Abstract
To determine the root causes or sources of variance of bad quality in supply chains is usually more difficult because multiple parties are involved in the current global manufacturing environment. Each component within a supply chain tends to focus on its own responsibilities and ignores possibilities for interconnectivity and therefore the potential for systematic quality assurance and quality tracing. Rather than concentrating on assigning responsibility for “recall” incidents, it would be better to expend that energy on constructing a collaborative system to assure product quality by employing a systematic view for the entire supply chain. This paper presents a systematic framework for intelligent collaborative quality assurance throughout an entire supply chain based on an expert system for implementing two levels of quality assurance: system level and component level. This proposed system provides intelligent functions for quality prediction, pattern recognition and data mining. A case study for wind turbines is given to demonstrate this approach. The results show that such a system can assure product quality improved in a continuous process.
Highlights
Quality is a critical requirement for customers, especially in the case of expensive and complex products
Rather than argue who ought to apologize to the “recall” incidents, it would be better to expend that energy on constructing a collaborative system to assure product quality by employing a systematic view for the entire supply chain
Three conceptual frameworks, including a management model, a technical model and a database management model, will be established to help guide the coordination of quality assurance along the whole supply chain. Intelligent functions such as quality prediction, pattern recognition and knowledge mining will be designed in the technical model to support two levels of collaborative quality assurance in Supply chain management (SCM): system level and component level
Summary
Quality is a critical requirement for customers, especially in the case of expensive and complex products. Chain management (SCM) was defined as the management of a network of interconnected businesses involved in the ultimate provision of product and service packages required by the end customers. It spans all movement and storage of raw materials, WIP inventory and finished goods from point-of-origin to point-of-consumption. With the consideration of the significance in closing above mentioned gaps, this paper will base on computational intelligence technologies to establish a collaborative quality assurance expert system for machinery products to ensure and improve their quality continuously.
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