system dynamics for market forecasting and structural analysisee00190/relatorios/mts_ver2.pdfthe...

James M. Lyneis is aSenior Vice Presidentwith Pugh-RobertsAssociates, a Division ofPA Consulting Group.He holds a PhD inBusinessAdministration from theUniversity of Michiganand was an AssistantProfessor at MIT’s SloanSchool of Management.He has applied systemdynamics to problems ofbusiness strategy in thetelecommunications,electric utility,aerospace, and financialservices industries.

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System Dynamics Review Vol. 16, No. 1, (Spring 2000): 3–25 Received February 1999Copyright © 2000 John Wiley & Sons, Ltd. Accepted October 1999

System dynamics for market forecastingand structural analysisJames M. Lyneisa

Abstract

Forecasts of demand, revenues, profits, and other performance measures are a common inputto managing a business. And, while managers intellectually appreciate the difficulties withforecasts, the use of assumptions about the future is inevitable and necessary. Since theforecasts that come from calibrated system dynamics models are likely to be better and moreinformative than those from other approaches, especially in the short- to mid-term, we musteducate our clients to make proper use of them. This article stresses three points: (1) systemdynamics models can provide more reliable forecasts of short- to mid-term trends thanstatistical models, and therefore lead to better decisions; (2) system dynamics models providea means of understanding the causes of industry behavior, and thereby allow early detectionof changes in industry structure and the determination of factors to which forecast behaviorare significantly sensitive; and (3) system dynamics models allow the determination ofreasonable scenarios as inputs to decisions and policies. The paper illustrates these pointswith examples from a model of the commercial jet aircraft industry. It shows how the modelwas used to identify important structural changes in the industry, to avoid unnecessarycapacity expansion, and to identify strategies to best ‘‘bridge’’ a business downturn. Theresults presented update and significantly expand upon work presented earlier. Copyright ©2000 John Wiley & Sons, Ltd.

Syst. Dyn. Rev. 16, 3–25, (2000)

We should never make predictions, especially about the future.Will Rogers, American Cowboy Philosopher, 1879–1935.

Use of forecasts in decision-making is inevitable

A forecast is a prediction, assumption, or viewpoint on some future event orcondition, usually as a basis for taking action. The use of forecasts in business,as in most of life, is widespread and seemingly inevitable. William Sherden(1998) in The Fortune Sellers notes that ‘‘The desire to know the future is adeep human psychic need. It lies at the foundation of virtually every religioncreated . . .’’ (p.1). He goes on to argue that ‘‘Though the title of ‘second oldestprofession’ usually goes to lawyers and consultants, prognosticators are therightful owners.’’ (p.2).

Assumptions about future demand and performance are essential for manybusiness decisions, for example: how much to produce; how much capacityand other resources to acquire; what products to develop; and how muchfinancing will be needed by the business. Sometimes managers use the ‘‘naive’’forecast of assuming that the future will be like the past, or the past trend. But,more often, companies devote significant effort to estimating future demand

aJames M. Lyneis, Pugh-Roberts Associates, 41 William Linskey Way, Cambridge, MA 02142, USA.

4 System Dynamics Review Volume 16 Number 1 Spring 2000

for their products, and to determining the consequences of that demand onbusiness performance (e.g., via spreadsheets and accounting models). Sherden(1998) estimates that the forecasting industry, broadly defined, generates $200billion a year in revenues. A search of the Dialogweb database revealed morethan 500 publications and journals with ‘‘forecast’’ or ‘‘forecasting’’ in theirtitle.

While the use of models for forecasting is widespread, there is reluctance inthe system dynamics community to encourage the use of system dynamicsmodels for forecasting. In one of the first publications in the field, Forresterargued that ‘‘. . . a dynamic model should be used for determining the behaviorcharacter of a system but not its specific state.’’ (Forrester 1961, 436). He laternoted that ‘‘. . . forecasting is not an appropriate or valid test for either aneconometric model or a system dynamics model, and . . . one should examinemodels in the context of how different policies within the model change thenature of ongoing behavior.’’ [Forrester, 1980, p. 574]

Several factors seem to underlie the viewpoint that models should not beused for forecasting the future:

1. Forecasts are likely to be wrong. Inaccuracies in forecasts of economicgrowth and inflation are widely documented in the business press, and inmore scholarly studies (Sterman 1988; Sherden 1998). Some of this errorcan be attributed to inaccurate or overly simplistic models. However, asForrester clearly demonstrated, even an accurate model can produce pointpredictions that diverge from reality as a result of unknowable randomelements impinging on a system (Forrester 1961, Appendix K).

