文章编号: 042727104 (2005) 0220201206
Received date : 2003209229
Biography : WAN G Qi2fan (1936—) ,male , professor , supervisor of Doctoral Candidate.
Advantages of System Dyna mics Approach in Managing
Project Risk Dyna mics
WANG Qi2fan ,NING Xiao2qian , YOU J iong
( School of M anagement , Fudan U niversity , S hanghai 200433 , Chi na)
Abstract : The fast changing environment and the complexity of projects has increased risk exposure and project risk man2 agement has become an important part of project management . However the ineffectiveness of traditional tools and tech2 niques in dealing with project risk dynamics calls for new approaches. It is discussed the application of system dynamics methodology in project risk management , especially its features and advantages of in managing project risk dynamics.
Keywords : system dynamics ; complex system ; project risk management CLC number :F 224. 12 Document code :A
1
Int roduction
During t he years p roject risk management p ractice has evolved p rimarily around t he assumption t hat risks are independent entities t hat do not affect each ot her. Numerous tools and techniques have been developed around t his assumption. For some risks t hese tools and techniques work effectively , but in many cases t hey are t rying to manage t he wrong t hings. In reality t he risk environment is in most case a series of conse2 quences , a risk network1 .
When risks are considered as individual entities , our attention will be directed away f rom t he risks t hat , when looked at individually don’t seem so important to be managed but when seen as a part of a risk continu2 um can p rove to be enormous in magnit ude. It is impossible to quantify t he net result of a set of risks wit hout modeling t he interrelations of risks t horoughly. The limitation of t he t raditional tools and techniques call for f urt her develop ment s in p roject risk management .
2
The nat ure of p roject risks
Detailed analyses of several major p roject s show clearly t hat it is t he it is t he interaction between different t ypes of risks causes t he most damage to a p roject2 . Thus , in addition to considering a wider range of risk categories , it is important to consider more t han just t he risks t hemselves—as a list or mat rix—but also t heir impact s on one anot her. Moreover , when one risk occurs toget her wit h ot her risks t hey can form a portfolio where t he impact of t he whole is greater t han t he sum of t he part s.
Analyses show t hat t here are complex ramified consequences t hat drive vicious cycles , where risk gener2 ates self - sustaining disasters2 . For example , consider t he following , all - too - familiar scenario . A con2 t ractor wins a p roject where it s scope is not f ully defined initially. Soon it becomes apparent t hat underesti2 mated changes are reducing act ual p roductivity and causing ext ra work. The p roject falls behind it s ambitious schedule. The delay exposes t he p roject to unexpected technological and regulatory changes , and hence more rework and lower p roductivity. Suppose t hat t here is no p rovision in t he p roject schedule and budget for any of
t hese. Furt her delay and cost escalation p rompt s t he client to change t he number of tasks to be p roduced ,in attempt to stay wit hin budget limit . Then t he cont ractor , in an attempt to salvage t he p roject , agrees to an overly ambitious , success - oriented , and potentially disast rous“rescue plan”. The impact s of t hese factors are delayed , nonlinear , indirect , and self - reinforcing. Hence , before t he fact s or even after t hey have occurred t heir f ull significance is difficult to perceive. Worse still , because t heir effect s can be quite counter - int uitive , t hese factors often p rompt dysf unctional management actions.
Therefore when planning for t he p roject and considering risks , mitigating actions , which appear sensible when seen in isolation relating to a specific risk event s , might p roduce obvious cont radictions and counterint u2 itive effect s when alongside ot her risk event s and mitigating actions. It is important to consider risks as sys2 temic in order to accurately rep resent what act ually happens
But , t here must be an ability to see bot h t he“big pict ure”and t he details. Int roducing t he view t hat risks should be seen as systemic widens t he view of risks as well as forcing a more holistic app reciation.
3
System dynamics overview
System Dynamics was initially developed by Dr. Forrester3 in t he late 1950’s at M. I. T as a met hod for analyzing and understanding t he dynamic behaviors of complex systems and has experienced a sharp increase in pop ularity during t he last decade. It s application to p roject management has also been growing imp ressively , wit h numerous successf ul applications to real life p roject s4 .
