Wenjing Yan

A Web Services-enabled B2B integration approach for SMEs

Effective exchange and sharing of information across supply chains play a key role in business-to-business integration (B2Bi). Web Services enable companies to collaborate closely with their business partners and gain access to needed information and business logic. However, helping small and medium enterprises (SMEs) to participate in B2B collaborations and make use of the advances of the service oriented technologies are challenging tasks. In an effort to bridge the gap between small and big companies, a Web Service-enabled B2B integration approach for SMEs was developed. It provides a feasible and cost-effective solution for SMEs to take part in B2B collaborations by taking advantage of Web servicespsila characteristics and lightweight IT infrastructure for SMEs. In this approach, two mechanisms namely pull and simulated push mechanisms of Web Services are designed. This paper provides a detailed discussion and merits of such a mechanism for enabling B2B collaborations for SMEs.

Practical lot release methodology for semiconductor back-end manufacturing

This paper presents a new method and system that has been developed to solve production lot release problems in a discrete semiconductor back-end manufacturing environment, wherein there is always a huge product mix and a multitude of capacity constraints. The methodology is a multi-constraint based finite capacity production control mechanism to plan lot release of the desired mix of products for the semiconductor assembly and test operations. Practical lot prioritization considerations, order release policies, finite capacity constraints and a novel technique of multi-level loading pattern for minimizing machine conversion are discussed in detail. The system and methodology presented in this paper has been successfully implemented in a semiconductor back-end factory in Asia.

An integrated modeling framework for capacity planning and production scheduling

Efficient system modeling can eliminate capacity discrepancy between mid-term capacity planning and by consistently delivering promised capacity. This paper proposes an integrated modeling framework which consists of capacity constraints and configurable constraints for capacity planning and production scheduling to address the issue of capacity discrepancy. The capacity constraints that derived from machine timeline, production rate and machine allocation preference matrix can resolve the conflicting objectives in capacity planning and production scheduling; whereas the configurable constraints that are designed and implemented for special concerns in planning and scheduling functions will facilitate the pursuing of optimized production plans and schedules. The paper depicts an implementation of the proposed framework in the semiconductor back-end assembly environment.

Spatio-temporal route mining and visualization for busy waterways

Route mining for busy waterways is a challenging task. Complicated shipping routes may be generated due to vessels of different types congesting in a narrow water way, frequently changing navigational direction and weaving through multiple crossing traffic. The traditional way using visual bearing and ship-stationed techniques may mitigate hazards of ship collision but lack macroscopic information for safe and efficient shipping navigation. In this paper, we proposed a spatio-temporal mining method to explore vessels shipping patterns in Singapore Strait. The frequent shipping routes can be automatically extracted using a local polynomial regression based algorithm. Time series clustering across spatial areas is used to associate spatial pattern with temporal pattern. The aim of this study is to provide support for decision-making process in optimal shipping route planning and maritime traffic management. Mapping the pattern information to a virtual geographical information platform enables users to intuitively acquire the knowledge of vessels shipping patterns.

Weighted clustering of spatial pattern for optimal logistics hub deployment

Optimal logistics hub deployment is a strategic challenge in logistics planning and management. Selecting a proper location for the logistics hub could be significantly impacted by long-term geospatial characteristics of logistics operations including spatial distribution of target customers, convenience of traffic access and operational cost. This paper describes a method using clustering of weighted spatial patterns to find optimal locations for logistics hubs deployment. The underlying concept of this method is that an optimal location of the hub could be determined by logistics operation patterns mined from logistical spatial and temporal data. A logistics spatial pattern can be produced by spatial association rules mining and clustering. The spatial patterns weighted by characteristics of logistics operations are then be clustered to generate the final hub location. In this study, the method is validated with a real data sets of pick-up and delivery business. The experimental results demonstrated that the method was able to generate an optimal location for logistics hub deployment with reduced travel distance to frequent customers locations.

