A Tutorial on Ergonomic and Process Modeling Using QUEST and IGRIP Figure 1 : QUEST Examines Labor Impact on the Overall System 4 ERGONOMIC LABOR ASSESSMENT USING

This paper will discuss the benefits of sharing data between discrete event process simulation and ergonomic workplace assessment using software developed by Deneb Robotics Inc. A modeling exercise has been developed to illustrate the integration of ergonomic and discrete event process modeling. This paper will provide an overview of ergonomic and process modeling, a summary of features and functions of ergonomic and process modeling, and a demonstration of model creation, analysis, and data sharing. The advantages of integrating these simulations into a combined analysis will be demonstrated. 1 WHY INTEGRATED ANALYSIS? Digital Manufacturing is becoming an indispensable tool in streamlining the design to implementation process. Engineers are creating “Virtual” factories, true 3dimensional (3D) environments that allow visualization of the production process much earlier in the product’s life cycle. The quality and quantity of data is continuously increasing, with many companies standardizing on 3D solid modeling of both components and manufacturing hardware. With the implementation of MRP and ERP systems, this data is becoming available throughout the enterprises. The widening availability of digital data has provided greater opportunities for experimentation during the design phase by using highly accurate simulations. Within this virtual environment, the present challenge focuses on developing methods to maximize the benefits from possessing this digital data. In many cases, the same data is passed through several analytical tools in the digital manufacturing process to minimize build risk. How do you effectively and easily pass this relevant, shared information between software packages that have distinct analytical purposes? Combining information from different packages to provide more accurate analytical results would be better still, since in many occasions the results obtained from performing one analysis may affect the results of another. 297 This methodology challenge exists within the manufacturing simulation field, where the same geometry, timing, and process data are examined from several different perspectives. Simulation model accuracy and integrity are essential items for ensuring that digital analysis can be used to predict actual production system behavior. Sharing data and combining the results of different simulation tools, such as ergonomic workplace assessment and discrete event process simulation, can assist in creating a highly realistic picture during the analytical phase of the design process. This tutorial addresses the benefits of combining ergonomics and process simulation, by providing a technique to share digital data and results between two specific software packages developed by Deneb Robotics, Inc. Each package’s basic functionality will be discussed, and an integration approach will be introduced. Finally, suggestions on applying this procedure will be reviewed. 2 LABOR ANALYSIS SHARES DATA Ergonomic workplace assessment and discrete event process simulation are both used in analyzing labor requirements. The analyses, however, yield results that achieve different objectives. Ergonomic workplace assessment addresses an operator’s work assignment from the perspective of whether that operator is physically capable of performing the assigned tasks, possibly within a specified time. Discrete event process simulation examines labor at a macro level, including the number of operators a task requires, the availability of operators to perform tasks, and the operators’ impact on production. Thus, ergonomists and industrial engineers simulate labor activities from different perspectives. Both types of labor analysis are essential for ensuring that the actual real-world process will work as designed. A task’s workplace may be designed ergonomically for operators of any size, yet without applying process variability with discrete event simulation, that task can be the bottleneck for the entire production scheme. Conversely, a one-operator task may meet cycle