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Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, Crawford, DeJong, Towill-Bevis and others. It explores the differences and similarities between the various models and examines the key properties that Estimators and Forecasters can exploit. A discussion about Learning Curve Cost Drivers leads to the consideration of a little used but very powerful technique of Learning Curve modelling called Segmentation, which looks at an organisation’s complex learning curve as the product of multiple shallower learning curves. Perhaps the biggest benefit is that it simplifies the calculations in Microsoft Excel where there is a change in the rate of learning observed or expected. The same technique can be used to model and calibrate discontinuities in the learning process that result in setbacks and uplifts in time or cost. This technique is compared with other, better known techniques such as Anderlohr’s. Equivalent Unit Learning is another, relative new technique that can be used alongside traditional completed unit learning to give an early warning of changes in the rates of learning. Finally, a Learning Curve can be exploited to estimate the penalty of collaborative working across multiple partners. Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists, as well as students of cost engineering.
Risk, Opportunity, Uncertainty and Other Random Models (Volume V in the Working Guides to Estimating and Forecasting series) goes part way to debunking the myth that research and development cost are somewhat random, as under certain conditions they can be observed to follow a pattern of behaviour referred to as a Norden-Rayleigh Curve, which unfortunately has to be truncated to stop the myth from becoming a reality! However, there is a practical alternative in relation to a particular form of PERT-Beta Curve. However, the major emphasis of this volume is the use of Monte Carlo Simulation as a general technique for narrowing down potential outcomes of multiple interacting variables or cost drivers. Perhaps the most common of these in the evaluation of Risk, Opportunity and Uncertainty. The trouble is that many Monte Carlo Simulation tools are ‘black boxes’ and too few estimators and forecasters really appreciate what is happening inside the ‘black box’. This volume aims to resolve that and offers tips into things that might need to be considered to remove some of the uninformed random input that often creates a misinformed misconception of ‘it must be right!’ Monte Carlo Simulation can be used to model variable determine Critical Paths in a schedule, and is key to modelling Waiting Times and cues with random arisings. Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.
Probability, Statistics and Other Frightening Stuff (Volume II of the Working Guides to Estimating & Forecasting series) considers many of the commonly used Descriptive Statistics in the world of estimating and forecasting. It considers values that are representative of the ‘middle ground’ (Measures of Central Tendency), and the degree of data scatter (Measures of Dispersion and Shape) around the ‘middle ground’ values. A number of Probability Distributions and where they might be used are discussed, along with some fascinating and useful ‘rules of thumb’ or short-cut properties that estimators and forecasters can exploit in plying their trade. With the help of a ‘Correlation Chicken’, the concept of partial correlation is explained, including how the estimator or forecaster can exploit this in reflecting varying levels of independence and imperfect dependence between an output or predicted value (such as cost) and an input or predictor variable such as size. Under the guise of ‘Tails of the unexpected’ the book concludes with two chapters devoted to Hypothesis Testing (or knowing when to accept or reject the validity of an assumed estimating relationship), and a number of statistically-based tests to help the estimator to decide whether to include or exclude a data point as an ‘outlier’, one that appears not to be representative of that which the estimator is tasked to produce. This is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.
Best Fit Lines and Curves, and Some Mathe-Magical Transformations (Volume III of the Working Guides to Estimating & Forecasting series) concentrates on techniques for finding the Best Fit Line or Curve to some historical data allowing us to interpolate or extrapolate the implied relationship that will underpin our prediction. A range of simple ‘Moving Measures’ are suggested to smooth the underlying trend and quantify the degree of noise or scatter around that trend. The advantages and disadvantages are discussed and a simple way to offset the latent disadvantage of most Moving Measure Techniques is provided. Simple Linear Regression Analysis, a more formal numerical technique that calculates the line of best fit subject to defined ‘goodness of fit’ criteria. Microsoft Excel is used to demonstrate how to decide whether the line of best fit is a good fit, or just a solution in search of some data. These principles are then extended to cover multiple cost drivers, and how we can use them to quantify 3-Point Estimates. With a deft sleight of hand, certain commonly occurring families of non-linear relationships can be transformed mathe-magically into linear formats, allowing us to exploit the powers of Regression Analysis to find the Best Fit Curves. The concludes with an exploration of the ups and downs of seasonal data (Time Series Analysis). Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.
Written by international contributors, Learning Curves: Theory, Models, and Applications first draws a learning map that shows where learning is involved within organizations, then examines how it can be sustained, perfected, and accelerated. The book reviews empirical findings in the literature in terms of different sources for learning and partial assessments of the steps that make up the actual learning process inside the learning curve. Traditionally, books on learning curves have focused either on cost accounting or production planning and control. In these books, the learning curve has been treated as a forecasting tool. This book synthesizes current research and presents a clear picture of organizational learning curves. It explores how organizations improve other measures of organizational performance including quality, inventory, and productivity, then looks inside the learning curve to determine the actual processes through which organizations learn.
"This book presents the latest research, case studies, best practices, and methodologies within the field of IT project management, offering research from top experts around the world in a variety of IT project management applications and job sectors"--Provided by publisher.
This book investigates the fundamental rethinking required by the transition to a production system whose guiding intelligence is self-organizing networks. Utilizing an exploding literature in the science of complexity and evolutionary economics, plus six detailed case studies of complex technologies that have experienced repeated innovation, this study identifies distinct innovation patterns and explores what happens when changes in these patterns occur. This volume also identifies the conditions that signal the approach of such changes and investigates the appropriate strategy and policy responses used to deal with them.
How many hate or bias incidents occurred on your campus this past year? Did any students opt out of filing formal charges? How many completed a formal resolution process, and what happened? Would you have liked to have other conflict resolution options?
This toolkit of facilitation techniques should provide readers with all they need to develop and hone their facilitation skills. Complete with case studies of the techniques in action the book covers traditional techniques and many new approaches such as the use of music, drama and storytelling.
Biotechnology and industry; Celltech in 1990; Celltech's genesis; The early years: finding a role; Building a base; Expanding ambition; Building a creative organisation; Technological learning; Building external networks; The strategic management of learning; Lessons from the learning firm.
New competitive realities have ruptured industry boundaries, overthrown much of standard management practice, and rendered conventional models of strategy and growth obsolete. In their stead have come the powerful ideas and methodologies of Gary Hamel and C.K. Prahalad, whose much-revered thinking has already engendered a new language of strategy. In this book, they develop a coherent model for how today's executives can identify and accomplish no less than heroic goals in tomorrow's marketplace. Their masterful blueprint addresses how executives can ease the tension between competing today and clearing a path toward leadership in the future.
Neoclassical economics, in particular the orthodox theory of the firm, offers little insight into the question of company strategy. It contributes even less to the understanding of the strategic management of technological change. In this volume, a number of international scholars from a variety of related disciplines explore the possibility of a more unified approach to linking company strategy and technological change. Each author examines the contributions from his own discipline, (economics, sociology, organization and systems theory), in order to build new multidisplinary theories of the firm, which will contribute to the debate surrounding the effects of new technology on company strategy and economic growth. Key Features * Links evolutionary economics to sociological analysis * Presents new case studies featuring this synthesis
By presenting ideas from instructional psychology, cognitive science, mastery learning, and performance based assessments, and then relating these findings to the workplace, the authors offer a new way to look at learning in the workplace.

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