PlantTwin by Amalgama

Discrete manufacturing simulation tool

Creating manufacturing digital twins based on simulation models made for decision support in mechanical engineering, aerospace industry, shipbuilding

PlantTwin by Amalgama

Discrete manufacturing simulation tool

Creating manufacturing digital twins based on simulation models made for decision support in mechanical engineering, aerospace industry, shipbuilding

PlantTwin is developed by Amalgama

PlantTwin is a discrete manufacturing simulation tool

PlantTwin plans and simulates the production plants’ operations.

The tool considers production routes, operating schedule of factory shops, production units and personnel, equipment maintenance and repair schedules, operations of external contractors, consumption and replenishment of purchased component stocks, transportations within production plant.

PlantTwin supports decision making for strategic and medium-term production planning of factory shops, enterprises and groups of enterprises.
Key application areas of PlantTwin are: machine-building, aerospace, ship-building industries

Examples of problems being solved

Estimating production capacity

Evaluating adequacy of productive capacity of a shop, enterprise or group of enterprises for production target delivery

Justification of investment

Justification of investment in modernization of existing and creation of new production facilities

Validation of plans

Checking production plan feasibility using simulation modelling and Monte-Carlo analysis

Scenario analysis

Scenario analysis of work distribution and coordination policies between plants of a holding company

Production plan

Generating a feasible schedule to complete production plan on time

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PlantTwin clients and partners

FCS

digital engineering

a company that specializes in engineering of manufacturing systems and increasing of effectiveness of machine-building industry based on digital engineering digitalfabrika.ru

AGAT

research institute

leading research institute of aerospace industry

URSC

United Rocket and Space Corporation

a Russian corporation on development, production and repair of rocket and space equipment

Key application areas of PlantTwin are machine-building, aerospace, ship-building industries

Discrete-production simulation models are widely used to support decision-making

PlantTwin allows to simulate a production plan generated both by external systems and by the integrated scheduling module. The integrated planning module allows to test the theoretical feasibility of a production program and generate a production schedule using one of several planning strategies.

PlantTwin considers

1

Product structure

Hierarchical BOMs structure
2

Production routes

Production routes, including assembly operations
3

Transportation

Products transportation parameters
4

Schedule

Operating schedule of factory shops, production units and personnel
5

Contractors

Operations of external contractors
6

Stocks

Consumption and replenishment of purchased component stocks
7

Batches sizes

Sizes of production batches
8

Maintenance schedules

Equipment maintenance and repair schedules
9

Capacity

Buffers and storage facilities capacity

Route map

  • PlantTwin allows users to create and edit the structure and the route map of a product
  • PlantTwin contains the graphical visualization of the product structure as a tree shown in 2D-mode
  • PlantTwin simulates single and group production operations as well as operations executed by third-party contractors
  • PlantTwin editor is used to set the product properties including the replenishment parameters of the purchased components

Scheduling mode

  • PlantTwin scheduler generates an optimized schedule of production operations and displays it on a Gantt chart
  • The scheduler does its best to schedule the production of each assembly as close as possible to its planned production date to reduce the WIP
  • The work centers schedule analysis table shows the expected utilization of work centers and the maximum expected operations queue
  • The schedule considers the work centers’ schedules and planned maintenance
  • The scheduling horizon is not limited and can be anything from one shift to several years

Simulation mode

  • Simulation model reproduces the operation of all work centers. The schedule of production operations is shown and dynamically updated on an interactive Gantt chart
  • Graph of the work centers operations queue helps to quickly evaluate the uniformity of the work centers’ load
  • Users can follow the executing of each work center task during the simulation experiment
  • All tables and graphs can be copied or exported to Excel files for the further analysis
  • In the simulation mode with rescheduling the initial schedule is rebuilt based on the current state of the simulated system when a failure or an operation time-based maintenance occurs
Amalgama technologies are used in various companies

Projects

Amalgama has implemented more than 30 successful commercial planning and simulation projects.

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Clients and partners

Three modules of PlantTwin allow performing the tasks of planning in a more efficient way

PlantTwin allows to simulate a production plan generated both by external systems and by the integrated scheduling module

Scenario editor

Prepares inputs for scenario analysis, forms an interrelated data set and maintains correctness and consistency of the data

  • User-friendly interface for editing strongly interrelated data
  • Import and export of any data to MS Excel with built-in error checking
  • Automatic check of data completeness and correctness

Scheduler

Verifies theoretic feasibility of a production program, identifies bottle necks in a system

  • Generation of feasible schedule for realization of a production program
  • Identification of bottlenecks, scarce resources and critical paths of the schedule
  • Representation of the plan as a set of interactive graphs, tables and diagrams

Simulation model

Verifies feasibility of a production program considering real-world variabilities and interdependencies

  • Estimation of the likelihood of on-time completion of production program
  • Consideration of random factors, such as delays in fulfillment of operation, components supply disruptions
  • Ability to verify plans generated by other systems like MES