maintaining service quality under pressure from investors: a systems dynamics model as a hands-on...

~ Pergamon European Management Journal Vo[. 15, No. 2, pp. I28-137, I997

© 1997 Elsevier Science Ltd All rights reserved. Printed in Great Britain.

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Maintaining Service Quality under Pressure from Investors: a Systems Dynamics M o d e l as a H a n d s - o n Learning Tool ANN VAN ACKERE, Associate Professor of Decision Sciences, London Business School; KIM WARREN, Associate Professor of Strategic ManagemenL London Business School; ERIK LARSEN, Marie Curie Fellow, Department of Management, University of Bologna

This article, by Ann van Ackere, Kim Warren and Erik Larsen discusses how system dynamics models can help understand a service company's growth potential and its limitations, as well as the use of such models for executive training. The ideas are illustrated using a model built for a European restaurant chain which grew close to 200 outlets in less than a decade. The model is being used for educating the emerging generation of managers who will have to cope with the tensions described in the model. The authors use the model to study how management policies affect the achievable rate of growth. The issues discussed are relevant to any service based company facing the problem of maintaining and improving service quality against the pressure of performance expectations set by shareholders or corporate owners. © 1997 Elsevier Science Ltd

Introduction

System dynamics simulation can improve understanding of managerial issues. Van Ackere et al focused on the use of this methodology in the context of business process redesign. Here we illustrate how system dynamics can

help understand a service company's growth potential and limitations by focusing on a European restaurant chain, which grew to about 200 outlets in less than I0 years. Our model studies the impact of service quality and the budgeting process on the acceleration and slowing down of growth.

Over the last I0 years the use of simulation models has expanded from focusing only on predicting the future state of a system, to also being used as a tool to help a team of managers understand the company's problems and opportunities, both current and future. This new use of simulation models, often in direct collaboration with a management team, has been shown to greatly enhance the team's understanding of and ability to manage dynamic complexity. See for example Morecroft (1992).

The system dynamics approach is appropriate when a system is known to contain, and be heavily influenced by critical 'levels' (number of restaurants, quality of menu, service and environment, and investment support), which are known to adjust slowly over time and to decay, and when dynamic feedback is known to occur, (e.g. marketing expenditure drives customer numbers up, this drives crowding up, thus driving customer numbers down again). System dynamics is

128 European Management JournalVo115 No 2 April 1997

MAINTAINING SERVICE QUALITY UNDER PRESSURE FROM INVESTORS

Customer ~ Base " ~ ~)

0 Customer Service . ~ Satisfaction Quality S ~ ~ , ~ Number of staff

Goal

Im

Time

Figure 1 Examples o f a B a l a n c i n g L o o p and Characteristic Behaviour

particularly suitable for capturing such level-changes and feedback in an intuitively simple manner. Another advantage of system dynamics is its ability to take 'soft' variables into account. Most system dynamics models incorporate such variables, and the model described here explicitly represents customers' perception of restaurant crowding and service quality as well as corporate expectations of profit growth. Other models have focused on issues of morale, customer satisfaction, quality etc.

While system dynamics was founded over thirty years ago (see for example Forrester, 1961), there has been a renewed interest in applying system dynamics to business policy and strategy problems during the last 10 years. This interest has been driven by a number of factors. New, user friendly, high level graphical simulation programs (ithink, Powersim and Vensim) 1 have been developed, enabling the decision maker (or the model user) to get a better understanding of the model. New popular books describe the use of the method and its results. Examples include Morecroft and Sterman (1994) and Senge (1991). Finally, a process has been developed which allows the user of a model to get actively engaged in its development. This process has been used extensively over the last 10 years and has been shown to be very effective, as documented in Morecroft and Sterman (1994).

Applications can be found in a variety of industries, for example in health care (Ittig, 1976), energy (Ford, 1987; Morecroft and van der Heijden, 1992), utilities (Bunn and Larsen, 1992; Bunn and Larsen, 1994), chemicals (Sterman, 1988), transport (Gottschalk, 1983; Sterrnan, 1988), telecommunications, (Lyons, 1994; Lyneis, 1994), bio-technology (Morecroft et ai.,1991), financial services (Senge and Sterman, 1992), and Business Process Re- engineering (van Ackere et al., 1993).

using simple examples, which turn out to be building blocks of the restaurant chain growth model discussed below. For a more complete overview of system dynamics, see Morecroft and Sterman (1994) and Senge (1991).

