Peter Bock developed Getting IT Right in direct response to discussions with the directors of R&D departments in large industrial firms who are frustrated by the lack of coherent and consistent methodologies in the R&D projects they manage.
The Symptoms: Think about your R&D work environment. Do any of these statements ring a bell?
· Research results cannot be reproduced
· Knowledge is precarious and kept primarily in people’s heads
· Speculations are represented as rigorous conclusions
· Data collection is haphazard and hindered politically
· Experiment methods are chaotic: try this, try that
· Project processes cannot be audited because there are no logs or records
· Documentation is incomplete or nonexistent
· Statistical reduction of data is naïve
· Data visualization techniques are poor
· Project activities are isolated and inbred
The Problem: Most engineers and scientists have little formal training in the design and conduct of complex R&D projects. As the focus in industry and academia shifts rapidly toward projects involving complex human-machine interaction, traditional R&D methods are coming up short.
The Solution: The complexity of industrial and academic research and development projects in the 21st century demands a consistent and coherent methodology for their design and execution. This book presents a complete and rigorous methodology for planning and conducting R&D projects, both large and small. The emphasis is focused squarely on Getting It Right:
· Strict adherence to a modernized version of the classical Scientific Method
· Consistent and precise terminology, summarized in an extensive Glossary
· Hierarchical and recursive project planning
· Stepwise refinement and inside-out task design
· Sensible assignment of project resources and management roles
· Political and technical issues for experiment data acquisition
· Fail-soft methods for recursive error recovery
· Rigorous design of carefully controlled experiments
· Comprehensive analysis of potential sources of bias and error
· Statistical methods for data analysis and reduction
· Data visualization techniques and documentation standards
· Highlighted Tips for dealing with common critical problems
· Real-world case studies augmented by detailed figures and tables
· Useful summaries of essential concepts
· Philosophical, ethical, and epistemological constraints and perspectives
Acknowledgments
Foreword
Biographies
Part 1 : Introduction
Chapter 1 : Research and Development
-
1.1 Motivation
-
1.2 Background
-
1.3 R & D Problems
-
1.4 Primary Objective
Chapter 2 : Process and Preparation
-
2.1 The Methodology
-
2.2 Tools and Resources
Part 2 : Project Organization
Chapter 3 : The Project Hierarchy
-
3.1 Bottoms Up
-
3.2 Top-Down Project Planning
Chapter 4 : The Project Task
-
4.1 Task Domain
-
4.2 Task Method
-
4.3 Task Range
Part 3 : Knowledge Representation
Chapter 5 : An Epistemological Journey
Chapter 6 : Categories and Types of Knowledge
-
6.1 Speculative Knowledge
-
6.2 Presumptive Knowledge
-
6.3 Stipulative Knowledge
-
6.4 Conclusive Knowledge
Chapter 7 : Roles of Knowledge Propositions
-
7.1 Governing Propositions
-
7.2 Factors
-
7.3 Range Knowledge
Chapter 8 : Limits of Knowledge
-
8.1 Accuracy and Error
-
8.2 Uncertainty
-
8.3 Precision
-
8.4 Knowledge, Truth, and Humility
Part 4 : The Scientific Method
Chapter 9 : Overview
-
9.1 History of the Scientific Method
-
9.2 The Modern Scientific Method
Chapter 10 : Analysis
-
10.1 Describe Problem
-
10.2 Set Performance Criteria
-
10.3 Investigate Related Work
-
10.4 State Objective
Chapter 11 : Hypothesis
-
11.1 Specify Solution
-
11.2 Set Goals
-
11.3 Define Factors
-
11.4 Postulate Performance Metrics
Chapter 12 : Synthesis
-
12.1 Implement Solution
-
12.2 Design Experiments
-
12.3 Conduct Experiments
-
12.4 Reduce Results
Chapter 13 : Validation
-
13.1 Compute Performance
-
13.2 Draw Conclusions
-
13.3 Prepare Documentation
-
13.4 Solicit Peer Review
Appendices
A : Bibliography
B : Glossary
C : Tips
D : Summaries and Guidelines
E : Case-Study Figures and Tables
F : Sample Experiment Protocol
G : An Algorithm for Discovery
Index
