A Research on Complex Event Processing Technology Based on Smart Logistic System

2014 ◽  
Vol 722 ◽  
pp. 430-435
Author(s):  
Bin Bin Fu ◽  
Jie Zhu
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Information Transfer ◽  
Supply And Demand ◽  
Complex Event Processing ◽  
Processing Technology ◽  
Event Processing ◽  
Supply Chain System ◽  
Technology Application ◽  
Demand Prediction

With IOT technology developing and the cost reducing, Its application in supply chain is a matter of time. Smart logistic system is one of the IOT technology application in supply chain which solve difficult problems, such as acquisition underlying data, information transfer and so on. we need to achieve higher level application and solve more complex problems such as improving inventory management accuracy, reducing supply chain management cost, improving accuracy of supply and demand prediction, supply chain's rapidly react ability,these need to use complex event processing technology. It will introduce how to apply complex event processing technology to supply chain system based on IOT. By this way we can sort out valuable information by processing a large number of simple event.

Research of IOT Context-Aware Processing Framework Based on Complex Event for Supply Chain Application

2012 ◽  
Vol 263-266 ◽  
pp. 1677-1687 ◽  
Author(s):  
Jun Tao Li ◽  
Gang Lin ◽  
Xiao Lin Cheng
Keyword(s):  
Supply Chain ◽  
Contextual Information ◽  
Situation Model ◽  
Complex Event Processing ◽  
Event Processing ◽  
Context Aware ◽  
Supply Chain System ◽  
Flow Situation ◽  
Event Flow ◽  
Internet Of Things Technology

In order to solve the logic fault of Internet of things technology applied in the supply chain system, this paper presents a supply chain oriented context-aware Framework which is based on Complex Event Processing. The frame is subdivided into five levels and proposes an active event flow situation model based on Complex Event Processing. Besides, it can provide a semantic senior situation for the application of the upper layer through processing the uncertainty contextual information under the semantic rules, as a support for the top decision.

Implementing Supply Chain Strategies

2019 ◽  
pp. 78-109
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Data Science ◽  
Supply Chain System ◽  
Chain Management ◽  
Supply Chain Strategy ◽  
Corporate Strategies ◽  
Inventory Turnover ◽  
Order Fulfilment ◽  
Outsourcing Decision

This chapter emphasizes the key elements required to implement supply chain strategy in a firm. It highlights the differences in supply chain strategies, its alignment with corporate strategies, and the associated drivers of supply chain management. This chapter also highlights the dynamics associated with inventory and success of supply chain of a firm. It tries to provide a framework to resolve the supply chain managers' dilemma as to hold inventory for order fulfilment or to enhance the inventory turnover ratio to maximize profitability. The chapter discusses all facets of inventory management – it includes inventory management of constant as well as dynamic demand. This chapter introduces the concept of adaptive inventory control for non-stationary demand. There are situations when all assumptions of conventional approach may fail and hence points out the importance of application of artificial intelligence and data science in inventory management. This chapter brings out the varied dimensions of contracts that are crucial to have an effective supply chain system. Here the author attempts to put forward an outsourcing decision framework to facilitate make or buy decision. This chapter relates the concept of materials requirement planning (MRP) with independent items. Since supply chains are going global, this chapter introduces the concepts behind global sourcing including the significance of INCO (international commercial) terms.

scholarly journals The Role of Internet of Things on the Development of Ports as a Holder in the Supply Chain

2018 ◽  
Vol 54 (1) ◽  
pp. 61-73
Author(s):  
Mladen Jardas ◽  
Čedomir Dundović ◽  
Marko Gulić ◽  
Katarina Ivanić
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Information Transfer ◽  
Information Technologies ◽  
Business Models ◽  
Business Processes ◽  
New Technology ◽  
Supply And Demand ◽  
Business Environment ◽  
Weak Links

The new technology greatly affects the way of production, consumption, communication, service delivery and ultimately on the entire supply chain. All stakeholders in the business process must invest in new knowledge and develop new business models to adapt to the changing business environment. Connecting devices over internet (Internet of things) and stakeholders’ synergy open up opportunities for new market achievements as well as for the improvement of business processes both in the supply chain and in ports. The development of information technologies has an impact on the reduction of errors, costs, time of information transfer and transport, inventory reduction and thus on better customization. There should be no weak links in the supply chain, which is especially related to the port and port processes that are the basis of the supply chain network. The port is the core of all activities of the supply chain and is also a place where supply and demand meet.

Modeling of Supply Chain Decision Logic Using PID Controllers

2004 ◽  
Author(s):  
Tung-King See ◽  
Edward M. Kasprzak ◽  
Tarunraj Singh ◽  
Kemper E. Lewis
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Critical Issue ◽  
Decision Makers ◽  
Pid Controllers ◽  
Supply Chain System ◽  
Number Of Factors ◽  
Human Decision ◽  
Decision Logic ◽  
Ordering Strategies

Most manufacturing takes place in the context of a supply chain. Each station in the supply chain must not only manufacture a product but also decide how much to produce. This decision is influenced by the supply of materials/components from the next station down in the supply chain and the demand from the next station up. With the advent of increased customization, inventory management is increasingly becoming a critical issue in the manufacturing process. In this paper we model the decision logic at each stage of a supply chain system through the use of system identification and PID controllers. The goal is to investigate and manage the costs of manufacturing a product in the context of a supply chain. It is assumed that the supply chain has well-understood interactions between individual positions, allowing for a focus on the ordering decision logic. A review of ordering strategies is presented, and a discussion of the difficulties in determining PID gains for human decision makers is included. The results show a range of correlation between the PID simulation and measured supply chain inventories. This stems from a number of factors, which are discussed. Additionally, ordering strategies to optimize the supply chain are investigated.

scholarly journals Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259284
Author(s):  
Hailan Ran
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Inventory Management ◽  
Management System ◽  
Prediction Method ◽  
Spare Parts ◽  
Collaborative Computing ◽  
Demand Prediction ◽  
Industrial Enterprises ◽  
Inventory Management System

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

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