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In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the 'distributed process mining' feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring 'doing the right things' within the workflow. An example of implementation is given, based on the case of slider manufacturer. [ABSTRACT FROM AUTHOR]

Global supplier selection has a critical effect on the competitiveness of the entire supply chain network. Research results indicate that the supplier selection process appears to be the most significant variable in deciding the success of the supply chain. It helps in achieving high quality products at lower cost with higher customer satisfaction. Apart from the common criteria such as cost and quality, this paper also discusses some of the important decision variables which can play a critical role in case of the international sourcing. The importance of the political-economic situation, geographical location, infrastructure, financial background, performance history, risk factors, etc., have also been pointed out in particularly in the case of global supplier selection. Supplier selection problem related to the global sourcing is more complex than the general domestic sourcing and as a result it needs more critical analysis, which could not be found properly in past available literatures. This paper discusses the fuzzy based Analytic Hierarchy Process (fuzzy-AHP) to efficiently tackle both quantitative and qualitative decision factors involved in selection of global supplier in current business scenario. The fuzzy-AHP is an efficient tool to tackle the fuzziness of the data involved in deciding the preferences of the different decision variables involved in the process of global supplier selection. The triangular fuzzy numbers are used to transform the linguistic comparison of the different decision criteria, sub-criteria and performance of the alternative suppliers. The pairwise comparison matrices help in deciding the synthetic extent value of each comparison and finally, the priority weights of one alternative over another are decided in this paper. An example from a manufacturing industry searching for the global supplier for a critical component is used to demonstrate the effective implementation procedure of proposed fuzzy-AHP technique. The proposed model can provide the guidelines and directions for the decision makers to effectively select their global suppliers in the current competitive business scenario. [ABSTRACT FROM AUTHOR]

In today's increasingly competitive environment, firms are experiencing growing pressure to reduce the product development lead time to meet market expectations. In this paper, a Responsive Product Development System (RPDS), which is used to model the product development process and the components of the process with object technology, introduces a dynamic product information schema characterized by its ability to provide design practitioners with a product data exchange standard, thus transforming data to information and then knowledge. To validate the feasibility of the proposed schema, a case study has been conducted in a plastic product factory based on the suggested approach. Following feedback from these companies, a further review of the design of the proposed system has been conducted to ensure efficient information flow across the heterogeneous computing environment. A measure of loss to society associated with RPDS in product development time is also included as a system evaluation. [ABSTRACT FROM AUTHOR]

Considering the growing concerns and importance of environmental issues, manufacturing industrialists, in particular those in electronics manufacturing, are seeking methods to evaluate the environmental performance of their manufacturing processes. These environmental evaluation tools should be capable of performing detailed analysis on the environmental performance of each individual process unit, identifying the environmental improvement opportunity and providing adequate decision support to environmental engineers for process modification and operational change. This paper modifies and improves the environmental impact evaluation model already developed and provides directions for decision-making at various stages of the analysis. The decision algorithm in this revised model adopts a hierarchical environmental impact evaluation approach and uses five impact categories related to ecological health and seven categories related to human health to form the base of an evaluation hierarchy. The algorithm, with its evaluation results presented in all levels of the hierarchy, is proposed as a means of tracking, controlling and improving the environmental performance of a process. A printed circuit board case study shows the effectiveness and applicability of the algorithm. The results indicate that the electroless copper process has a higher impact on ecological health than on human health, and that the phosphoric acid in the waste components is the identified major source of the impact on ecological health. [ABSTRACT FROM AUTHOR]

Purpose This paper aims to investigate the characteristics of demand and supply in relation to the real-world supply chain strategies of local urban fresh food supply chains (FFSC). It generates insights into how a range of strategies is adopted by urban retailer businesses in attempting to cater for the particular requirements of food-literate urban consumers and small-scale local growers.Design/methodology/approach Using a multiple case study method, 12 urban local fresh food retailers in Sydney were studied and interview data were analyzed using thematic analysis.Findings Local fresh produce has characteristics of both functional and innovative products. Retailers with strong upstream and downstream collaborations adopt hybrid strategies for increased time efficiency and product variety. The dominance of strategies for time efficiency in downstream activities is aimed at maximising the product's freshness and taste, while product range improvement strategies mean innovative retailers are working with growers to introduce new product types and offering new recipes to consumers that encourage a wider use of products. Urban retailers of local fresh produce leverage on their relationships with upstream and downstream supply chain entities in implementing hybrid strategies.Implications Policymakers will make use of the new knowledge generated about the real enablers of contemporary urban food systems in designing developmental policies; findings will inform urban FFSC retailers about how harmonious relationships can be leveraged for sustainability.Originality/value The study generates new knowledge on the implementation of a leagile approach by studying the adoption of innovative hybrid strategies by urban local FFSCs in relations to demand and supply characteristics and the utilization of strong vertical relationships in a short supply chain. [ABSTRACT FROM AUTHOR]

