Author details

preparation of managerial decisions which are based on real statistical data that take into consideration the interaction of subsystems in production system. Therefore, together with the use of predictive models IIoT helps not only enhance the level of automation and reduce a certain part of personnel production expenses but also consider such factors as increasing power intensity and resources consumption of productions, inertness of integration and management processes in production systems, and the situations that are connected with repair

We have investigated the question how to use and apply under existing conditions the approaches that search feasible and optimal solutions in the tasks of efficient management and planning (taking into account time factor). The changes that affect the setting and solution of tasks can be explained by the shift to automated and automatic enterprises, by the shift from mass production to single-part production. In this connection, the current situation requires operational rearrangement of ongoing production processes; we need to increase global economics mobility, i.e. the variability of external environment where production systems

The approach that is described in the chapter is relevant as it tackles management tasks given as optimization tasks; besides, it helps deal with the phenomenon of NP is the completeness of

The obtained results are sensitive to the quality of forecasts and lack time lags; more than that, we can observe a change in production volume that creates additional increased capacities for

That is why, the shift to the concepts Industry 4.0 gives not only evident momentary advantages, but also outlines new areas for studies, i.e. the solution of tasks that take into consideration the inertness of production system and expenses that arise due to changes in production volume and risk metrics, that appear upon interaction with external systems (for example,

The development of mathematical formalization of these areas of studies can lead to additional effects in future and underlie the appearance of industrial concepts of next generations.

The author thanks the government of Perm Krai for the support of the project for "Development of software and economic and mathematical models for supporting innovation project management processes in production systems", which is being implemented in accordance

The reported study was partially supported by the Government of Perm Krai, research project

production system (related to the change in production schedule).

delayed delivery, the delivery of faulty parts, return of goods etc.).

actions, equipment mortality, procurement failures, change in demand and prices etc.

operate.

obtained tasks.

90 Digital Transformation in Smart Manufacturing

Acknowledgements

No. C-26/058.

with decree No. 166-п of 06.04.2011.

Leonid A. Mylnikov

Address all correspondence to: leonid.mylnikov@pstu.ru

Perm National Research Polytechnic University, Perm, Russia
