David M. Chiang

The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres

Among distribution centre operations, order picking has been reported to be the most labour-intensive activity. Sophisticated storage assignment policies adopted to reduce the travel distance of order picking have been explored in the literature. Unfortunately, previous research has been devoted to locating entire products from scratch. Instead, this study intends to propose an adaptive approach, a Data Mining-based Storage Assignment approach (DMSA), to find the optimal storage assignment for newly delivered products that need to be put away when there is vacant shelf space in a distribution centre. In the DMSA, a new association index (AIX) is developed to evaluate the fitness between the put away products and the unassigned storage locations by applying association rule mining. With AIX, the storage location assignment problem (SLAP) can be formulated and solved as a binary integer programming. To evaluate the performance of DMSA, a real-world order database of a distribution centre is obtained and used to compare the results from DMSA with a random assignment approach. It turns out that DMSA outperforms random assignment as the number of put away products and the proportion of put away products with high turnover rates increase.

Improved customer satisfaction with a hybrid dispatching rule in semiconductor back-end factories

On-time delivery is a vital factor for customer satisfaction in the competitive semiconductor manufacturing industry, and to optimize on-time delivery manufacturers must continuously improve their management of work-in-progress (WIP). However, in undertaking to optimize WIP, managers are also concerned with short cycle times, high throughput, and high utilization. In an attempt to find the most satisfactory solution to these potentially conflicting requirements with regard to WIP, the present study employs fuzzy analytic hierarchy process (AHP) to determine an appropriate set of acceptable WIP deviation levels (AWDLs). These AWDLs are then used in a proposed hybrid dispatching rule to determine the operational priorities of jobs. A simulation model using real-world data is then constructed to examine the proposed mechanism for improving customer satisfaction. The findings of the study confirm that the proposed mechanism is capable of simultaneous consideration of various goals and the achievement of enhanced performances with respect to due-date delivery from semiconductor back-end processes.

Data mining based storage assignment heuristics for travel distance reduction

Among the warehousing activities in distribution centres, order picking is the most time-consuming and labour-intensive. As a result, order picking may become a bottleneck preventing distribution centres from maximizing the effectiveness of their warehousing activities. Although storage location assignment or product allocation is a tactical decision, it is especially influential on the effectiveness of order picking. In previous studies, most storage assignment approaches considered the order frequency of individual products rather than that of product groups, which often are purchased together. This paper proposes a new association measure, weighted support count WSC, based on association rule mining, to represent both the intensity and nature of the relationships between products in a distribution centre. This paper presents two storage assignment heuristics, the modified class-based heuristic MCBH and the association seed based heuristic ASBH, designed to facilitate efficient order picking by applying WSC. The real-world data set of a grocery distribution centre is used to verify the effectiveness of the proposed approaches. From the computational results, MCBH cuts at most 4% from the travel distance for order picking per month, as compared with the traditional class-based approach. Meanwhile, ASBH achieves at most a 13% reduction in travel distance.

Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method

To use information from genetic polymorphisms and from patients (drinking/exercise habits) to identify their association with stone disease, the main analytical and predictive tools being discriminant analysis (DA) and artificial neural networks (ANNs).

Optimal supply chain configurations in semiconductor manufacturing

As a semiconductor supply chain becomes widespread and the competition pressure is very fierce, the detrimental effects of increasing varieties and variations are magnified in the supply chain. But many important issues, such as different service priorities, adaptability, controllability and scalability of performance metrics, have not been addressed in the literature. Conventional modelling techniques of supply chain operations are no longer effective for supply chain configuration. Therefore, a proposed empirical model was first built to catch up the relationship between supply-chain configuration and metrics under the influence of the variability sources. Next, an optimal supply chain configuration model is formulated as a polynomial goal programming model to accommodate different goal objectives. Finally, an efficient solution methodology is further developed to find out the optimal supply chain configuration. Our results show that our proposed approach can easily be adapted to the practices in semiconductor supply chain, and the solution methodology developed in this paper is truly promising.

Delivering Superior Customer Perceived Value in the Context of Network Effects

Network effect refers to the phenomenon that the value of connecting to a network depends on the present number of customers connected to that network. The self-reinforcement characteristic of the network effect strengthens the strong firms and weakens the weak firms. In the extreme form, network effects may produce a winner-take-all market. Therefore, competitive strategies are critical for companies in markets characterized by network effects. To succeed in the market, a firm must be able to deliver superior value to customers compared to its competitors. This study proposes a conceptual model for describing the market situations characterized by network effects. Based on this model, this study clarifies how companies can deliver superior customer value in the context of network effects. This study proposes two major approaches: increasing total customer value and reducing total customer costs. Various practical methods are proposed for implementing these two approaches.

Multi-objectives exception management model for semiconductor back-end environment under turnkey service

On-time delivery is the need for customer satisfaction which is a critical factor in the survival of the semiconductor industry. The on-time-delivery performance of the whole semiconductor turnkey service depends on the performance of the back-end factories. Unfortunately, undesirable and inevitable production variations make it difficult to maintain and improve a factorys performance and more objectives such as cycle time, throughput rate and the due-date accuracy need to be simultaneously considered. This paper presents an exception management model in order to compromise the contradicting needs of delivery accuracy, throughput rate and cycle time. The exception model can be divided into three parts: (1) an integrated performance index is proposed to compromise multiple performance measures; (2) an AWDL (available WIP deviation level) determination model is designed to gather proper AWDLs for triggering exceptions and (3) a WIP correction action is proposed to make abnormal WIP levels back to normal levels as soon as possible. To evaluate the proposed WIP exception management model, a simulation model is constructed and experiments are then conducted. The simulation results show that the proposed model helps back-end factories to set proper exception triggering conditions, reduce uncertainty occurrences and achieve better performances on due dates.

Partner selection model for design chain collaboration

Due to increasing global competition and shorter new product life cycles, brand owners collaborate more often with design chain partners to bring a new product to market. This paper proposes a design partner selection model to minimise the overall design chain cost, taking into account communication cost, time-to-market, and quality. The model can be formulated as a stagecoach problem. Next, a dynamic programming algorithm is developed for this model to select the optimal partners through the design chain. Finally, a numerical example and a sensitivity analysis are presented. The results also reveal that design quality and manufacturing quality positively influence the total cost, and when brand owners need to speed up time-to-market to keep up with a competitor, the design quality will become less important.

The impact of estimation error on the dynamic order admission policy in B2B MTO environments

When swarming demands cause stringent capacity situations, order promising becomes a challenging job. However, a dynamic order admission policy by utilizing the concept of revenue management may find a good way to solve the problem. Unfortunately, the expected profit under different dynamic order admission policies is sensitive to the estimation error of order forecasts. In this paper, the impact of estimation error is investigated under various order structures. The post analysis is performed and shows significant statistical difference among the optimal unbiased DSKP policy, biased DSKP policy, and FCFS policy. The results reveal the robustness and superiority of DSKP policy in most scenarios.

Consumption Externalities: Review and Future Research Opportunities

An extensive literature review of consumption externalities is presented in this article. The review is classified into four sections: network externality, indirect network externality, congestion externality, and cross-consumer externality. The review of network externality is a fundamental part of this paper. In this paper, we first suggest two dimensions and provide a classification on consumption externalities. Similarities and dissimilarities among them are contrasted. We next review the literature on consumption externalities in four sections. Finally, we raise research opportunities, which can be further explored in four fields.