To understand complex business situations, system dynamics uses causal loop diagrams to represent relationships between concepts. As these maps are developed, 'loops' are 'closed' to recreate the behaviour observed in the real system. Consider the relationship between service quality and customer satisfaction. A 'linear' way of thinking would state that as service quality increases, customer satisfaction grows. A system dynamics view of the same problem would close the loop as in Figure 1. This loop adds the key observation that the increase in customer satisfaction subsequently leads to a decline in service quality.

An additional concept, customer base, has been added. For a given level of resources (say number of staff), higher customer satisfaction leads to more customers and thus a decrease in service quality (unless resources are increased). The notation S and O in Figure 1 is the system dynamics way of labelling the direction of a relationship between two concepts. If the link is labelled S, the two concepts 'move' in the same direction, i.e. if Customer Satisfaction goes up (down), then Customer Base goes up, (down). If the link is labelled O the two concepts 'move' in opposite directions, i.e. if Customer Base goes up Service Quality goes down. The double bar (\\) on the connection between Service Quality and Customer Satisfaction indicates a delay. Such a loop is called balancing or goal seeking. This is indicated by a B in the centre of the loop. The reason for this can be seen on the graph in Figure 1 which shows the typical behaviour of a balancing loop. The delay causes the loop to overshoot and undershoot its goal before reaching the equilibrium state.

System Dynamics

The main ideas and concepts of the system dynamics approach are briefly discussed here. These are illustrated

A second type of loop is known as reinforcing. Consider for instance the relationship between menu attractiveness and a restaurant's profitability, as shown in Figure 2.

European Management JournalVo115 No 2 April 1997 129


Customer Base

Profitability

Menu Attractiveness

S Spend on menu

development

Time

Figure g Example of a Reinforcing Loop and Characteristic Behaviour

Higher profitability enables management to invest more in menu development. This leads to a more attractive menu and therefore more customers, which in turn leads to higher profit. Whereas the loop in Figure 1 (a balancing loop) had a goal seeking behaviour (the goal might be implicit or explicit) the reinforcing loop has no limitations. In isolation this loop exhibits exponential growth as shown in Figure 2. This is indicated by an R in the centre of the loop. The number of customers keeps increasing and soon exceeds realistic numbers. When there is a reinforcing loop, therefore, there must be one or more balancing loops to prevent indefinite growth.

A number of structures which recur frequently in business situations are commonly known as 'archetypes' (see Senge, 1991). These archetypes are a combination of reinforcing and balancing loops. A common archetype, known as limits to growth, is shown in Figure 3. This archetype combines the loops from Figures 1 and 2 into a single structure. Initially the reinforcing loop 'drives' the development, yielding exponential growth as in Figure 2. But the rapidly growing customer base causes service quality to deteriorate, and customers become less satisfied. The growth of the customer base slows down, resulting in stagnation or even decline.

The Growth of the Restaurant Chain

The model developed here reflects the history of a real business - - the Beefeater restaurant chain - - which started around 1980 and grew to dominate the UK market for mid-priced restaurants over the subsequent decade. The first restaurants were developed from a few of the 6,000 pubs owned by the UK brewer Whitbread plc. Attractive, suburban sites were substantially redeveloped, and equipped with some 60-100 seats and an open kitchen area, all within a radical and dramatic design theme. A simple, but interesting menu was offered, together with intensive and friendly service, and frequent redecoration and restyling of the restaurants occurred. Some sites were so busy that, on Friday and Saturday evenings, each seat might be 'sold' three times.

These first sites, being some of the best available, generated spectacular returns, and Whitbread allocated considerable resources to grow the chain. With this support, and access to more of the company's best sites, Beefeater grew rapidly to over 100 units. However, several pressures began to arise, both from the market and from within the company itself.

Number of staff

S

Service 0 S Quality " ' - " ~ S

Customer

Customer Satisfaction

Profitability

Base ~ Spend on menu development

Y ~",-.~ Menu J Attractiveness S

Figure 3 Example of a ~Limits to Growth' Archetype

].30 European Management JoumalVo115 No 2 April 1997


As the chain grew, the sites available to it were increasingly of lower quality than the early units, so that more effort had to go into product and service quality to maintain customer numbers. At the same time, the early units were deteriorating. The busier they were, the faster they deteriorated and the more maintenance was required. Meanwhile, consumers were beginning to take for granted the quality of food and service, so money was needed for developing the menu. This spend was also needed to extend the restaurants' appeal to new groups of customers, such as families and older adults. These new groups were a particularly useful addition to the customer base, since they were available at times outside the traditional trade peaks.

