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Traditional computer‐based material requirements planning (MRP) systems are usually implemented on large, mini‐computer systems. The MRP module is frequently one, albeit a central one, of a complex of modules together referred to as MRP II or Manufacturing Resources Planning. These systems, although ideal for real‐life, day‐to‐day use, can present a problem when used for training purposes. The sheer number of facilities available and consequent complexity of use often means that a lengthy training period is required. Describes a PC‐based MRP simulation package which encompasses the main features of existing MRP packages available in industry. As such, the package is ideal for training purposes. It allows typical MRP activities to be performed and basic knowledge of the most important MRP features to be gained. Great emphasis has been placed on a user‐friendly interface. Describes the PC‐based training system, the first of a series which will eventually cover several MRP II modules.

The implementation of Just‐in‐Time (JIT) techniques is an important issue for management. The wide range of activities making up JIT has posed real problems for its implementation. This fact motivated the development of a PC‐based software package which simulates JIT manufacturing. The package serves as an important educational training aid which may be used to illustrate various JIT principles, and as a valuable pedagogic tool for management. The simulation (or game) is interactive so that the user may make a sequence of decisions and then view the effect of these on a simulated production line. Extensive use is made of the colour graphics facilities of the PC for visual displays. The software is based on an existing manual version of the game.

Describes an expert system which goes some way towards providing detailed advice for implementing Just‐in‐Time (JIT) concepts in a manufacturing environment. Advises managers as to which techniques should be realized in order to begin implementation of JIT, or to achieve further improvement if a commitment has already been made to JIT implementation. Specifically, focuses on small‐to‐medium manufacturing companies producing high volume discrete components. The advice is based on an expanded two‐stage process for JIT implementation.

Many traditional cost accounting methods can result in distorted cost information as they allocate overheads in proportion to labour. This can result in a low technology product being overcosted and a high technology product being undercosted. Activity‐based costing (ABC) allocates costs more accurately and pinpoints areas of waste. Describes software which provides a tutorial introduction to ABC and highlights the difference between ABC and conventional cost accounting by means of simulating a production environment for the user to explore. The simulator should foster improved understanding of the opportunities of ABC.

This paper aims to introduce a method based on the optimizer of the particle swarm optimization (PSO) algorithm to improve the efficiency of a Kriging surrogate model.

PSO was first used to identify the best group of trend functions and to optimize the correlation parameter thereafter.

The Kriging surrogate model was used to resolve the fuselage optimization of an unmanned helicopter.

The optimization results indicated that an appropriate PSO scheme can improve the efficiency of the Kriging surrogate model.

Both the STANDARD PSO and the original PSO algorithms were chosen to show the effect of PSO on a Kriging surrogate model.

The purpose of this paper is to apply a numerical simulation of stochastic processes to the problem of real estate investment appraisal.

These uncertain operating costs are integrated into an enhanced dynamic simulation. To model the dynamics in the uncertainty of the cost schedule, a range of different types of stochastic processes is used. The operating costs are classified by cost drivers and an appropriate stochastic process is determined for each of the derived cost clusters. To optimise the capital structure in this application, heuristic optimisation with genetic algorithms is used.

The application of the model to real world investment situations shows that linear and deterministic modelling underestimates the risk‐generating effect of uncertain operating expenses, which often can lead to inefficient investment decisions.

In a further application of the model, the authors demonstrate the effect of uncertain operating costs on the optimal capital structure of real estate investments.

In contrast to models in the literature that are usually focussed on the income side, here the focus is on the uncertain dynamics of real estate operating costs as a key factor affecting return.

The purpose of this paper is twofold. First, to combine a holistic model – in our case the balanced scorecard – with the time-driven activity-based costing model. The inspiration for this stems both from Kaplan and Norton and from the intense discussions and use of business analytics (BA) and performance management (PM). Second, to use numerical experiments – more specifically Monte Carlo simulation – to design and explore four hypothetical scenarios within such a holistic model. The paper also aims to emphasise the role played by statistics in increasing the confidence in using such a framework.

The author runs four numerical experiments using different assumptions to show how a decision-maker can improve the outcome by making small changes in the key performance indicator (KPI) input variables.

The paper gives recommendations for the assumptions that each decision-maker has to consider when setting out to conduct this kind of analysis. Small changes in some input variables may completely change the output and hence the decision result.

The paper shows why practitioners and researchers need to better understand the limitations of deterministic analysis to make realistic models when combining more accounting models. To choose the relevant probability distributions for the input resources is an important issue for the decision-maker as they have a very large impact on the result.

The real value of the paper lies in making students and practitioners as well as researchers aware of the opportunities for stochastic modelling and also to point at the problems and limitations of combining elements from BA with performance measurement and management.

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.