2. Forecasts are a part of a system’s decision structure, and therefore cancontribute to problematic behavior. Forecasts that involve trend extra-polation increase a system’s instability (Lyneis 1980). In addition, actionstaken on the basis of forecasts that mis-estimate demand create adverseconsequences. Underestimates of demand can lead to self-fulfilling proph-ecies as feedbacks, often through product or service availability, drive salesto equal the capacity provided to meet the forecast (Lyneis 1980). The busi-ness press contains numerous examples of such behavior. Decisions taken onthe basis of overestimates of demand can lead to over-capacity and financialdifficulties. Barnett (1988) cites examples from the electric utility industryin the 1970s, the petroleum industry in the 1980s, and the personal computerindustry in the 1980s.

3. There is a desire to shift managerial emphasis to understanding and policydesign. As a result of forecast inaccuracies and potential misuse in decisions,many system dynamics practitioners desire to shift managerial emphasis

James M. Lyneis: Market Forecasting and Structural Analysis 5

away from forecasting and towards understanding and policy design. Ster-man argues that ‘‘. . . the purpose of modeling is not to anticipate and reactto problems in the environment, but to eliminate the problems by changingthe underlying structure of the system.’’ (Sterman 2000, pp. 655–6).

It seems to me that blind faith in either extreme (forecasts are or can beaccurate; use of forecasts should be avoided) can lead to suboptimal perform-ance, if not disaster. Accurate and consistent prediction, on a point basis, offuture conditions has not been, nor is likely to be, possible, and reliance on thepoint accuracy of a forecast too often leads to significant wasted expenditureand poor decisions (not preparing for alternatives; self-fulfilling prophecies,etc.). On the other hand, the belief that one cannot predict the direction andapproximate magnitude of important variables is also wrong. It can lead toreacting rather than anticipating, and/or designing policies for too wide a rangeof potential conditions (such that overall performance is sub-optimal).

Business will inevitably use assumptions about the future as a basis for mostdecisions. The proper use of system dynamics models for market ‘‘forecasting’’and structural analysis can therefore add value to clients in several ways:

1. System dynamics models can provide more reliable forecasts of short- tomid-term trends than statistical models, and thus lead to better decisions.In many systems, structural momentum dominates over ‘‘noise’’ in the shortterm. While it may not be possible to change the system’s behavior in thistime frame, actions can certainly be taken to improve a company’s situationduring this period.

2. System dynamics models provide a means of understanding the causes ofindustry behavior, and thereby changes in industry structure, as part of anearly-warning or on-going learning system.

3. System dynamics models allow the determination of reasonable scenariosas inputs to decisions and policies.

These points are illustrated with a case example from the commercial jetaircraft industry. The discussion and focus in this paper is on system dynamicsmodels of industries or markets, for example, automobiles, chemicals, com-mercial aircraft, etc. These models are usually developed instead of or inaddition to company-specific models, because:

, Understanding demand is important and involves complex dynamics., The decisions to be taken, based on the forecasts, are (seemingly) obvious., Market models are less threatening, and more standard procedure, than com-

pany models (especially ‘‘policy’’ or strategy models).


Case example—worldwide commercial jet aircraft industry

Structural dynamics of the commercial aircraft industry

The commercial aircraft industry, now dominated by Boeing and Airbus, haslong experienced boom and bust cycles. Our work in this industry was con-ducted between 1987 and 1992, first for a manufacturer of aircraft, and laterfor a supplier of parts. As the work is no longer commercially sensitive, I candiscuss some of the details. In addition, I have updated the model assumptionsto reflect recent trends in the industry.

