1. Introduction
Testimony of Fréderic Delattre manager, PhD in electronics and mechanics, with 25 years experience in R&D management, Quality and project portfolio management. With EXPERING, an engineering and supply chain transition management firm, we take the time to analyze the challenges of Artificial Intelligence in the R&D professions, including the engineering of new products. The possibilities offered by Artificial Intelligence are becoming ever wider, and we have grouped them into 4 categories of contribution for R&D professions:
2. THE 4 CONTRIBUTION CATEGORIES
Testimony of Fréderic Delattre manager, PhD in electronics and mechanics, with 25 years experience in R&D management, Quality and project portfolio management. With EXPERING, an engineering and supply chain transition management firm, we take the time to analyze the challenges of Artificial Intelligence in the R&D professions, including the engineering of new products. The possibilities offered by Artificial Intelligence are becoming ever wider, and we have grouped them into 4 categories of contribution for R&D professions:
- Assist the business or project manager in his or her role of administering his or her activity: examples are technological monitoring, sorting and filing of intra and extra information, management of meetings and rituals, reporting, resources, …); this assistance is not specific to R&D and falls within the scope of AI for managerial efficiency.
- Enhance the efficiency of those involved in the business by automating (robotizing) regulated, systematic processes, such as the automation and optimal availability of business rules, data capture and filing, monitoring of test equipment, control, etc.
- Address new complexities such as multiphysics optimal design, Dependability requirements, certification requirements, by analyzing consistency and lack of justification, assisted identification of optimal concepts, coverage and completeness analysis, …
- Assist in the creation of new knowledge, and therefore new services such as feedback analysis, cross-functional integration opening up inter-business collaboration, identification of patterns to improve product control and performance, etc.
3. Conclusion
Given its power, its requirements in terms of access to clean data and its learning constraints, integrating Artificial Intelligence has a major impact on work organization. This is a change management project, part of a digital transition strategy, closely associating R&D business expertise, AI business expertise and HR/GPEC expertise, under the guidance of a project manager from the R&D business. This project must be conducted using a Lean Engineering approach, enhanced by best practices in change management. While large industrial groups have already embarked on such an approach with their internal resources, SMEs and ETIs can now do so by calling on an R&D transition manager with an AI culture, supported by the EXPERING skills network and linking up with AI experts from the formidable ecosystem of French start-ups recognized at the highest world level. Don’t hesitate to contact us to assess with you the opportunity of such an approach; because it’s now that the AI culture needs to penetrate R&D professions and processes to reap the immediate benefits and be ready for the next technological breakthroughs. To find out more about AI & Engineering inculturation initiatives :
- System X launches AI program for augmented industrial engineering
- Description of the IRT SystemX AMC project
Frédéric DELATTRE