Dusan Stefanovic

Supply network modelling and simulation methodology

Abstract Supply network management in today’s business climate characterized by high uncertainty, globalization of business, outsourcing, shorter product life-cycles, and high customer expectations is extremely challenging. Simulation can be a valuable tool for supply network analysis, planning, optimization, evaluation, and risk management. This paper presents a methodology for modelling both structure and dynamics of complex supply networks based on process approach. It also describes the model-driven simulation methodology and the main components of the simulation software solution: model database, process library, knowledge base, and execution engine. Finally, simulation results of the case supply network are presented.

Methodology for modeling and analysis of supply networks

The analysis and modeling of business processes are the basis on which management methodologies, simulation models and information systems are developed. The goal of this paper is to point out the possibility of establishing relationships between processes in supply networks and functioning of the whole system. In this integrated system, all relevant factors for supply network management, both at the global level and at the single process level, could be observed. The idea is to form a process library of the supply network, which would contain process description, inputs, outputs, and the way the process is realized. Every record in the library presents the single instance of that process. The relationships of one process with another depend on process structure and the way of its realization. Every instance of a process represents its realization. The assembly of mutual compatible instances of all processes represents one realization of the supply network. The key problem, triggering the process realization, is solved by specific production expert system. Process realization is very similar to a real system, because the environment influence, uncertainty, and available resources are taken into consideration. As the output, the aggregate of relevant parameters for the evaluation of model functioning are derived. This concept presents the basis of virtual framework for supply network simulation.

Supply chain business intelligence: technologies, issues and trends

Supply chains are complex systems with silos of information that are very difficult to integrate and analyze. The best way to effectively analyze these disparate systems is the use of Business Intelligence (BI). The ability to make and then to process the right decision at the right time in collaboration with the right partners is the definition of the successful use of BI. This chapter discusses the need for Supply Chain Business Intelligence, introduces driving forces for its adoption and describes the supply chain BI architecture. The global supply chain performance measurement system based on the process reference model is described. The main cutting-edge technologies such as service-oriented architecture (SOA), business activity monitoring (BAM), web portals, data mining, and their role in BI systems are also discussed. Finally, key BI trends and technologies that will influence future systems are described.

Application of Data Mining for Supply Chain Inventory Forecasting

This paper deals with data mining applications for the supply chain inventory management. It describes the use of business intelligence (BI) tools, coupled with data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions. The methodology is designed to provide out-of-stock forecasts at the store/product level. The first phase of the modelling process consists of clustering stores in the supply chain based upon aggregate sales patterns. After quality store-cluster models have been constructed, these clusters are used to more accurately make out-of-stock predictions at the store/product level using the decision trees and neural network mining algorithms. The methods for evaluation and accuracy measurement are described. Also, the specialized front-end BI web portal that offers integrated reporting, web analytics, personalization, customization and collaboration is described.

Total coliforms and data mining as a tool in water quality monitoring

Total coliforms, as a microbiological indicator of water quality, have been tested on the basis of condition, dynamics, as well as on the dependence on other physicochemical and biological parameters, by methods and models of data mining. Using a combination of intelligent approaches, cluster analysis and classification, total coliforms have been analyzed and modeled on the examples of the

Management information system of lakes and reservoirs

Information technologies provide a significant support in the management of lakes and reservoirs. Existing information systems mostly did not comprise all the necessary data and enabled collecting only a limited set of reports. More complex data analyses such as statistical analyses and data research were conducted in separate environments and recquired additional preparation of the data. Information system of lakes and reservoirs in Serbia (SeLaR) was produced in order to provide all the neccessary information for lake and reservoir management. The information system integrates a large data set and provide users with necessary information in the form of reports and charts. The users of the system are scientists and experts who study these issues, as well as students with the objective of continuous education. Furthermore, an interface was produced to enable linking with data research software. In this way a unique working environment was created to provide users with an efficient model of acquiring all the information necessary for the management and to find out new facts in data interaction, and also to make a forecast. This study presents an overview of the information system, its production model, the information available and the possibilities of data research.

Application of Business Intelligence for Business Process Management

Companies require highly automated business process management (BPM) functionality, with the flexibility to incorporate business intelligence (BI) at appropriate stages throughout the workflow. Business Activity Monitoring (BAM) unifies these two technologies and provides real-time access to critical performance indicators to improve the speed and effectiveness of business operations. This paper discusses BPM technologies in the context of the supply chain and presents the comprehensive BAM solution that utilizes latest BPM, BI and portal technologies in order to enable decision makers to access and assimilate the right information to make well-informed, timely decisions.