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Thales uses @RISK to Measure Feasibility of New Business Tenders Thales is a leading provider of mission-critical electronic information systems for aerospace, defence, and security markets around the world. With operations in 60 countries and 70,000 employees, it develops products for dual markets in recognition that civil and military systems benefit from many of the same technologies and innovations. Risks and Challenges Addressed with @RISK Bringing products of this calibre to market is costly both in terms of time and resource. Consequently, for every opportunity to compete for new business (which in itself can be a long, and therefore expensive, process), Thales must be confident that it has a reasonable chance of success. It must also take into consideration that, while winning a contract is favourable, the long-term cost of then maintaining its market share can actually be prohibitive to pitching for the prospect in the first place. For example, once developed, sophisticated electronic systems for aircraft require continual upgrading—the cost of which may outweigh the original benefit (and profit projections) of the initial deal. Thales uses @RISK software from Palisade to assist it in making these business-critical decisions. @RISK is an Excel add-in using Monte Carlo analysis to show all potential scenarios, as well as the likelihood that each will occur. @RISK enables Thales to calculate the competitiveness of complex markets, measure probabilities for project costs, quantify rate of return, and even account for the effects of cumulative business, thereby providing decision-makers with the most complete picture possible. @RISK Quantifies Complex Markets For example, Thales might need to decide whether to bid for developing a radar system for a particular type of combat aircraft. By developing a distribution model with @RISK, Thales can input unknown quantities such as production forecasts for that aircraft and account for them with distribution functions. Thales also inputs 'known' quantities, such as which countries and organisations are currently flying the same machine, and is then able to forecast the potential market for the product once it is completed. On the civil aviation front, the 25-year lifespan of aeroplanes offers the potential for several equipment upgrades during that time. For example, in-flight entertainment systems have changed dramatically over the past quarter of a century, with personalised systems that allow passengers to watch their individual choice of film now providing airlines with competitive advantage when selling tickets. Again, combining @RISK's sophisticated prediction capability with human expertise and research enables Thales to ascertain whether the potential market is sufficient to warrant competing for a contract. @RISK Weighs Up Project Cost vs. Probability To then calculate the probability of breaking even on a project, Thales must factor in the cost of preparing a tender with the probability of winning the business. Although bidding against three other operations could be perceived as offering a 25 percent chance of success, Thales believes it makes better business sense to err on the side of caution when making this estimation. @RISK Measures Rate of Return @RISK Accounts for Cumulative Business Taking this one step further, Thales can also calculate when it makes commercial sense to use this tactic strategically—for example, will winning a piece of business in one region be beneficial when tendering for another contract? @RISK helps Thales understand whether securing a contract with one region will be a route in to doing business in another. @RISK Enables Business-Critical Decisions
More Detail Key @RISK software features used by Thales
@RISK enables the user to view the input distributions graphically, which Thales finds is a very good way to communicate the model to colleagues who are not familiar with the product. It also facilitates the capacity to view the outcome of the Monte Carlo modelling as a continuous graph, and then to slide the cursors up and down to read off instantly the probabilities of being above or below specific values. Again, Thales believes this is very useful for communicating with people who are unfamiliar with modelling. Distributions used by Thales
Thales selects the statistical distribution to reflect the nature of the market in which it is working. Thus, for example: When Thales is modelling the win or loss of one or more such large contracts, it uses the binomial distribution to reflect the fact that it either wins it all or loses it all—there is no in-between position. On the other hand when modelling a wide market where there are hundreds of customers, and each customer might purchase a small or large quantity of various products, Thales would use a continuous distribution such as normal. Setting the standard deviation of the normal distribution depends on how Thales thinks the market might behave: if the buying decisions in a market are independent of one another, the standards deviation could be smaller; but if buying decisions are influenced by what others have purchased, then the standard deviation should be larger to reflect potential swings in the marketplace. When using @RISK for Project, there are different considerations to determine the distribution of the duration of an event in a programme. If the work is well defined, resources must be applied to the task, and the work will be done, then a normal distribution would is appropriate as it takes into account the usual risks to schedule such as sickness. However the project may involve achieving a technical advance, for example developing some software or a piece of hardware, and proving that it satisfies the design criteria and performance levels and Thales has to bear in mind that some developments do not go according to plan, and designs sometimes have to be revised and reworked in order to meet the criteria. In these cases, a lognormal distribution is an easy way of reflecting life: the distribution is mostly around the mode, but with a longer tail to the right to reflect the possibility of having to undertake extra effort to solve the most intractable of problems.
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