The forgotten art of using experience curves to improve management practices

In an increasingly competitive business environment, a management teams ability to accurately forecast its costs and productivity is a key driver of business value. Experience curve theory is a powerful analytical methodology that can support a management team in determining realistic and measurable forecasts based on specific industry benchmarks and achievements.

This article outlines Deloittes point of view on how the experience curve can be leveraged to support leadership teams in setting realistic targets and then managing value. With a focused approach, the experience curve forecasting and monitoring process becomes a key enabler to realising superior productivity enhancements.

The experience curve theory has a wide spread of applicability for any organisation that uses a repetitive process. The theory suggests that for every doubling of a unit of production, productivity tends to constantly improve by a factor called the Progress Ratio (PR). The construction of large engineering projects is one example where experience curve theory is readily applied. The principle of experience curve theory is captured in Figure 1 below. Quite simply, if a construction firm is building 8 engineering structures where the first structure has cost $100m, the second unit will cost $85m, the 4

th unit will cost $72m and the

last unit will cost $61m.

Figure 1: 85% Progress Ratio (PR) over eight units.

It can be seen that a considerable saving of $39m has been achieved when building the 8

th unit, if the initial

cost was estimated at $100m.

As companies grapple with current operating environment complexities, the real value of experience curves has become somewhat underutilised because companies do not translate the calculated results into business strategy. In essence, when used as part of the budget setting and performance management processes, experience curve theory is an effective tool.

In Deloittes experience, management teams that link quantitative results to business strategy tend to achieve greater success. Using experience curve theory in this practical manner is a key differentiator to drive operational efficiency and improve profitability forecasting.

Interpretation: Learning curve vs. Experience curve

Wideman

1 noted that confusion arises in the

construction and mining industries regarding the use of the term learning curve. Because work typically takes place under unique conditions at a unique site, it is useful to differentiate between productivity improvement due to learning and that due to experience.

Wideman1 uses the following example to distinguish

between the two terms. As craft apprentices learn their trade, productivity increases. However, when skilled craftsmen perform a specific task on site and repeat it a number of times, there is a similar productivity increase. The former increase is due to learning the skill, while the latter is due to acquiring experience of the particular site conditions associated with the work activity at the time, which also can be called organisational learning.

Therefore, learning can be traced back to individual performance capabilities, which will differ for each person and therefore be variable, while experience is more related to particular site conditions. Unfortunately, in reality these two conditions are difficult to dismantle. However, from this aforementioned example one can conclude that the combination of these two terms should be variable.

1 Wideman, M., 1994, A Pragmatic Approach to Using Resource Loading,

Production and Learning Curves on Construction Projects, Canadian Journal of Civil Engineering, Vol. 21, pp. 939-953.

$61m

$0m

$100m

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Experience Curves:

A powerful management tool to drive

operational efficiency and improved forecasting

1 2 3 4 5 6 7 8 9 10 11 12

Co

st

Units produced or timeConventional Curve PR = 90%

Conventional Curve PR = 92%

Applied methodology

0 5 10 15 20 25 30 35

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mu

lati

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ear

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Units produced or time

Variable learning

Linking experience curve theory to business strategy

Deloitte has developed an applied methodology which caters for variable experience curve rates that are a result of planned interventions / innovations (Figure 2).

Figure 2: Illustration of how the rate of learning can differ but still reach the same end result after several units are produced.

From this applied methodology, Deloitte is able to link the results to operational strategy. This is done through using cumulative learning to predict three different management phases. In Figure 3, the unit or time period at which the cumulative learning is 65% and 95% from the optimum cumulative learning is predicted. By identifying the phase the company finds itself in, Deloitte is able to identify when new productivity interventions are needed for performance optimisation. Thereafter, an intervention roll out strategy can be developed, further linking the mathematical theory to the business and operational logic.

Figure 3: Illustration of how simulated cumulative learning varies with consecutive units produced.

Case study

The following case study shows the relevance of the applied experience curve theory within the electrical utility industry. Data was obtained for a major US utility. Contractor labour cost on construction, plant overhaul and modification projects were analysed and an applied experience curve was fitted (Figure 4)

2.

Figure 4: Continuous work process improvement at a major utility from 1993 through early 2001.

2 Picard, H.E., 2002, Construction process measurement and improvement,

Construction management consultants.

Management phase:

This phase indicates that several interventions are in place to reduce cost or time and that management should monitor and manage these interventions to ensure progress.

Planning phase:

At this stage management should still monitor and manage the implemented interventions. However, research and planning should be done regarding what the next innovative intervention or interventions should be.

Implementation phase:

In this phase the planned intervention should be implemented. After implementation, a new segmented experience curve should be obtained and the management phase will start again.

Optimum learning

Planning phase

Implementation phase

Management phase 95% of optimum

learning 65% of optimum learning

10%

15%

20%

25%

30%

35%

40%

45%

50%

0 2 4 6 8 10 12 14 16 18

Half years

Applied experience curve

Historical data

Implemented intervention (illustration)

Management phase

Planning phase

Implementation phase

Unproductive contracted labour utilization

From Figure 4, it is shown that the management phase is between zero and three (zero and 1.5 years) while the planning phase is between three and six (1.5 and three years). The more data points become available the more accurate the prediction becomes.

Figure 4 also indicates how a roll-out of a planned intervention should impact contracted labour utilization.

Conclusion

In conclusion, experience curve theory can be used to improve managements forecasts, as well as affording management the ability to track performance, and

identify where strategic intervention is required. The Deloitte approach to using the experience curve enables management to drive for value in a more disciplined manner.

Contact information:

Mike Vincent

Deloitte Consulting

Director: Strategy & Innovation

Tel: +27 11 806 5400

Email: mivincent@deloitte.co.za

Anton Torlutter

Deloitte Consulting

Executive Lead: Strategy & Innovation

Tel: +27 11 806 5400

Email: atorlutter@deloitte.co.za

Stephen Povey

Deloitte Consulting

Lead: Strategy & Innovation

Tel: +27 11 806 5400

Email: spovey@deloitte.co.za

Dr Werner van Antwerpen

Deloitte Consulting

Lead: Strategy & Innovation

Tel: +27 11 806 5400

Email: wvanantwerpen@deloitte.co.za

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