Современные технологии имитационного моделирования и их применение в информационных бизнес-системах. Пленарный доклад
Russian mining industry experiences a rapid growth providing an attractive market for suppliers of mining equipment. However, it's a customer-dominated market. The customers set stringent requirements for the initial equipment supply, as well as for the after-sale service and spare parts supply. The manufacturers of mining equipment are thus facing the need to rigorously analyze their supply chain, to develop a reasonable customer service policy and to adjust the supply chain to meet the requirements of the service policy. To fulfill the after-sale maintenance requirements, the manufacturers must stock spare parts within the supply chain and to design inventory control policies. This paper presents an agent-based simulation approach for estimation of the supply chain's key performance indicators and to explore the service - cost trade-off. The conceptual model and the description of its implementation in Anylogic 8.4 software are provided. A case-study of a global mining equipment manufacturer's branch operating in Russia is described. The model is used to estimate the inventory control policy parameters required to meet the target customer service level, to compare make-to-order against make-to-stock supply strategies, as well as to explore the service level - cost trade-off.
The paper focuses on the questions of analysis, selection and monitoring of management systems development programs, relying on comparison of the program related expenditures and dynamics of the system’s maturity level, using simulation modeling. In this regard, integrated indicators, which characterize effectiveness of the development program, its financial aspects, as well as its efficiency and duration, are considered. Specific features related with of calculation of the development program integrated indicators using the results of simulation modeling and appropriate statistical metrics are disclosed.
This work discusses a possibility to assess the probability of company default using system dynamic model. This approach is based on Monte Carlo Simulation with various inputs for a system dynamic model. The results are compared with the estimations of rating agencies.