Matching Items (2)
Description
This is a creative thesis project on the topic of the third party logistics industry, and the improvements that are possible through the implementation of goods to person technologies. The scope of the project entails the relationship between Company X, which is a third party logistics provider, and Company Y, a major toy retailer. This thesis identifies current trends for the third party logistics industry such as rising operating costs and average savings achieved through these business relationships. After identifying the negative trends that Company X is vulnerable to such as high human resources costs, and cost of quality issues. Given the findings derived from industry data, a final recommendation was settled on to improve productivity and ultimately reduce the use of temporary labor for Company X. The implementation of a goods to person technology solution provides the opportunity to reduce hours of operation, man hours, as well as direct and indirect costs such as labor. Research has proven that firms operating in the retail industry rely heavily on temporary labor to handle the seasonal demand brought by the holidays, thus this recommendation could be applied to a variety of operations. The data compiled throughout this thesis have major implications for the third party logistics industry and achieving long term profitability in operations management.
ContributorsFonseca, Tanner (Author) / Printezis, Antonios (Thesis director) / Kellso, James (Committee member) / Department of Supply Chain Management (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Factory production is stochastic in nature with time varying input and output processes that are non-stationary stochastic processes. Hence, the principle quantities of interest are random variables. Typical modeling of such behavior involves numerical simulation and statistical analysis. A deterministic closure model leading to a second order model for the product density and product speed has previously been proposed. The resulting partial differential equations (PDE) are compared to discrete event simulations (DES) that simulate factory production as a time dependent M/M/1 queuing system. Three fundamental scenarios for the time dependent influx are studied: An instant step up/down of the mean arrival rate; an exponential step up/down of the mean arrival rate; and periodic variation of the mean arrival rate. It is shown that the second order model, in general, yields significant improvement over current first order models. Specifically, the agreement between the DES and the PDE for the step up and for periodic forcing that is not too rapid is very good. Adding diffusion to the PDE further improves the agreement. The analysis also points to fundamental open issues regarding the deterministic modeling of low signal-to-noise ratio for some stochastic processes and the possibility of resonance in deterministic models that is not present in the original stochastic process.
ContributorsWienke, Matthew (Author) / Armbruster, Dieter (Thesis advisor) / Jones, Donald (Committee member) / Platte, Rodrigo (Committee member) / Gardner, Carl (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2015