The acquisition of
data and the development of different options in production system and factory
planning requires up to 2/3rds of the total needed time resources. The
digitization of production systems offers the possibility of automated data
acquisition. Nevertheless, approaches concerning fully automated data
acquisition systems are not widely spread among SME (small and medium sized
enterprises).  Furthermore, the
advantages of The Digital Twin are not sufficiently known due to the lack of
competence in SME concerning matters of Industry 4.0. In order to transfer
knowledge about the benefits of digitalization, the development of
demonstrating platforms is crucial. This article introduces a learning factory
based concept to demonstrate the potentials and advantages of real time data
acquisition and subsequent simulation based data processing.

Primarily, this paper
proposes methodology for implementation Learning factory based on Skoltech’s
machining equipment .  The production
cell included industrial robot for part handling, loading and unloading, several
milling and lathe machines with installed sensors.  All components had no connection between each
other and information from sensors didn’t bring any sense and use. An
automation upgrade of the cell is proposed, involving Technomatix Plant
Simulation tool, programmable logic controllers, RFID tags.   In this framework, the digital validation of
the real manufacturing system is performed through Discrete Event Simulation to
analyze the performance of the manufacturing system in terms of productivity
and utilization of resources under different alternative scenarios, avoiding
the risks associated with physical experimentation on the real system. Moreover,
Digital twin approach increase a flexibility of the system  like Distributed planning, dynamic
rescheduling, improved decision support, automatic planning and execution of
offers/orders and wider the range of products.

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Finally, working
example of Learning factory has been implemented at the Skoltech’s Masterskaya.  It is empowered with several features such as
Discrete Event Clone gathers information from sensors, remote control of
manufacturing execution, automatic rescheduling system, individual program of
metal treatment or handling operations for each bar. The test has shown that
installed system is capable to execute more variety of assemblies with better
performance compare to ordinary manufacturing system.

The comparison between
the Digital Twin and common tools of process optimization, e.g. VSM, is carried
out and shows the benefits of digitalization in a vividly manner. Benefits of
the proposed new approach for the analysis and modification of production
systems can be experienced by participants in practical training sessions,
especially continuous data acquisition, automated derivation of optimization
measures and capturing of motion data. The physical implementation of the
system enabling the Digital Twin based on Discrete event simulation is the
first step to enter Industry 4.0 era. To increase components utilization of the
system and enable It with scalability further researches should be performed
Including optimization methods, machine learning, Genetic algorithm and etc.