Computational Modelling: building productivity, insight and foresight for your business


Friday 22 March 2019

BEIS Conference Centre
1 Victoria Street, Westminister, London. SW1E 5ND

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This event is for decision-makers and managers in business and industry, especially those who are not yet using computational modelling methods.

A mix of case studies and workshops:

  • hear how others have used computational modelling to improve productivity, sustainability and profitability;
  • find out ‘how to' from getting started to hands-on introductions to some of the latest developments.

This event is free to attend, as a partnership between the Department of Business, Energy, Innovation and Skills and The Operational Research Society, which has been the home of computational modellers and modelling for 60 years.

Computational modelling supports decision-making and management

The Council for Science and Technology’s Blackett Review highlighted the power and importance of computational modelling in supporting decision-making and management. There have been countless successful applications in industries large and small, private and public sector, across the world; but there are still many organisations where it is underused. Computational modelling can transform your business; but only if it is the right modelling for you, with design, use and support adapted to your needs, and properly implemented.

There will be examples and workshops to show how you can use modelling effectively, to help:

  • cut costs and speed up throughput via tighter, better-targeted operations
  • improve sustainability and profitability through better costing and business planning
  • identify and try out your options for change and improvement, safely and collaboratively

Read about our 2019 Workshops here

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What is computational modelling?

Computational modelling uses a representation of how we think something works e.g. manufacturing processes, customer behaviour, distribution network, etc.. The model is a set of equations that we use to understand the likely effect of actions to change the system, or to find feasible options and opportunities and pick the best, to guide decisions on real-world changes.

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