The problem faced by the manufacturer is illustrated in Figure 1—orders forcommercial jet aircraft exhibited highly cyclical behavior. At the time the pro-ject began, critical questions were: Are orders at another peak? Should we beadding more capacity? When will be the best time to introduce the next gen-eration of aircraft (i.e., when will the market bottom and grow again). Inaddition, the client was interested in a number of alternative scenarios: Howwill future orders, in total and by size category, be affected by the speed andsuccess of ‘‘liberalization’’ of the European airline industry. By growth in the‘‘freight’’ business? By future oil prices and economic conditions? And from amore academic perspective: What causes industry cycles? To what extent arethey due to external changes? To internal industry structure and decisions?

Fig. 1. World-wideorders for new aircraftare highly cyclical


Fig. 2. Structure of theindustry as a supplychain

As illustrated in Figure 2, the industry has characteristics similar to a supplychain or production–distribution system. Starting at the left of the figure, econ-omic conditions (GDP, personal income) affect passenger demand for businessand recreation. Demand for travel drives changes in the airlines’ fleet util-ization. Changes in fleet utilization, in turn, causes airlines to change theirorders for aircraft, and so on down the chain. Figure 3 shows that the systemcreates amplification down this chain. Demand growth fluctuates more thanchanges in GDP; airline orders vary significantly more than the fluctuations indemand growth.

The feedback structure of the industry, at the simplest level, is illustrated inFigure 4. There is one major ‘‘negative’’ or balancing feedback loop and threeamplifying positive loops. Contained within these loops are several importantstocks—travel demand; order backlog; airline capacity (available seats, an agingchain); fares; and manufacturer production capacity. While the dynamics ofthese feedbacks have been described in detail elsewhere (Lyneis 1999; Lyneisand Glucksman 1989), it is worth noting several key features. First, the sig-nificant delays around the major controlling loop inherently produce ampli-fication of cyclical variations in economic conditions, in part because theairlines in total fail to completely account for what is in the supply change,and in part because of amplifying factors noted below. Second, forecasting andcompetitive policies of the airlines increase amplification around the majorloop. Empirical evidence from calibrating the model indicates that airline fore-casts are largely extrapolations of recent trends. Such extrapolation is one


Fig. 3. Amplificationdown the supply chain:(a) demand variationsexceed GDP variations;(b) order variationsexceed demandvariations


Fig. 4. Basic dynamics ofthe industry

source of amplification. Competition for market share in business upturns cre-ates additional amplification. Many airlines, all trying to gain share, can easilyproduce a situation in which the demand they are trying to fill significantlyexceeds 100% of the market. And third, the three reinforcing feedbacks notedin Figure 4 further amplify key components—demand changes are reinforcedby experience effects; demand changes are reinforced by the influence of unitcosts on fares (a significant percentage of airline costs are fixed in the shortterm); and aircraft ordering is reinforced by the need to project further ahead,and to compete for delivery slots, as delivery lead time for aircraft increases.Together, these feedbacks produce a boom-like over-expansion in the industry,with a resultant later bust.

The dynamics just described are the essential causes of cycles in the aircraftmanufacturing industry. External factors, denoted by the ovals in Figure 4, caninfluence the dynamics: GDP, population, fuel and other prices, interest rates,and the target fleet utilization of the airlines. While these external factors affectindustry behavior, as shown below, they are less important than the internalindustry dynamics.

There are, however, additional feedbacks affecting the industry. As illus-trated in Figure 5, the used aircraft market often acts to amplify cycles. Whennew, replacement aircraft are delivered, the stock of used aircraft for sale


Fig. 5. Used market andfinancial dynamicsreinforce cycles

increases. This creates a supply to absorb further demand, and acts to depressprices and encourage the purchase of used rather than new aircraft, therebyprolonging a downturn. In addition, financial dynamics (cash flow and pro-fitability) act to reinforce cycles—when the industry is in an upswing, highprofits and cash flow encourage investment, and conversely when the industrydeclines.

While the dynamics described above create the cycles in the aircraft industry,such a simple model would not have served the decision needs of the aircraftmanufacturer. Detail and calibration were necessary to answer questions aboutthe timing and size of the peak, the need for more capacity, and the prospectsfor particular size categories of aircraft. Detail was added to the model (referback to Figure 2): demand was disaggregated into domestic and internationalcomponents (different size and operating characteristics of the aircraft) andinto major regions (because of significantly different growth potential). Airlineswere similarly disaggregated by region. The numerous prime manufacturersthat existed at the time were represented. However, the same basic feedbackstructure underlies the detail.

