Heller Stephen
Stephen Heller & Associates, Houston, TX 77084, USA.
J Hazard Mater. 2006 Mar 17;130(1-2):58-63. doi: 10.1016/j.jhazmat.2005.07.067. Epub 2005 Nov 17.
Some relatively easy techniques exist to improve the risk picture/profile to aid in preventing losses. Today with the advent of computer system resources, focusing on specific aspects of risk through systematic scoring and comparison, the risk analysis can be relatively easy to achieve. Techniques like these demonstrate how working experience and common sense can be combined mathematically into a flexible risk management tool or risk model for analyzing risk. The risk assessment methodology provided by companies today is no longer the ideas and practices of one group or even one company. It is reflective of the practice of many companies, as well as the ideas and expertise of academia and government regulators. The use of multi-criteria decision making (MCDM) techniques for making critical decisions has been recognized for many years for a variety of purposes. In today's computer age, the easy accessing and user-friendly nature for using these techniques, makes them a favorable choice for use in the risk assessment environment. The new user of these methodologies should find many ideas directly applicable to his or her needs when approaching risk decision making. The user should find their ideas readily adapted, with slight modification, to accurately reflect a specific situation using MCDM techniques. This makes them an attractive feature for use in assessment and risk modeling. The main advantage of decision making techniques, such as MCDM, is that in the early stages of a risk assessment, accurate data on industrial risk, and failures are lacking. In most cases, it is still insufficient to perform a thorough risk assessment using purely statistical concepts. The practical advantages towards deviating from strict data-driven protocol seem to outweigh the drawbacks. Industry failure data often comes at a high cost when a loss occurs. We can benefit from this unfortunate acquisition of data through the continuous refining of our decisions by incorporating this new information into our assessments. MCDM techniques offer flexibility in accessing comparison within broad data sets to reflect our best estimation of their importance towards contribution to the risk picture. This allows for the accurate determination of the more probable and more consequential issues. This can later be refined using more intensive risk techniques and the avoidance of less critical issues.
存在一些相对简单的技术来改善风险状况/概况，以帮助预防损失。如今，随着计算机系统资源的出现，通过系统评分和比较来关注风险的特定方面，风险分析相对容易实现。像这样的技术展示了如何将工作经验和常识通过数学方式结合到一个灵活的风险管理工具或风险模型中，用于分析风险。如今公司提供的风险评估方法不再是一个群体甚至一家公司的想法和实践。它反映了许多公司的实践，以及学术界和政府监管机构的想法和专业知识。多年来，多标准决策（MCDM）技术在各种目的下用于做出关键决策已得到认可。在当今的计算机时代，这些技术易于获取且用户友好，使其成为风险评估环境中的一个不错选择。这些方法的新用户在进行风险决策时会发现许多想法直接适用于其需求。用户会发现他们的想法稍作修改就能很容易地适应，以使用MCDM技术准确反映特定情况。这使其成为评估和风险建模中一个有吸引力的特性。决策技术（如MCDM）的主要优点是，在风险评估的早期阶段，缺乏关于工业风险和故障的准确数据。在大多数情况下，仅使用纯统计概念进行全面的风险评估仍然不够。偏离严格的数据驱动协议的实际优势似乎超过了缺点。行业故障数据往往在损失发生时成本很高。通过将这些新信息纳入评估，不断完善我们的决策，我们可以从这种不幸的数据获取中受益。MCDM技术在广泛的数据集中进行比较时提供了灵活性，以反映我们对它们对风险状况贡献重要性的最佳估计。这有助于准确确定更可能和更具后果性的问题。之后可以使用更深入的风险技术进行完善，并避免不太关键的问题。
