Machine Learning Adoption : The Interplay of Cost, Time, and Systems Integration
Simpia, Leslie Joy (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024052415291
https://urn.fi/URN:NBN:fi:amk-2024052415291
Tiivistelmä
The logistics sector, which has evolved from strategic military operations, is now shifting to a data-driven environment where the integration of digital and physical is crucial to remain competitive in the intricate sector while navigating disruptions in a dynamic global market. Ushered by further research, technological advancement and the proliferation of data, machine learning (ML) as a tool offers potential solutions in diverse industries with its strength in analysing massive data sets, providing quick actionable insights.
The study explores the necessary aspects of ML to understand its nature. ML applications across different sectors will be explored, emphasising the logistics sector. While most intelligent applications involve ML, many enterprises could still be at the crossroads of adopting the technology. Thus, this paper aims to provide a clearer understanding of the technology by providing fundamental business cases. Therefore, a qualitative method is conducted for this purpose.
This study highlights the necessary considerations for successful adoption, particularly emphasising time, cost and systems integration factors. ML applications have different facets; thus, it can be used to solve various problems. However, adopting machine learning also presents multifaceted challenges that could be overcome with strategic and proactive considerations. By implementing these recommendations, organisations can position themselves in the ML market, navigating the complexities of time, cost, and systems integration.
The study explores the necessary aspects of ML to understand its nature. ML applications across different sectors will be explored, emphasising the logistics sector. While most intelligent applications involve ML, many enterprises could still be at the crossroads of adopting the technology. Thus, this paper aims to provide a clearer understanding of the technology by providing fundamental business cases. Therefore, a qualitative method is conducted for this purpose.
This study highlights the necessary considerations for successful adoption, particularly emphasising time, cost and systems integration factors. ML applications have different facets; thus, it can be used to solve various problems. However, adopting machine learning also presents multifaceted challenges that could be overcome with strategic and proactive considerations. By implementing these recommendations, organisations can position themselves in the ML market, navigating the complexities of time, cost, and systems integration.