Innovation Analytics: Tools For Competitive Advantage

✍ Scribed by Nachiappan Subramanian, S. G. Ponnambalam, Mukund Janardhanan

• Publisher: World Scientific
• Year: 2023
• Tongue: English
• Leaves: 324
• Category: Library

✦ Synopsis

Innovation analytics is an emerging paradigm that integrates information/knowledge, analytics, digital twins and artificial intelligence to support and manage the entire lifecycle of a product and process from inception, through engineering design and manufacture, to service and disposal of manufactured products. Innovation analytics is set to become an integral part of the innovation lifecycle to help make smart, agile decisions and accelerate business growth. Innovation Analytics: Tools for Competitive Advantage provides a comprehensive overview of the challenges and opportunities behind the latest research surrounding technological advances driving innovation analytics; the transition of analytical ideas to interdisciplinary teams; the development of deep synchronicity of skills and production innovation; and the use of innovation analytics in multiple stages of product and process evolution. In exploring the impact of emerging developments in the current climate, researchers and academics will be able to gain insight into real-world usage of analytics for innovation and its contribution toward society. As such, students, scientists, engineers, academics, and management professionals alike will find this title beneficial.

✦ Table of Contents

Contents
About the Editors
Chapter 1 Introduction
1. Introduction
2. Innovation Overview
3. Analytics
4. Review of Research Studies
5. Analytics and Innovation
5.1. Product innovation
5.2. Process innovation
6. Summary of Chapters
6.1. Product and process innovation
6.2. Artificial intelligence
6.3. Data engineering
References
Part 1 Product and Process Innovation
Chapter 2 Consumer Product Innovation and the Opportunities for Data Analytics
1. Introduction
1.1. Problem: The tempo of innovation
1.2. Data-driven models for marketing innovation
2. Major Themes of the Competitive Landscape
2.1. Key drivers of big data perceptions and needs
2.2. Innovator unmet needs
3. Information-Driven Opportunities and Technologies
3.1. Finding emerging technologies and products
3.2. Using search and sales data
4. Discussion
5. Further Research
6. Conclusion
References
Chapter 3 R&D in Product and Process Innovation — System Design of Multidisciplinary Products by Applying Mass Customization Approaches
1. Introduction
2. Literature Review
2.1. System design
3. System Architecture Using Function–Behavior–State Modeling
4. A Method to Develop Product Architecture
5. Results and Discussion
5.1. Step 1: Analysis of the existing products
5.2. Step 2: FBS modeling
5.3. Step 3: Identify similarities and differences in FBS models
5.4. Step 4: Product architecture development in SA-CAD
5.4.1. Construction of building blocks for architecture modeling
5.4.2. Knowledge base development at SA-CAD
5.4.3 Metamodel development in SA-CAD
5.4.4 System modeling in SA-CAD
5.4.5 System architectures as a parameter network
5.4.6 Display entities and parameters in design structure matrix
5.5. Step 5: Identify similarities and differences among the products in terms of their entities
5.6. Step 6: Product platform development
6. Managerial and Social Implications
7. Conclusion
References
Chapter 4 Business Model Innovation Analytics for Small to Medium Enterprises
1. Introduction
1.1. The need for a data-driven approach for BMI
2. Theoretical Background
2.1. The evolution of innovation management
2.2. What is business model innovation?
2.3. Dynamic capabilities for BMI
2.4. Data analytics
3. Toward a Business Model Innovation Analytics Framework
3.1. Framework proposal
3.2. Framework usage
3.2.1. Recommendation 1: Start small but start
3.2.2. Recommendation 2: Build multidisciplinary teams
3.2.3. Recommendation 3: Rethink structure, solidify BMI analytics capability
4. Concluding Remarks
Acknowledgment
References
Chapter 5 Analysis of Factors Influencing Product and Process Innovation for Smart Manufacturing
1. Introduction
2. Literature Review
3. Methodology
3.1. Suggesting compromise solu
4. Case Study
4.1. Results and discussion
4.2. Compromise solution
5. Conclusion
5.1 Future scope
Appendix A
A.1. Summary of the notations used in this study
A.2. Sample calculation to compute three indices: Utility, regret and VIKOR
References
Part 2 Artificial Intelligence
Chapter 6 AI-Driven Innovation: Leveraging Big Data Analytics for Innovation
1. Introduction
2. Literature Review: The Role of AI/Big Data Analytics in Innovation
3. Innovation Analytics: Using AI to Drive Innovation
3.1. Innovation analytics for radical innovation
3.2. Innovation analytics for incremental/sustaining innovation
4. AI-driven Innovation: Integrating AI/BDA to Drive Innovation
5. Conclusion
References
Chapter 7 A Survey of IIoT and AI-Enabled Manufacturing Systems: Use Case Perspective
1. Introduction
1.1. Background and context
1.2. Top 10 disruptors for manufacturing systems
2. Conceptual Framework: IIoT as Sensory Organs of Manufacturing Systems
2.1. Eye: Sense of vision/sight
2.2. Ear: Sense of sound/hearing
2.3. Skin: Sense of proximity/touch
2.4. Nose: Sense of smell/odor
