Home page

“… there is absolutely
no inevitability
as long as
there is a willingness
to contemplate
what is happening.”

“The Medium is the Massage”
Mashall McLuhan
& Quentin Fiore
Bantam, 1967
Decision analysis as a basis for more effective agricultural innovation

Part 3: Tacit knowledge & performance

Hector McNeill1


In terms of project sensitivity and risk analysis, there is a tendency to emphasize the performance impacts of changes that occur in the environment surrounding a project. It is often assumed that internal capabilities and processes are fixed. However, in reality, the capacity of human resources to improve project performance, changes all the time. The quantitative benefits of changes in human performance can be measured and forecast. This provides for a more transparent and realistic assessment of risks facing a project because the ability to compensate for any externally-imposed changes is enhanced.

In this article we explore how the Navatec System manages two types of essential knowledge to ensure that teams not only have the right information on hand but that the system becomes a learning environment that enhances team capabilities and quality of work.

Essential knowledge

The most important factors in project design and implementation performance are the ability to enhance the capabilities of team members, clarify their task assignments and to provide support for their work. Individual capability depends upon qualifications in terms of training, experience and the ability to apply know how effectively as well as to learn on the job. The acquisition of capabilities depends upon two forms of knowledge:

  • Explicit knowledge
  • Tacit knowledge

Explicit knowledge

Explicit knowledge is that information that can be communicated by word of mouth, in written form, sound, imagery and musical notations or sounds which today can be distributed, stored, accessed and processed through digital technology. The main structure to this information is language, mathematical logic, such as Boolean logic and verbal argument. Mathematical formulae are a useful component of explicit knowledge as are ways and means of building decision analysis models and applying analytical techniques and simulation. In summary, explicit knowledge can be passed on from one person to another and from generation to generation in the form of records and it is the medium used in educational and training activities.
Riding a bicycle depends on tacit knowledge

The best example of the meaning of tacit knowledge is the process of learning to ride a bicycle. It is possible to describe how to ride a bicycle but in reality, beyond that, each individual needs to learn to ride the bicycle, gain the appropriate balance and control over motion through their own efforts.

Tacit knowledge

Tacit knowledge is an operational capability, or skill, associated with each individual in the carrying out of a task. Tacit knowledge is developed as a result of an individual becoming acquainted with the application of a technique and applying it on a repetitive basis. The difference in quality of output and efficiency in execution of an experienced practitioner over a novice is the difference in their levels of accumulated tacit knowledge. Tacit knowledge is usually related to the well-established phenomenon of the "learning curve". The effect of the learning curve was explained by Theordore Wright in a paper in 19362. The learning curve impact described by Wright has also been referred to as Wright's Law has turned out to be a reliable basis for predicting the impact of tacit knowledge on the productivity of organized processes involving a combination of technologies and humans.

Learning curve and index

The learning curve is the phenomenon of the occurrence of measurable reductions in the resources used (including time) in the production of an object in association with the cumulative quantity of throughput.

The heuristic, or rule of thumb, is that the time required or resources used to complete action fall by a constant percentage for every historic doubling of output. This means that the gain from learning on a production amount of x units will be the same as the gain from 2x units and this will be the same as 4x units and 8x units should have the same gain in performance.

This gain in performance, because of the historic doubling of throughput factor signifies that there are diminishing marginal returns which complies with production theory.

Geometry of the learning curve

The learning index is the percentage reduction in resources used with each historic doubling of throughput. This is also expressed as a percentage curve thus more capital intensive processes might have a 90% curve signifying 10% reductions and a more labour–intensive process might have an 80% curve indicating a 20% reduction. An example of a 80% curve is shown on the right.

Some general relationships influencing learning curves

Belkaoui4 cites a summary by Hirschmann5 of the basic doctrine of the learning curve as:

  1. Where there is life there is learning
  2. The more complex the life, the greater the rate of learning.
  3. Man-paced operations are more susceptible to learning or can give greater rates of progress than machine-paced operations
  4. The rate of learning can be sufficiently regular to be predictive.
  5. Operations can develop trends which are characteristics of themselves
  6. Projecting such established trends is more valid than assuming a level or performance or no learning

In general terms the learning curve effect is more pronounced if production processes are labour-intensive and less pronounced if production processes are more capital-intensive or automated

Throughput and quality of output

Throughput is measured in terms of quantity and quality. The acquisition of tacit knowledge through learning tends to be associated with higher quantities of output per unit time and improved quality of output. The quality of output is measured by the quantity of output that meets a specified quality. The yield of a process is the percentage of output that meets quality standards. Usually an individual will have a predefined maximum capacity for production (Cp) according to a the process equipment, tool sand applied technique. This is measured in terms of units of output per hour or day. The achievement of that maximum quantity of throughput depends upon the time allocated each day to the task (Ta) and the capacity utilization (Cu). Therefore the quantity of physical output is given by:

