Simulation software is a powerful tool that allows you to model, analyse, visualise and optimist almost any conceivable process, whether manufacturing and production, or car hire tracking and public transport planning. Quite simply, simulation software let's you get the most from your operations and eliminate the glitches before the occur.
What does Simulation Software Do?
Simulation software allows you to test test unlimited numbers of scenarios and parameters to see the subsequent consequences on your daily operations. What happens if you reduce safety stock to 4 hours? Simulation software will show you. What would be the risk to production, and ultimately satisfying customer demand, if you increased you total work content and standard operation working times? Simulation software will show you. Any scenario can be input and simulated over a day, a week or even a year to show the outcome.
The Outputs Of Simulation Software
Simulation software will deliver its findings in a series of easy to read and analyse reports. 3D graphics, histograms and pie charts come as the norm. You also have the possibility of creating endless bespoke reports from the collected data. Everything you need to analyse your as-is state at the click of a mouse.
Simulation of complex systems has evolved into a research discovery tool but has not yet been employed to the degree called for in many energy technology development areas. Such models and simulations, drawing upon the dramatic scale up of computational power and associated architectures and algorithmic innovation, can address complex systems with many degrees of freedom and with multiple length and time scales of interest. These system characteristics are important for many energy technology challenges. They are crucial for characterizing natural systems such as the atmosphere, ocean, biosphere couplings crucial for understanding climate and for social systems such as the global economy and its response to energy and environmental policy change or the dynamics of emerging "gigacities." Advancing the methodologies for analyzing and simulating complex nonlinear systems will be important for avoiding the unintended consequences that so often arise when energy issues are addressed in isolation from complex technical, policy, social and behavioral feedbacks.
The Advanced Systems, Modelling and Simulation Research Group undertakes leading research into challenging complex systems problems involving enterprises, people, processes and technologies. Our research advances modelling and simulation methods to provide greater insight and understanding, encompassing a wide range of methods from abstract mathematical representations, sophisticated computer simulations through to hardware in the loop simulation. The focus of this research is not only on development of better modelling and simulation methodologies, but also on the application of state of the art techniques to help understand and predict the behaviour of complex systems. Applications for our research span many industry sectors including aerospace, automotive, construction, counter-terrorism, defence, energy, healthcare, manufacturing, transport and virtual engineering.
At the heart of our approach is the strong desire to balance stakeholder perspectives with input from engineering and non-engineering disciplines. This application of a multi-disciplinary systems engineering methodology is key to performing the cross-domain trade-offs required to deliver future optimal solutions.
Verification, validation and assurance are foremost in our minds as we develop appropriate model based systems engineering solutions.
Monte Carlo simulation is a versatile method for analyzing the behavior of some activity, plan or process that involves uncertainty. If you face uncertain or variable market demand, fluctuating costs, variation in a manufacturing process, or effects of weather on operations, or if you're investing in stocks, developing a new drug, or drilling an oil well -- you can benefit from using Monte Carlo simulation to understand theimpact of uncertainty, and develop plans to mitigate or otherwise cope with risk. This page introduces Monte Carlo simulation and explains why you might need it, and what you need to know (or learn) in order to use it.
What is Monte Carlo Simulation?
The Monte Carlo methodwas invented by scientists working on the atomic bomb in the 1940s, who named it for the city in Monaco famed for its casinos and games of chance. Its core idea is to use random samples of parameters or inputs to explore the behavior of a complex system or process. Specifically, using a model of the situation, system, or process being looked at, a random sample of each uncertain input (such as sales volume, etc.) is taken. Next the model is recalculated and the key results (such as net profit) saved. this is done repeatedly and the results saved each time. Once complete the range and shape of the results can be examined visually and numerically. Since the 1940's, Monte Carlo methods have been applied to an incredibly diverse range of problems in science, engineering, and finance -- and business applications in virtually every industry.
Why Should I Use Monte Carlo Simulation?
Whenever you need to make an estimate, forecast or decision where there is significant uncertainty, you'd bewell advised to consider Monte Carlo simulation -- if you don't, your estimates or forecasts could beway off the mark, with adverse consequences for your decisions! Dr. Sam Savage, a noted authority on simulation and other quantitative methods, says "Many people, when faced with an uncertainty ... succumb to the temptation of replacing the uncertain number in question with a single average value. I call this the flaw of averages, and it is a fallacy as fundamental as the belief that the earth is flat."
Most business activities, plans and processes are too complex for an analytical solution -- just like the physics problems of the 1940s. But you can build a spreadsheet model that lets you evaluate your plan numerically -- you can change numbers, ask 'what if' and see the results. This is straightforward ifyou have just one or two parameters to explore. But many business situations involve uncertainty in many dimensions -- for example, variable market demand, unknown plans of competitors, uncertainty in costs, and many others -- just like the physics problems in the 1940s. If your situation sounds like this, you may find that the Monte Carlo method is surprisingly effective for you as well.
What Knowledge Do I Need to Use It?
To use Monte Carlo simulation, you must be able to build aquantitative modelof your business activity, plan or process. One of the easiest and most popular ways to do this is to create aspreadsheet model using Microsoft Excel -- and use Frontline Systems'Risk Solver Platform as a simulation tool. Other ways include writing code in a programming language such as Visual Basic, C++, C# or Java -- with Frontline's Solver SDK Platform-- or using a special-purpose simulation modeling language. You'll also need to learn (or review) the basics of probability and statistics. To deal with uncertainties in your model, you'll replace certain fixed numbers -- for example in spreadsheet cells -- with functions that draw random samples from probability distributions. And to analyze the results of a simulation run, you'll usestatisticssuch as the mean, standard deviation, and percentiles, as well as charts and graphs. Fortunately, there are great software tools (like ours!) to help you do this, backed by technical support and assistance.
