As modern industrial processes have become increasingly complex, complicated multi-factor problems have emerged. These complex problems end up costing companies millions of dollars every day. Existing problem-solving techniques are only effective to a certain point. This book provides a solution to a myriad of industrial problems by using first principles and rigorous hypothesis testing. Key topics covered within the work include: - How to use the latest research, advanced modeling, big data mining, analytical testing, and many other techniques to systematically create and test hypotheses surrounding why a process is malfunctioning - How to use scenario development to frame a team’s understanding of why a process is malfunctioning - How to approach today’s lack of experienced industrial workers, whose failure to approach problem solving from first fundamentals are causing myriad of inefficiencies in industry - How to use multiple methodologies together with an emphasis on first principles and mechanistic math modeling as a basis to industrial problem solving
Engineers of any discipline working in both research and development of manufacturing environments, along with professionals in any industrial discipline looking to reduce costs will be able to use this work to both understand and pragmatically solve the pressing issues we see in today’s industrial market.
Table of Contents
1. Introduction 6
2. First Principles Problem Solving - why first principles? 15
3. Opportunity identification/definition - initial team formation 35
4. Hypothesis testing 46
5. Scenario development 56
6. Value of project 57
7. Team management and roles 61
8. Stakeholder Identification, Analysis, and Communication plan 94
9. Generating, Selecting, and Implementing Solutions to the Problem 151
10. Sustaining the Gains and Control plans 190
11. Project Wrap-up 221
12. The big world of problems 224
13. Corrosion problems 236
14. Mechanical issues 239
15. Color 247
16. Emphasis is on speed, low cost, productivity and right answer 256
17. Literature 258
18. Scientific Method 263
19. Process Data Analysis 265
20. First Principles Modeling 283
21. Analytical 296
22. Engineering disciplines examples 309
23. Six Sigma, LEAN and Continuous Improvement 315
24. Theory of Constraints 326
25. Visual presentation 339
Appendix
1. Scientific Method Practice Levels 346