The money to be spent on supporting the business had to come from the chain's profitability and growth. Head office, accustomed to strong profits growth and good returns, continued to hold high expectations, which the chain's managers strived to meet. The temptation was to cut back on service staff, on restaurant maintenance and on menu development to meet budget targets and thus win the capital needed to drive further growth. Price increases were another possible means of boosting profits. These pressures were exacerbated by the 1985 downturn in consumer spending. However, by thorough discussion of the dilemma faced by the management team, a satisfactory balance was achieved between the performance expectations from head office and the investment in service quality, environment and product that was essential to longer-term success.

Having weathered the recession of the mid-1980s, the business was again in good shape, and growth resumed for the rest of the decade, to the point that the chain was able to eliminate its former major competitor, Bemi. That firm was managed in a somewhat different manner, with high expectations and incentives for financial performance. Its managers cut back on staff, maintenance and menu support, and pushed prices upwards, so that customer satisfaction with their restaurants declined and sales volumes fell back. This further damaged financial returns, so that yet more cost reductions occurred, a vicious spiral from which there was no escape.

Although Beefeater recovered strongly and renewed its growth to over 200 units, the late 1980s also saw the upsurge of a second rival, Harvester. This chain benefitted from access to good sites, and Beefeater was only just able to maintain a lead in reputation with customers. Harvester was able to grow to some I00 sites, and become a major threat. The year 1990 saw difficult market conditions return, along with the same conflict for both companies between financial performance requirements and business support. Once again, though, Beefeater retained sufficient profitability to continue investment in service, environment and product development to survive this difficult period. Harvester could not match this commitment, and was subsequently offered for sale.

The story graphically illustrates the conflicting pressures many managers face, on the one hand, to ensure they

keep meeting the demands of customers even when market conditions are tough, and on the other hand to keep delivering profits for their investors. This pressure is similar, whether the 'investors' are true shareholders or corporate owners. A dynamic model of the trade-offs involved should, therefore, be helpful both to management researchers and to managers who find themselves in such situations.

The Model

The key elements of the model are briefly discussed here. Further details can be found in Larsen et al., (1996). We should point out that the model is a known simplification of reality. Issues not central to the model's purpose (e.g. the downturn in consumer spending and the performance of rivals) were left out. Our model focuses on two conflicting pressures:

• 5 ° customer-perceived value: how is customer- perceived value managed and what effect might value have on the growth of the restaurant chain7

°:° allocation of capital for growth: how does the parent company decide on capital allocation, and how does the performance of the division in the previous year affect its allocation?

The key elements of the model are customer-perceived value and capital allocation.

Customer-perceived Value Overall customer-perceived value is made up of four main components: service quality, restaurant standard, menu attractiveness and crowding. Service quality depends on the number of staff relative to the number of meals sold, with insufficient staffing leading to low levels of quality.

The standard of the restaurants depends on the amount of money spent on maintenance, with a larger number of customers creating a need for more maintenance. Even with very few customers some maintenance is needed just to keep restaurants up-to-date. If profitability is low, management is under pressure to cut back on maintenance, as there is strong pressure to keep the return on capital employed (ROCE) at a level which will win enough capital from head-office to maintain growth.

Just like any other business, a restaurant chain needs to innovate, in this case by continuous investment in new menu items. For restaurant chains this is an almost scientific exercise which involves identifying trends in customers' taste, developing new dishes within the constraints of the existing kitchens, testing the items in selected restaurants etc.

To be appealing to customers, restaurants need to be busy, but not too much so; few people like to go to restaurants that are empty, but if they have trouble

European Management JournalVo115 No 2 April 1997 I 3 1


getting a table, they get annoyed and are less likely to visit again. There is thus a certain range of 'crowding' that is acceptable, and consumers become dissatisfied if actual crowding is above or below this range.

