The Application of Spreadsheet Model Based on Queuing Network to Optimize Capacity Utilization in Product Development

Modeling of a manufacturing system enables one to identify the effects of key design parameters on the system performance and as a result make the correct decision. This paper proposes a manufacturing system modeling approach using computer spreadsheet software, in which a static capacity planning model and stochastic queuing model are integrated. The model was used to optimize the existing system utilization in relation to product design. The model incorporates a few parameters such as utilization, cycle time, throughput, and batch size. It is predicted that design changes initiated as a result of analysis using the model reduced subsequent manufacturing costs significantly and also can reduce the launch program by a few years, because confidence in the model justified the commissioning of full-scale manufacturing equipment when the product was still only at the concept stage

Time Value of Commercial Product Returns

Manufacturers and their distributors must cope with an increased flow of returned products from their customers. The value of commercial product returns, which we define as products returned for any reason within 90 days of sale, now exceeds US $100 billion annually in the US. Although the reverse supply chain of returned products represents a sizeable flow of potentially recoverable assets, only a relatively small fraction of the value is currently extracted by manufacturers; a large proportion of the product value erodes away due to long processing delays. Thus, there are significant opportunities to build competitive advantage from making the appropriate reverse supply chain design choices. In this paper, we present a simple queuing network model that includes the marginal value of time to identify the drivers of reverse supply chain design. We illustrate our approach with specific examples from two companies in different industries and then examine how industry clockspeed generally affects the choice between an efficient and a responsive returns network

A User's Guide to the Brave New World of Designing Simulation Experiments

Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models.In this paper, we discuss a toolkit of designs for simulationists with limited DOE expertise who want to select a design and an appropriate analysis for their computational experiments.Furthermore, we provide a research agenda listing problems in the design of simulation experiments -as opposed to real world experiments- that require more investigation.We consider three types of practical problems: (1) developing a basic understanding of a particular simulation model or system; (2) finding robust decisions or policies; and (3) comparing the merits of various decisions or policies.Our discussion emphasizes aspects that are typical for simulation, such as sequential data collection.Because the same problem type may be addressed through different design types, we discuss quality attributes of designs.Furthermore, the selection of the design type depends on the metamodel (response surface) that the analysts tentatively assume; for example, more complicated metamodels require more simulation runs.For the validation of the metamodel estimated from a specific design, we present several procedures.