In some cases forecasting policies are built into a model, and in others theyare represented by ‘‘exogenous’’ decision inputs. In this model, the forecastingof travel demands and aircraft required by the airlines was built into the


decision structure of the model. However, forecasting by the manufacturers forcapacity expansion was not included dynamically in the original version ofthe model (it was later added for the work done for the parts supplier). Rather,because a manufacturer was the client, manufacturing capacity was inputexogenously as a means of testing alternative expansion strategies and scenarios(‘‘How much capacity should we add?’’ ‘‘When, and in what sizes?’’ ‘‘What ifthe other manufacturers expand more, or less, aggressively?’’).

Forecasting industry cycles

Because of the severity of the industry cycles, and the costs of adding newcapacity and introducing new aircraft, manufacturers and industry associationsdevote considerable effort to forecasting aircraft orders. These efforts fall intotwo categories: (1) top-down, statistical time series and econometric models;and (2) bottom-up, aircraft life-cycle models. The statistical models, describedin more detail below, typically forecast aircraft orders as a function of pastorders (time-series analysis), or of changes in economic conditions (e.g., econ-omic growth, fuel prices, etc.). They are primarily used for short-term fore-casting. The bottom-up models, at their simplest, use regression forecasts toestimate travel demands and the need for additional aircraft, and detailed fleetmodels to estimate retirements. More sophisticated versions track the routestructures and flight requirements of individual airlines. These models areprimarily used for long-term forecasting.

In spite of these large efforts, industry models have not been good for short-to mid-term forecasting. According to a respected industry journal:

Manufacturers’ macro forecasts of long-range product demand have beenaccurate through the years . . . Manufacturers’ 1–2 year market assess-ments have been abysmal . . . There are numerous examples of productiontaps being closed off prematurely . . . The result has been lost sales orslowing down production too late to avoid interest charges from ‘‘whitetails’’, or unsold new aircraft, because an upturn was foreseen but failedto develop on schedule. (Aviation Week and Space Technology 1995c).

As illustrated in the next section, industry forecasting models have not done agood job of forecasting because these models do not capture the structure ofthe industry which creates behavior over time:

, reinforcing and balancing feedback loops;, stocks and aging chains, which create delays and inertia; and, non-linearities in decisions processes, e.g., hysteresis in demand, extra-


polation, increased competition during boom times, financial constraints dur-ing downturns, etc.

While ‘‘judgmental’’ adjustments to econometric forecasts are often made toaccount for some of these factors, structural modeling allows this to be donein a more rigorous, consistent and scientific fashion.

The system dynamics model was calibrated to historical data, and used toproduce a forecast of future orders by the airlines. Initial simulations with themodel in 1987 indicated that the peak in orders was at hand (as indicated inFigure 1). However, although hard data for the industry was not yet available,anecdotal evidence and order data at the one manufacturer indicated that ordersfor aircraft were still increasing. The manufacturer did not believe that itsmarket share was increasing. This discrepancy between forecast and ‘‘data’’initiated further discussions with marketing and sales staff at the manufacturerand several airlines. These discussions indicated that an important structuralchange was occurring: leasing companies, which had previously been strictlyfinanciers of aircraft ordered by the airlines, were now placing significant ordersfor their own ‘‘fleet’’, to be leased to the airlines on an operating basis. As aresult, structure was added to the model to reflect this change. Two key assump-tions were required to represent this: (1) the ‘‘market share’’ targets of theleasing companies; and (2) how long it would take for the airlines to reflect thischange, and what fraction of this capacity they would include in their orderingdecisions. Best estimates were obtained from the manufacturer, and this becamethe basis for the ‘‘Base Case’’ forecast shown in Figure 6.

With the detailed and calibrated model, the peak and subsequent downturnwere accurately predicted. As a result, the manufacturer avoided unnecessarycapacity expansion because it was clear that a significant portion of the ordersin the 1989 peak were positioning or double orders, and would be cancelledor delayed when the bottom fell. The manufacturer was also able to introducea new family of aircraft into the upturn. Having a detailed, calibrated modelthat produced accurate forecasts resulted in better decisions and significantsavings to the manufacturer.

