In one of our previous articles, Harnessing the Power of Optimisation, have gone through the basics of Process Optimisation, where we said that if you know what you want to achieve, you can achieve that goal by understanding what variable values to use through modelling & simulation and algorithm-based models. 

A model can be physics-based or chemistry-based, or it can be built using your data, which, together with correct data analysis & the proper visualisations, can enable a business to ensure that they take the right decisions and to ensure the right mechanisms are adopted to achieve the desired result.

Process Modelling & Simulation is crucial in many types of businesses, especially ones where getting it right is a must. Some businesses incur huge losses if they fail in achieving the optimal state or a 0 Loss scenario and therefore, through Process Modelling, variables are calibrated in such a way so as to ensure the achievement of the goal or the best possible outcome. There isn’t only the monetary element at stake, but sometimes certain situations have an impact on a national scale, or the wellbeing of people, and so Process Modelling & Simulation is a must to ensure that the right thing is done and to take the right decisions.

Let’s take the scenario of a company that wants to build a new manufacturing line in which the target end-of-line output is the manufacturing of 1000 packets a minute according to a set of criteria or specifications (e.g. 1000 packets, containing 10 units each, of a certain quality, length, weight, with the right amount of materials per unit and so on). If you wanted to carry out Process Modelling you need to start with creating a virtual/digital representation of the manufacturing line. This helps to identify the problems that are to be avoided in real-life scenarios when the physical manufacturing line is built. Through various modelling and simulation techniques, one of which is the “Discrete Event Simulation”, or DES, you can change the variables with no repercussion, as all is done on a virtual environment. By testing such variables, you can identify inefficiencies and constraints, you can assess the impact of any changes to the system, you can identify the most important variables/parameters, and through data analysis & visualisation and specific testing you can ensure data-based decisions are made as well as ensuring optimisation for the best throughput of your system. This reduces waste thus saving costs, as well as ensuring that no disruption of operations takes place in the eventual real-life scenario. 

In a manufacturing line, some components, from raw material to end product are run in parallel and some others run in sequence. This is already complex, as there are many moving parts. Through Process Modelling we can understand the specifications needed, the quantity of equipment and resources for each subprocess to get to the Output the Company is looking for. One of the core tenets of Process Modelling is that it can be used to model any process: from the simplest chemical reaction to a whole factory or even a national infrastructure. In this case the infrastructure would be split into its individual sub-components. These sub-components can be “described” (modelled) using a mathematical description of the process (be it physical or chemical) and then simulated. This can be quite complex in nature, when you have many relationships or variables to assess, and those variables being coupled, i.e., one affects an other, which can affect an other, or even two variables affecting each other.

Through virtual simulation you can integrate and assess various variables, then multiple scenarios (tens, hundreds or thousands) are tested in parallel. In real life scenarios you have various operations, resources and processes running in parallel and in sequence, as well as automated and manual steps that need to be carried out. Therefore, by “understanding” your system through Modelling & Simulation, you will be certain that you are using the best possible “settings” of the right variables, to ensure these work together in an optimum way so that the goal, the best possible outcome is achieved.