Introduction to Production: Philosophies, Flow, and Analysis

Production is a fundamental societal and economic activity. Production has to do with the transformation of raw materials into useful objects and includes the knowledge to complete the transformation effectively. Thus, production is a board topic ranging from philosophies about how to approach production such as lean and quick response manufacturing, how to organize production facilities, how to analyze production operations, how to control the flow of materials during production, the devices used to move materials within a facility, and strategies for coordinating multiple production facilities. An integrated introduction to production is presented in a set of learning modules. In significant part, these learning modules are based on over 20 years of interactions with the professional production community in the West Michigan region where Grand Rapids and Holland are the principal cities. This community consists almost exclusively of small and medium size companies engaged primarily in high mix, low volume manufacturing. Students in the Bachelor of Science in Engineering and Master of Science in Engineering programs at Grand Valley State University often work in production for these companies. Thus, interactions are facilitated particularly though master’s degree capstone projects, several of which are referenced in the learning modules. The learning modules are well-grounded in established production concepts. Emphasis is placed on proven procedures such as systematic layout planning, factory physics, various production flow control techniques such as kanban and POLCA, and discrete event simulation. Professional practice is a focus of the learning modules. Material from processional groups such as the Lean Enterprise Institute and the Material Handling Institute (MHI) is integrated. The opportunity to read and discuss professional publications presenting production improvement projects is provided. Students are referred to professional videos and web sites throughout the learning modules. All materials provided are referenced are open access and free of charge. When downloading the main file, it is important to also download and use the Main File Support as it contains supplemental materials.https://scholarworks.gvsu.edu/books/1022/thumbnail.jp

REDUCING CUSTOMER WAIT TIME AND IMPROVING PROCESSES AT ABC’s ATV RENTALS

This project serves to explore the system bottlenecks of a small, family owned ATV rental company. The main objective is to reduce the average time a customer spends in the system, focusing on customer wait time as well as other areas that can be improved. This was done by collecting time studies and inputting the values into simulation software, which was run to represent the current system as well as various other possible scenarios encountered by rental companies. While creating the simulation, adaptive techniques were incorporated into the simulation. These techniques aim to increase the durability and reusability of the simulation for future use. An example of incorporating adaptive simulation is through having the simulation software draw values from an Excel spreadsheet. This example of adaptive simulation targets the efficiency of use, as values and formulas are easier to calculate and visualize in Excel than the simulation software. Through the scenarios created in the simulation software, the main system bottleneck was discovered to be the company’s trailer fleet size. Several scenarios were then created to further explore the theory and resulted in confirming it. The results of this analysis conclude that to reduce customer wait time in the system, the company should increase its fleet size by one trailer. A secondary, no cost solution is to eliminate ATV load/unload times by moving ATVs to the dunes prior to customer arrival instead of loading them on a customer by customer basis

Determining the efficacy of additive manufacturing for the aerospace spare parts supply chain

This thesis investigates how additive manufacturing (AM) based on-demand part production can supplement or replace the traditional production and inventory in typical aerospace’s spare parts supply chain systems. This study focuses on the operational characteristics of AM and its impacts on the overall logistics of plant-level operations. To capture the microscopic operational aspects of the AM production, a discrete-event simulation based approach was adopted, with key AM operation resources (e.g. AM system, operator) and attributes (e.g. AM manufacturing speed, individual part characteristics and demands) accounted for in the modeling process. In addition, a benchmark warehouse inventory model was also established separately based on classic theories, which was subsequently utilized to create a cost/benefit analysis for the AM based part supply strategies versus the traditional strategies. The results from virtual experiments with these models were analyzed in order to gain an understanding of the operational characteristics (e.g., production cost, system utilization, lead time) as a function of various production policies such as machine/operator configurations and part prioritization. Data analysis shows cost savings for AM as an alternative to warehousing under high penalty scenarios. Results also indicate higher cost savings with the addition of extra machines over extra operators to meet capacity. Finally, analysis shows that reprioritizing orders waiting in a queue has higher savings when assessing due date and penalty outcomes

Scheduling With Alternatives Machine Using Fuzzy Inference System And Genetic Algorithm.

