Nenad 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.

Proactive Supply Chain Performance Management with Predictive Analytics

Todays business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

Composite Web Information System for Management of Water Resources

Management of water resources such as lakes and reservoirs involves many stakeholders and a huge amount of data that comes from various information systems and in different formats. These systems are typically very difficult to integrate, analyze and customize. This requires a new breed of information systems capable to answer the challenges of modern water information management. In this paper, we introduce the new application model for designing such systems and accompanying i-SeLaR software system - composite, service-oriented and multilayered web information system for management of Serbian lakes and reservoirs. We describe architecture and services of the application model, together with structure and functionalities of the software solution. i-SeLaR integrates data, tools and applications for collaboration, data and knowledge management, process automation, and analytics. Architecture and features of i-SeLaR provide scalability and flexibility, so the system can be scaled, customized and implemented in organizations of different types and sizes. i-SeLaR web portal is the central component which serves as a unique gateway to different data sources and services, and provides a seamless and unified web interface for monitoring and management of water resources. Practical results showed several benefits in terms of better integration and flexibility, enhanced collaboration, higher data quality, knowledge-based analysis and more informed decision making.

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

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.

Developing Adaptive Business Intelligence Systems for Agile Supply Chain Analytics

Agile supply chain analytics is about ability to adapt business intelligence (BI) systems to meet the changing needs of the supply network and its environment. Agile BI incorporates both technical and process-oriented approach to development, management, and delivery of BI solutions. To achieve agile BI, supply chains must provide and establish the necessary infrastructure, architecture, tools and processes in a way that enables them to deliver BI systems capable of quickly and effectively adapting to the rapidly changing business needs and environment factors. However, most supply chains still lack methods, processes and tools to successfully design and implement these systems. The paper first provides an analysis of current situation and a concise background research, followed by the introduction of the integrated and adaptive supply chain business intelligence model. This model enables design of pervasive analytical systems for collaborative decision-making, monitoring and management of the supply network. The main components of the model are described, as well as the architecture and features of the specialized supply chain intelligence web portal which demonstrates usefulness and applicability of the proposed model. This system allows creation of a new breed of flexible and agile BI systems which ultimately results in more efficient, responsive and adaptive supply chains.

Innovative OER Model for Technology-Enhanced Academic and Entrepreneurial Learning

In today’s globalized, networked and fast-changing environment, knowledge became invaluable asset for everyone—students, workers and organizations. Lifelong learning became crucial to the future of our society. In order to foster more effective and collaborative learning and knowledge exchange it is necessary to utilize modern information technologies and services. If used in the right way, information technology can transform and energize educational process, stimulate learning in anyplace and anytime, promote creative thinking and make better use of existing individual and organizational resources. Although modern web technologies and open educational resources (OER) have great potential to bring new opportunities for learning and knowledge exchange, at the same time, developing successful OER systems is very challenging. Design of an innovative and open learning software environment needs a holistic approach, which comprises specific methodology, software architecture, and various services, applications and tools. The rationale of this chapter is to introduce the innovative open educational model and supporting software system that is capable to answer the major challenges of modern education. The proposed OER model and software platform enables creation of agile and adaptable educational ecosystem that can transform the learning experience, and connect individuals, educational institutions and companies.