System dynamics models can provide more reliableforecasts than statistical (non-structural) models

The system dynamics model was able to quite accurately forecast the cyclicalpeak, and the subsequent downturn. While not all forecasts turn out as accurateas those shown in Figure 6, a system dynamics model offers the potential for


Fig. 6. Base case forecast

greater accuracy than statistical models. Statistical models tend to be of twotypes. One, time-series models in which forecasts are based on averaging and/ortrends in the underlying data stream. In these models, there is no attempt tounderstand the underlying structure of the industry that created the datastream. And two, regression/econometric models. In theory, the statistical tech-niques underlying these econometric approaches are a means of estimatingparameters for any structural model, including system dynamics models. Inpractice, limitations with the availability of data and with the estimation tech-niques themselves (e.g., the need for data without measurement error) producemodels that are based largely on macro-economic factors (see Mass and Senge1980 for a discussion of statistical techniques applied to dynamic models).

As a result, statistical models largely ignore industry structure. But in manyindustries, behavior is determined more by industry structure than by changesin exogenous, macro-economic factors. Therefore, structural models offer thepotential for more accurate forecasts. In addition, the dominance of structureoften allows structural models to produce reasonable short to mid term fore-casts in the face of noise and uncertainties in the exogenous inputs.

An example using trend extrapolation and simple regression will illustrate.Sterman (1988; 2000) demonstrates that many forecasts, especially those thatrely on judgment or judgmental adjustments, are accurately captured by extra-


Fig. 7. Trend forecastwith 3-month average, 1-year average, and 2 yearhorizon (in order tocompare to actualdemand, the forecastshown for any givenperiod in time is thatgenerated by the trend 2years earlier

polations of trends in the data. He presents equations for determining suchtrends, in which a ‘‘forecast’’ of the current condition is determined by takingthe perceived present condition and adding to it the trend extrapolated forwardover some projection period. If the projection period is the averaging time forthe perceived present condition, then the forecast simply attempts to recoverthe current condition of the system (in this case the data). This is illustratedby the Trend Forecast—3 Month Average shown in Figure 7. The data for ordersare taken as the actual condition of the system. In the aircraft industry, data onorders are reported regularly and therefore a three-month averaging time isreasonable. Using a four-year time constant to determine the referencecondition, and then extrapolating forward the three months, the ‘‘trend fore-cast’’ of the present orders is quite good.

However, trend extrapolation begins to produce less accurate ‘‘forecasts’’under two conditions. First, when it takes longer to obtain the data, the ‘‘fore-cast’’ of the current condition begins to noticeably lag the actual (for example,Figure 7 shows that with a one-year, rather than three-month averaging time,the forecast is less accurate). And second, when we need to look further intothe future, extrapolating the trend over a longer period inevitably increases thevariability of forecasts in a cyclical industry. A manufacturer of aircraft, or asupplier to manufacturers, needs to know airline orders for aircraft several


years into the future in order to plan production and obtain additional capacity.Figure 7 also illustrates the consequences of extrapolating two years into thefuture. Such a forecast, with a cyclical change in the input, significantly devi-ates from the true condition.

Can a regression model do any better? Several relatively simple models weretested: aircraft orders (as a fraction of the size of the fleet) as a function of GDPgrowth (as a measure of economic activity) and fuel price (as a key driver ofcosts and therefore fares). The models tested are listed below with the summaryR2 given in parentheses:

, GDP growth (R2 = 0);, GDP growth lagged one year (R2 = 0.2);, GDP growth lagged two years (R2 = 0.39);, GDP growth lagged three years (R2 = 0.07);, GDP growth lagged two years, fuel price change (R2 = 0.70);, GDP growth lagged two years, fuel price change lagged one year (R2 = 0.55).

Figure 8 illustrates a simulation using the model that produced the best fit:

Orders/Fleet = 3.66+1.36* GDP Growth Lagged 2 Years −0.04 Change inFuel Price.

Except for the early years, the fit to historical data from 1977 on is good. Note

Fig. 8. Regression fit overhistorical data period


Fig. 9. Forecastsproduced by the threemethods at beginning of1988

that the intercept of 3.66 would correspond to a replacement demand averaging27 years, which is reasonable though a little low.