Owing to the shockwaves brought by the recent financial tsunami, most enterprises are facing tremendous challenges in maintaining the good liquidity of their own companies. In order to sustain a desirable level of cash flow for expanding business, inventory needs to be well organized because unnecessary inventory that ties up the capital in the business would prevent the enterprises from making investments. Because the existing approaches to replenishment are inflexible and unsophisticated, a new customer-based responsive replenishment system embracing online analytical processing, fuzzy logic and genetic algorithm is proposed in this paper. This system could determine accurate and realistic order quantities based on all possible and relevant variables that affect the order quantity for each item that needs to be replenished. Once the quantity has been accurately identified, the company can increase the level of customer satisfaction while minimizing stocks. Furthermore, rather than static rule repositioning, the proposed dynamic rule refining ability makes the replenishment system self-ameliorating by using genetic algorithm to investigate the possible fuzzy rule candidates for a more accurate inventory management model. A study has been conducted in a case company for the validation of the feasibility of the proposed system. After performing a spatial analysis, the results obtained indicate that the proposed responsive replenishment system is capable of ensuring improved inventory control performance in the case company. [ABSTRACT FROM AUTHOR]

Reactive ion etching (RIE) is a process in the fabrication of semiconductor devices. The ability to predict the influence of the process parameters of RIE is crucial in terms of machine performance as they may have a serious impact on product quality as well as on the probability of machine failure. To address this issue, this correspondence paper presents a novel performance tradeoff function for evaluating the overall suitability of adopting the predicted control parameters suggested by domain experts, taking into full consideration their impact on the performance of the machine involved. An experiment using the RIE machine is provided to validate the practicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Purpose — This paper aims to develop a genetic algorithm (GA)-based process knowledge integration system (GA-PKIS) for generalizing a set of nearly optimal fuzzy rules in quality enhancement based on the extracted fuzzy association rules in a supply chain network. Design/methodology/approach — The proposed methodology provides all levels of employees with the ability to formulate nearly optimal sets of fuzzy rules to identify possible solutions for eliminating the number of defect items. Findings — The application of the proposed methodology in the slider manufacturer has been studied. After performing the spatial analysis, the results obtained indicate that it is capable of ensuring the finished products with promising quality. Research limitations/implications — In order to demonstrate the feasibility of the proposed approach, only some processes within the supply chain are chosen. Future studies can advance this research by applying the proposed approach in different industries and processes. Originality/value — Because of the complexity of the logistics operations along the supply chain, the traditional quality improvement approaches cannot address all the quality problems automatically and effectively. This newly developed GA-based approach can help to optimize the process parameters along the supply chain network. [ABSTRACT FROM AUTHOR]

In today’s globally competitive industries, high-quality and high-reliability products play an important role in achieving customer satisfaction, and insisting on quality is always the only way to survive in an enterprise. Studies indicate that automating quality audits and adding decision support in quality improvement is an attractive idea. In this environment, production workflow mining is an approach for extracting knowledge from different manufacturing processes in order to assist real-time quality prediction and improvement. This papers attempts to propose an intelligent production workflow mining system (IPWMS) embracing online analytical processing (OLAP) and data mining technology, together with the use of artificial intelligence combining artificial neural networks (ANNs) and fuzzy rule sets to realize knowledge discovery and decision support in high-quality manufacturing. To validate the feasibility of the proposed system, a prototype is developed and evaluated in a company, and a description of this case example is covered in this paper. [ABSTRACT FROM AUTHOR]

The pivotal idea central to engineering education is the cultivation of a motivation-based environment. There is little doubt that students learn better, particularly with regard to deep learning if they are allowed to focus on areas of a curriculum in which they are strongly motivated On the other hand, engineering education can also be regarded as an integrated study for students, which foster the cultivation of basic skills and the talent to follow well-defined patterns of procedures in order to solve problems. This paper presents the design and implementation of an educational simulation which mimics a microworld of business activities. The objective of such educational simulation is to present an integrated environment for students to undergo a motivation-based learning experience based on an integrated study methodology. The design and implementation of the educational simulation is intended to motivate students to accept personal responsibility for behavior in a simulated environment by recognizing and taking pride with a sense of self achievement. [ABSTRACT FROM AUTHOR]

During the 1990s, Computer-Aided Design (CAD) and Computer-Aided Process Planning (CAPP) have emerged as effective means of improving productivity and efficiency, resulting in widespread research work related to CAD and CAPP integration in the hope of achieving seamless data flow among the relevant computer systems. Unfortunately, the efficiency of process planning systems still needs significant improvement due to the problems of design data loss or design data mis-interpretation. This paper attempts to introduce an intelligent computer-integrated system for reliable design feature recognition in order to achieve automatic process planning. Moreover, it describes a rule-based computer-integrated system for recognizing features of components stored as STEP format, analyzing the design files and designing manufacturing processes. In this approach, the STEP design file is used as the information source for generating detailed manufacturing process. The significance of this research is that the product designs with dissimilar formats from various CAD systems can now be interconnected and automatically coded for multiple manufacturing purposes. It presents an effective method that rule-based reasoning is used to create optimal process plans for metal cutting. [ABSTRACT FROM AUTHOR]