System Dynamics model is an abst raction of a real object or system. Modeling a system means capt uring and abst racting t he system’s component s , relationship s and behavior , according to t he model’s objective. System Dynamics modeling is p rimarily based on cause - effect relationship s and t he most powerf ul feat ure of System Dynamics modeling is realized when multiple cause - effect relationship s are connected forming a circu2 lar relationship , known as a feedback loop . The concept of a feedback loop reveals t hat any actor in a system will event ually be affected by it s own action5 .
Fig. 1 A feedback loop diagram of interacted project risks
Fig. 1 illust rates t he main feat ures of a feedback loop diagram. The arrows rep resent influences between t he different factors ; t he plus or minus sign indicates whet her a positive change in t he p receding factor has a positive or negative effect in t he next . In Fig. 1 , schedule p ressure might induce several reactions f rom p roject team. For example , t he p roject manager hires more labors to relieve schedule p ressure. A higher labor level result s in a greater p rogress rate , which in t urn alleviates schedule p ressure. These arrows suggest a complete
balancing feedback loop indicated by B2 in t he figure. However t he intended balancing loop is not so easily achieved , as t here might be cont rary effect s of management actions , which tend to off set t he intended effect s of management actions and aggravate schedule p ressure , such as t hose indicated by reinforcing feedback loop
R1 , R2 and R3 in Fig. 1. The relative st rengt h of balancing feedback loop s and reinforcing feedback loop s determines t he combined effect s of management actions , which is complex and difficult to understand and p re2 dict .
Based on feedback loop diagram , a System Dynamics simulation model can be established by calibrating corresponding st ruct ures and parameters. System Dynamics simulation models p rovide an effective way to sim2 ulate t he behaviors of complex systems and st udy how t he behaviors are driven by t he system st ruct ures.
4
The application of System Dynamics app roach in p roject risk management
The system dynamics app roach to p roject management is based on a holistic view of t he p roject manage2 ment p rocess. In cont rast wit h t raditional p roject management met hodology , t he p rimary objective of a system dynamics model is to capt ure t he major feedback p rocesses responsible for t he p roject system behaviors , wit h less concern abort t he detailed p roject component s. There is a st rong focus on human factors and managerial policies as t hese are considered to dominate t hat feedback st ruct ures. Concerning p roject risk management , t he main advantages of System Dynamics app roach lie in risk identification , risk analysis and risk response planning as t hese p rocesses involve many factors which are subjective and dynamic and cannot be effectively dealt wit h by t raditional tools. This section discusses t he feat ures and st rengt hs of System Dynamics ap2 p roach , typically of feedback loop diagrams and System Dynamics simulation models , in p roject risk dynamics
management .
4. 1 Risk identif ication
Project risks can be identified qualitatively t hrough t he analysis of feedback loop s in feedback loop dia2 grams. Risks are mainly identified in t he following t hree scenarios. Firstly , balancing feedback loop s t hat lim2 it a desired growt h or decay . For example , when t he p rogress rate is high and t he p roject is perceived ahead of
schedule , t he negative schedule p ressure might induce relaxed mood in p roject team , i. e. decrease t he motiva2 tion of p roject team , which tends to lower p roductivity and p rogress rate and p ush p roject back to it s sched2 uled deadline. This p rocess is described by balancing feedback loop B1 in Fig. 1. Secondly , reinforcing feed2 back loop s t hat lead to undesired growt h or decay. For example , high schedule p ressure leads to deteriorated p ractice quality and increased rework , which slip s p roject f urt her , t hereby reinforces schedule p ressure ( see loop R6 in Fig. 1) . Thirdly , external factors t hat exacerbate any of above two types of feedback loop s , for ex2 ample t raining delays exacerbate undesired reinforcing loop R2 in Fig. 1 , t hat is t he more t he new labors hired in t he later stages , t he heavier t he schedule p ressure due to t raining overheads.
Analysis of feedback loop s can make apparent some risks , which ot herwise might be too vague to arouse enough notice. For example , p roject managers seldom pay enough attention to t he t raining and communica2 tion overhead induced by hiring new labors , while experience show t hat t hese overheads might be t he major causes to deteriorate p roject schedule performance , especially in high2tech p roject s , such as software develop2 ment p roject s , where p rofessional skills and effective communication are essential6 .
Anot her advantage of System Dynamics app roach in p roject risk identification is it s effort and capability to quantify most of risks.