The combination of these four factors yields the overall service quality experienced by the customer. A change in any one of these factors will affect the attractiveness of the restaurant and thus the customer's experience of eating there. However, such a change does not impact the customer's perception of quality instantly. It takes time for the customer base at large to change their expectations. The model assumes that the longest delay is associated with forming an opinion regarding the change in restaurant standard, while the three other factors have a shorter delay. Because of these delays, a change in any one factor, for better or worse, will not be reflected instantly in the restaurant's sales: it takes time for customers to notice the improving or declining standards, and adapt their behaviour.

Finally, to determine whether customers actually visit the restaurants or not, the overall perceived quality is com- pared to the price level. Relatively modest quality may be acceptable if prices are rather low, and exceptional quality may offer the chance to push price upwards. Inconsistency between the two, however, will cause trouble, resulting in either overcrowding (high quality and low prices) or empty restaurants (high prices and low quality).

Capital Allocation

The second main theme of the model is capital allocation. Diversified companies often face strong

internal competition for resources and one of the main functions of the corporate headquarters is to allocate capital among these competing divisions. See for instance Barney (1991) and Goold and Campbell (1983) for a discussion of these issues. In our model the amount of capital allocated to a division depends on the performance of that division in the previous year and on expectations of future growth. There are two criteria for capital allocation: absolute performance (i.e. return on capital employed, ROCE) and relative performance (i.e. actual performance versus expectations).

When profit performance exceeds expectations, the headquarters uprate the amount of capital provided for expansion. However, the expected return depends on how well the division has performed in the past. As the division improves its profitability, the headquarters' expectations creep up. In many diversified companies, such expectations are a critical issue for business unit managers. How does one keep presenting better and better results, without creating unrealistic expectations/ This is one example of a commonly occurring structure, known as the 'eroding goals' archetype: the goal adjusts to the level of performance.

The Full Mode l

Figure 4 sketches the main loops of the model. The loop R1 is the main profit growth mechanism: more revenue leads to more spending on quality (staffing, maintenance, menu development), higher quality, better value for money, and thus more customers and more revenue. The loops B2 and R3 represent the two aspects of crowding: popularity is good, but overcrowding hurts. Loop B3

Number of S ~ Customers /f restaurants ~ Total

: restP:rrant " ~ : i nC::~tt: /

/ / Value for6~ 0 ~ Se~v~eSue ~ Cr~w"~ding S l R3~ siney ~Pric( " ] OF'~~ / ~ allC::~tt~n

I Popularity \ S ~ s / \ \ o / ~k P:ru:/ityd R-~ \ Spe~nd on \ / / \ ~ , ~ quality R ~ I ROCE ]

k - S : 0 S k N"~C°st /~Expected/ Waiting ~ S - - ROCE - -

S~ time ~ . ~ ~ S Figure 4 The Main Feedback Structures

"[,32 European Management JournalVo115 No 2 April 1997


180

170

160

tSO

140

I30

t20

r i o

tO0

9O

80

70

60

50

40

30

20

10

0 1976

i

/

I 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986

D Simulated data + Historical data

Figure 5 The Growth of the Restaurant Chain over 10 Years

captures the negative impact of spending on revenue. Loop R2 is the expansion engine: better performance leads to more capital being allocated and further expansion, which increases revenue. Loop B1 shows the need to manage expectations: better performance leads to higher expectations, and lower capital allocation in the future if management do no more than repeat the past.

The Base Case

Figure 5 shows simulated and actual data for the growth of the restaurant chain over the period 1976--1986, indicating an acceptable fit. If it is necessary to fit a set of empirical data precisely, a much larger and more detailed model is required. Data beyond 1986 (240 restaurants in 1991) is not relevant for our purpose, as further growth was affected by poor economic conditions and the acquisition of a competitor. Neither of these factors are included in this model.

Next we highlight a few results from the model. Figure 5

18

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la [-

!

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T i m e (Quarters) - - Profit * Profit Tariet

310 40

Figure 7 Actua l Profit versus Target Profit

12

tO

9 F 8 i

0 10 210 3;0

T ime (Quarters)

40

Figure 6 Capital Al locat ion f rom the Parent Company

shows the growth of the restaurant chain, ending up with around 170 outlets after 10 years. The model starts with 3 restaurants and grows slowly over the first 5 years to about 35 outlets. A major expansion takes place during the next 3 to 4 years. The growth rate declines as potential sites become less attractive and more expensive. The maximum growth rate is reached around 1983, when the chain is growing at a rate of up to 8 new restaurants per quarter.

