Simulation Validation

Digitalization of machinery and processes has become an integral part of operations management because of the direct and immediate impact it has on efficiency, flexibility and time to market. Axcend, Simulation & Validation team provides you the expertise to build 3D models of your machinery and processes and validate key performance parameters with real time data. Our simulation solutions provide for applications in Advance planning and management, Performance Analysis, Production management and Manufacturing intelligence The team is multi-platform trained and can help with the appropriate tools as demanded by the project.

  • Complete 3D simulation of machines, plant floor processes and human interactions can be modeled and tested with real time data.
  • Provides a very effective means to identify bottlenecks in the line / factory and has an immediate impact on plant / machine efficiency
  • Advance planning, scheduling, sequencing and real time inventory can be planned with accuracy that any other system can match

Collect Data

Collecting various existing process & product data from ERP/shop floor such as variant details, Process time, Set-up time, Buffer capacity, MTBF (Mean Time between Failures), MTTR (Mean Time to Repair), BOM (Bill of Material) and Availability.

Data Analysis

The data collected from ERP/shop floor is crucial & has to be accurate to build a simulation model, hence the data will be analysed before developing the model with the concerned team. Insufficient / invalid data leads to incorrect results from the simulation model.

Build Model

A digital Simulation model is built based on the inputs collected. All the inputs will be configured in the simulation model to simulate production requirements. The digital model built is a digital replica of physical facility considering all the inputs as-is in the physical facility.

What if Scenarios/Target

Define & configure the various what if scenarios such as – availability of resources, variant sequencing, Demand & takt time in the existing model built to identify the effectiveness of changes in the parameters defined to achieve desired target such as throughput, cycle time, identify bottlenecks, resource utilization and sequencing of variants/products.

Optimise throughput

Throughput of the existing facility will be optimized by identifying the bottlenecks & simulating defined input parameters. Sequencing of the variants will be carried out using genetic algorithms; the optimum buffer capacity is identified/calculated by experiment manager. Various experiments/trials will be carried out to achieve the optimum throughput by using the defined input parameters and target.


The results of simulation (Digital) model and the physical (Factory) model are compared using digital twin concept. Digital twin refers to a digital replica of physical assets, processes and systems that can be used for various purposes. The real-time data/output of the physical model will be captured using IoT (Internet of Things).


  • Simulate production requirements without altering the existing facility
  • Reduce new system cost marginally
  • Identifying the bottlenecks in the existing facility
  • Make fast, reliable, smarter decisions in the early stages of production planning
  • Optimize material flow, resource utilization and throughput
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