Use simulation to discover new ways of improving the business, without risking it
Most business decisions, whether strategic or operational are made without any option to 'undo' and mistakes can be costly. Furthermore, we only have one physical reality to try something new. For these reasons simulation models allow managers to assess the impact of a decision before it is made. With a rich history dating back to the 1950's, this is one of the earliest applications of computers in management science.
We have a long track record of working with clients to define, implement and maintain simulation models for decision support across many industries, functions and time scales. From near-operational, day-to-day planning of production lines in manufacturing plants to long term scenario analyses of cycles in asset-heavy industries using System Dynamics.
Our consultants have experience working with all the main simulation paradigms and many associated tools, including:
- System Dynamics (Vensim, AnyLogic, Jitia)
- Agent-Based Modeling (AnyLogic, Python, C)
- Discrete Event Simulation (Arena, AnyLogic)
- Monte Carlo simulation (R, MatLab, SPSS, Python, Excel)
We also build custom simulations, visualisations and integrations, e.g. running the simulation on a tablet, in the browser, offline in Excel, or scaled up on a powerful cloud cluster.
How to build a simulation model
Analyse the nature of the business problem
Identify internal and external sources, stage and clean data
Confirm blueprint with stakeholders
Align model with reality - measurable and perceived
Gradually expand scope/dimensionality/detail until fit for purpose
Use model to visualise results, show sensitivities and inform business decisions
How this approach has helped our clients
Problem:
Solution:
The tool and simulation was first prototyped in Excel and later integrated into daily floor routines via a bespoke tablet app that allowed to live-update the simulation in real-time.
Problem:
Solution:
Dynamic simulation models are based on an explicit understanding of market drivers and their interdependencies, including feedback loops.
Problem:
Due to the complexity of the results and the variety of underlying assumption, communicating key advantages of their tests to key stakeholders and payers proved difficult.
Solution:
What our clients say about us:
We'll simulate your plant, process or industry
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