The growth rate can be seen more clearly in Figure 6, which shows the amount of capital allocated to the division for expansion. It takes quite a long time before substantial amounts of capital are allocated on a quarterly basis. This reflects investors' genuine caution about over-investing in what is still a relatively small and unproven business. Because of this, the reinforcing process starts relatively slowly and it is not until quarter 18 that we see a sharp increase in the amount of capital allocated. We then see how fast reinforcing processes work (both ways!). This growth period is followed by a dramatic decline, due to increasing restaurant costs and quality problems resulting from crowding. Up to the

1.9

1.8

1.7

1.6

t.5

t.4

1.3

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0.8

0.7

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T ime (Quarters)

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Figure 8 Perceived Overal l Quali ty

European Management JournalVo115 No 2 April 1997 I33


llo 210 310 40

Time (Quarters) ~rowdin t -- Menu attractiveness o Maintenance ~ Service quality

Figure 9 The Evolution of the Components of Perceived Overall Quality

peak in capital allocation, profits fairly consistently exceed the target profit set by the parent company. However, as soon as the actual profit falls significantly behind the target profit, the capital allocation is cut back, see Figure 7.

Figure 8 shows the evolution of perceived overall quality (the weighted average of the four quality measures described above). There are large variations in overall quality, which can best be described as cyclical behaviour with a period of about 10 quarters. The overall quality can be interpreted as a proxy for the restaurant chain's popularity. To understand this, it is necessary to look carefully at the components which make up overall quality, as shown in Figure 10: crowding, menu attractiveness, maintenance standard and service quality.

Menu attractiveness grows steadily from 1 to around 1.15, and the perceived restaurant standard fluctuates between 1 and 1.1, driven by the number of customers (1.0 is set as a reference level of each quality variable). The service quality is perceived to be high, (although

1.1

it\ 0.8

0.7

0.6

0.5 -

0.4 I 112 : : 218 3r2 316 0 4 O 16 20 24 40

Time (Quarters) o Base case + Permanent inc rease o Temporary i nc rease

Figure 10 Scenarios: Perceived Crowding Quality

there are some fluctuations) until the end of the simulation period, when a sharp drop occurs. The real problem is perceived crowding. This is due to the popularity of the restaurant chain, which is too high for its own good. The first 'dip' in crowding is around quarter 8. This is solved relatively fast as customers begin to stay away from the restaurants and the quality improves: fewer customers implies shorter waiting times. However, as the quality improves, customers come back, and a new cycle of crowding begins. In the third cycle the crowding quality index reaches a low of 0.60 in quarter 32 where over 2,000 people a week turn up at a restaurant designed for a thousand.

Scenarios

The previous section described the behaviour of the model in the base case. It is now possible to use the model to ask 'what if' questions to gain insight in the dynamics of the business. The model provides a tool to challenge one's thinking and check that one's expectations about what should happen are in line with what is actually happening in the model. A simulation model of this type is nothing more than a collection of assumptions agreed by the model-building team. If the outcome differs from expectations, this can only be for one of two reasons (assuming that there are no 'technical' errors in the model):

*$* The expectations were wrong. This can be checked and understood by going back and following the causal links that have lead to the unexpected outcome. This way the user improves his or her understanding of the 'reality' the model aims to represent, which should lead to better decisions being made.

**** The expectations were actually correct, and the model's results are wrong. However, as the model reflects the users' assumptions, this indicates a need to re-examine these assumptions (e.g. the way customers react to changes in service quality). In this case the users learn by having to revise their

180 170 160 ! 50 140 130 120 ltO l O0 90 80 70 60 50 40 30 20 10 0

4 B 112 116 2 I0 2J4 2P8 312 3r6 40

Time (Quarters)

Base case + Permanent inc rease o Temporary i nc rease

Figure 1 1 Scenarios: Number of Restaurants

134 European Management JournalVo115 No 2 April 1997


assumptions - - again leading to improved decision making.