How successful are these trend, regression, and simulation models in fore-casting forward? Figure 9 compares forecasts from 1988 to 1994 for the threeapproaches, as if they were made in 1988 but using actual GDP growth overthe entire period for the regression model, and actual orders data for the trend.(The system dynamics forecast is labeled ‘‘Base91’’ because the assumptionsabout future external conditions were updated in that year. Regrettably, I donot have a record of the assumptions used to produce the original 1988forecast.) Only the system dynamics simulation does a reasonable job of fore-casting. Because of the need to extrapolate, the trend forecast significantlyovershoots the peak. Because the changes in GDP and fuel prices were notsignificant (and do not drive industry dynamics), the regression forecast com-pletely misses the peak.

In a dynamic industry, a well-calibrated model that captures those dynamicscan be an accurate short- to mid-term forecasting tool. Such models tend to beinsensitive in the short-term to exogenous driving inputs such as GDP or oilprices. For example, Figure 10 shows the forecast produced by the model forseveral different input assumptions:

, ‘‘flat’’ GDP growth between 1987 and 1995, at the actual average for thoseyears;


Fig. 10. Sensitivity offorecast to inputassumptions

, a larger cycle in GDP, closer in amplitude and timing to historical cycles thanthat which actually occurred; and

, a decline in real oil prices of 1% per year (close to what actually happened),rather than the assumed increase.

The forecast is largely insensitive to these inputs.However, the forecast is sensitive to industry dynamics. Figure 11 compares

the Base Case to two simulations in which key drivers of industry dynamicswere neutralized from 1987 on:

, the leasing companies as owners of aircraft were removed; and, manufacturing delivery delay remained at the 1987 value.

In both cases, the ‘‘forecast’’ provided by the model would miss the peak andtrough significantly, both in timing and amplitude.

System dynamics models provide a means ofunderstanding the causes of industry behavior

If a well-calibrated model is capable of providing very good short- to mid-termforecasts, then that model becomes a means of detecting changes in industry


Fig. 11. Sensitivity offorecast to industrydynamics

structure. As new data and other information become available, they are com-pared to the model’s forecast. When significant deviations are detected, themodel provides a means of determining the source of the deviation. If sufficienttime has passed since the last model update, it is possible that changes inexternal inputs (‘‘noise’’) may have caused the simulation to deviate from actualbehavior. Alternatively, industry structure may have changed in some way. Forexample, the sensitivity of the airlines to growth trends, profits, and deliverydelays may have changed, perhaps because there are many new entrants, orbecause the industry has consolidated.

One example of such structural change, the emergence of the leasing com-panies as owners of aircraft, was described above. In that case, the changerequired adding new structure to the model. Another example was the deregu-lation of the US industry in 1979. Representing this required changing a numberof parameters in the model that reflect airline decision-making, including air-line preference for flight frequency versus larger aircraft, and the impact ofcompetition on target load factors, operating margins, and ordering sensitivity.As the industry seems to be reconsolidating, some parameters in the modelmay again need to be changed to reflect this.

Any model must be updated regularly if it is to remain current and if truelearning is to occur (see Lyneis 1999 for discussion of strategy management).


The forecast of slow recovery being produced by the system dynamics modelin 1991 (Figure 11) was consistent with most industry forecasts, even in the1995 time period. For example:

Boeing forecast that orders ‘‘will remain subdued until well into the nextcentury. (Financial Times 1995)

[Industry analyst] Greenslet believes it will be 2000 before there is anyglobal ‘surge’ of orders. Moreover, some officials estimate that the priceof new aircraft will have to come down substantially to trigger such awave. [‘‘U.S. Airline Profitability May Be Short-Lived’’, (Aviation Weekand Space Technology 1995b)].

However, even by the end of 1994 the simulated demand was below the data.Had we been actively engaged with a client and updating the model and dataduring this time period, this deviation would have raised warning flags.Updating the economic inputs to reflect actual data between 1991 and 1994did not improve the fit. Further analysis indicated that the simulation wasunderestimating demand, primarily because of an exogenous effect from theassumed impact of ‘‘congestion’’. During the late 1980s and early 1990s, con-gestion at airports and in the air traffic control systems of the world (primarilyEurope and the USA) was causing significant disruption to the on-time per-formance of the airlines. Management at our client aircraft manufacturer specu-lated that this congestion would get worse and begin to affect travel demand.Hence, the exogenous effect was added. However, it rather appears that pass-engers and airlines adjusted to the increased traffic without significant impacton demand. In the USA anyway, airlines increased the scheduled duration oftheir flights to reflect this congestion. So, while the flight times were the same(if not longer), passengers’ perception of the delays (late arrivals) was probablyreduced (at least until recently).

