In this paper we examine the product innovation in a supply chain by a supplier and derive a model for a supplier's product innovation policy. The product innovation of a supplier can contribute to the long-term competitiveness for the supply chain, and as it is for many supply chains a major factor, it should be considered in the development of strategies for a supplier. Here, we evaluate the effectiveness of supplier product innovation as a strategic tool to enhance the competitiveness and viability of supply chain. This paper explores the dynamic research performance of a supplier with endogenous time preference under a given arrangement of product innovation. We find that the optimal effort level and the achieved product innovation obey a saddle point path, or show tremendous fluctuations even without introducing the stochastic nature of product innovative activity. We also find that the fluctuation frequency is largely dependent both on the supplier's characteristics such as supplier's product innovative ability and on the nature of product innovation process per se. Short-run analyses are also made on the effect of supply chain cooperation in the product innovation process.
In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize parameters in surface grinding processes where multiple conflicting objectives are present. The relationships between surface grinding process parameters and the performance measures of interest are obtained by using experimental data and particle swarm optimization intelligent neural network systems (PSOINNS). The results showed that particle swarm optimization is an effective method for solving multi-objective optimization problems, and an integrated system of neural networks and swarm intelligence can be used in solving complex surface grinding operations optimization problems. In this paper the key grinding process models and relationships that were discovered by previous research efforts have been unified in the form of a particle swarm optimization intelligent neural network systems.
The capability to concurrently design the product and the supply chain is becoming a key competence in manufacturing companies. In spite of this development, this competence is still underdeveloped in industry. Research has not been able to fill this industrial capability gap partly because there is a lack of convergence of the methodologies for concurrent product and supply chain design in the research community. Today, businesses depend on strategic collaboration with their suppliers and customers to create value to develop product and to obtain better market-share. Designing products to match the processes and supply chains processes to match product platforms and supply chains, and supply chains to match the product platforms and process are the ingredients in todays fast developing markets. If this co-design is done well upfront with sufficient focus on product development process managing, product will cost much less overall and the time to market will decrease substantially. This paper presents a supply chain collaboration dynamic model with two innovative R&D sectors for each supplier and buyer: A vertical R&D sector that improves the quality of existing differentiated products and a horizontal R&D sector that creates new differentiated products. The supplier and buyer exchange differentiated products and beneficiate from knowledge spillovers (possibly impulsed by R&D subsidies). The long term policy effects of R&D subsidies in this context had been studied in this paper. In this contribution, we have realized an attempt to integrate the product development model in a supply chain collaboration framework. This enables us to discuss of the optimal research policy integrating some feedback effects from innovation and knowledge spillovers. Our main result is that the effect of a subsidy to vertical R&D (the only subsidy that has a long term effect) depends on the relative innovative capacities of the supplier or buyer that realized this policy.
Manufacturing strategy research aims at providing a structured decision making approach to improve the economics of manufacturing and to make companies more competitive. The overall objective of this paper is to investigate how manufacturing companies make use of different manufacturing practices or bundles of manufacturing practices to develop certain sets of capabilities, with the ultimate goal of supporting the market requirements. The authors propose a technique that can effectively take managerial preferences and subjective data into consideration, along with quantitative factors. The tool that is proposed here relies on the use of a more effective version of the Analytical Hierarchy Process (AHP) called the Analytical Network Process (ANP) to help integrate managerial evaluations into a more quantitatively based decision tool, data envelopment analysis (DEA). In this paper, these two techniques, when used together, can provide subjective and objective evaluations for manufacturing strategy decision makers. An illustrative example provides some insights into the application of this methodology. The research contributes to several insights to the research area of manufacturing strategy and to practitioners in manufacturing operations. A model that investigates process improvement investments, assuming that alternative process improvement initiatives exist, is then presented.
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