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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS<br />

JUNE 2011<br />

VOL 3, NO 2<br />

• Interpret output from the simulation model to provide answers for what-if scenarios;<br />

• Respond to interactive queries and generate reports;<br />

Market<br />

data<br />

Push<br />

System<br />

production<br />

control<br />

Data<br />

Production control<br />

Simulation<br />

model<br />

Decision<br />

support<br />

data<br />

base<br />

DSS<br />

Operating<br />

system<br />

Command<br />

language<br />

Data<br />

analysis and<br />

display<br />

Solution<br />

candidate<br />

generator<br />

Statistical<br />

analysis<br />

manager<br />

Digital<br />

simulation<br />

manager<br />

Dynamic<br />

programming<br />

model manager<br />

Decision<br />

logic<br />

Strategic<br />

level<br />

Tactical<br />

level<br />

Operational<br />

level<br />

Solution<br />

optimization<br />

Company<br />

wide<br />

data<br />

Solution<br />

optimization Solution<br />

validation<br />

Decision maker<br />

Figure 3. The DSS framework for the transition to a pull system<br />

The statistical analysis manager includes a data base management system (DBMS) and provides<br />

the communication between the digital simulation manager and the model manager. The main<br />

functions of the staffstical analysis manager are to:<br />

• Use an internal DBMS to organize simulation output statistics;<br />

• Receive data from the DBMS and run multiple regression to identify the significant<br />

performance measures;<br />

• Export results to the model manager;<br />

• Respond to interactive queries and generate reports.<br />

COPY RIGHT © 2011 Institute of Interdisciplinary Business Research 166

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