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Analysis of business partnership process development with a proof of concept through the adoption rate: the AiSight case in the predictive maintenance field.

Lucrezia Ferretti

Analysis of business partnership process development with a proof of concept through the adoption rate: the AiSight case in the predictive maintenance field.

Rel. Abdollah Saboori. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Della Produzione Industriale E Dell'Innovazione Tecnologica, 2023

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Abstract:

This study delves into the intricate dynamics of business partnership process development, emphasizing the practicality and viability of such partnerships. Through a detailed examination of the AiSight case in predictive maintenance, the research investigates the crucial factors that influence the adoption rate of business partnerships in today's evolving landscape. The AiSight case serves as an example, showcasing how organizations can harness the potential of predictive maintenance through strategic partnerships. By employing a systematic approach, this study unravels the intricate phases and elements involved in the process of fostering successful business collaborations. Key components explored in this analysis include the identification of suitable partners, alignment of goals and objectives, effective communication strategies, and the implementation of effective and valuable partner management practices. By dissecting each of these elements within the context of the AiSight case, this research provides valuable insights into the mechanisms behind partnership development. Moreover, the study underscores the significance of measuring the adoption rate as a tangible indicator of partnership success. Through quantitative and qualitative analysis, this research highlights the correlation between effective partnership strategies and increased adoption rates, elucidating how businesses can leverage partnerships to enhance market penetration and growth.

Relatori: Abdollah Saboori
Anno accademico: 2023/24
Tipo di pubblicazione: Elettronica
Numero di pagine: 117
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Della Produzione Industriale E Dell'Innovazione Tecnologica
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-33 - INGEGNERIA MECCANICA
Aziende collaboratrici: AiSight GmbH
URI: http://webthesis.biblio.polito.it/id/eprint/28184
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