Operational Research  

“Research is creating new knowledge." - Neil Armstrong

Every business decision that a company makes has the potential to be disastrous to the wellbeing of the company. It is therefore quite imperative to make sound decisions and in order to make those decisions you would need to have all the available and relevant information on hand. This is where operational research comes in. Operational research uses advanced statistical analysis and mathematical modelling to calculate best or optimal solutions.

Azexcel has numerous statistical experts, operational researchers and Six Sigma Master Black Belts that possess a wealth of industry experience and the deep understanding required to successfully utilise the principles and methodologies of operational research in your business, regardless of the industry. 

Tools and Techniques we use 

  • Linear and nonlinear programming

  • Game theory 

  • Integer programming 

  • Dynamic programming 

  • Markov Process 

  • Network scheduling - PERT/CPM 

  • Symbolic model 

  • Value theory 

  • Decision theory 

  • Queuing theory 

  • Inventory modelling and theory 

  • Goal programming

  • Transportation problem 



We begin by identifying all problems and the factors that are causing the problem to exist. Observance, meetings and research help in this phase. This stage is vital in ensuring the validity of the entire process. 

This stage is a detailed and operational description of the difference between the existing situation and the desired situation. We attempt to make our definitions as concrete and detailed as possible, so as to allow us to be able to effectively address the problems.

In this stage we create mathematical models of the current situation. The purpose of these models are to help define the interdependencies among variables and formulating constraint equations. The model has to be tested and revised in a real world environment before moving on. 


In the final step we look at solution implementation  We also look at the gap between the organisation and the analysis. Successful solutions implementation  will invariably come down to behavioural tendencies of the different individuals within the company, so this has to be nurtured carefully 

At this stage we combine the collected data and the mathematical models created. We are then able to arrive at solutions to problems that were identified in the earlier steps. However, we do not implement the solution yet, not until limitations and imperfections in the model have been identified.

At this stage we collect all the data from the various relevant sources and then start to collate the data. The data that is gathered is vital in helping to test the models that have been created in previous steps.