When a business needs to minimise cost, maximise throughput, optimise design, model uncertainty or start performance improvement projects, there are a number of strategies and tools that can help achieve these results including Six Sigma, Quality Function Deployment and simulation among others.
However, understanding how they work and whether they are suitable for a specific purpose can be challenging, given that adopting the wrong tool can often lead to mediocre results while requiring considerable effort.
This article reviews some of the most important tools for making decisions in business and engineering, especially tools that are computational in nature, which can forecast detailed and measurable results.
Classified into three general categories, Monte Carlo simulation, optimisation and discrete-event simulation, the tools cover a wide range of decisions in business and engineering, and have been used in every industry including retail, manufacturing, healthcare, defence as well as government.
Monte Carlo Simulation
Monte Carlo simulation is a method for studying systems that contain uncertainty. When constructing a model, a set of relationships that relates inputs to outputs is first defined. For example, sales forecast, materials and labour costs affect the revenue and profit. Probability distributions are next assigned to one or more of the inputs.
The simulation is then run using a Monte Carlo simulation software such as Crystal Ball, which gives the probabilities of achieving various outputs, thereby providing more complete information for making decisions compared to deterministic models.
The analyst can then simulate the effects of changing the input on the output such as reducing product defects by switching to a more reliable production machine. These 'tweaks' to the model are normally performed manually but commercial simulation packages typically offer an optional optimiser that helps to automate this task.
Monte Carlo simulation should be used when uncertainty plays an important part in the system and when the model can be reasonably represented as a snapshot over time. Popular Monte Carlo simulation packages include MS Excel add-ins due to their ease of use and adaptability to existing Excel documents.
Optimisation is a class of methods that aims to achieve some optimal result including for instance, minimising cost/downtime or maximising profit/throughput. The model consists of relationships between inputs and the objective, with some of the inputs allowed to vary and known as decision variables.
For example, in a transportation model describing how products should be shipped along various routes, decision variables may include how much product should be shipped along each route in order to minimise transportation costs. The optimisation software is then used to obtain optimal combination of decision variables that leads to the minimum cost.
A very flexible tool, optimisation has been used in financial portfolio management, engineering design, capital budgeting, staff scheduling, transportation and capacity planning. Optimisation tools include MS Excel add-ins such as Premium Solver and What’s Best! as well as standalone applications and development tools such as LINGO and LINDO API.
A snapshot model over a period of time is favoured in optimisation. Discrete-event simulation should be considered if the system is dynamic and changes over time.
Discrete Event Simulation
More specific to modelling repetitive processes such as manufacturing and services operations, discrete event simulation should be used when it is important to model the behaviour of the system over time and when it is necessary to model the intricacies of the system operation in detail.
Once the user specifies how the system should behave such as part and vehicle routing rules, worker shifts, machine processing rates and downtimes, the discrete event simulation software then simulates the detailed system operation over time, imitating the actual system.
The simulation screen shows the movement and activity of individual elements in the system with the software providing statistics such as inventory levels, downtimes, throughput, vehicle/people movements and costs that enable the user to track the performance of the individual elements.
The system can be improved by performing virtual experiments, involving the tweaking of various parameters and observing the effect on some parameter of interest. Optimiser add-ons are also available to help automate this task. These capabilities make discrete-event suites well suited for Six Sigma and Lean projects.
Discrete-event simulation software such as ProModel, MedModeland ServiceModel suites provides highly detailed representation of the actual system, and therefore has the potential to yield more accurate results. Cheaper, stripped down versions such as the Process Simulator provide a convenient starting point.
Hearne Scientific Software offers advice on selecting the most appropriate tool and software for businesses.