The rare blend of skills, methods and experience within decisionLab offers genuinely unique industry insight and methodologies. We use best of class tools and invest time to innovate, develop, understand and apply new techniques and technologies.

Our clients know they are working with established experts – a single consultancy that offers a blend of services and techniques to support multiple areas of their organisation

With our flexible decision support you can

  • Cover a specific skills gap
  • Enlist our Simulation and Optimisation experts to share knowledge with your dedicated Operational Research team
  • Confidently outsource your project to decisionLab
If you choose to work with us, our combination of process consultation and expert consultants, means you’ll benefit from a more collaborative way of working and people-focused project delivery.

Through our Specialist Business Units, we work in sectors where our team has in-depth knowledge and the on-the-ground experience needed to make a real difference.





Optimisation is the mathematical technique used to evaluate a number of viable alternatives. Faced with a proliferation of viable alternatives, it can be impossible to intuitively decide the best strategy to solve complex real-world problems.

decisionLab works with industry-leading software to apply powerful optimisation approaches and identify the best possible option to:

  • Improve operational efficiency
  • Increase productivity
  • Gain competitive advantage
  • Reduce financial risk.

We are driving advances in Water Resources management with our deployment of EBSD+ models, using multi-criteria Optimisation, multiple generation of alternatives, and info-gap approaches, to produce robust plans ensuring that water companies are meeting demand for years to come.

Additionally, we have experience optimising across Outcome Delivery Incentives, which involves the achievement of different customer-focused objectives within the water industry. We also have our asset risk model, which can be used to find the best plan with a fixed budget, or the cost to maintain risk at a certain level, taking into account the ageing and deterioration of assets, criticality and future health.

We love sharing ideas, get in touch with your optimisation challenge.


Simulation is the imitation of the operation of a system or process, over time. It is normally performed by changing parameters in an existent model and observing its evolution and outputs. The science of modelling a system consists of building a framework (e.g. software) that facilitates the understanding of the system and predicts its behaviour.

  • Conceptual Modelling is normally a framework structure such as a diagram or a schematic made of the composition of concepts that will enable to build a model.
  • Mathematical Modelling consists of building a model based on mathematical equations.
  • Analytical Modelling is a set of equations describing the performance of a system that can be displayed within a defined domain for performing analytical analysis.
  • Agent Based Modelling is a modelling technique normally applied to dynamic and complex systems for describing the actions and interactions of autonomous agents within the system, with the aim of assessing their effects on the system as a whole. E.g. simulating the traffic flow in an airport.
  • Multi Method Modelling consists of modelling by applying different techniques such as Agent Based, System Dynamics or Discrete-Event.
  • Discrete Event Simulation is a modelling technique that consists of making an approximation of the continuous real-world processes of a system to a sequence of well-defined and ordered events that work in a discrete time bases. It allows quick analyses of the system over time.
  • System Dynamics is a modelling technique that permits the understanding of the behaviour of non-linear systems over time by using stocks, flows, internal feedback loops, and time delays.


Descriptive analytics

The purpose of descriptive analytics is to summarise what happened. You may already be using descriptive analytics techniques, including data modelling, trend reporting and regression analysis, to assimilate the large volumes of data readily available in your organisation. Well-designed, these spreadsheet models can quickly and effectively extract the insights you need to help you understand past business performance and inform decisions to improve future performance.

Predictive analytics

uses a variety of statistical, modelling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about what might happen in the future.

Prescriptive analytics

goes beyond descriptive and predictive models by recommending one or more courses of action, and showing the likely outcome of each decision.


This is a flexible software tool that allows you to exploit your asset data to determine a robust, evidence-based asset management plan. While the majority of Enterprise Asset Management systems record & report ‘what is’ to help with short-term operational planning & scheduling; our model enables you to ask ‘what if?’ Decision-makers have used the tool to find the best plan for a fixed budget; ascertain the budget required to maintain a population at their chosen risk level; and establish the best mix of replacement, refurbishment and maintenance.


Asset Lifecycle Management aims to minimise the costs associated with the maintenance, replacement and overhaul of equipment. Suspending operations is expensive, while replacing equipment too early increases equipment costs over the lifetime of a system.  However, unanticipated stoppages due to failure of equipment can be even more costly.

Our expert problem solvers apply the right combination of Simulation to explore and define a range of feasible options; then Optimisation to determine the best possible solution to meet your process efficiency, safety, profit and/or environmental objectives.



A software platform designed for developing modelling applications for solving large-scale Optimisation and scheduling-type problems.

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A software platform designed for developing different types of modelling applications based on Java programming language.

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A cloud-computing platform and infrastructure created by Microsoft for building, deploying, and managing applications and services through a global network of Microsoft-managed data centres.

Azure Logo

A collaborative, drag-and-drop tool used to build, test, and deploy predictive analytics solutions based on data. It publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.

Azure Logo

Business intelligence software that allows anyone to easily connect to data, visualize it, and create interactive and sharable dashboards.

Azure Logo

An optimisation software to solve mixed-integer programming problems.

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A programming language