Throughput = Cp x Ta x Cu

and the quantity of output of the required quality is given by:

Quality output = Cp x Ta x Cu x Y

The capacity utilization and yield are strongly correlated to tacit knowledge or skill in the carrying out of tasks. Capacity utilization can be related to the operator applying the correct equipment settings or following efficient procedures to take full advantage of the technique being applied. The quality yield is usually related to the level of skill applied to operations. The table below should typical relationships associated with experience of operators:

The relationship between learning and performance

ExperienceTacit knowledgeCpTaCuYQuantityQualityLoss (waste)Unit cost of required quality
up to 2 yearsLow20,000 units/day2 days0.800.8040,00025,600
2-5 yearsIntermediate20,000 units/day2 days0.920.9240,00033,856
>5 yearsHigh20,000 units/day2 days0.990.9940,00039,204

As can be observed unit costs and waste decline with learning while output quality and capacity utilization and yield increase with the level of tacit knowledge.

The scope and of relevance the learning curve to agricultural innovation projects.

The main characteristic of the learning curve is its reliance on repetitive application of a technique. It is therefore evident that the impact is greater the more extensive the time during which specific techniques are applied. Sometimes, within the space of a project there will not be sufficient experience built up to have any significant impact. However, innovation projects have an unusual context which involved several stages as illustrated in the diagram on the left. These stages include prioritized research, proof of concept, prototype creation and following feasibility studies, investment for production. The longest "run" or operational time would normally be associated with the investment in the form of the commercial production where learning curve advantages usually become more apparent. However, some of the projects leading up to that stage can become be altered to switch from being one-off projects to continuous processes as an integral part of the production chain supplying the commercial operation. Therefore, the relevance of learning curve projections will vary according to the innovation stages.

How Navatec System integrates learning curve projections

Navatec System make use of the learning curve relationships to provide projections of the impact of rises in human capability in the application of given technologies and techniques. This contribution to project performance is related to the use of resources and time to complete each operation. Navatec System has an embedded learning curve server side utility (SSU)w that enables raw performance data to be input to calculate the learning index and then project costs or timing. If benchmark data is available this can be input to obtain the same results.

Screenshot from Navatec System Learning Curve projection

Reviewing impact of learning using Monte Carlo Simulation

Beside generating accurate evidence-based projections for learning impact the Navatec System also provides the means to simulate different scenarios using Monte Carlo Simulation (MCS) to establish feasible targets for performance within given timeframes. The image below is a screen shot of one of the simulators input dialogs for the MCS that deals with quality and quantities of throughput.

Screenshot from Navatec System simulation data input bank dialogue to trace tacit knowledge influence

Screenshot from Navatec System simulation output with annotations added

Project design and appropriate training

Irrespective of any gains that might accrue from the build up of tacit knowledge, the maximisation of the contribution of techniques is related to the deployment of the most appropriate best practice in terms of efficiency, effectiveness and economy within the constraints facing a project.

What defines best practice is normally based on experience and benchmark data where the current practice has benefited from the contributions of tacit knowledge in refining the techniques. Thus within each technique there is a constant innovation in how it is applied and in the attainable performance.

Usually the benchmark performance will accord with the current levels of experience of team members in applying the technique in question. Training has an important role in making sure that:
  • the appropriate technique is selected
  • the technique is demonstrated in a practical manner
  • those applying the technique have mentoring support to ensure the initial appropriate application and to ensure progress in capability over time
No matter what technique is applied the person applying it can benefit from clear descriptions of the process to be applied and the reasons why a specific technique is being used. Precautions and guidelines associated with technique also assist those applying them to initiate work. As can be appreciated all of the training is expressed through explicit knowledge but the targets and actual state-of-the-art techniques have evolved through accumulated enhancements and adjustments introduced through the accumulation of tacit knowledge and incremental improvements.

Naturally, each team member needs to descend the learning curve and they cannot start applying a technique with the competence of a skilled and experienced person. However, with an adequate knowledge of performance benchmarks that relate to learning curve coefficients, it is possible to forecast the rate of increase in performance associated with experience in applying the technique. With use of a technique in a work environment the person concerned will build up an appreciation of a sequence of actions and their relationship to the quantity and quality of output achieved. Quality of output, which is measured by applying explicit knowledge will become clearly related to the way in which specific manipulations are carried out so the tendency to refine those actions result in the more experienced person producing higher quality output. Cumulative tacit knowledge has an important role in raising the performance of activities.

1 McNeill, H. W., "The State-of-the-Art & Future of Decision Analysis", SEEL, The George Boole Foundation, BSI,HPC, October, 2009
2 Wright, T., "Factors Affecting the Cost of Airplanes", Journal of Aeronautical Science, Volume 3, No.2, 1936, pp. 122-128.