Sergey M. Yampolsky - Associate Professor, Department of Business Analytics, School of Business Informatics, Faculty of Business and Management, National Research University Higher School of Economics. 
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation.
E-mail:syampolsky@hse.ru

Anatoly S. Shalamov - Researcher, Department of Statistical Problems of Informatics and Management, Institute of Informatics Problems, Russian Academy of Sciences.
Address: 44, Vavilova Street, Moscow, 119333, Russian Federation.
E-mail:a-shal5@yandex.ru

Alexander P. Kirsanov - Professor, Department of Business Analytics, School of Business Informatics, Faculty of Business and Management, National Research University Higher School of Economics. 
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation.
E-mail:ki@hse.ru

Eugene V. Ogurechnikov - Senior Lecturer, Department of Business Analytics, School of Business Informatics, Faculty of Business and Management, National Research University Higher School of Economics. 
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation.
E-mail:eogurechnikov@hse.ru

      The article considers the issues of technical product life cycle management in the field of spare parts delivery organization and management within the framework of after-sales service.
      It provides an examination of a Petri net model, describing the cause-effect relations between events that are linked to delivery planning and management, based on a probabilistic analytical model for after-sales service of technical products and a program-based risk analysis system based on technical and economic criteria. The result of a given model’s performance is planning of an acceptable balance between the cost and quality of products and their current maintenance, which includes detection and minimization of financial risks.
      An example that illustrates automated planning of spare parts delivery is given. Dynamics of operated technical products’ quantity variation is represented in the integrated graphic type, providing an opportunity to predict an average factor of technical product’s serviceability, determined both by a number of serviceable technical products in a warehouse of the customer and productivity of repair agencies.
      The earned value method application is proved to be an effective tool for risk analysis of schedule variance in the field of spare parts delivery. Monitoring of the earned value of finances permits to forecast not only the probability of successful completion of spare parts delivery, but also the risks of both cost and schedule variance.
      An example of automated risk analysis is provided. Estimated coincidence degree of actual cost and planned value is calculated by means of the effectiveness index, which is used to analyze the quality of customer’s subdivisions performance and to correct further functioning. For a selected year, the effectiveness index can be defined and optimized for the predetermined serviceability factor, assigned for every customer during the process of automated planning of spare parts delivery.
      The approach presented in the article can be considered quite universal, which predetermines an opportunity to apply it in order to provide solutions for product and service life cycle management problems in various organizational technical and economic systems.