The Analytical Report and Infographic behind this practice: how ISE professionals should navigate information sources, and what actually predicts successful AI adoption in manufacturing.
This report was completed in the role of an intern in a department's professional development office. The goal: research the most valuable information sources for ISE professionals, evaluate them, and provide recommendations for colleagues preparing for a symposium. The research covered three professional organizations (IISE, ASQ, INFORMS), their journals, online communities, and an upcoming conference, and examined three major issues facing the field: AI and automation in manufacturing, supply chain resilience, and the workforce skills gap.
Article analyzed
Kinkel, Baumgartner, & Cherubini (2022) · published in Technovation
Surveyed 655 manufacturing companies across 16 countries. Main finding: digital skills - software development, hardware development, data science, and machine learning - are the strongest predictor of AI adoption, stronger than company size or R&D spend.
Infographic
Prepared by Sesar Macias, UTEP Industrial and Systems Engineering
Projected annual U.S. productivity growth from generative AI, illustrating the potential impact on efficiency across sectors through 2030, according to McKinsey & Company. Estimates range from 0.5% to 0.9%.
Map the process, pilot with cloud tools, train your team, lock down your data, then measure before you scale.
Find the repetitive task before you find the tool.
Test a low-cost SaaS platform before investing in custom systems.
Train your team to work with the tool, not replace it.
Set clear security rules before you scale up.
Let real results decide what's next.
Sources: McKinsey & Company; Vena Solutions; American Enterprise Institute; IBM; Goldman Sachs; Akinrinola et al., SME AI Adoption Research.
A side-by-side evaluation of IISE, ASQ, and INFORMS as information sources for ISE professionals.
| Criterion | IISE | ASQ | INFORMS |
|---|---|---|---|
| Primary Focus | Industrial & Systems Engineering | Quality management & certification | Operations research & analytics |
| Key Journals | IISE Transactions; ISE Magazine | Journal of Quality Technology; Quality Progress | 17 peer-reviewed journals; OR/MS Today |
| Certifications | Lean, Six Sigma, CAP | CQE, CQA, Green/Black Belt | CAP (Certified Analytics Professional) |
| Annual Conference | IISE Annual Conference & Expo | ASQ World Conference on Quality | INFORMS Annual Meeting |
| Navigation Ease | Clear, well-structured | Content buried 3-4 menu levels deep | Clean layout, direct access |
What actually predicts AI adoption across 655 manufacturing companies in 16 countries, organized by the Technology-Organization-Environment (TOE) framework.
| Factor (TOE Category) | Direction | Significance | Key Finding |
|---|---|---|---|
| Digital Skills (Organizational) | Strong positive | p < 0.001 | R² increased from 0.19 to 0.31 when added |
| Company Size (Organizational) | Positive | p < 0.01 | Larger firms adopt AI more frequently |
| R&D Intensity (Organizational) | Positive (Model 1 only) | p < 0.001 | Effect absorbed by digital skills in Model 2 |
| Product Complexity (Technological) | Positive | p < 0.001 | Complex products drive higher AI adoption |
| Industrial Sector (Environmental) | Not significant | n.s. | Sector effects absorbed by intra-firm factors |
| Country of Origin (Environmental) | Minimal (Japan/Korea) | p < 0.05 | Geographic proximity to AI innovators matters |
Journals, professional organizations, and forums each fill a different gap in an ISE professional's information diet.
Digital skills, not company size or budget, are what actually determine whether a company can adopt AI.
AI adoption, supply chain resilience, and the workforce skills gap are not separate problems - they compound each other.
Start with peer-reviewed journals, use professional organizations as a baseline, treat forums as supplemental only, and build digital skills early.
Related viewing
Directly on-topic with the research above - industrial AI transformation and the human/AI skills gap.
Markus Lorenz - "Industry 4.0: How Intelligent Machines Will Transform Everything We Know" (TED)
Sylvain Duranton - "How Humans and AI Can Work Together to Create Better Businesses" (TED)