Increasing Technology Adoption Efforts Facilitates Value Creation During R&D Process
A common challenge faced by researchers during material R&D is from a data perspective, such as availability of highly fragmented information due to data systems that vary from application to application, fluctuating data quality and inability to access curated external research activities effectively. Integration of digital technologies such as Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Big Data Analytics etc. can successfully addresses challenges faced by R&D teams and reducing time to market.
Innovations in Digital Technologies Transforming Material R&D provides an overview of how digital technologies can overcome the challenges currently faced during R&D of materials and chemicals. The technological advancements in digital platforms have been captured emphasizing on how each digital technology can contribute and influence material R&D (Research and Development) process.
This research service also provides a comprehensive overview of key stakeholders who are developing platforms that can be used by material developers for various R&D and new product developmental efforts. Various strategies opted by material stakeholders in the industry such as technology licensing, joint developmental efforts to implement digitization tools in collaboration with the solution providers are also highlighted.
Table of Contents
1. Strategic Imperatives
1.1 The Strategic Imperative 8™
1.2 The Impact of The Top Three Strategic Imperatives on Digital Technologies for Material R&D
1.3 About The Growth Pipeline Engine™
1.4 Growth Opportunities Fuel The Growth Pipeline Engine™
1.5 Research Methodology
2. Executive Summary
2.1 Research Scope
2.2 Digitization Expected to be a Game Changer Across Functionalities in Material Industry
2.3 Machine Learning Based Algorithms Can Augment their Accuracy Over Time by Leveraging the Results of Previous Experiments
3. Need for Digitization in Material R&D
3.1 Data Science and Algorithms can be Used to Simulate R&D Processes
3.2 Key Digital Technologies: Artificial Intelligence and Machine Learning
3.3 Key Digital Technologies: Digital Twin
3.4 Key Digital Technologies: Internet of Things (IoT)
3.5 Key Digital Technologies: Big Data
3.6 Key Areas of Implementation of Digital Technologies in R&D Processes: Modeling and Optimization
3.7 Advantages of Implementing Digital Tools for Material R&D
3.8 Challenges of Implementing Digital Tools for Material R&D
4. Companies to Action
4.1 Artificial Intelligence based Platforms to Expedite Research in Chemicals Industry
4.2 Use of Proprietary Technologies to Extract and Harvest Materials Microstructural Information
4.3 Advanced Open Source Software & Data Analytics for New Materials
4.4 Unique AI Platform Expediting Decision Making Process for Materials Development
4.5 Artificial Intelligence Combined With Data for Advancements in Chemicals & Materials Industry
4.6 Unique Data Ingestion and Analysis tool Accelerating R&D Efforts in Material Development
4.7 AI Powered Platform For Identifying Materials With Desired Properties
4.8 Deep Learning Technology Powered AI Tool Capable of Efficiently Analyzing Sparse and Noisy Data Sets
4.9 SaaS Platform for Effective Steel Development
4.10 Machine Learning and Artificial Intelligence for Coatings and Battery Optimization
4.11 Cloud-based Software for Materials Development
4.12 Hierarchical Machine Learning Platform for Materials Development
4.13 Using AI to Optimize Chemical Extraction Process
4.14 Cloud-based IoT Platform for Chemical Industry
4.15 Advanced Analytics Platform for Chemical Manufacturing
5. Growth Opportunities
5.1 Growth Opportunity 1: Development of Integrated Platforms With Easy-to-Use Interfaces
5.2 Growth Opportunity 2: Open Source Platforms
6. Key Contacts
7. Next Steps
7.1 Your Next Steps
