SELECTION AND JUSTIFICATION OF ARCHITECTURE OF VIRTUAL PRODUCTION SYSTEM FOR PLANNED PORTFOLIO OF ORDERS

Ксения Олеговна Западня, Олег Александрович Гайденко, Вадим Алексеевич Пуйденко

Abstract


The task to form and research the virtual production system (VPS) operation for the planned portfolio of orders (PPO) has been stated and solved. Due to the complexity of the proposed task, the solution is implemented in several stages. At the first stage the research of the main indicators of the formed VPS has been made. First of all, this concerns the composition of the VPS that according to its characteristics should meet the basic requirements of the PPO. For this purpose a set of potential enterprises – VPS candidates – have been formed using a precedent base (PB). A qualitative scale in the form of Latin alphabet was used to evaluate each enterprise in terms of key indicators and requirements of PPO. By lexicographic ordering of options in the PB, the most suitable enterprises are selected to make the VPS. To adapt the enterprises (equipment, technologies) to the specific requirements of PPO, the choice of activities for the modernization of production is made. The adaptation costs have been selected as an objective function to evaluate the set of activities. The time to make the activities and the acceptable risks are taken as limitations for the task. In order to study the VPS operation an agent simulation model has been constructed. The analysis of the VPS "supply-production-marketing" logistics chain has been carried out on the basis of this model. The results of the simulation make it possible to estimate the time of PPO implementation, the impact of the risks and, if necessary, to make the further restructuring of the VPS architecture. The proposed approach is expedient to be used at the initial stage of VPS architecture development taking into account the basic requirements associated with the planned portfolio of orders

Keywords


virtual production system; planned portfolio of orders; production adaptation; cost optimization

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DOI: https://doi.org/10.32620/aktt.2018.1.08