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Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data is available:
ISBN 9781119447757 (Hardcover)
ISBN 9781119453741 (ePDF)
ISBN 9781119453727 (ePub)
Cover Design: Wiley
Cover Image: © Thirteen/Shutterstock
I dedicate this book to my mother and father, my wife Rosmary Wubbel, and son Leslie Wubbel for all their love and support. I especially want to thank three strong ladies, sisters to me for their guidance, care, and constructive comments. Thanks to my best friend and son Leslie for his reviews and the many conversations and topic discussions that helped make this book possible. Thanks to my nieces, especially Catherine Mintmire, for producing the graphic art work. And finally, too numerous to mention the many business friends and mentors in software engineering and data science fields including Philip Douglas Brown for his insights and perspectives on making the connections in data for advancing analytical capabilities.
| 1.1 | JMP® CONNECTIONS Capability Maturity Model Levels 0 and 1 |
| 1.2 | JMP® CONNECTIONS Capability Maturity Model Levels 2 and 3 |
| 1.3 | JMP® CONNECTIONS Capability Maturity Reference Model |
| 1.4 | Maturity through Level State Transitions |
| 1.5 | Common Cycle Time to Build and Publish a Weekly Dashboard |
| 1.6 | Transition from Level 1 to Level 2 |
| 1.7 | Achieving On-Demand Capability |
| 2.1 | Business Case Basics |
| 2.2 | Dashboard |
| 2.3 | Balanced Scorecard |
| 2.4 | Graphical Ontology Example: Human Resources |
| 2.5 | Micro-warehouse Value Tool |
| 2.6 | Generating Value—Leveraged Knowledge Asset |
| 2.7 | KPI Development Timeline |
| 2.8 | BRF Risk Assessment |
| 2.9 | Example—Large Application Spreadsheet Type |
| 2.10 | Financial Regulation Documents |
| 3.1 | Conceptual Illustrations of the JMP® CONNECTIVIY Platform |
| 3.2 | Disk Operating System (DOS) Command Prompt |
| 3.3 | Definition of the Time-Constant Learning Curve Function |
| 3.4 | Total Productive Maintenance Conceptual Diagram |
| 3.5 | Sequence Column and OEE Values Column in a JMP® Table |
| 3.6 | Learning Curve Formula Specification |
| 3.7 | Configuration for Gauss-Newton Iteration |
| 3.8 | Nonlinear Fit Program |
| 3.9 | Nonlinear Fit Output |
| 3.10 | Nonlinear Fit Results |
| 3.11 | Learning Curve Formula for Estimating OEE |
| 3.12 | Estimating OEE Formula |
| 3.13 | Setting Up to Display the Control Chart |
| 3.14 | Control Chart with Forecasting Errors Out of Criteria |
| 5.1 | Data Virtualization Server |
| 5.2 | Federated Virtualization Engine |
| 5.3 | Federated Concept |
| 5.4 | Small-Scale Cookie Batch |
| 5.5 | Recipe Ingredient Metrics |
| 5.6 | Hierarchical Data Format |
| 5.7 | Cookie Data in a Table Format |
| 7.1 | Senders push and receivers pull |
| 7.2 | Junctured KPI Thought Process for Connecting the Dots |
| 7.3 | Information Linkage between Parent–Child KPIs |
| 7.4 | Quick Response Code |
| 7.5 | JMP® GUI Window with JMP® Table |
| 7.6 | Office Temperature Study |
| 7.7 | Chocolate velvet anyone? |
| 8.1 | Public Domain: William Thomson Oil Painting |
| G.1 | Tangential View |
| G.2 | Transversal Maturity Model View |
| G.3 | Office Temperature Study |
JMP® Pro® is the centerpiece software that is capable of saving your business in difficult economic times. JMP® CONNECTIONS (herein referred to as “the Model Platform”)1 illustrates the technical means and financial variables that will leverage peak productivity. JMP® CONNECTIONS provides a clear pathway toward quickly generating actionable intelligence from your organization's raw data for optimal decision‐making purposes. The prime reason for describing a CONNECTIONS platform is the fact that JMP® Pro® enables computational in‐memory statistical analytical capability second to none in the business, engineering, and scientific world. When a person is able to make a connection, what most often happens is a decision and this fact should generate broad discussion as well as potentially collective performance improvements for groups, teams, or large organizations.
More than ever before, metrics are playing the most important role in the conduct of a business on the competitive stage today. In typical fashion, software comes with a wealth of features, functions, and extensibility. In many cases several software packages may be required to satisfy or facilitate common business functions in support of the operation. Office suites come to mind as an example.
When business conditions are challenging or when strategic goals continually set the bar higher for better performance, innovation is a key factor toward contributing to results that exceed expectations. Consequently, the task of producing metrics must become an innovation as well. As a result, one must visualize a model of capability when it comes to designing, developing, generating, and reporting within your own company, division, or all the way down to the department level. Given the nature of today's office suites, metrics tend to be produced once a week, once a month, or quarterly with each having a cycle time to completion. JMP® CONNECTIONS suggests a model, or innovation, that eliminates cycle time so that there is a reduction in full‐time equivalents (FTEs) for metric production purposes whereby the metrics produced are real‐time or, in other words, “metrics on‐demand.”
The key to understanding how this type of innovation can lessen tough economic times is through improved business decision making. It is innovative by differentiating between cycle time methods versus metrics that are available with either the latest available data or real‐time aggregate raw data material, transformed into usable knowledge.
JMP® Pro® is the central hub and can become your command and control center for managing and executing a business operating system on many varied scales. The journey in building a real‐time metric production system is simplified through a series of capability maturity steps. Pooling the data from disparate silos starts with data aggregation and integration forming a repository. Mining the repository for conducting statistical analysis, the journey transitions through three levels leading to a final maturity level of predictive modeling and analytic goals. The goals are supportive of the key performance indicators required by the strategic objectives set forth for proper performance management. This book will not only discuss the model but help an organization implement the model with their own people.