed NPD process. The article concludes with lessons learned and implications for engineering managers based on the case study.

On the adoption of lean manufacturing principles in process industries

Traditionally, the lean paradigm has been applied to discrete manufacturing of items that can be easily put together and taken apart. The process industry, on the other hand, transforms raw materials into cohesive units that are basically blended into a final product with parts that cannot be disassembled and then reassembled. The current lean literature provides numerous commendable examples of theory and practices of lean principles in discrete manufacturing. However, its application in process industry is limited. Furthermore, there is no systematic accounting of the lean literature in this sector, which may have contributed to lesser awareness in the industry. This paper provides a state-of-the-art review of lean manufacturing literature with respect to its applications in process industry. It contributes to the classification of literature in a manner which helps to identify strategies suitable for the adoption of lean concepts in process industry. The paper seeks to synthesise the literature with an emphasis on identifying the scope for lean in process industry and associated benefits. The review also presents an analysis of the lean tools and techniques that have been applied or have potential application in the process industry and the challenges to implement lean. We believe that such a comprehensive review will not only facilitate the adoption of lean in process industry but will also provide agenda for further research by exposing voids in the knowledge base.

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

Insights and learnings from lean manufacturing implementation practices

Lean manufacturing as a set of principles is now fairly rooted in the literature. Barring some voices of discontent (Gordon, 1995; Berggren, 1992) regarding the adoption and ultimate effectiveness of lean production, many case examples exist to demonstrate how companies are changing their production methods and management practices to become more lean and fit. The main aim of this study is to first collect information on fundamental lean principles and then investigate the level of lean implementation in the automotive industry. Furthermore, it describes some learnings from actual implementation practices particularly in the USA, UK and Indian automotive sectors. Attempts are made here to discover the inside stories and present the gaps between the principles and actual practices.

Bayesian belief network-based framework for sourcing risk analysis during supplier selection

Increasing trend in global business integration and movement of material around the world has caused supply chain system susceptible to disruption involving higher risks. This paper presents a methodology for supplier selection in a global sourcing environment by considering multiple cost and risk factors. Failure modes and effects analysis technique from reliability engineering field and Bayesian belief networks are used to quantify the risk posed by each factor. The probability and the cost of each risk are then incorporated into a decision tree model to compute the total expected costs for each supply option. The supplier selection decision is made based on the total purchasing costs including both deterministic costs (such as product and transportation costs) and the risk-associated costs. The proposed approach is demonstrated using an example of a US-based Chemical distributor. This framework provides a visual tool for supply chain managers to see how cost and risks are distributed across the different alternatives. Lastly, managers can calculate expected value of perfect information to avoid a certain risk.

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.

Implementation of benchmarking concepts in Indian automobile industry – an empirical study

Purpose – This paper aims to present existence comprehensive analysis of state of implementation of benchmarking concepts in Indian automotive companies. Design/methodology/approach – The research is carried out through a mixed method of research approach comprising of a survey of 300 auto companies in India. Out of 300, 48 valid responses together with three additional case studies were used in the data analysis. Inclusion of case studies was aspired to get deeper insight into the issues pertaining to adoption of best practices, and subsequently the implementation of benchmarking activities. Findings – Benchmarking has been unanimously accepted as an effective performance and productivity improvement tool by Indian auto companies. However, Indian automobile manufacturers still see benchmarking as a tool to compare product attributes, quality attributes, operations, and processes. Moreover, it has been perceived as being less applicable at strategic level. Results also show that benchmarking is in its pri...