To illustrate how the model can be used to assess the impact of proposed policy changes, we consider various policies regarding marketing expenditure. In the base case, management spends 5 per cent of revenue on marketing. Consider the following scenario. By quarter 9, perceived crowding quality is improving, but management is worried as this is largely the result of a decrease in sales: meals per restaurant per week are down from over 1550 in quarter 6 to barely a thousand in quarter 9,, and the trend is downwards. It is decided to double the marketing budget from quarter 10 onwards in an attempt to recover volume, a necessary condition for further growth. Figures 10 and 11 show the unfortunate consequences of this decision. It takes time for the results of the increased marketing effort to come through, the only immediate impact being a reduction in profits which, in the longer term, leads to less capital for growth. But after a few quarters, the marketing effort results in increased customer numbers, and overcrowding (Figure 10), causing the newly attracted customers to be turned away. This leads to a temporary improvement in crowding quality, but the negative impact on profits of this inappropriate marketing effort leads to no further capital for growth, and the chain gradually dying out (Figure 11).

The next scenario starts as the first one, with a doubling of marketing expenditure in quarter 10. But, as sales revenue resumes an upwards trend (back to 1,150 by quarter 13), the marketing budget is brought back to its normal level from quarter 14 onwards. As shown in Figure 11, although growth is somewhat slower, the future looks good. But Figure 10 warns us not to be overconfident: the policy changes have resulted in increased quality fluctuations, which risk getting out of control.

These scenarios illustrate the importance of understanding the structure and relations in a business situation, as it is very easy to 'kill' the potential for growth. Note the widely differing outcomes that can arise even when, as in this model, there is neither a rival, nor an economic downturn to contend with. This implies that business performance may be more directly in the hands of management than is generally realised, and one should carefully consider whether poor performance may be caused by policy failure, rather than outside factors, before blaming the latter.

Using Models as Teaching Tools

Over the years, simulation has increasingly been used as the basis for management 'games'. There is a large literature on game design, and the impact of using games for team learning. For an overview, see for example Lane (1995). These issues have also attracted attention in the systems dynamics area, where the focus has been on

transferring the learning to people not closely involved in the development of the model. The first breakthrough came in the late 1980s when it was realised that simulation models turned into management games could provide a useful tool for creating understanding and commitment in organisations. See Morecroft and van der Heijden (1992), Senge and Sterman (1992) and Sterman (1988) for details. These games, often referred to as 'management flight simulators', have now been used in a number of organisations and have proved to be effective as well as entertaining for the participants. They are being used increasingly in business schools and for executive training.

The effectiveness of these games is still open to question. Emerging evidence (Langley, 1995) suggests that they may both increase learning and speed up the learning process. Further research is required to confirm these findings, and to gain insight into how the learning from a game can be transferred to the real world. Examples include Bakken et al., (1992), Langley and Larsen (1995a) and Langley and Larsen (1995b).

System dynamics based games are characterised by narrow, clearly defined learning objectives (see Langley and Larsen, 1995a), and tend to be combined with the case method. This approach leads to a new and more comprehensive way of teaching, which increases the involvement of course participants. First participants go through the standard discussion phase, and come up with a proposed strategy. They then move on to the implementation phase, where the gaming simulator allows them to discover the results of their chosen strategy (see Ginsberg and Morecroft, 1995). Observing the consequences of their decisions and experimenting with alternative options leads to a deeper understanding.

To accompany the Beefeater Restaurants model, we have produced a teaching package which contains a conventional case study (Warren, 1992), describing the development of the restaurant chain and the issues the growth raised. This conventional case series is supported with an interactive, computer-based game (Warren and Langley, 1996) where the participants have to manage the growth of the restaurant chain on a quarter by quarter basis, setting the price of meals, number of staff, amount spent on maintenance, etc.

As, mentioned earlier, the model we build is a deliberate simplification of reality. Issues not central to the model's purpose, although relevant to the overall dynamics, are left out. Examples of this include changes in the external environment, specifically the 1985 and 1991 downtums in consumer spending and the performance of rivals. The model shows that irrespective of such external pressures, prospects are largely in the hands of the management - - decisions and policies that seem perfectly sensible in isolation (a low-price position or heavy marketing or reacting to parent company pressure with cost-cutting) can lead to failure to grow and/or business failure. Including exogenous factors would risk making the model less useful as a learning tool, as participants would



be able to blame poor performance on such external influences (which in practice are unknowable at any point in time), rather than on their own decisions.

This interactive game is a useful tool to enhance team learning and team decision making. The model can be used to illustrate that 'local' decision making, in which for instance marketing or staffing decisions are made in isolation from other considerations, can be disastrous. This implies that team-negotiation of coordinated decisions is critical (e.g. don't do marketing if perceived quality is poor, or if the restaurants are over-crowded), making the game particularly useful for the study of the dynamics of team behaviour.

