Editorial

Citation

Moutinho, L. (2006), "Editorial", Journal of Modelling in Management, Vol. 1 No. 3. https://doi.org/10.1108/jm2.2006.29701caa.001

Editorial

Here we are. The third issue of Volume 1 of the Journal of Modelling in Management (JM2). It has been a learning process and I would like to thank all the editorial support we have received as well as to the authors for their submissions and patience while waiting for the reviewing process.

In this final issue of the first volume, we have an array of challenging topics in modelling that range from environmental uncertainty, change management and customer support performance to ant colony optimisation modelling and manufacturing safety simulation.

In the first paper, Pramod and Garg deal with the analysis of flexibility requirements under uncertain environments. Flexible manufacturing system (FMS) is one of the options to meet the uncertainty in demand and high variety of products. This paper reviews the definition, classification, and measurement of manufacturing flexibility concerned with manufacturing flexibility management. As a consequence of this process three primary flexibility dimensions are identified: volume, variety and machine. A simulation approach is used to study the behaviour of FMS under different demand scenarios and levels of flexibility. For any flexibility level, as the traffic density increases the system utilization increases, as the traffic density increases the throughput time increases and as the number of part type increases the system utilization decreases. It is concluded that partial flexibility is better as compared to no flexibility and total flexibility. The value of this research also rests on the effect that a specific design variable has on a specific system level flexibility type can change with the level of part processing flexibility present and a flexibility trade-off in manufacturing systems is not inevitable.

Professors Subhas Misra, Vinod Kumar and Uma Kumar's paper “An actor-dependency technique for analyzing and modeling early-phase requirements of organizational change management due to information systems adoption” is the second paper in this issue. Because of the competitive economy organizations today seek to rationalize, innovate and adapt to changing environments and circumstances as part of business process reengineering (BPR) efforts. Irrespective of the process reengineering program selected and the technique used to model it, BPR brings with it the issues of organizational and process changes, which involves managing organizational changes. Change management is non-trivial, as organizational changes are difficult to accomplish. Though some attempt has been made to model change management in enterprise information systems using conventional conceptual modeling techniques, they have just addressed “what” a change process is like, and they do not address “why” the process is the way it is. These authors' approach is novel in the sense that it presents an actor-dependency-based technique for analyzing and modeling early-phase requirement of organizational change management that provides the motivations, intents, and rationale behind the entities and activities.

The third paper of this issue, deals with modeling customer support performance, the case of the Indian IT hardware industry. Customer support assumes strategic importance for branded IT-hardware products. An authorized service centre and a stream of specialized service centres undertake field services and represent a sales territory's support network. `Time-constrained' service men have to deliver customized service meeting a promised time-standard. The stochastic demand for support services severely mars the customer response resulting in poor service quality. A manufacturer has to address the following decisions under these conditions. What is the ideal staffing level in a territory considering restricted server availability? What will be the impact of changing the staffing levels on customer service level? This study by Ravishanker, Vijayaraghavan and Narendran develops an analytical model to address these decisions. The study identifies the variables underlying stochastic service demand through a field survey and determines the demand distribution. Applying stochastic principles the study derives a relation between field staffing level and customer response considering server time constraint. The outcomes of the analysis reveal that increasing field staffing levels obscures the significant difference between the customer waiting times under very high levels of uncertain demand.

Entrepreneurs operate in conditions of dynamic uncertainty; identifying and exploiting opportunities presented by the business environment. Opportunistic search is core to entrepreneurial activity, but its dynamics are rarely explored. Groups of entrepreneurs are attracted to the same potential business opportunities. They have no incentive to cooperate, they may not even know of the existence of others. However, over time, clusters of entrepreneurs interested in the same opportunities develop. Ant colony optimisation modelling is used by Butel and Watkins to simulate the activities of entrepreneurs in an opportunity rich environment. The entrepreneurs must identify the locations of the appropriate resources. Three simulations were run to observe entrepreneurial success in different environments. The findings revealed that a random search of the business environment for resources by individual entrepreneurs was unproductive. Once the entrepreneurs learned to read the business environments and so refine their search, they were increasingly efficient. This was even more pronounced when time allowed for search was constrained and weaker entrepreneurs had little influence. The computer simulations demonstrate how a cluster of entrepreneurial activity may begin. Using a multi-agent search model to simulate dynamic interaction of a number of entrepreneurs in the same business environment demonstrates early cluster formation without the protagonists relying on cooperative, competitive or value chain interaction.

Finally, Charles-Owaba and Kazeem Adebiyi focus on the development of a manufacturing safety programme simulator. It was the goal of this study to develop a simulation model for predicting the performance of a manufacturing safety programme (SP). The principles of system-dynamics were applied to identify the relevant safety-related components and their relationships. A simulation model for evaluating periodic performance of a manufacturing safety programme was then developed. A set of dynamic equations for predicting factory accidents or preventions and the monetary saving were the performance measures. Two sets of factory data were collected and the parameters of the model were estimated using the first set while it was validated with the second and the associated monetary saving computed. Solutions to factory accidents or preventions yielded exponential functions. There were no significant differences between the predicted and actual for the accidents and preventions, respectively, at 5 per cent level. The model is a useful took for setting profitable manufacturing safety standards and effective safety programme management.

I hope you enjoy reading this diversity of scholarly work related to modelling issues.

See you!

Luiz Moutinho