promotion image of download ymail app
Promoted

請幫忙我翻譯這一篇文章急件謝謝喔

Generating Risk Profiles

The risk quantification procedure should be workable for the project management team.

Our approach is somewhat similar to the minimalist first pass approach of Chapman and Ward (2000).

Similar to theirs, our approach expects project management to provide the probability of occurrence and the impact of the risk factors on the activities of an activity group under a best case scenario and a worst case scenario.

The GUI prompts the project manager to answer a list of scenario based questions aimed at the quantification of a risk and its impact on the duration increase of the activities of a certain activity group.

Upon validation, the answers provided by the project managers are directly entered into the GUI and provide the following input: the overall occurrence frequency q of the risk, an estimate b of the time (in days) by which the duration of an affected activity is extended in the worst case scenario, the frequency of appearance ζ(b) of the worst case scenario in similar previously executed projects, the estimated duration prolongation а of an affected activity (in days) in the best case scenario, and the requency of appearanceζ(a) of the best case scenario.

The frequencies ζ(a), ζ(b), and q can be larger than one if the project manager expects several occurrences of the risk during the project.

The data are entered into the GUI after passing a validation test checking whether

q≧ζ(a)+ζ(b).

If the data entered do not pass this test, they must be revised by the project manager.

From the two extreme case point estimates (a and b), a triangular probability density function f(x) and its cumulative distribution function F(x) for the impact χ are generated.

Update:

A triangular distribution is completely defined by three parameters: the lower

limit, the mode, and the upper limit.

The first step to generate f(x) is the determination of c, the most likely estimate for the impact.

Update 2:

Asking the project manager for an estimate of c has been shown to be difficult because of the fuzziness inherent to the “most likely” concept.

Update 3:

For the time being, we assume that the best case scenario a, and the worst case scenario b, are the lower and upper limit of f(x), respectively, and calculate c as

c =(aζ(a)+bζ(b)) / ( ζ(a)+ζ(b)) (1)

Update 4:

The reasoning behind this formula starts from the idea that project managers never think in terms of point estimates f(x) for a continuous distribution.

We do, however, assume that the fractionζ(a) / ζ(b) is a correct estimate of P(a)/ P(b), where

1 Answer

Rating
  • 1 decade ago
    Favorite Answer

    生成風險簡介

    風險量化的程序應是可行的項目管理團隊。

    我們的做法有點類似於最低限度首次通過的辦法查普曼和沃德( 2000年) 。

    他們的相似,我們的做法預計項目管理提供發生的可能性和影響的風險因素的活動的一項活動組根據最好的情況下和最壞的情況。

    界面提示的項目經理回答名單情況的問題,以量化的風險及其影響的持續時間增加的活動有一定的活動小組。

    經審定,答案所提供的項目經理直接進入圖形用戶界面,並提供下列輸入:整體出現頻率Q的風險,估計B的時間(天) ,其中持續時間受影響的活動延長在最壞的情況下,頻率的外觀ζ (二)最壞的情況類似以前執行的項目,估計持續時間延長а受影響的活動(天)在最好的情況下,和requency的appearanceζ (一)最好的情況下。

    ζ的頻率(一) , ζ ( b )項和q可以大於1 ,如果項目經理預計數發生過程中的風險項目。

    這些數據輸入到圖形用戶界面經過驗證測試檢查是否

    q ≧ ζ (一) + ζ ( b )項。

    如果輸入的數據沒有通過這次測試,他們必須修改的項目經理。

    從兩個極端的情況下點估計值( A和B ) ,一個三角形的概率密度函數f ( x )及其累積分佈函數f ( x )的影響χ產生。

    一個三角形的分佈是完全確定的3個參數:低

    限制,模式和上限。

    第一步生成函數f ( x )是確定的c ,最可能的估計的影響

    就目前而言,我們認為,最好的情況下一個,和最壞的情況下情況B ,是降低和上限f ( x )的,分別計算c視

    的C = ( aζ (一) + bζ ( b )項) / ( ζ (一) + ζ ( b )項) ( 1 )

    背後的推理這個公式出發的想法從來沒有項目經理的角度來考慮的點估計值f ( x )的連續分佈。

    然而,我們確實認為fractionζ (一) / ζ ( b )是一個正確的估計的P (一) /峰( b )項,其中

    • Commenter avatarLogin to reply the answers
Still have questions? Get your answers by asking now.