Removal of this exogenous effect produces the simulation labelled Base94 inFigure 12. The actual data, as it subsequently transpired, has been added to thefigure. While the Base94 forecast turns at about the correct time, in retrospect itlags the speed of the early run-up in orders. Why? There are a number of possiblereasons. Again, had we been working closely with industry managers, we mighthave fine-tuned the forecast, or at least developed alternative scenarios, at thattime. One possible explanation for the accelerated order pattern, once the recov-ery started, could be the ‘‘early’’ retirement of aircraft due to noise regulations.Certainly such speculation was noted in the industry press at that time:

The market forecasts of the Boeing Commercial Aircraft Group for the restof this decade continue to be driven by looming aircraft noise requirements,


Fig. 12. Updatedforecasts

Meskill [Boeing market outlook project director] said . . . The order upturn,when it comes, likely will be pronounced. U.S. carriers must operate all-Stage 3 fleets by Dec. 31, 1999. European carriers are under similar pressure,although they have until 2001. (Aviation Week and Space Technology 1995a)

Assuming accelerated retirement of old aircraft, and adding an Asia crisis, pro-duces the Base98 forecast shown in Figure 12. Much of the difference in the peakbetween Base94 and Base98 results from the accelerated retirements in 1995 and1996, although the Asia crisis has a more significant impact than would normalGDP variations because of the severity of the crisis in a high-growth region.

Used in this way, a system dynamics model becomes an effective tool formerging structural theory, data, and expert judgments about external eventsthat impact the industry. The purpose of such use of forecasts is to fosterimproved, early understanding of changes in the environment, as a guide fordesigning adaptive mechanisms.

System dynamics models allow the determination ofreasonable scenarios as inputs to decisions and policies

System dynamics models can provide important inputs to specific businessdecisions and to the design of policies that improve performance. Every


decision or policy is based on some assumption, or forecast, about the future.What assumption should be made? One of the dangers of forecasting is thatpeople focus too much on one prediction, and fail to recognize the uncertaintyin that prediction. A structural model can help avoid this. By understandingwhy, we can also determine ‘‘why not’’—what are the risk factors that mightsignificantly alter the forecast. This is more than simply putting a probabilitydistribution around uncertain inputs and computing a probabilistic range forthe outputs. In an industry where structure dominates, this is likely to resultin a narrow distribution, and does not lead to insight regarding which factorsare really important in affecting behavior.

Understanding of dynamics, and the ability to do simulations and full sen-sitivity tests, allows us to:

, determine those uncertainties to which the forecast is most sensitive—thereal risks (e.g., excess capacity, capacity shortages) and ask what might causedemand to change in such a way to cause this risk to occur;

, provide more reliable, or better thought out ranges for the ‘‘forecast’’ andscenarios, given the key uncertainties; scenarios are not what will happen,but what could happen; and

, identify factors to monitor that can provide early warning or risks or alter-native scenarios.

An understanding of the drivers of industry performance can be important for‘‘one-off’’ types of decisions. For example, when we made our initial pro-jections with the model in 1987–1988, assumptions regarding leasing com-panies could only be estimated. However, recognizing their importance to theforecast, the client examined a plausible high–low range. This is illustrated inFigure 13. While the precise assumption affects the point forecast, it was clearfrom these results that with high degree of certainty the industry would experi-ence significant over-ordering in the 1989–1990 peak. This gave the client theconfidence to abandon plans to add significant extra capacity. But it also poin-ted out an assumption that needed to be closely monitored.

