![]() ![]() Layout Optimisation: Factory layout reconfigurations are usually time consuming and expensive.Validation of New Technology Introductions: DES can be used as a validation tool to evaluate the effects of technological improvements such as automation and new tooling to provide more accurate or valid estimates of the incremental costs and benefits of alternatives on KPIs such as throughput, resource utilization, WIP etc.The ‘what-if’ scenarios that could be carried out using DES are as follows: Once validated, the model can then be utilised to run various experiments/scenarios.įigure 1: Steps of a DES study 7.1.2 ‘What-If’ Scenarios of a DES In order to make sure this model represents the real system, it needs to be verified and validated by comparing model outputs to real-life system performance indicators. This data is used to build the baseline (‘as-is’) model. First, the data mentioned in Table 1 needs to be collected. The steps taken in developing a discrete event simulation model are presented in Figure 1. These experiments are commonly known as ‘what-if’ scenarios' and can be used to assess a wide range of changes on the KPIs. After areas for improvement have been identified, any proposed changes to the running of the baseline factory or any of its processes can be simulated in the model reducing the risk of implementation. The process flow model provides insights into numerous KPIs such as: resource utilisation of both staff and equipment, bottleneck location, throughput, buffer capacity, and work in progress (WIP). Remove Toenail, Tendon Cut and Insert HooksĬarcass Trim Inspection and Removal (Check Point 1)Ĭarcass Health Mark Stamp and Kill NumberĬarcass Inspection and Trim (free from visible physical contamination)Ĭarcass Rapid Chill -18 ☌ to – 30 ☌ (footnote)Ĭarcass Chiller: Carcass temperature <7 7☌ in 24 hours, target <5 5☌ Polish / Automatic Singe Livestock Unloading (Temperature Ambient) Lairage and Slaughter Processes and Resources. The effect of this uncertainty can be incorporated into the model through the use of statistical distributions. As an example, process timings may not be exact and therefore difficult to capture, especially for manual processes, which are likely to vary depending on operator skill. ![]() ![]() In DES models, input variables can be stochastic (or random variables). An example of the information needed for the generic abattoir simulation is presented in Table 1. It can capture uncertainty in the operations and more detailed information than an analytical model or spreadsheet analysis hence providing accuracy and a precise forecast.Ĭertain data requirements must be met in order to construct a DES model. One particular area where DES is employed to great effect is within the manufacturing sector, where simulation and evaluation of factory flows, and production processes is common practice (footnote) (footnote) (footnote).ĭES provides a risk-free environment to gain deeper insights and test decisions prior to making changes in the real-life manufacturing operations. This non-uniform time step makes the simulation computationally efficient and means that this technique is perfectly suited for modelling flows through a system. 7.1 Generic Abattoir Simulation Model 7.1.1 Discrete Event Simulation as a Methodologyĭiscrete Event Simulation ( DES) (footnote) is a modelling technique whereby the simulation clock progresses from one event to the next. ![]()
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