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.
In the paper an approach to modeling of requirements management process associated with IT projects is considered. The requirements management model includes three stages. The first stage is related with the choice of business requirements, which are described in the project solution and define the project scope. The second stage includes development of a model that is associated with accepting, rejecting, clarification or classification as ‘additional task’ for every of incoming user requirements. On the third stage for all the accepted user requirements priorities of system requirements are formulated; these priorities are subsequently used for project planning. The decision making models are based on the methods of analytic hierarchy process (AHP) and analytic network process (ANP), as well as on SuperDecisions decision support system.
The companies that are IT-industry leaders perform from several tens to several hundreds of projects simultaneously. The main problem is to decide whether the project is acceptable to the current strategic goals and resource limits of a company or not. This leads firms to an issue of a project portfolio formation; therefore, the challenge is to choose the subset of all projects which satisfy the strategic objectives of a company in the best way. In this present article we propose the multi-objective mathematical model of the project portfolio formation problem, defined on the fuzzy trapezoidal numbers. We provide an overview of methods for solving this problem, which are a branch and bound approach, an adaptive parameter variation scheme based on the epsilon-constraint method, ant colony optimization method and genetic algorithm. After analysis, we choose ant colony optimization method and SPEA II method, which is a modification of a genetic algorithm. We describe the implementation of these methods applied to the project portfolio formation problem. The ant colony optimization is based on the max min ant system with one pheromone structure and one ant colony. Three modification of our SPEA II implementation were considered. The first adaptation uses the binary tournament selection, while the second requires the rank selection method. The last one is based on another variant of generating initial population. The part of the population is generated by a non-random manner on the basis of solving a one-criterion optimization problem. This fact makes the population more strongly than an initial population, which is generated completely by random. Comparing of ant colony optimization algorithm and three modifications of a genetic algorithm was performed. We use the following parameters: speed of execution and the C-metric between each pair of algorithms. Genetic algorithm with non-random initial population show better results than other methods. Thus, we propose using this algorithm for solving project portfolio formation problem.
This article discusses the characteristic changes in management practices occurring in the context of the digital transformation of business. It shows the mutual interconnections of these changes, as well as the links to changes in the organizational culture of the organization. Among the new management practices reviewed are those both at the level of the enterprise as a whole (digital products, digital business models, digital management of value creation chains, digital business processes), as well as on the local level in adoption of management decisions - unlimited knowledge and management of the enterprise in real time (Real Time Enterprise). The article demonstrates the need for formation of certain cultural norms in the organization, including total knowledge management and an orientation to rapid changes. Review is made of the succession and qualitative distinctions of traditional automation from digitalization of enterprises. We discuss the possibility of using theories and methods connected with such concepts as complementary assets for research into new forms of organization for the digital enterprise. The article also presents a research program conducted in the framework of a program for digital transformation of activities of the OJSC Surgutneftegaz, Orbita 2.0. In the given research program the accent is placed on analysis of the problem of sustainability of the organization. In order for the organization to be flexible and changeable, it should periodically be in a condition of instability. In the contrary case, strong resistance to change will develop in it. The search for principles and forms of organization ensuring the controllability of sustainable organizations _ is an important area of this research.
For practical, important tasks in the fi elds of economics and logistics, as well as in a number of technical applications, it becomes necessary to solve the traveling salesman problem (TSP). Quite often, the features of these problems lead to the traveling salesman problem in asymmetric formulation (asymmetric traveling salesman problem, ATSP). Moreover, in some practical applications it is desirable to obtain an exact solution. One of the known exact algorithms for solving the ATSP is an algorithm that implements the well-known branch and bound method. The known experimental estimates of its complexity on the average are exponential. However, this does not mean that for small dimensions of the problem (currently, no more than 70–75), the expected time for solving the individual problem is unacceptably high. The need to reduce the time for solving individual problems dictated by practice is associated with the use of various modifi cations of this algorithm, of which a modifi cation that involves storing truncated matrices in the search decision tree is one of the most eff ective. In this article, the authors rely on this modifi cation. Other possible improvements in the time effi ciency of the software implementation of the branch and bound method are related, among other things, to obtaining the initial approximation by heuristic algorithms. As a result, we get a combined algorithm, in which, at the fi rst stage, some heuristics works to obtain the initial solution, from which the branch and bound method starts. This idea has been discussed for a long time, but the problem is that to reduce time, such a heuristic algorithm is needed that delivers a solution close to optimal which will be found quite fast. One of the possible solutions to this problem is the subject of this article.