Purpose - This paper proposes an infrastructural framework, involving various emerging technologies, for the development of an agent-based quality enhancement system with the distinct feature of the ability to cope with the potential quality problems from various customers' requirements. Design/methodology/approach - The proposed system is equipped with the "distributed problem solving" feature which automatically identifies and divides the potential quality problems into sub-problems and directs straight to the associated agents that are capable of interacting and negotiating with each other to support customer needs. The new feature of this agile quality enhancement system is characterized with the incorporation of the quality mining engine through the capabilities of the online analytical processing, case-based reasoning, fuzzy logic and artificial neural networks, achieving the optimization of the performance of organizations. Findings - In this paper, the detailed architecture and the components included in the proposed system are described with a case study to validate its feasibility of application in actual environment. Originality/value - The proposed system is to enhance customer satisfaction by automating problem solving procedures in all business processes. [ABSTRACT FROM AUTHOR]

To cope with the requirements of high dimensional accuracy for injection molding components, it is important to optimize the process parameters in order to sustain the high level dimensional quality of the molded parts. In this respect, a study in the domain of process optimization is of paramount importance in terms of determining the optimal set of injection molding parameters. To this end, a methodology to establish an integrated model which consists of both fuzzy logic reasoning and a genetic algorithm is proposed. These two artificial intelligence techniques can complement each other to form an integrated model which capitalizes on the merits and at the same time offsets the pitfalls of the involved technologies. To validate the feasibility of the proposed model, a case study related to injection molding optimization is also covered in this paper. [ABSTRACT FROM AUTHOR]

Recent research related to the aircraft container loading and scheduling problem for airfreight forwarding business has seen significant advances in terms of load plan optimization, taking into account the cost and volume of packed boxes. In today's competitive industrial environment, it is essential that freight forwarders are able to collaborate with carriers (airline companies) to achieve the best possible selection of logistics workflow. However, study of contemporary research publications indicates that there is a dearth of articles related to the design and implementation of an intelligent logistics system to support decision-making on carrier selection, aircraft container loading plans as well as carrier benchmarking.This paper presents an intelligent logistics support system (ILSS) which is able to provide expert advice related to the airfreight forwarding business, enhancing the logistics operations in relevant activities within the value chain of tasks. ILSS comprises a heuristics-based intelligent expert system which supports carrier searching and cargo trading planning as well as load plan generation. The proposed approach is meant to enhance various operations in the airfreight forwarding business, adopting computational intelligence technologies such as rule-based reasoning to provide domain advice and heuristics to support the generation of load plans. After potential outcomes are generated by the heuristics-based intelligent expert system, a neural network engine is applied to support prediction of unexpected events. To validate the viability of this approach, a production system using the ILSS has been developed and subsequently applied in an emulated airfreight forwarding environment. The application results indicate that the operation time from searching for potential carriers to the execution of the order is greatly reduced. In this paper, details related to the structure, design and implementation of the ILSS are also covered with the inclusion of the actual program codes for building the prototype. [ABSTRACT FROM AUTHOR]

Attempts to introduce a quality measurement scheme (QMS) that is able to assess the immediate feedback of customers globally in real time, followed by a data mining process, which is an interactive process that involves assembling the data into a format conducive to producing a multi-dimensional analysis using an online analytical processing (OLAP) approach. In addition, an XML schema, which provides a universal syntax to facilitate the exchange of data, is used in the design of the QMS to support the data mining process. To validate the feasibility of QMS in real industrial situations, a case example is covered, showing promising test results based on the proposed scheme. [ABSTRACT FROM AUTHOR]

In today's competitive environment, rapid advances in economic globalization and information technology have forced many organizations to anticipate and respond to increasing volatility and competitive pressures. Numerous researches have focused on developing techniques on the analysis, design and management of whole logistics chains in particular in the domain of data mining, data analysis and data classification. While the data mining techniques and computational intelligence techniques are good independently for solving specific tasks, they can be synergized through the formation of an integrated and unified model, which can take advantage of the goods and offset the flaws of the two techniques. In this paper, an intelligent information infrastructure, which is characterized by its ability to encompass a rich collection of knowledge representation formalisms for dealing with a logistics information flow problem, is presented. Such intelligent information infrastructure includes a two-step strategy that embraces the combination of online analytical processing and neural networks to support knowledge discovery. In addition, extensible markup language is used to support the overall infrastructure, in order to facilitate the seamless data interchange within an enterprise. [ABSTRACT FROM AUTHOR]