SYSTEM DYNAMICS IN ACTION

One of the interventions most frequently used to illustrate the power of the system dynamics approach is that conducted by the MIT System Dynamics Group with Hanover Insurance (see Senge and Sterman in Morecroft and

Sterman, 1994; Maani and Cavana, 2000; Cavaleri and Obloj, 1993). The case reveals that apparently rational action taken by management to reduce the costs of settling claims and to maintain customer satisfaction actually led to an erosion of quality of service and increased settlement costs. The system dynamics study demonstrated, through an analysis of the interacting feed-back loops, exactly why this happened, suggested less obvious but more e⁄cacious ways of tackling the problems faced and led to the development of a microworld and learning laboratory to spread the learning obtained throughout the company.

Hanover Insurance had undergone an amazing transformation in the 1970s and early 1980s pulling itself from the bottom of the industry to become a leader in the property and liability ¢eld. During this period it grew 50% faster than the industry as a whole. Nevertheless, it could not escape the many problems and resulting runaway costs that impacted on the industry during the 1980s. Automobile insurance premiums doubled causing a public backlash, the number of product liability cases increased massively and the average size of claims settled in court increased ¢vefold.

It was easy to blame dishonest policy holders, biased juries, greedy lawyers and the increased litigiousness of society. Senior managers in Hanover, however, determined to look at how their own management practices were contributing to the problem situation. A good starting point was the claims management operation responsible now, because of increasing numbers and complexity of claims, for more than 67% of total company expenses.

The project began with a team from Hanover, consisting of the senior vice-president for claims and two of his direct subordinates, meeting regu-larly with some MIT researchers. A vision statement expressed the desire to be pre-eminent among claims organizations and to provide ‘fair, fast, and friendly’ service. From this it was possible to derive an image of the ideal claims adjuster and the performance measures he or she would be required to meet. The problem was ¢nding a coherent path from the reality to the ideal. There were lots of candidate strategies, but these seemed disjointed.

A more systemic solution was required.

STELLA was used with the Hanover team to build computer-based simulation models. These were subject to basic reality checks and employed to test the results of current strategies and to seek improvements in manage-ment practices. Expert judgemanage-ment was used, alongside whatever quantitative data were available, to estimate the many ‘soft variables’ involved and their e¡ects. The ¢nal model was both sophisticated in its treatment of problem dynamics and fully owned by the Hanover team. Figure 5.4, reproduced

System dynamics in action 75

from Senge and Sterman, is a causal diagram that expresses the problem dynamics.

The existing, implicit strategy of Hanover, in the face of pressure from increased claims, was captured in the productivity loop and the work week

Figure 5.4 System dynamics of claims processing in the insurance industry. Feed-back loops controlling claims settlement, with processes causing self-reinforcing erosion of quality and increasing settlement costs. Arrows indicate the direction of causality. Signs (‘þ’ or ‘
’) at arrowheads indicate the polarity of relationships: a ‘þ’

denotes that an increase in the independent variable causes the dependent variable to increase, ceteris paribus (and a decrease causes a decrease). Similarly,‘
’ in-dicates that an increase in the independent variable causes the dependent variable to decrease. Positive loop polarity (denoted by (þ) in the loop identifier) indicates a self-reinforcing (positive feedback) process. Negative (
) loop polarity indicates a self-regulating (negative feedback) process.

From Senge and Sterman (1994), reproduced by permission of Productivity Press.

loop of Figure 5.4. The operating norm for claims adjusters was simply to work faster and work harder. Working faster helped reduce pending claims because less time was spent per claim and therefore more claims could be settled. Working harder helped reduce pending claims because of the e¡ect on productivity of working longer hours and taking shorter and fewer breaks. In the short term these ‘¢xes’ might appear to work. In the longer term, however, other relationships bring about unintended consequences (initially hidden because of the existence of ‘delays’) that make matters worse rather than better. They are examples of the ‘¢xes that fail’ archetype.

The unintended consequences of working faster are shown in the settle-ment cost loop of Figure 5.4. Spending less time per claim reduces the quality of settlements, and this leads to increased settlement costs. Less time to investigate and negotiate claims means that in£ated settlements are agreed. Other customers become dissatis¢ed with the amount of time devoted to them by the adjuster and are more ready to resort to law. Where litigation results, inadequate documentation means longer preparation time and less successful outcomes.

The unintended consequences of working harder are shown in the burnout loop and turnover loop of Figure 5.4. Working harder leads to fatigue, ill health and sta¡ burnout, which impacts adversely on productivity.

Burnout also increases sta¡ turnover, which means fewer assessors and even greater time pressure on those that remain.

Through the settlement cost, burnout and turnover loops, therefore, the initial ¢xes provoke longer term unintended consequences that reduce quality, increase time pressures and increase costs. Because this feedback is delayed and its causes are not easy to trace, management will tend to react by relying further on the original ¢xes. In an insurance industry facing runaway costs the temptation to require claims adjusters to work even faster and harder was virtually irresistible. The idea of addressing the problem by hiring new claims adjusters seemed ridiculous. In archetype terms, therefore, the burden was shifted from capacity expansion to quality erosion.

Hanover, at the time of the study, had the highest number of assessors per claim in the industry. Nevertheless, because the Hanover team had been involved in building the model themselves and, through the model, had come to appreciate the interconnections that produced the counterintuitive behaviour of the claims adjustment system, they were prepared to embrace the only fundamental solution to the problem. This, as is shown by the capacity loop in Figure 5.4, was increasing adjuster capacity. However well it compared with the rest of the industry, only by hiring new adjusters and

System dynamics in action 77

training them properly could Hanover genuinely address issues of service quality and increased costs.

The next challenge for the project was to extend the learning gained to the entire company. As implied earlier, there was a tendency to blame outside factors, and not internal management practices, for the travails of the insur-ance industry. Furthermore, in Hanover responsibility for decision-making is widely distributed. If there was to be a real change in the way the company behaved it was essential that all those managers with in£uence experienced for themselves the counterintuitive behaviour of the claims processing system. To achieve this a claims learning laboratory, incorporat-ing a computer simulation game (or management £ight simulator), was developed. Managers were familiarized with causal loop diagrams and helped to think through the variables and relationships associated with the claims system. The design of the game ensured that they were forced to make explicit their mental models and to challenge them when the results of the strategies they championed de¢ed their expectations. As a result

‘double-loop’ learning was facilitated.

As is demonstrated by the many excellent examples in Sterman’s (2000) Business Dynamics, system dynamics is applicable to a wide range of industrial and public policy issues.