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

Advances in Repurposing and Recycling of Post-Vehicle-Application Lithium-Ion Batteries

Increased electrification of vehicles has increased the use of lithium-ion batteries for energy storage, and raised the issue of what to do with post-vehicle-application batteries. Three possibilities have been identified: 1) remanufacturing for intended reuse in vehicles; 2) repurposing for non-vehicle, stationary storage applications; and 3) recycling, extracting the precious metals, chemicals and other byproducts. Advances in repurposing and recycling are presented, along with a mathematical model that forecasts the manufacturing capacity needed for remanufacturing, repurposing, and recycling. Results obtained by simulating the model show that up to a 25% reduction in the need for new batteries can be achieved through remanufacturing, that the sum of repurposing and remanufacturing capacity is approximately constant across various scenarios encouraging the sharing of resources, and that the need for recycling capacity will be significant by 2030. A repurposing demonstration shows the use of post-vehicle-application batteries to support a semi-portable recycling platform. Energy is collected from solar panels, and dispensed to electrical devices as required. Recycling may be complicated: lithium-ion batteries produced by different manufacturers contain different active materials, particularly for the cathodes. In all cases, however, the collecting foils used in the anodes are copper, and in the cathodes are aluminum. A common recycling process using relatively low acid concentrations, low temperatures, and short time periods was developed and demonstrated

Reducing Lead Times in a two-process cell using lean and simulation.

An existing power system production process includes two operations and produces twelve different part types. The first operation fills WIP carts used by the second operation. A combined lean and discrete event simulation study supported by the analysis of order history information stored in a corporate information system is presented. The goal was to identify operations alternatives that could be used to reduce customer lead time from the current 3 to 3 days to 1 to 3 days. The application of lean methods included the examination of the order history data that showed that 80% of parts ship to a single primary customer and 20% to many secondary customers. The lean part of the study further concluded that the number of WIP carts should equal the number of different products, that each WIP cart should be associated with one and only one product and that each WIP cart should be should refilled daily after orders are processed. Using a discrete event simulation model, four order processing sequencing alternatives for improving on-time delivery were evaluated. The percent of orders delivered in 1 day was maximized, with the lowest variance, by sorting all orders for the primary customer first from smallest in size to largest. The orders for the same product from the remaining customers are processed immediately after the order for the same product from the primary customer. The value of the synergistic effect of combining lean tools with simulation supported by order data extracted from the corporate information system is demonstrated

A Case Study of Laser Wind Sensor Performance Validation by Comparison to an Existing Gage

A case study concerning validation of wind speed measurements made by a laser wind sensor mounted on a 190 square foot floating platform in Muskegon Lake through comparison with measurements made by pre-existing cup anemometers mounted on a met tower on the shore line is presented. The comparison strategy is to examine the difference in measurements over time using the paired-t statistical method to identify intervals when the measurements were equivalent and to provide explanatory information for the intervals when the measurements were not equivalent. The data was partitioned into three sets: not windy (average wind speed measured by the cup anemometers ≤ 6.7m/s) windy but no enhanced turbulence (average wind speed measured by the cup anemometers \u3e 6.7m/s), and windy with enhanced turbulence associated with storm periods. For the not windy data set, the difference in the average wind speeds was equal in absolute value to the precision of the gages and not statistically significant. Similar results were obtained for the windy with no enhanced turbulence data set and the average difference was not statistically significant (α=0.01). The windy with enhanced turbulence data set showed significant differences between the buoy mounted laser wind sensor and the on-shore mast mounted cup anemometers. The sign of the average difference depended on the direction of the winds. Overall, validation evidence is obtained in the absence of enhanced turbulence. In addition, differences in wind speed during enhanced turbulence were isolated in time, studied and explained

Beyond Lean: Simulation in Practice, Second Edition

Lean thinking, as well as associated processes and tools, have involved into a ubiquitous perspective for improving systems particularly in the manufacturing arena. With application experience has come an understanding of the boundaries of lean capabilities and the benefits of getting beyond these boundaries to further improve performance. Discrete event simulation is recognized as one beyond-the-boundaries of lean technique. Thus, the fundamental goal of this text is to show how discrete event simulation can be used in addition to lean thinking to achieve greater benefits in system improvement than with lean alone. Realizing this goal requires learning the problems that simulation solves as well as the methods required to solve them. The problems that simulation solves are captured in a collection of case studies. These studies serve as metaphors for industrial problems that are commonly addressed using lean and simulation.https://scholarworks.gvsu.edu/books/1006/thumbnail.jp

