An operator can be influenced and changed in different ways in order to improve the performance of the human-machine system. One method is to use various types of selection processes in order to try to identify and select those people who are best suited for the particular type of work. The question of selection will not be dealt with here, primarily because in most cases it is a difficult method to apply, and one whose value is highly disputable. If selection tests are to be used, these need to be properly evaluated with known validity and reliability. However, tolerance to stress might be a personality factor to be considered. More difficult is the issue of introverted and extroverted personalities in control room work. Only relevant case-valid systems should be used. On the other hand, a refined training method is often of great value. Many of the guidelines found in the common educational literature are also applicable to the training of control centre operators. In this context, therefore, the emphasis here will be on the form of training that is of particular interest to the training of operators in process industries.
Traditionally, it has been possible to identify three types of learning: factual learning, attitude modification, and job skill training. To these can be added: skills for lifelong learning, training for team working, skills for self-directed learning, learning how to learn, and creative skills development. It is also important to consider one’s own preferred style of learning and being creative. Over the past decades different forms of technology have been developed to support processes of learning. The past decade has seen an explosive development of e-learning and other forms of Web-supported and self-directed learning. This has partly emerged from distance learning using radios, TV, and ordinary postal services. Historically this is also related to simple early technological aids for learning, for example, the introduction of computers created computer-based training (CBT). The more sophisticated techniques were also developed earlier in the form of simulations of processes. Quite soon the nascent aviation industry made use of the early simulation technologies. Aircraft pilots used the first types of simulator training, which were forms of shadow techniques and were used to prepare the pilots for night-time flying. In this technique the learners learned from ‘controlling’ their own vessel or aircraft in relation to the shadow images of other craft or buildings displayed in front of them. Similar techniques were later also used for the shipping industry and simulating the manoeuvring of ships in difficult approaches, and navigating vessels in narrow archipelagos. In the era of computerisation there have emerged extremely sophisticated simulators for aviation and shipping training. Today there is a very rapid development in the area of using simulators for training and learning.
Use of simulators in aviation training is probably one of the most important areas of applications. For commercial aviation, simulator training has taken over a very large proportion of the training of commercial pilots. In theory they can be used for more or less 100% of the training input. However, in practice, this is not feasible. Traditionally the strength of aviation simulators has been in dynamic skills training. Nowadays simulators are also used for factual learning, for example, in navigation training for both maritime and aviation applications. More recently, simulator training has begun to be used increasingly in more diverse application areas. It has become more common, for example, in maritime training, but its use has also been increasing within other industries, for example, nuclear power facilities, power plants, and electrical distribution centres. Simulator training has a high potential to be used in most types of modern control centres. Simulators can also be used for redesign and development of industrial and administrative processes. Other examples include simulations that replicate market movements in an economy and simulators on which trainees may hone their skills in trading currencies, stocks and shares, and other financial instruments. The potential in this area is very large, particularly if the simulator training or related development tools integrate the use of advanced stochastic mathematics combined with modern software for the creation of simulation programmes.
However, the main use and experience of simulators is in aviation training and development. Here, scientific evaluation has been carried out in order to determine the advantages and disadvantages of using simulators. In other areas, similar scientific research is found only to a more limited extent. A large part of the experience from within aviation can be transferable to other areas with a certain degree of confidence. Experience within simulator training shows that the advantage of simulators is in the training of sensory-motor job skills and the ability to handle different types of procedures (read off instrument A; if the value exceeds X adjust 4, and so on). On the other hand, simulator training has been regarded as less suitable for the more classic types of factual learning or for attitude modification. Nowadays, we know differently (see below).
A prime advantage of simulator training is economic. In simulator training, learning periods can be considerably shortened. This is an attractive feature in industries, such as aviation, where time is money—a factor that is increasingly so in all industries. Simulator training is also important in that it enables training in unusual or dangerous situations that are seldom encountered in real life. By their nature, simulators allow training in especially important situations that could, for instance, lead to serious accidents in real life. Obviously, there is very high potential for simulation training of extreme and very rare situations in economics, in trading, and in energy distribution. Simulator training can be used both for the initial learning and training, and for follow-up training, especially in connection with the unusual real-life situations.
It is, however, important to remember that simulator training can replicate elements of the real world but it can never wholly replace training on the real system. In spite of sophisticated technical advances, a simulator will only be a replication of reality. It will never be the same as the reality itself. A simulator for training in particularly dangerous tasks must resemble reality as closely as possible. In other words very good mathematical models need to be incorporated into the programme design. On the other hand, if it is to be used for training job procedures and routines, the simulator can in most cases be considerably simpler. It may sometimes even be sufficient just to have a simple mock-up without electrical connections (that is, without any realistic responses on instruments to different control movements by the operator). This simple mock-up type of simulator can also be used for factual training, such as memorising start-up routines, names and design of instruments and controls, or emergency procedures.
In the early 1980s, Scandinavian Airlines used simulators for team training and management learning. In this situation, the simulator training proved to be a very good method for attitude changes. In this context, it became possible to educate cockpit crews to accept role demarcation, for example, between the copilot and the captain. This usage of simulators for team training has also spread to other areas of learning how to work in groups. In control rooms and other control centres, this is a very important organisational skill in which employees need to be trained. In new types of control centres—for example, in energy trading or other financial centres—this type of training can enhance efficiency and output of the whole organisation.
When planning simulators for training, it is important to take account of educational expertise from simulator training. There is always a risk that in such training one may build in small deviations from reality, which could lead to a ‘negative transfer’ effect in the training. This may result in someone handling the real system as though it were the simulator, which in turn could result in the operator making critical errors. This risk of negative transfer seems to be larger in very high-tech applications where the simulation very closely resembles real-world experience (that is, the simulation is so good that the user doesn’t expect any deviations from real- world conditions).
The training officer who designs the training programme on the simulator must therefore carry out a very careful skills analysis of the real-life job at the beginning, in order to determine which factors in the job are critical from the point of view of education or training. Of particular importance is to be conscious of the long-term need for new skills and also as a process of lifelong learning. Only then is it possible to determine how the education and training programme should be put together. Furthermore, a simulator designed for learning can easily also be used for creative work and for the development of the work process and of the simulator process itself. In the beginning, we discussed the relationship between creativity and the process of learning. Simulator training can to a large extent be combined with creative work. To make this form of combined learning and creativity meaningful, it is necessary to create a high-ceiling environment that at the same time facilitates teamwork.