Authors: Anna Paula Tanajura Ellefsen, Joanna Oleśków-Szłapka, Grzegorz Pawłowski, Adrianna Toboła
Title: STRIVING FOR EXCELLENCE IN AI IMPLEMENTATION: AI MATURITY MODEL FRAMEWORK AND PRELIMINARY RESEARCH RESULTS
Keywords: Industry 4.0, Logistics 4.0, Artificial intelligence 4.0, Artificial intelligence, maturity levels
Background: In hereby article authors try to summarize how AI can be use by companies within production and warehousing. On the basis of previously developed Logistics 4.0 maturity model authors also propose Artificial intelligence maturity levels and on its basis a survey has been conducted in selected Polish and Norwegian companies and actual AI state of development and maturity levels has been recognized. However authors present preliminary stage of research as a multi case study which will be further developed and extended in order to identify branches and areas with a hugest potential to enhance AI utilization.
Furthermore paper presents potential directions of Artificial intelligence implementation as well as tools that can be useful to deal with big data and optimization problems predicted not only for big companies but also SMEs. Authors propose term Artificial Intelligence 4.0 to point out the actual trends in the scope of Industry 4.0 and Logistics 4.0 and revolution with respect to AI. Without doubt AI is a big challenge for manufacturing companies as well as Transport and Logistics Industry and its application should be increased and extended in solving practical problems.
Methods: Methodology applied by authors of hereby paper can be divided on following stages: literature analysis, enlargement of AI maturity model, development of a questionnaire, multi-case studies in Norway and Poland.
Results: The literature search showed a cognitive gap due to fact there is a little of literature dealing with problem of Artificial intelligence maturity models as well as Logistics 4.0 and Artificial Intelligence. Artificial intelligence maturity levels can be combined with Logistics 4.0 maturity models thus relations between actual level of logistics maturity and AI readiness in companies will be recognized. Due to such analysis it will be possible to develop complex roadmap with the organization’s strategic guidelines how to deal with Logistics 4.0 and AI. All the companies investigated in this preliminary study could be classified as AI Novices: Companies that have not taken proactive steps on the AI journey and are at best in assessment mode. Even the bigger companies with more automated solutions cannot visualize the benefits AI can bring.
Conclusions: Authors see potential to apply aforementioned model to investigate AI maturity levels in logistics companies and combine obtained results with previously developed Logistics 4.0 maturity model. Authors propose to introduce term Artificial Intelligence 4.0 to emphasize the importance of artificial intelligence with respect to Logistics 4.0 and Industry 4.0.
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|MLA||Ellefsen, Anna Paula Tanajura, et al. "Striving for excellence in AI implementation: AI Maturity Model framework and preliminary research results." Logforum 15.3 (2019): 5. DOI: 10.17270/J.LOG.2019.354|
|APA||Anna Paula Tanajura Ellefsen1, Joanna Oleśków-Szłapka2, Grzegorz Pawłowski3, Adrianna Toboła4 (2019). Striving for excellence in AI implementation: AI Maturity Model framework and preliminary research results. Logforum 15 (3), 5. DOI: 10.17270/J.LOG.2019.354|
|ISO 690||ELLEFSEN, Anna Paula Tanajura, et al. Striving for excellence in AI implementation: AI Maturity Model framework and preliminary research results. Logforum, 2019, 15.3: 5. DOI: 10.17270/J.LOG.2019.354|