Firstly , t he calibration of System Dynamics simulation models forces p roject managers to explicitly ex2 p ress t heir assumptions governing t he p roject p rocess , such as what are t he impact s of Schedule p ressure on p roductivity and p ractice quality , and when and how many new labors are hired. These assumptions are often unwritten and may reside only in managers’mind. This elicitation p rocess is helpf ul to uncover potential risks
t hat are not easily identified due to t heir intangible and subjective nat ure. For example , t he motivation of p ro2 ject team has great impact on p roject performance , however it is seldom exp ressed explicitly due to it s mea2 surement difficulty. Calibrating System Dynamics simulation model forces p roject managers to t ry to find suit2 able exp ression of motivation . St udying of t he past p roject s shows t hat , labors’desire to work hard is mainly reflected on t he effective working hours spent by labors. So effective working hours spent by labors might be a suitable equivalent to motivation5 . Through calibration , t he potential risk of decreased motivation , which is demonst rated by reduced effective working hours when p roject is ahead of schedule , can clearly identified.
Calibrating System Dynamics simulation models also gives p roject managers a chance to scrutinize history p roject data as t hey have to use t hese data as calibration base. This t hinking back p rocess help s p roject mangers to identify risks common to most p roject s and eliminate irrelevant risks p reventing unnecessary miti2 gating effort s.
Secondly , System Dynamics simulation models can uncover important quantitative information about p ro2 ject current stat us , which typically is not measured by t raditional techniques , such as undiscovered flawed tasks.
4. 2 Risk analysis
The likelihood and impact s of each risk can be qualitatively inferred f rom feedback loop analysis. Each feedback loop in t he feedback loop diagram might favor or counter t he occurrence of a specific risk. Through feedback loop analysis , it can be identified whet her a feedback loop is a dynamic force t hat p ushes t he p roject outcome towards or away f rom t he risk occurrence. The relative st rengt h of t he loop s related to a specific risk
determines it s occurrence likelihood and impact s.
Feedback loop diagram also describes how p roject risks are interacted to each ot her , which is one of t he most distinguishing feat ures of System Dynamics app roach. For example , in Fig. 1 , t he interaction of rein2 forcing loop R6 and balancing loop B4 shows how schedule risk is twisted wit h quality risk. Practice quality start s to decrease when schedule p ressure reaches a certain upper limit as labors are working nervously. In2 creased rework induced by decreased p ractice quality aggravates schedule p ressure f urt her. However , de2 creased p ractice quality also leaks more rework undiscovered , which tends to reduce tasks to be handled t hus alleviate schedule p ressure , but deteriorate p roject quality. The interaction of t hese two loop s clearly demon2 st rates t he t radeoff between schedule and quality , which is a common p henomenon in most p roject s.
The interaction of p roject risks can be quantitatively analyzed by System Dynamics simulation models. The most out standing feat ure of System Dynamics simulation model is it s effectiveness in simulating scenarios where several risks interact . The feedback nat ure of System Dynamics simulation model ensures t he cross im2 pact s of different risks are capt ured and t he direct and indirect impact s of risks are quantified. The impact s of interacted risks can be simulated and quantitatively analyzed by switching on/ off p rocedures. First , all risks are switched off , i. e. a risk f ree scenario is simulated and t he resulting behavior patterns are benchmarked as baselines. Then , specific risk is switched on one by one and t he resulting behavior patterns are compared wit h t he baselines to see t he impact s of t hat specific risk. For example , t he behavior patterns p roduced when scope changes are int roduced by client are compared wit h t he baselines when no scope changes are int roduced to see t he impact s of scope changes. Lastly , two or more risks are switched on at t he same time. By comparing t he resulting behavior patterns wit h t he baselines and t hose p roduced when only one of t hose two or more risks is switched on , t he cross impact s of t hese risks can be quantified. By t his kind of analysis , p roject managers can identify risks , which in isolation will not seriously deteriorate p roject performance , but when interact wit h ot her risks will cause terrible result s.
Anot her two feat ures of System Dynamics simulation model in p roject risk analysis are it s capability of capt uring nonlinearities and delays in p roject s.
