Simultaneous Engineering

Objectives

Designing a system to achieve maximum performance without exceeding capital budgets while also focusing on overall life cycle costs requires the effective coordination of  cross-functional teams, a discipline often referred to as “Simultaneous Engineering”. Working as a part of that team, Empirical Solutions Corporation develops models of the new process that drive decisions based on data. Our models provide members of all areas of the organization, including marketing, product engineering, manufacturing engineering, materials control and suppliers, with a dynamic view of the proposed system in operation, far in advance of the design and build phase of the project. This visualization of the system, and quantification of associated performance measures provides a cost-effective approach to obtaining team consensus regarding input parameters and operating assumptions. Typical objectives of a simultaneous engineering simulation project include:

• To align system capacity with market demand
• To implement lean layout principles
• To optimize life cycle costs
• To minimize direct and indirect labor costs
• To identify and quantify opportunities to improve overall system efficiency.

Performance Measures

Effective decision-making requires the evaluation of valid information and a clear understanding of the consequences of a particular action. Our systems analysis and modeling approach insures that we provide quantitative measures to support the decision-making process. Typical measures from a Simultaneous Engineering study include:

• Required line speeds and processing times to achieve a balanced or designed constraint system.
• System throughput
• Direct and indirect resource utilizations
• Work in process inventory statistics
• Throughput variability statistics

Benefits

Using a quantitative test bed to insure that a new system will achieve volume, efficiency and operating cost targets provides numerous benefits, including:

• Optimized capital investment levels
• Reduced operating costs
• Identification of high risk areas
• Accurate prediction of system capacity
• Reduced time to market
• Reduced launch curve
• Increased cross-functional communication