Browsing by Subject "Manufacturing resource planning"
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Item A comparative simulation study of manufacturing resource planning, just-in-time and theory of constraints in VAT classified flow shops facing smooth and lumpy demand(Texas Tech University, 2002-05) Buentello, Edmundo José GamasThree production planning and control methods currently vie for supremacy in realworld industrial application. One of the production planning and control methods in question is Manufacturing Resource Planning, which is founded upon the logic of its predecessor, Material Requirements Planning. The other two production planning and control methods concerned are Just-In-Time and Theory Of Constraints. Each one of these production planning and control methods purports to endow a production manager with a comprehensive conceptual framework for the planning and control of his or her industrial operations. The question of whether one of the three production planning and control methods mentioned is superior to the others is by logical extension a valid and important one for a research undertaking. Two previous simulation studies compare the performance of the three aforementioned production planning and control methods. One study was conducted by Fogarty, Blackstone, and Hoffmann (1991), and the more recent study was undertaken by Cook (1994). Both simulation studies contrast the three production planning and control methods in an "I" classified flow shop, which is characterized by a single production path. This dissertation compares the performance of the production planning and control methods referred to in flow shops with a more complex layout, known as "V" "A" and "T" classified flow shops, respectively, in accordance with the shape of their layout. It designs and constructs three simulation models, one to represent each of these three flow shop classifications. Each simulation model is run under each of the mentioned production planning and control methods, to satisfy either a smooth or a lumpy demand profile, and at four different total material buffer capacity levels.Item A synergistic methodology and adaptive model for material management in a dynamic environment(Texas Tech University, 1998-05) Carrigo, E. AllenThis dissertation and the associated research compares the three main manufacturing philosophies of Manufacturing Resource Planning (MRP), Just In Time (JIT) and the Theory of Constraints (TOC) with a new manufacturing concept termed the Adaptive Model. The Adaptive Model uses the synergistic effect of combining selected aspects of the JIT and TOC philosophies into a new manufacturing philosophy. Specifically, the adaptive model identified a second constraint in addition to the primary constraint. This secondary constraint was both optimized and given an increased buffer size. It was envisioned before conducting the research that the adaptive model would be superior to the other models. Three different manufacturing lines of five, nine and fifteen workstations were developed and modeled. Each of these models with different buffer sizes was simulated for approximately ten time periods of six months. Performance factors were identified and employed for evaluating the four philosophies. These factors included Throughput levels. Work in Process (WIP) quantities, Time-in-the-system periods, the maximum amount of idle time at the main constraint and the utilization rate for the main constraint. Data for each of the performance factors were collected and analyzed. Additionally, hypothesis tests were developed and conducted. Differences existed between the various manufacturing philosophies, according to the tests. Observations of the data suggest that the adaptive model produces sporadic superior results when compared with the other philosophies. However, the adaptive model exhibits higher costs of larger WIP levels, increased time-in-the-system lengths and increased costs of operating at an accelerated pace. The research conclusion is that the TOC model for a flow shop manufacturing environment is the best overall manufacturing philosophy.