How Will This Help Me in My Work or Career?
If your success depends on makinggood forecasts or managingactivities that involve uncertainty, you can benefit in a big way from learning to use Monte Carlo simulation. By doing so, you canavoid the trap of the Flaw of Averages. As Dr. Sam Savage warns, "Plans based on average assumptions will be wrong on average." If you've ever found that projects came in later than you expected, losses were greater than you estimated as "worst case," or forecasts based on averages have gone awry -- you stand to benefit!
Go Beyond the Limits of 'What If' Analysis. A conventional spreadsheet model can take you only so far. If you've created models with best case, worst case and average case scenarios, only to find that the actual outcome was very different, you need Monte Carlo simulation! By exploring thousands of combinations for your 'what-if' factors and you can get deep insight into the range of potential outcomes and how likely each is to occur to allow you to better set expectations and manage risk.
Know What Factors Really Matter. Tools such as Frontline's Risk Solver Platform enable you to quickly identify the high-impact factors in your model, using sensitivity analysis across thousands of Monte Carlo trials. It could take you hours to identify these factors using ordinary 'what if' analysis.
Give Yourself a Competitive Advantage. If you're negotiating a deal, or simply competing in the marketplace, having a realistic idea of the probability of different outcomes -- when your opponent or competitor does not -- can enable you to strike a better bargain, choose the price that yields the most profit, or benefit in other ways.
Be Better Prepared for Executive Decisions. The higher you go in an organization, the more you'll find yourself dealing with uncertainty. Simulation or risk analysis might not be essential for routine day-to-day, low-value decisions -- but you'll find it invaluable as you deal with higher-level, more strategic -- and higher-stakes -- decisions.
Consult ourtutorial to learn more. We'll take you step by step through the process of converting a simple spreadsheet model, that uses "flawed average" assumptions, into a risk analysis model that yields surprising insights with the aid of Monte Carlo simulation.
Continuous Simulation presented by Tony Kuch, Eng MSc A discussion of advantages of continuous simulation, supporting hydrology methods, management and processing of time series and hydraulic elements such as evaporation, exfiltration losses in ponds and others.
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When simulating an inventory control systems project, it is important to take stock of all the key elements of real-world inventory control without overlooking the critical real-world factors. A well-designed inventory control simulation should include data based on the recommendations of front-line employees who know where losses occur that might otherwise go unnoticed.
Inventory Control Simulation Key Factors
An inventory control system should take into account key factors such as demand fluctuation based on market trends, spoilage in unstable goods such as food or chemicals, shrinkage due to spills, product damaged in shipping, and shrinkage caused by staff. Demand fluctuation based on market trends can only be predicted in a general sense by analyzing past precedences with similar products and how they relate to new items. In contrast, the spoilage of unstable goods is usually a very predictable process and can be minimized by effectively estimating how much product will be sold before the shelf-life of the item expires, thereby eliminating over-purchasing. Within inventory control, estimating the market demand for unstable goods and ensuring that the company does not buy too much or too little of a product is among the most difficult of tasks, and must be supported by large volume data samples before an informed decision can be made. Shrinkage due to spills can be minimized through the employee training programs, although the exact amount of shrinkage will vary dramatically between locations and require some real-world data gathering. Shrinkage due to internal and external staff can be virtually eliminated by enhancing systemic security protocols, including adequate security monitoring and loss-prevention technology.
Determining an Inventory Control Strategy
Choosing an inventory control strategy for the simulation experiment requires an intimate knowledge of the specific nature of the business being analyzed. A small-scale greengrocer, for example, should focus their inventory control strategy on anticipating consumer demand and minimizing loss due to spoilage, whereas a large stable goods retailer such as Wal-Mart, Kmart or Target can afford to make large purchase orders of items, store them in a warehouse and distribute them internally while receiving volume purchasing discounts.
The Effectiveness of Inventory Control Simulations
The effectiveness of business simulation software is not universally accepted, but it has gained significant credibility by being used in at least three American universities. According to Entrepreneur Magazine, simulation software is being used by students under the supervision of professors or assistant professors at the University of Chicago, Seton Hall University and Michigan State University. Despite the growing popularity of business simulation software, its accuracy can only be as good as its programming, meaning that programs will be judged on a case-by-case basis. http://www.ehow.com/facts_5951438_effect-inventory-control-profit-making.html
In today's globalized and integrated economy, intense competition necessitates faster demand for goods and products, and manufacturers and producers cannot afford to stock more inventory than necessary. An inventory-control system (mostly automated or computerized) helps producers, warehouse owners and stockists to better manage inventory and control overhead expenses. It ensures that just enough inventories are kept in stock.
Real -Time Assessment
New--generation and highly sophisticated inventory control systems give near real-time assessment of inventory levels and movement of goods and products among various points in an organization's distribution, supply and marketing chain.
Ensures Control
Real-time assessment of inventory items and stockpile of goods ensures better control and availability of finished products and processed goods at wholesale, retail and customer-facing points and outlets.
Better Planning
Knowledge of supply, storage and accessibility of existing items, supplies and goods enables organization managers and planning personnel to better plan for present and future demand. Production scheduling and cost estimates for procurement are better managed.
Timely Issuance of Pay Orders
A computerized inventory system monitors inventory level at all times and issues pay orders to suppliers and other business partners as required. Invoices raised by external parties are also addressed swiftly.
Better Accountability
Modern businesses investing in the latest inventory-control systems can avoid waste on all fronts related to manufacturing, production, material supplies and labor and thus streamline operations.
What Is Simulation Software?
