http://directory.umm.ac.id/Data%20Elmu/jurnal/I/Information%20and%20Management/Vol37.Issue3.Apr2000/1858.pdf

Toward an empirical understanding of computer simulation implementation success

A materials handling system simulation (written using GPSS/H) was developed to predict the Automated Guided Vehicle requirements necessary for a major manufacturer to maintain desired levels of production in one of its automobile assembly plants. Rather than use the simulation as a representational DSS and risk complicating the user interface, validated simulation outputs were collected and used to produce a multivariate regression metamodel. This metamodel formed the centerpiece of a narrow-scope suggestion model DSS used on the factory floor to aid in day to day allocations of resources. This article looks at the metamodel development methodology and offers this technique as an effective means of producing a suggestion model DSS from a more complex representational DSS.

Multivariate regression metamodel: a DSS application in industry

Advances in information technology and explosion of internet technology is creating new professional users, across and within countries. These users are looking at technology to provide decision support for non-recurring tasks, to provide prototyping capabilities and to provide research support. However, organizational decision environment is also changing, creating havoc for system builders who must match changing technology to changing decision environment. This paper focuses on one such technology, namely, Decision Support System (DSS) and one such decision environment, namely, sequential decisions. It is argued that the next millennium DSS must focus on the 'D' of DSS because of the complexities of the decisions they are trying to support. These DSSs, called SDSSs, are further examined in the context of data-dialog-model-communication. Communication component is needed because of the complexity of sequential decision-making which spans across several hierarchical levels or involves several decision makers at the same level.

/pdf/a-taxonomy-of-sequential-decision-support-systems-4dxcm6wrc4.pdf

A Taxonomy of Sequential Decision Support Systems

A small business inventory DSS: design, development, and implementation issues

A simulation based-decision support system for multi-product inventory control management is developed The system permits management to obtain an inventory system-wide view of the effect of changes in decision variables on the performance measures of a furniture manufacturing firm The simulation model considers the effect of variations in demand, re-order point, stock-control level, time between reviews, and lead time The decision support system generates policy scenarios based on management specifications The effect of each scenario on system performance is then analysed A set of search methods is combined with the DSS to aid in the optimisation process The developed network consists of three basic sub-networks The first sub-network represents the demand, back- order, and stock components of the system The second network represents the periodic review process with stock control levels for each product The third sub-network performs profit and cost calculations under normal circumstances and when

A Simulation Model for Multi-Product Inventory Control Management:

Model-based decision support systems: an effective implementation framework

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Model-based decision support systems: an effective implementation framework