As another example, the aircraft manufacturers face the decision of whetheror not to build a ‘‘super-jumbo’’ (an aircraft with 550 to 650-plus seats, muchlarger than the Boeing 747). This is not a decision that is subject to traditionalsystem dynamics policy design. A model that captures the drivers of trafficgrowth on long-haul international routes (potentially including competitionfrom alternatives such as tele-conferencing) and the pressures that affect sizeof aircraft versus frequency of flights can help manufacturers make a moreinformed decision, certainly better than trend extrapolation and regression


Fig. 13. Sensitivity offorecast to leasingcompany assumptions

models. A structural model can also enhance mental models to the mutualbenefit of both when speculating about future trends.

Policy design also involves assumptions about the future. In some situations,policies can be improved regardless of the forecast. For example, in the ‘‘BeerGame’’, a fundamental cause of instability is that almost all those who play failto fully account for orders they have already placed and are in the supply line(Sterman 1989). To the extent that this failure also reflects a cause of ampli-fication in the industry, then adding a ‘‘supply-line correction’’ to the rule forordering beer from your supplier is a good thing to do, regardless of whatelse is happening in the system or external environment—everybody wins.However, once we have made this improvement (and perhaps others such assharing information or removing stages), additional incremental improvementsare likely to depend on what range of demand we expect. One ultimately getsto a tradeoff between stability and ability to respond to growth. In the BeerGame system, stability is improved by increasing response times (order aver-aging and inventory correction times). But sluggish response in the face of largeswings in customer orders, and general growth, can result in stockouts (andlost market share in the real world). This can be compensated for by carryingadditional inventory. But what is the best tradeoff between stability and inven-tory levels? Clearly it depends on the pattern of customer orders, and the better


one understands the likely range of orders the closer one will come to the‘‘best’’ policy. If future demand is likely to be dominated by growth, then shortresponse times and/or high inventory objectives make sense. If future demandis likely to be dominated by ‘‘noise’’, then long response times may be best.Having a good forecast and set of scenarios can improve the performance ofthe business. Of course, the model and forecast must be continually updatedto detect basic changes that might call for a shift in policies.

As a real example, going into the last downturn, the aircraft market model wasused to establish a likely range of scenarios to guide production and inventorydecisions by a supplier to the aircraft manufacturers. In prior downturns, theparts supplier cut production (laying off people and cutting orders to theirsuppliers) in order to avoid building any inventory. But in the subsequentupturns, the supplier often had difficulty rebuilding its workforce and partsinventories. Scenarios from the market model were used to test the performanceof the company’s manufacturing to different work-smoothing and inventory-building options (using a system dynamics model for the company’s manu-facturing). The forecast range from the model, and more importantly, thereasons for the forecast differences, narrowed the likely range such than an‘‘optimal’’ policy for the likely range of demand could be determined. Althoughour client did not go as far as we felt was justified, the power of the logic ofthe forecasts gave them the courage to adopt a new, performance-improvingpolicy for bridging the downturn.

System dynamics models allow the determination of appropriate buffers andcontingencies that balance risks against costs. Forecasts will be inaccurate, andthe successful companies will be those that recognize this and provide thenecessary buffers and contingencies. However, most buffers and contingenciesinvolve costs, and therefore some idea of the range of uncertainty with whichthey have to operate would allow companies to design cost-effective buffers.

Conclusions

All business decisions are based on forecasts, or assumptions about the future.By capturing the causes of industry dynamics, system dynamics models canprovide better forecasts than traditional approaches. In and of itself, this shouldallow managers to make better decisions. But in addition, the use of systemdynamics models for forecasting allows managers to:

1. get an early warning of industry structural changes;2. identify key sensitivities and scenarios; and


3. determine appropriate buffers and contingencies for forecast inaccuracies.

These benefits can further enhance business performance.The examples presented in this paper demonstrate the value of system

dynamics models in forecasting and understanding the dynamics of market orindustry models. In addition, system dynamics models can provide effectiveforecasting of internal company performance, and of the performance improve-ment that should result from strategic initiatives, investments, or policy chan-ges. In many cases in our consulting work, the structural system dynamicsmodel produced consistently better forecasts than other company models.

Acknowledgements

A number of colleagues contributed to the success of the cases discussed: RickPark, who passed away November 1998, and to whom I would like to dedicatethis work; Bill Dalton, of Pugh-Roberts; and my former colleagues MauriceGlucksman and Henry Weil (who participated in the earlier work described inLyneis and Glucksman 1989).

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