Wit h table f unctions , System Dynamics simulation model can conveniently describes various kinds of non2 linear relationship s between p roject variables , which are abundant in p roject but cannot be capt ured by t radi2 tional modeling techniques. For example , t he impact of schedule p ressure on motivation is nonlinear. When p roject is ahead of schedule. i. e. schedule p ressure is negative , motivation of p roject team tends to decrease as plenty of time is perceived to be left to finish remaining tasks. The decreased motivation is demonst rated by labors spending more time chatting or dealing wit h personal fairs , t hus reducing t heir effective working hours. When p roject is behind schedule , motivation increases wit h t he increase of schedule p ressure , as p roject team t ries to stay wit hin schedule. And t he motivation increase pattern depends on t he nat ure of t he p roject . How2 ever , once schedule p ressure increases above an upper limit , p roject team perceives it is impossible to finish p roject wit hin schedule even wit h it s f ull effort , t hen it s motivation begins to decrease again. System Dynam2 ics simulation model’s capability of capt uring nonlinearity enables p roject managers to identify risk more p re2 cisely , such as when motivation is a risk factor t hat should be cont rolled and when it is a facilitating factor which should be cultivated.
Delays are normal p henomenon in p roject s. In Fig. 1 , arrows marked wit h“=”describe delayed effect s of t he p receding variables on t he succeeding variables. For example , when labors just start to overwork , t here is almost no impact on p roductivity and p ractice quality t hrough fatigue. However , after continuously over2 working for a p rod , fatigue accumulates and it s negative impact s become more and more apparent . System Dynamics simulation model can describes different order of delays conveniently wit h it s ready2made formulas. Wit h it s delay describing capability , System Dynamics simulation model p rovides a more realistic app roach to describe p roject s. St udying p roject behavior patterns p roduced by t he models integrating delayed effect s facili2 tates p roject manager to pay more attention to t hose risks and actions wit h delayed effect s.
4. 3 Risk response planning
System Dynamics app roach is powerf ul to support t he effective risk responses develop ment . Feedback loop diagrams and System Dynamics simulation models are very effective in diagnosing and analyzing t he multi - factor causes of risks. These causes can be t raced - back along t he chains of cause - effect wit h effective so2 lutions being identified. For example , it can be clearly seen f rom Fig. 1 t hat schedule p ressure is determined by bot h p rogress rate and tasks to be handled. So to relieve schedule p ressure , t he effective app roaches are to accelerate p rogress rate and to reduce tasks to be handled. Quickening t he t ransfer of new labors to t he experi2 enced is beneficial bot h to p roductivity and p ractice quality , i. e. attenuates vicious reinforcing loop R2 and
R3 in Fig. 1 at t he same time , so shortening t raining delays is effective to alleviate schedule p ressure. However , risk2mitigating actions often have side effect s due to t he interaction of p roject risks. Feedback loop diagrams walking2t hrough also help s to identify t hese side effect s. For example , when p roject manager hires more labors to alleviate schedule p ressure , communication complexity in p roject team will increase , which tends to harm p roductivity and exacerbate schedule p ressure ( see lop R1 in Fig. 1) . Fig. 1 demon2 st rates t hat all t he reinforcing loop R1, R2 ,R3 , are side effect s of policy of hiring new labors while bot h t he reinforcing loop R4 and R5 are side effect s of policy of overwork. The cross effect s of certain schedule p res2 sure mitigating policy depend on t he relative st rengt h of balancing loop s and reinforcing loop s t he policy int ro2 duces and are difficult to p redict mentally. System Dynamics simulation model p rovides an experimental labo2 ratory , wherein t he impact s of various risk responses can be tested and analyzed at low cost and in a safe envi2 ronment wit hout harming real p roject p rocess. Wit h“what2if”analysis , p roject managers can better under2 stand how p roject behaviors are driven by it s st ruct ure , t herefore devise effective risk responses t hat can elimi2 nate existing vicious loop s or attenuate t heir influence on t he p roject behaviors wit hout int roducing ot her vi2 cious loop s.
5
Conclusion
Wit h it s feedback and endogenous perspective , System Dynamics app roach is effective in dealing wit h in2 teracted , dynamic p roject risks , to which most of t he t raditional modeling techniques are inapp rop riate. Sys2 tem Dynamics has t herefore a st rong potential to p rovide a number of distinctive benefit s to p roject risk man2 agement , particularly to risk identification , risk quantification and risk response planning.
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