As the manufacturing activities in today's industries are getting more and more complex, it is required for the manufacturing company to have a good shop floor production scheduling to plan and schedule their production orders. Industri pengeluarcim kini telah berkembang pesat dan aktiviti pengeluarannya semakin kompleks, dengan itu syarikat pengeluar memerlukan jadual lantai pengeluaran (shop floor) yang terbaik untuk merancang permintaan pengeluaran (product)

A Performance Evaluation of a Lean Reparable Pipeline in Various Demand Environments

Lean production and logistics processes were developed in the commercial sector to reduce total system costs of production while simultaneously providing high levels of customer service, increased productivity, and increased worker utilization, In 1993, the Air Force instituted the Lean Logistics program, which successfully implemented some commercial lean principles, enabling a reduction in the total reparable asset material requirement for the Air Force reparable asset pipeline. The Air Force is attempting to further implement lean production principles into depot repair in hopes of further enhancing reparable asset pipeline cost and customer service performance. However, the failure of reparable assets, which determines demand for Air Force depots can be extremely erratic and difficult to predict. A primary criticism of lean systems is their vulnerability in volatile demand environments. Therefore, the implementation of a full-scale lean approach to depot repair may not be conducive to operational success, The purpose of this research is evaluate whether the Air Force reparable pipeline operating under lean production and logistics principles can effectively support operational requirements in various demand environments. In an attempt to answer the research objective, multiple Arena simulation models of a lean reparable asset pipeline operating under various conditions were developed. A full factorial experimental design was employed and multivariate analysis of variance (MANOVA) was utilized to assess the effects of differing levels of demand variability, base and depot supply levels, and the use of premium transportation on cost and stockage effectiveness response variables

Scheduling and shop floor control in commercial airplane manufacturing

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2005.Includes bibliographical references (p. 73-75).Boeing is the premier manufacturer of commercial jetliners and a leader in defense and space systems. Competition in commercial aircraft production is increasing and in order to retain their competitive position, Boeing must strive to improve their operations by reducing costs. Boeing factories today still schedule and monitor the shop floor much as they have for the past 100 years. This thesis compares and contrasts several different methods for shop floor control and scheduling including Boeing's barcharts, Toyota production system, critical chain, and dynamic scheduling. Each system is will be analyzed with respect to how it handles variability in labor output required and how that affects which products are typically made under each system. In additional to qualitative comparisons, discrete event simulations comparing the various strategies will be presented. Areas for future simulation study are also discussed. The recommended approach for commercial airplane assembly is critical chain. A suggested implementation plan is presented along with methods to ease acceptance.by Vikram Neal Sahney.S.M.M.B.A

CHANGE-READY MPC SYSTEMS AND PROGRESSIVE MODELING: VISION, PRINCIPLES, AND APPLICATIONS

The last couple of decades have witnessed a level of fast-paced development of new ideas, products, manufacturing technologies, manufacturing practices, customer expectations, knowledge transition, and civilization movements, as it has never before. In today\u27s manufacturing world, change became an intrinsic characteristic that is addressed everywhere. How to deal with change, how to manage it, how to bind to it, how to steer it, and how to create a value out of it, were the key drivers that brought this research to existence. Change-Ready Manufacturing Planning and Control (CMPC) systems are presented as the first answer. CMPC characteristics, change drivers, and some principles of Component-Based Software Engineering (CBSE) are interwoven to present a blueprint of a new framework and mind-set in the manufacturing planning and control field, CMPC systems. In order to step further and make the internals of CMPC systems/components change-ready, an enabling modeling approach was needed. Progressive Modeling (PM), a forward-looking multi-disciplinary modeling approach, is developed in order to modernize the modeling process of today\u27s complex industrial problems and create pragmatic solutions for them. It is designed to be pragmatic, highly sophisticated, and revolves around many seminal principles that either innovated or imported from many disciplines: Systems Analysis and Design, Software Engineering, Advanced Optimization Algorisms, Business Concepts, Manufacturing Strategies, Operations Management, and others. Problems are systemized, analyzed, componentized; their logic and their solution approaches are redefined to make them progressive (ready to change, adapt, and develop further). Many innovations have been developed in order to enrich the modeling process and make it a well-assorted toolkit able to address today\u27s tougher, larger, and more complex industrial problems. PM brings so many novel gadgets in its toolbox: function templates, advanced notation, cascaded mathematical models, mathematical statements, society of decision structures, couplers--just to name a few. In this research, PM has been applied to three different applications: a couple of variants of Aggregate Production Planning (APP) Problem and the novel Reconfiguration and Operations Planning (ROP) problem. The latest is pioneering in both the Reconfigurable Manufacturing and the Operations Management fields. All the developed models, algorithms, and results reveal that the new analytical and computational power gained by PM development and demonstrate its ability to create a new generation of unmatched large scale and scope system problems and their integrated solutions. PM has the potential to be instrumental toolkit in the development of Reconfigurable Manufacturing Systems. In terms of other potential applications domain, PM is about to spark a new paradigm in addressing large-scale system problems of many engineering and scientific fields in a highly pragmatic way without losing the scientific rigor