In today's hyper-competitive world, organizations need to make high performance and continuous improvement their highest priority. From a variety of process improvement philosophies and methods, one has emerged as the clear winner: Lean. Based on work by pioneers like Frederick Winslow Taylor, and Frank and Lillian Gilbreth, matured by global organizations like the Toyota Motor Company, and adapted world-wide since the 1980's, companies that have embraced Lean have consistently risen to the top of their industries. This is true for both manufacturing and non-manufacturing organization, like hospitals. The heart of the Lean method for manufacturing is flow, the ability to do work as a continuous, uninterrupted process, without waste, mistakes, or delays. The more that work can flow, the closer the company gets to high profitability, fast response time, zero waste, happy customers, and a host of other benefits. All of the extensive tools of Lean are focused on this objective: to be able to flow work. More specifically, organizations need to flow work of different types, the concept of Mixed Model production. The Complete Guide to Mixed Model Line Design is a practical guidebook that explains the Lean line design method, step-by-step and in plain English. This data-driven approach has been implemented successfully thousands of times, and has been proved in every industry. The Complete Guide to Mixed Model Line Design, and the methodology it explains, should be a part of every organization's improvement strategy, and be a part of the training for everyone involved in continuous improvement.

Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-nav

In the 1950’s, the design and implementation of the Toyota Production System (TPS) within Toyota had begun. In the 1960’s, Group Technology (GT) and Cellular Manufacturing (CM) were used by Serck Audco Valves, a high-mix low-volume (HMLV) manufacturer in the United Kingdom, to guide enterprise-wide transformation. In 1996, the publication of the book Lean Thinking introduced the entire world to Lean. Job Shop Lean integrates Lean with GT and CM by using the five Principles of Lean to guide its implementation: (1) identify value, (2) map the value stream, (3) create flow, (4) establish pull, and (5) seek perfection. Unfortunately, the tools typically used to implement the Principles of Lean are incapable of solving the three Industrial Engineering problems that HMLV manufacturers face when implementing Lean: (1) finding the product families in a product mix with hundreds of different products, (2) designing a flexible factory layout that "fits" hundreds of different product routings, and (3) scheduling a multi-product multi-machine production system subject to finite capacity constraints. Based on the Author’s 20+ years of learning, teaching, researching, and implementing Job Shop Lean since 1999, this book Describes the concepts, tools, software, implementation methodology, and barriers to successful implementation of Lean in HMLV production systems Utilizes Production Flow Analysis instead of Value Stream Mapping to eliminate waste in different levels of any HMLV manufacturing enterprise Solves the three Industrial Engineering problems that were mentioned earlier using software like PFAST (Production Flow Analysis and Simplification Toolkit), Sgetti and Schedlyzer Explains how the one-at-a-time implementation of manufacturing cells constitutes a long-term strategy for Continuous Improvement Explains how product families and manufacturing cells are the basis for implementing flexible automation, machine monitoring, virtual cells, Manufacturing Execution Systems, and other elements of Industry 4.0 Teaches a new method, Value Network Mapping, to visualize large multi-product multi-machine production systems whose Value Streams share many processes Includes real success stories of Job Shop Lean implementation in a variety of production systems such as a forge shop, a machine shop, a fabrication facility and a shipping department Encourages any HMLV manufacturer planning to implement Job Shop Lean to leverage the co-curricular and extracurricular programs of an Industrial Engineering department

"It is a book for manufacturing companies that are fighting desperately for survival and that will go to any length to improve their factories and overcome the obstacles to success. One could even call this book a bible for corporate survival."Hiroyuki Hirano Known as the JIT bible in Japan, JIT Implementation Manual The Complete Guide t

Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.

Efficient assembly line design is a problem of considerable industrial importance. Assembly Line Design will be bought by technical personnel working in design, planning and production departments in industry as well as managers in industry who want to learn more about concurrent engineering. This book will also be purchased by researchers and postgraduate students in mechanical, manufacturing or micro-engineering.

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.