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This book helps the reader understand the power of informed decision making by covering a broad range of DSS (Decision Support Systems) applications in the fields of medical, environmental, transport and business. The expertise of the chapter writers spans an equally extensive spectrum of researchers from around the globe including universities in Canada, Mexico, Brazil and the United States, to institutes and universities in Italy, Germany, Poland, France, United Kingdom, Romania, Turkey and Ireland to as far east as Malaysia and Singapore and as far north as Finland. Decision Support Systems are not a new technology but they have evolved and developed with the ever demanding necessity to analyse a large number of options for decision makers (DM) for specific situations, where there is an increasing level of uncertainty about the problem at hand and where there is a high impact relative to the correct decisions to be made. DSS's offer decision makers a more stable solution to solving the semi-structured and unstructured problem.

Agile, fast and flexible production networks are a must in today’s global competition. The interrelations between manufacturing systems and processes are becoming more complex and the amount of data for decision making is growing. Manufacturing, engineering and production management decisions involve the consideration of multiple parameters. These often complex, interdependent factors and variables are too many for the human mind to cope with at one time. Agile production needs a management and evaluation tool for production changes, manufacturing system development, configuration and operations planning. A decision support system based on manufacturing simulation is one suitable solution. Discrete Event Simulation (DES) has mainly been used as a production system analysis tool to evaluate new production system concepts, layout and control logic. Recent development has enhanced DES models for use in the day-to-day operational production planning of manufacturing facilities. These "as built" models provide manufacturers with the ability to evaluate the capacity of the system for new orders, unforeseen events such as equipment downtime, and changes in operations. After a simulation model has been built, experiments are performed by changing the input parameters and predicting the response. Experimentation is normally carried out by asking "what-if" questions and using the model to predict the likely outcome. A simulation-based Decision Support System (DSS) can be used to augment the tasks of planners and schedulers to run production more efficiently (Figure 1). Some of the benefits of implementing an operational simulation scheduling system include: less effort required to plan day-to-day scheduling, customer order due date conformance, synchronisation of flow through the plant, minimisation of set-ups/changeovers, early warnings of potential problems, checks of critical resources and materials, and, naturally, “what-if” scenario analysis for capacity planning. Although dedicated software packages are currently available, there are limited examples of the use of simulation tools in the operational planning of manufacturing. This chapter also sheds light on development challenges and current development efforts to solve these challenges for this data and model-driven DSS. The major challenges are: 1) data integration, 2) automated simulation model creation and updates, and 3) visualisation of results for interactive and effective decision making.