Vessel movement analysis and pattern discovery using density-based clustering approach

Automatic identification system (AIS) has been widely equipped on vessels for maritime communication, positioning and traffic monitoring. The comprehensive data obtained by AIS provides spatio-temporal traces depicting the vessels trajectories and can be used as a coherent source of information for vessels behavior and the overall maritime traffic analysis, in supporting of the better traffic planning and service optimization. However, it is challenging to process and analysis such a large amount of AIS data that is associated with a great variety of vessels. In this paper, we propose an unsupervised data mining method using density-based strategy to analyze vessels trajectories and extract the traffic patterns from historical AIS data. It starts with stops and moves identification from vessels trajectories, followed by the extraction of stationary areas of interest from the stops and the detection of the main traffic routes from the moves using density-based clustering method, which takes both the speed and direction into consideration. Experiments on the real AIS data demonstrate the effectiveness of this work.

Towards better supply chain visibility — The design and implementation of a supply chain system S-ConTrol to support an operational HQ in Singapore

Visibility into supply chain can give company a competitive advantage in todays growing global operations. As Small and Medium Enterprises (SMEs) grow beyond Singapore, there is a need to help them to setup operational headquarters (OHQs), with Control Tower capabilities to better manage their supply chains, for better productivity and customers satisfaction. This paper discusses the challenges in designing, developing and implementing a supply chain system, SMEs Control Towers (S-ConTrol), to support such a Control Tower. A two-phase systematic approach for the system design and implementation was proposed. In the first phase, a top-down business analysis combined with target driven bottom-up data analysis process was executed to address the business needs and identify the challenges. The second phase was the implementation of the solution to fulfill the business needs. The system has been successfully deployed in a local company and its regional customer centres (RCCs) with tangible productivity gains achieved.

DMTT - An approach for business Document Mapping and Transformation in B2B collaboration

Business documents are frequently exchanged between business entities as well as within the enterprise. In the context of dynamic business to business (B2B) collaborations, where partnerships are formed in an ad-hoc manner, the relationships between the elements of these documents are often unknown to each. To translate the information from a document to another, a mapping and transformation of these elements are required. To identify the possible mappings automatically, a matching is performed. In the matching of business documents, one of the challenges identified is matching the multi-word used to define the element names. Our contributions in this paper are two folds. Firstly, we proposed an algorithm to address the matching of multi-word in XML business documents using their corresponding XML Schemas. Secondly, we proposed Document Mapping and Transformation Tool (DMTT), a solution to the mapping and transformation of XML business documents. DMTT employed combined matching techniques to derive the recommended correspondences of XML schemas with the consideration of both element and structural aspects of schemas.

A Priority-Driven Finite Capacity Planning System with Enhanced Shifting Bottleneck Algorithm

This paper presents the details of a priority-driven finite capacity planning system to address the capacity aggregation issue of the traditional rough-cut capacity planning (RCCP) approach. The system overcomes the limitation of infinite capacity consideration in the traditional RCCP approach through detailed system modeling. The capacity planning process starts by establishing capacity availability through the user-defined production calendars and machine unavailability time periods such as planned machine preventive maintenance schedule. The available capacity information is represented by building machine time lines with finite time buckets down to an increment of minute. Machine loading preferences and standard processing times are then specified in the form of capacity matrices. With the detailed capacity modeling approach, all information needed to model the capacity constraints could be precisely stated. The enhanced priority-driven finite capacity engine can be configured to consider weighted product and machine priorities; product forecast ratio, linkages to critical tooling and fixture constraint, as well as, the ability to cater for shifting bottleneck during the dynamic capacity allocation process. Through the intelligent capacity planning algorithms, the demands are assigned to the available capacity on a level-by-level approach. The priority-driven finite capacity planning system has been implemented in a few companies in the semiconductor backend assembly environment and it has proven to be a practical and effective capacity planning solution based on the encouraging feedback from the end users.

A Cognitive Planning Model for Transport and Logistics

It is known that human teams assisted by a computer software system can make planning more accurately and quickly in uncertain situations than teams of just people. Therefore, there are a lot of planning systems developed based on the traditional theories such as rational choice theories and rule-based expert systems which mainly focused on human intelligence. However, these systems could not emulate and explain well that people are prompted to make different decisions for actions according to a same plan based on different emotional feelings in uncertain conditions. These human factors are diverse and could be categorized into biological, social and/or cognitive ranging from the feelings such as happy, fear, anger, interest, curiosity, stress and so on. Rather than continue in the traditional methods, this paper presents a novel approach to research and development of a more realistic computational planning model to simulate planning process by extending traditional planning model with human factor in the context of transport and logistics.