Beyond Lean: Simulation in Practice

Lean thinking, as well as associated processes and tools, have involved into a ubiquitous perspective for improving systems particularly in the manufacturing arena. With application experience has come an understanding of the boundaries of lean capabilities and the benefits of getting beyond these boundaries to further improve performance. Discrete event simulation is recognized as one beyond-the-boundaries of lean technique. Thus, the fundamental goal of this text is to show how discrete event simulation can be used in addition to lean thinking to achieve greater benefits in system improvement than with lean alone. Realizing this goal requires learning the problems that simulation solves as well as the methods required to solve them. The problems that simulation solves are captured in a collection of case studies. These studies serve as metaphors for industrial problems that are commonly addressed using lean and simulation.https://scholarworks.gvsu.edu/books/1001/thumbnail.jp

Using Expert Systems for Simulation Modeling of Patient Scheduling

Modeling the scheduling of patient appointments is an important issue in simulating a health care delivery facility. A simulation model must include the control logic of appointment scheduling software and the explicit and implicit decision rules used by the human scheduler in selecting an appointment time. Expert systems provide one way of modeling such control logic and decision rules. We describe a structure for an expert system that models patient appointment scheduling and the integration of such an expert system within a simulation model. An example expert system for a small animal veterinary clinic is presented

Floating Laser Pulse Technology: A Strategy for Great Lakes Hub Height Offshore Wind Assessments

The principal purpose of this project is to conduct a wind assessment study of Lake Michigan and to advance the body of knowledge that will allow successful offshore commercial wind energy development on the Great Lakes. The project involves the permitting and installation of the first offshore wind power assessment meteorological (MET) facilities in Michigan’s Great Lakes, utilizing Laser Pulse Technology (LPT). In addition to validating the technology, other important research that will contribute to the deployment of offshore wind technologies are being undertaken based on the guidelines established by the Michigan Great Lakes Wind Council (GLOW Council). The project has created opportunities for public dialogue and community education about offshore wind resource development. Project collaborators include: U.S. Department of Energy, Michigan Public Service Commission, WE Energies, Sierra Club of the Great Lakes, Grand Valley State University, University of Michigan, Michigan Technological University, and Michigan State University

Intelligent Transportation System Real Time Traffic Speed Prediction with Minimal Data

Purpose: An Intelligent Transportation System (ITS) must be able to predict traffic speed for short time intervals into the future along the branches between the many nodes in a traffic network in near real time using as few observed and stored speed values as possible. Such predictions support timely ITS reactions to changing traffic conditions such as accidents or volume-induced slowdowns and include re-routing advice and time-to-destination estimations. Design/methodology/approach: Traffic sensors are embedded in the interstate highway system in Detroit, Michigan, USA, and metropolitan area. The set of sensors used in this project is along interstate highway 75 (I-75) southbound from the intersection with interstate highway 696 (I-696). Data from the sensors including speed, volume, and percent of sensor occupancy, were supplied in one minute intervals by the Michigan Intelligent Transportation Systems Center (MITSC). Hierarchical linear regression was used to develop a speed prediction model that requires only the current and one previous speed value to predict speed up to 30 minutes in the future. The model was validated by comparison to collected data with the mean relative error and the median error as the primary metrics. Findings and Originality/value: The model was a better predicator of speed than the minute by minute averages alone. The relative error between the observed and predicted values was found to range from 5.9% for 1 minute into the future predictions to 10.9% for 30 minutes into the future predictions for the 2006 data set. The corresponding median errors were 4.0% to 5.4%. Thus, the predictive capability of the model was deemed sufficient for application. Research limitations/implications: The model has not yet been embedded in an ITS, so a final test of its effectiveness has not been accomplished. Social implications: Travel delays due to traffic incidents, volume induced congestion or other reasons are annoying to vehicle occupants as well as costly in term of fuel waste and unneeded emissions among other items. One goal of an ITS is to improve the social impact of transportation by reducing such negative consequences. Traffic speed prediction is one factor in enabling an ITS to accomplish such goals. Originality/value: Numerous data intensive and very sophisticated approaches have been used to develop traffic flow models. As such, these models aren’t designed or well suited for embedding in an ITS for near real-time computations. Such an application requires a model capable of quickly forecasting traffic speed for numerous branches of a traffic network using only a few data points captured and stored in real time per branch. The model developed and validated in this study meets these requirements