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
Thales operates in a highly competitive environment, with technologically advanced countries continuously providing tough opposition when it tenders for contracts. It is imperative therefore that Thales keeps up with the relentless pace of technology development—something made additionally challenging by the sophistication of the products in question. In addition, the critical and sensitive nature of its customers' businesses demand that the equipment Thales develops and produces is rugged, robust, and failsafe.
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
The risk analysis undertaken by Thales enables it to quantify and understand how the market for a particular product area may work, and from there whether it is worth investing in trying to win the business offered.
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
With the market potential 'measured', Thales needs to balance the overall cost for bringing the completed project to market against the probability of winning the business. Figures fed into the @RISK model include costs for staffing, new office openings, licensing, etc, as well as the adjustments needed for different countries and regions, such as providing instructions in different languages. Thales must also include a sum for 'unknown' or ‘unexpected’ costs.
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
Adam Ogilvie-Smith, Senior Consultant at Thales Management Consultancy, explains: “In theory it would be tempting to try and win every piece of business that we are offered. But in the sophisticated and complex markets in which we operate, closer inspection of each case reveals this often is not possible, practical, or desirable. @RISK's complex risk analysis capabilities provide us initially with a figure that is essentially an internal rate of return on the funds used to tender for and win a project—that is, will financing a business pitch and the subsequent product development bring an equivalent rate of return as investing the same amount of capital in a bank over the same period of time? In addition, the @RISK model helps us to determine the probability of securing the contract, and therefore whether it is strategically viable overall to pitch for the business.”
@RISK Accounts for Cumulative Business
Commercial agreements rarely occur in isolation. Thales' @RISK decision models therefore ascend to another level by taking into account the cumulative effect of winning new business, for example including conditions such as winning project A being a prerequisite for pitching for project B. It also enables Thales to work with scenarios such as the initial probability of securing contracts in countries X and Y being factored as 40 percent and 30 percent respectively, but winning the contract in country X then making it easier to succeed in the next territory—country Y.
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
Ogilvie-Smith concludes: “Our commercial success is determined by our ability to quantify both our internal operations, as well our position in the wider environment in which we operate—which is both dynamic and sensitive. It is essential therefore that we use a versatile and robust modelling tool that can handle the complexity of our queries. @RISK combines its powerful risk analysis capability with the added benefit of being easy and intuitive to use, and applicable for almost any task. As a result it is a key strategic tool for Thales, assisting us in our process of reaching informed business-critical decisions.”
@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.
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.