Modeling Adaptive Supply Chain Management
MAGAZINE №5 (100) October 2020
AUTHORS ANDRONOV S.A., YARTSEVA A.A.
CATEGORY Simulation modelling Supply chains reliability and sustainability The uncertainty and risks in supply chains Supply chain management
ABSTRACT
Supply chain enterprises operate under the influence of many uncertain factors, such as, for example, fluctuations in demand, instability of supply, competitors' activity, weather conditions, etc. These issues in real supply chains are the responsibility of managers or of a provider firm.
When modeling processes in supply chains, coordination and adaptation functions are taken over by the corresponding blocks of the chain model. This study is devoted to the topical problem of improving the efficiency of the chain, taking into account the factors noted, namely, the study of management models for adaptive supply chains.
Due to the stochastic nature of the supply chain environment, modeling typically uses a simulation approach. Studies have shown that models that do not include self-organization blocks are not able to provide the required quality of management in conditions of uncertainty. Therefore, for adaptation at the operational level, it is proposed to consider the agent model in combination with the most appropriate methods of the automatic control theory. The presence of the human factor in supply chain management implies the presence of inertia inherent in people, which determined the applied principles: "quick response" - inertia and predictive nature when smoothing demand fluctuations to control the level of production, a fuzzy logical conclusion as a "soft" regulation inherent in humans, as well as error control based on the PID controller.
The model implemented in the Anylogic program has shown the effectiveness of applying the considered control methods under conditions of uncertainty and stochasticity of the parameters of the external environment. So, for example, the application of the quick response principle ensures the stability of the chain at the given levels of the studied impacts. So, in particular, in the framework of combined management, the model showed the best result - no more than 10% of outstanding orders. The addition of a continuous inventory control module to the model made it possible to reduce the failure rate by an average of 50%. The greatest effect is achieved as a result of the use of integrated control which takes into account the simultaneous change in the parameters of the chain links.
The considered control algorithms can be tested on empirical data of a specific chain, applied in the development of digital twins of chains, used by the provider as a means of automating the real supply chain management.
Keywords: supply chains uncertainty simulation AnyLogic control theory fuzzy logic
Methods and Tools of Intelligent Data Analysis for Digital Logistics and Supply Chain Management
MAGAZINE №4(87) August 2018
AUTHORS
SEROVA E.G. - Cand. of Ec. Sc., Associate Professor Management Department, National Research University Higher School of Economics (St. Petersburg, Russia)
CATEGORY Analytics and reviews Information technologies in logistics and SCM
ABSTRACT
Success of any logistics enterprise in the context of digital economy progress directly depends on regular and effective innovations in the area of improving analytical applications and information systems in such actively developing fields of knowledge as strategic management, distribution networks development, and supply chain management. In an attempt to ensure a sustainable economic circumstance under conditions of strong competition, the most perspective companies are increasingly focusing on the development and introduction of modern methods and tools for intelligent data analysis. The article focuses on the consideration of issues related to the use of modern simulation approaches and such components of the soft computing concept as neural networks, fuzzy logic and evolutionary computations in solving problems of multifunctional logistics and supply chain management.
Keywords:
