Study on Construction Project Variation Management System Design

This paper analyses the necessity of developing variation information management of construction project. The structure scheme of variation management system has been put forward, which is based on intranet or internet scheme, with the structure of Browser or Server. The system consists of eight modules, which are variation account management, variation application, variation evaluation, variation execution, variation price management, variation report management, variation information query and system maintenance. With this system, remote management and controlling in real time for variation of construction proiect can be carried out.

Managing Variation and Resolving the Trade-off Implications of Global Supply

The reduction of wasteful variation within the supp ly chain is naturally suited to high and stable volumes, as epitomised in the lean practices of the automotive industry. However, with the growth of innovative pr oducts, typically characterised by lower volumes and uncertain demand, it is als o important to develop strategies to effectively manage the externally imposed v ariation resulting from volatile market demand. Such strategies often come under the banner of quick response or agile supply. System variation is the common denominator, for whereas lean strategies emphasis e the need for stability and the reduction of wasteful supply chain variation, a gile emphasises the need to respond to externally imposed demand variation. Simi larly, lean supply is closely associated with level scheduling and investing in inventory to decouple demand variation from the supply chain, whereas, agile sup ply is associated with investing in responsive capacity as inventory requirement s cannot be effectively predicted. This distinction between lean and agile supply, highlights a trade-off between investing in responsive capacity and decoupling inventory, which is closely related to the conflicting manufacturing tasks of fast response and low cost. The implications of this conflict have been more widely felt in recent years, as companies have opted to outsource manufacture to low cost overseas suppliers. T he rationale is to reduce manufacturing costs, but the trade-off is a dramatic loss of responsive capacity with higher dependency on decoupling inventory. As with lean and agile supply, the trade-off implications of such choices need to be acknowledged, conceptually understood and then separate out or otherwise r esolved. This paper introduces the business need before exploring the significance of var iation and the concept of managing variation in a delivery system using responsi ve capacity and decoupling inventory. These concepts will then be used to unpack the lean and agile paradigms before applying the thinking to a more detailed in dustrial case concerning offshore supply decisions. The paper concludes with som e reflections.

PGG:An Online Pattern Based Approach for Stream Variation Management

Many database applications require efficient processing of data streams with value variations and fluctuant sampling frequency. The variations typically imply fundamental features of the stream and important domain knowledge of underlying objects. In some data streams, successive events seem to recur in a certain time interval, but the data indeed evolves with tiny differences as time elapses. This feature, so called pseudo periodicity, poses a new challenge to stream variation management. This study focuses on the online management for variations over such streams. The idea can be applied to many scenarios such as patient vital signal monitoring in medical applications. This paper proposes a new method named Pattern Growth Graph （PGG） to detect and manage variations over evolving streams with following features： 1） adopts the wave-pattern to capture the major information of data evolution and represent them compactly; 2） detects the variations in a single pass over the stream with the help of wave-pattern matching algorithm; 3） only stores different segments of the pattern for incoming stream, and hence substantially compresses the data without losing important information; 4） distinguishes meaningful data changes from noise and reconstructs the stream with acceptable accuracy. Extensive experiments on real datasets containing millions of data items, as well as a prototype system, are carried out to demonstrate the feasibility and effectiveness of the proposed scheme.