Giving a group of people from different parts of the business the opportunity to run the model as a team enables them to experience these interactions the hard way (e.g. by failing to drive business growth), but without having to face the real-life consequences of their actions. This experience becomes a constructive starting point for discussion on how to cope with these interactions.

If participants come from the business being modelled, key learning concerns the policy choices made by teams in trying to manage the system, and the interdependency between those policies. Further debate focuses on how users' mental models of the business compare with the actual model in the simulator. Such users can also be asked to consider those issues that the model does not contain, (e.g. staff morale or competition) and debate what difference such factors might make.

Where users come from a different business background, the debriefing needs to include a debate on the parallels and differences between the model and their own business. For example, most businesses need to keep up a good level of customer-perceived value, and most business unit managers know about the problem of delivering expected performance without creating unrealistic investor expectations of future performance.

Conclusion

We have illustrated how system dynamics can improve understanding of the growth of a service company. The simulation model we developed enabled us to reproduce a growth pattern similar to that experienced by the real restaurant chain over a 10 year period. The model includes examples of how soft variables, such as service quality, crowding and value for money, can be included in what many perceive as hard models. We illustrated how the model can be used for 'what if' analysis, providing a vehicle for exploring the impact of important policy variables

This type of model has a number of advantages, as well as some disadvantages, one should be aware of. Two of the advantages are listed below

Being involved in a model building exercise like this is an engaging activity for a management team. Although it is time-consuming, there is usually general agreement that the return on this time investment is high. The model building process itself is of significant value. The models include soft variables, which are often as important, if not more so, than the hard variables one is used to see in more traditional models (e.g., spreadsheets). We may not be able to get as precise a definition of soft variables (e.g. customer reputation, or investor support). However, managers are usually able to describe what factors influence a soft variable, such as morale. Being unable to measure something directly is not a good reason to leave it out. The 'error' resulting from leaving out such variables is much larger than the 'error' arising from having no precise measurement of them in the model, as such variables are often critical to the understanding of a given problem.

One should keep in mind that these models do not produce forecasts. They are built to improve understanding and learning. Teams tend to focus on the exact numbers resulting from the simulation, rather than on the general behaviour of the model. To fully benefit from this approach, one must distance oneself from the numbers and focus on the overall picture. We believe that such tools have the potential to increase the rate of learning in management education.

Note

Powersim is a registered trademark of Modeldata AS, ithink is a registered trademark of High Performance Inc., Vensim is a registered trademark of Ventana.

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ANN VAN ACKERE, London Business School, Sussex Place, Regent's Park, London N W I 4SA, E-mail: A VANACKERE @Ibs.lon.ac.uk [Ann van Ackere@ internet ].

Ann van Ackere obtained her undergraduate degree from UFSIA (Belguim) and her PhD from the Stanford

Graduate School of Business. Upon graduation she joined London Business School, where she is Associate Professor of Decision Sciences. Her research interests centre on the management of congestion in manufacturing and service operations, using various approaches which include system dynamics, game theory, micro-economic modelling and queueing models. She has published among others in Operat ions Research, M a n a g e m e n t Science, the European Journal of Operat ional Research and the Rand Journal of Economics.

KIM WARREN, London Business School, Sussex Place, Regent's Park, London N W I 4SA.

Kim Warren is Assistant Professor, Strategic and International Management at the London Business School. After positions with ICL Shell and Arthur D. Little in the oil and petrochemicals

sector, he was Retail Strategy Director for the brewing and retail group, Whitbread plc during the group's I980's growth to dominate several restaurant and drink sectors. He has been with LBS since I990, teaching strategic management on MBA and executive programmes. As a member of the school's system dynamics group, his work focuses on simulating change in industry conditions and strategic responses, together with the use of such techniques to anticipate change and enhance organisational learning.

ERIK LARSEN, University of Bologna, Departimento di Discipline Economico-Aziendale, Piazza Scaravilli, 2, 40126, Bologna, Italy.

Erik Larsen is Marie Curie Fellow in the Department of Management, University of Bologna. He has held previous appointments at London Business School and Copenhagen Business School. Author of more than 30 papers and a forthcoming book. Goodeve Medal Winner (I994) of the UK Operational Research Society. His work focuses on the privatisation and regulation of newly privatised utilities. Another area of research is the use of complexity theory to help understand fundamental issues in strategy and organisational theory.


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