Strategic Cost Management · Simulation

Introduction to Simulation and Its Applications

Updated 11 October 2026 · Fact-checked

Simulation is a technique that builds a model of a real system and runs it repeatedly on assumed inputs, often random, to see how the system behaves. It is used when exact formulas are hard or the system is too risky to test. You answer by defining the problem, building the model, running trials and drawing a decision.

Understand Introduction to Simulation and Its Applications

Think of a simulation as a rehearsal on paper or in a computer. A real system, such as a warehouse, a queue at a bank or a product launch, is too costly or risky to experiment with. So you copy its key features into a model and test different situations on the model instead.

The model uses inputs that vary, such as daily demand, lead time or machine breakdowns. When these inputs are uncertain, you give each a probability distribution and draw values using random numbers. Each run is one trial. After many trials you study the results, such as average profit, average stock-out or average waiting time.

Features to remember: it is a trial-and-error, descriptive technique; it imitates reality rather than solving for one exact answer; it handles uncertainty; and it is repeated many times. It is a tool to evaluate chosen alternatives, not one that automatically produces the best answer.

Types you should know: deterministic simulation (inputs fixed, no chance element) and stochastic or probabilistic simulation (inputs random). Monte Carlo simulation is the stochastic type using random sampling. Also note static versus dynamic (time not involved versus time-dependent), and discrete versus continuous (changes at points in time versus continuously).

Applications in management include inventory control, queuing at service points, production scheduling, capital budgeting risk analysis, cash management, project time estimation, and profit or cost forecasting under uncertain demand and prices.

Simulation versus optimisation: optimisation techniques such as linear programming give the best solution under stated assumptions. Simulation shows how a system behaves under given decisions and does not by itself guarantee the best one. Use simulation when the system is complex, has chance elements, or cannot be written as a neat equation.

Key rules to remember

Cumulative probability: Cumulative probability = running total of probabilities of the values, ending at 1.00; Used to build the table for assigning random numbers.
Random number allocation: Range for a value = (previous cumulative % + 1) to its own cumulative %; With two-digit numbers, 00 to 99 gives 100 numbers. A probability of 0.25 gets 25 numbers.
Average of simulated results: Average = Σ simulated values ÷ number of trials; Results from a few trials are only indicative; more trials improve reliability.

How to solve Introduction to Simulation and Its Applications questions

Use this approach for theory and for short application questions on simulation.

1Read what is asked: meaning, types, merits and limits, or an application to a given business case.
2Define simulation in one line: a model of a real system run repeatedly on assumed or random inputs to study behaviour.
3Identify the uncertain variables and whether the case is deterministic or stochastic.
4Name the type that fits: Monte Carlo for random inputs, static or dynamic, discrete or continuous.
5State how it would be run: probability distribution, cumulative probability, random number ranges, trials, then averages.
6Link to the decision: what the results tell the manager and which alternative looks better.
7Add a balanced view: one or two advantages and limitations specific to the case.
8Close with a clear conclusion or recommendation.

Quickest way: Five-point answer frame

When to use it: Use for 4 to 7 mark theory questions or short case scenarios when time is tight.

Meaning in one line.
Two or three features: imitation, trial and error, repeated runs, handles uncertainty.
Types: deterministic and stochastic, plus Monte Carlo.
Applications matched to the case given.
Advantages and limitations in two bullets each, then a one-line conclusion.

Common mistakes in Introduction to Simulation and Its Applications

Saying simulation gives the optimal solution
Students mix it up with linear programming and other optimisation methods.
Fix: Write that simulation evaluates alternatives and shows likely outcomes; it does not guarantee the best answer.
Calling every simulation Monte Carlo
Monte Carlo is the most-practised type in problems.
Fix: Monte Carlo is the stochastic type that samples random numbers. A model with fixed inputs is deterministic.
Listing advantages without limitations
Students recall only the positives.
Fix: Always give both sides: it can be costly and time-consuming, results depend on the model quality, and it gives approximate results.
Giving generic applications unrelated to the case
Rote learning of a standard list.
Fix: Pick the uses that match the scenario, for example queues for a service centre or stock levels for a retailer.
Treating a few trials as proof
Textbook problems use small runs of 10 or so trials.
Fix: State that results from few trials are indicative and more runs improve reliability.

Worked examples

Example 1

A manufacturer wants to test several reorder policies for its raw material, where daily demand and supplier lead time are uncertain. Explain why simulation suits this problem and name the type used.

Show the solution

Demand and lead time are uncertain, so inputs are random variables.
Testing policies in the real plant would risk stock-outs and extra holding cost.
A model of the stock system can be built and run for many simulated days.
Probabilities of demand and lead time are converted into cumulative probabilities and random number ranges.
Each run gives stock-out days and holding cost for a policy; averages are compared.
Because chance inputs are used, it is a stochastic simulation, specifically Monte Carlo.

Answer: Simulation suits it because the system has uncertain inputs and cannot be tested safely in practice. It is stochastic (Monte Carlo) simulation, used to compare reorder policies by their average stock-out and holding costs.

Example 2

Daily demand for a product is 10 units with probability 0.2, 11 units with 0.5 and 12 units with 0.3. Allocate two-digit random numbers (00 to 99) to each demand.

Show the solution

Check probabilities: 0.2 + 0.5 + 0.3 = 1.0.
Cumulative probabilities: 0.2, 0.7, 1.0.
Convert to percentages: 20, 70, 100.
Demand 10: 00 to 19, which is 20 numbers.
Demand 11: 20 to 69, which is 50 numbers.
Demand 12: 70 to 99, which is 30 numbers.

Answer: Demand 10: 00-19; demand 11: 20-69; demand 12: 70-99. A random number such as 47 gives demand of 11 units.

Exam tips

Theory questions often ask for meaning, advantages and limitations together; give each its own short heading or bullet group.
In cases, tie the use of simulation to the uncertainty in the scenario rather than quoting a generic list.
If asked to compare with optimisation, state the key point: simulation describes outcomes, optimisation prescribes the best choice.
When allocating random numbers, check that ranges cover all numbers without overlap.
Write a concluding line telling the manager what the simulation outcome suggests.

Practice questions from Simulation

Introduction to Simulation and Its Applications: frequently asked questions

What is simulation in quantitative techniques?

It is a technique that imitates a real system through a model and runs it repeatedly on assumed or random inputs. You study the results to understand behaviour and compare decisions. It is used when the system is too complex or risky for direct experiment.

What are the main advantages and limitations of simulation?

Advantages: it handles complex, uncertain systems, allows safe what-if testing and needs little advanced mathematics. Limitations: it can be costly and time-consuming, gives approximate results and depends on how well the model and inputs reflect reality.

What is the difference between simulation and optimisation?

Optimisation, such as linear programming, finds the best solution under stated assumptions. Simulation tests how a system behaves under chosen decisions, often with random inputs, and does not itself identify the best option.

Is Monte Carlo the same as simulation?

Not exactly. Monte Carlo is one type of simulation, the stochastic kind that uses random numbers to sample from probability distributions. Simulation is the wider term.