As companies are attributing growing importance to planning, designing, and controlling of operations management, simulation appears as a strong problem-solving tool, significantly aiding the firm’s decision support process.
Supported by high computational capacity, digital twins (simulation models) allow to test alternative scenarios and designs through a time horizon, recording and comparing the performance of each one of them before choosing the best. Notwithstanding, it requires the gathering of accurate data and solid validation of all the assumptions made in the model to assess its representativity of the real system.
Also, a simulation tool makes possible to model and analyze the operation of a real system by managers who are not well versed in programming languages.
With all its unique features, simulation modelling finds applications across many industries and use cases. Depicting a manufacturing plant in a simulation model will reveal expected bottlenecks and travelling times between machines, on which you can support your next capital investment or layout rearrangement. Pedestrian and vehicle dynamics are very useful to understand the effects of congestion when designing a warehouse. Simulating the daily operations of a retail supply chain can shed light on the hidden interactions between demand variability, delivery fleet size and schedules, stock levels and service levels.
Digital twin is all about business agility and unlock powerful capabilities. Here are some benefits of a digital twin strategy.
With simulation modeling, you can define and test multiple scenarios, replicating the reality and testing solutions in a risk-free environment.
For example, the simulation of the production environment allows the estimation of relevant KPIs for any given layout before it is implemented in the real world.
An interesting use of simulation is to make sensibility and what-if analysis. In order words, to understand the impact of different parameters on the final solution and show how a system would behave under varying input parameters and/or constraints.
In a simulation model, it is easy to represent time-related and casual dependencies and analyze the in-depth performance analysis of alternative scenarios. That way, it can guide the decision towards more robust solutions to variations rather than the “best” solution in high uncertainty scenarios.
Simulation adds value essentially because complex systems have emergent properties. In other words, agents that can be governed by relatively simple rules, when placed in a situation where they interact with each other or compete for resources, exhibit a complex collective behavior that could not be inferred from individual characteristics.
Thus, it is a very useful tool to provide management with great comfort in testing solutions before implementing them, avoiding surprises.
Let’s imagine the example of a simulation model of a supply chain. Tweaking an input variable such as the number of vehicles available, warehouse productivity or inventory management policies will affect the entire system.
Having the ability to understand the impact of each variable in the supply chain as a whole leads to an holistic view of the chain and enables scenario testing with a panoply of variables and parameters, and understand the true cost-benefit trade-off of each decision.
A simulation model can embody several scales of detail of the operation, providing, at the same time, a bird-view of and a detailed view of the operations. Collecting metrics at different levels of aggregation leads to a better understanding of the impact decisions for all levels in the organization.
Another advantage of simulation models is the visual component. Being able to visualize the model in operation is a plus that improves the perception of reliability, interpretability, and “salability” of the solution to senior management.
Simulation can be combined with other techniques, such as optimization. For example, an optimization model optimizes the input parameters of a simulator, which validates this result by reorienting the optimization model, and so on.
A simulation model is truly a company asset, on which you can carry out analyzes for different purposes and update over time.
It becomes clear that it will be increasingly used also at an operational level. However, technical challenges should not be underestimated.
Simulation models take time and several validation stages to be put to work, should be tailored to each company’s reality and people in organizations must be encouraged to take an active part in the development of these tools.
Only an analytics-ready company will have the skills, data infrastructure and culture needed to use a simulation tool with effectiveness.