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A review of recent literature is presented relating to general Just‐in‐Time (JIT) philosophy, its supply bases and on the formalised and mathematical models. JIT has become a subject of much interest and some little controversy in recent years and has attracted the interest of many major manufacturing organisations in the West as well as in Japan where it was originally developed. A growing and developing literature on this subject has been published particularly since the late 1970s. An attempt is made to draw major conclusions regarding benefits and relevance to Western manufacturing industry.

In this study, an adaptive controller of a solar tracking system is designed and its performance is evaluated through computer simulation. To achieve this goal, two tracker systems based on open-and closed-loop control strategies are studied. The instantaneous and total errors that represent the differences between the tilt and rotation angles of both the open-and closed-loop systems are analysed. An adaptive controller that regulates the electric signals to the motors is designed. Owing to uncertainty in the sun position data, the performance of solar tracker suffers degradation. Towards this end, sun position data are analysed to extract the error sources. The designed control system objective is to keep the tracker perpendicular to sunlight at all times during the day and eliminate modelling errors such as sun position data deviations, friction and environmental changes. System performance is verified through computer simulation where the controller corrected for modelling errors and date changes from the date used for algorithm design. More specifically, in the first case the square error reduces from 15.43 to 12.18 degrees 2 for azimuth angle and from 39.05 to 19.09 degrees 2 for altitude, whereas in the latter case the square error reduces from 236.92 to 105.90 degrees 2 for azimuth angle and from 402.82 to 40.40 degrees 2 for altitude.

In this study, the authors consider a solar system which consists of a solar trainer that contains a photovoltaic panel, a DC centrifugal pump, flat plate collectors, storage tank, a flowmeter for measuring the water mass flow rate, pipes, pyranometer for measuring the solar intensity, thermocouples for measuring various system temperatures and wind speed meter. The various efficiencies of the solar system have been predicted by an artificial neural network (ANN) which was trained with historical data. The ANN fails to predict the efficiencies accurately over the long-time horizon because of system parts degradation, environmental variations, date changes within the year from the modelling date and presence of modelling errors. Therefore the ANN is adapted using the error between the ANN-predicted efficiency and the efficiency measurement from the appropriately selected sensors and efficiency laws to update the network's parameters recursively. The adaptation scheme can be performed online or occasionally and is based on the Kaczmarz's algorithm. The adaptive ANN capability is demonstrated through computer simulation.

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