2.5. Tongue: Sense of quality/taste
3. AI as the Mind and Central Nervous System in Manufacturing Systems
3.1. Unsupervised learning
3.2. Supervised learning
3.3. Deep learning
3.4. Reinforcement learning
4. Manufacturing IIoT Landscape: Use Case Perspective
4.1. Product design
4.2. Warehousing and logistics
4.3. Core manufacturing
4.3.1. Planning and scheduling in manufacturing systems
4.3.2. Inventory management systems
4.3.3. Predictive maintenance
5. Futuristic/Emerging Applications: IT–OT Integration, Edge Computing, and Technological Aspects
5.1. Technology aspects
6. Conclusions and Directions for Future Research
6.1. Directions for future research
Acknowledgments
References
Chapter 8 Fighting Food Waste: How Can Artificial Intelligence and Analytics Help?
1. Introduction
2. Some Definitions on Food Waste
3. Food Waste Reduction Under Different Tracking Conditions
3.1. Temperature monitoring and no control
3.2. Temperature monitoring and control
3.3. No temperature control and monitoring
4. A Multi-Criteria Decision-Making Framework and Analytics Implementation Approach
4.1. Innovative analytics approach to reduce food waste
5. Scope for Further Research Involving Technology Demonstration and Case Studies
Acknowledgment
References
Chapter 9 Intelligent Traffic Solutions (Role of Machine Learning and Machine Reasoning)
1. Introduction
2. Goals & Objectives
3. Literature Review
3.1. Innovative data acquisition
3.2. Review of machine learning and machine reasoning
3.3. Intelligent parking tools and techniques
4. Traffic Solutions
5. Traffic Solution Technology Backgrounds
5.1. Passive infrared detectors
5.2. Active infrared detectors
5.3. Pneumatic tubes
5.3.1. Inductive Loop
5.4. Piezoelectric
5.5. Thermal imaging
5.5.1. Video imaging
5.6. Manual counting
6. Parking Solutions
6.1. Smart parking tools and technologies
6.1.1. Wireless sensor network
6.1.2. Parking guidance system
6.1.3. VANET
6.1.4. Inductive proximity sensor
6.1.5. Active ultrasonic sensor
6.1.6. Radio frequency identification (RFID)
6.1.7. LIDAR
6.1.8. Camera detection
6.1.9. Magnetometer
7. Problem Statement
8. Methodology
9. Results
9.1. Portland, Oregon
9.2. Chicago, Illinois
9.3. New York, New York
9.4. San Francisco, California
9.5. Pittsburg, Pennsylvania
9.6. Austin, Texas
9.7. Columbus, Ohio
10. Technology Comparison Tables
10.1. Traffic solution technology comparison table: Pedestrians and bikes
10.2. Traffic sensor technology landscape analysis: Vehicles
10.3. Traffic sensor technology landscape analysis: Connected vehicles
10.4. Parking sensor technology landscape analysis: Vehicles
11. Expert Panel
12. Use Cases
13. Considerations When Designing a Solution
14. Recommendations
15. Peek into the Future
16. Suggestions for Future Research
17. Conclusion
References
Appendix A: Expert Survey Questions
Appendix B: Tables
Part 3 Data Engineering
Chapter 10 Mitigating the Proclivity Toward Multiple Adjustments Through Innovative Forecasting Support Systems
1. Introduction
2. Literature Review
2.1. Forecasting
2.2. The human factor in supply chain forecasting
2.3. Forecast support system
2.4. Judgmental adjustments
2.5. Multiple adjustments in forecasting
3. Methodology
4. Data Analysis and Findings
5. Conclusion
Acknowledgments
References
Chapter 11 Fuzzy Logic-Based Multi-Objective Decision-Making Model for Design Evaluation in an Open Innovation Environment
1. Introduction
2. Terminologies in Multi-Criteria Decision-Making
2.1. Alternatives
2.2. Criteria
2.3. Weights
2.4. Decision-Makers (DMs)
2.5. Decision matrix
3. Basics of Fuzzy Sets and Fuzzy Numbers
4. Fuzzy Number-Based Multi-Criteria Decision-Making Model
4.1. Formulate the number of evaluation criteria and the design alternatives
4.2. Perform linguistic rating of the design alternative using domain experts and decision-makers
4.3. Develop fuzzy sets and fuzzy numbers for transforming the linguistic variable in a numerical order scale
4.4. Select suitable weights for the criterion to describe the relative importance
4.5. Apply intersection on the fuzzy numbers of the performance ratings with respect to all the criteria for each alternative as aggregated evaluation
5. Application of Proposed Method for Design Evaluation of Mobile Robot Chassis: A Case Study
5.1. Linguistic ratings of mobile robot chassis
5.2. Formulation of fuzzy sets
5.3. Weighted logical decision-making function
6. Conclusions
Acknowledgments
References
Chapter 12 A Sentiment-Based Approach for Innovative Product Sales Forecasting
1. Introduction
2. Literature Review
2.1. Importance of UGC for sentiment approach
2.2. UGC-based innovative analytics for sales prediction
3. Methodology
3.1. Research design
3.2. Sentiment analysis
3.3. Opinion selection and sentiment analysis
3.4. Variables summary
3.5. The prophet forecasting model
4. Findings
4.1. Model comparison with performance matrix
4.2. Variable improvement
5. Discussion of Results
6. Conclusion and Implication
6.1. Limitation and future direction
Acknowledgment
References
Chapter 13 Conclusion
1. Product and Process Innovation
1.1. Customer perspective
1.2. Manufacturing perspective
1.3. R&D perspective
1.4. Business model perspective
2. Artificial Intelligence
3. Data Engineering
References
Index