TOWARDS CIM OPTIMIZATION

This paper describes initial work carried out in constructing a strategy-based quantitative technique, which aims not only to formulate a strategy for Advanced Manufacturing Technology (AMT) investment (incorporating elements of a financial justification for the investment), but also identifies means of monitoring investment performance against strategy. The approach is further extended through the use of simulation to deal with problems when the relevant probability distributions can be postulated. Simultaneous considerations of the revenues, production costs, salvage values and economics (many of the costs and system characteristics for individual firms) require considerable effort. The framework for computer implementation of the methodology and the various sources from which the needed information may be obtained for a successful implementation of the methodology are discussed. Together with earlier works and feasibility studies, a schematic representation of the information flows among various functions and activities in a typical manufacturing business system have been carried out1,2. In addition, a management information system should also identify the costs of capacity, lead time performance, flexibility performance, and the cost of product driven by technology. A computer aided model is an additional tool that decision makers can use to consolidate all efforts to realistically consider many of the tangible and intangible elements. The model does have implicit in its design the capability to play out alternative strategies of operations for the manager. This evolutionary development of an optimization model for the operational evaluation of an integrated manufacturing system does not seek to answer any identifiable problem, but to provide the mechanism for future investigations. It is therefore intended to be a generalized model applicable to various manufacturing environments elected by the user. Many operational research techniques have been applied over the years to investigate problems indentified in the management of the manufacturing environment. Commonly, the techniques have sought to achieve an optimum solution to the problem being investigated. However, very often the expected level of optimization is not necessarily achieved when applied to the real production environment where the original problem resides. Ultimately, it is feasible to consider the progression from this model in operation as a simulation and optimization tool, playing out hypothetical environments, to provide a true description in real time of the system in operation. With increasing computational power becoming cost effective, the multiple parameters in a manufacturing environment can be re-permutated in a realistic time-frame to give true emulation of the system in operation.