Exploratory research on digitalization transformation practices within supply chain management context in developing countries specifically Egypt in the MENA region
With no doubt, the adoption of Artificial Intelligence applications in customer service reduces the time to market. Nevertheless, the question remains whether or not its adoption in production and logistics will transform the supply chain into a more agile one in developing countries context. Manufa...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Taylor & Francis Group
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/8e3c17624fff4aeabf531c0124956c43 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | With no doubt, the adoption of Artificial Intelligence applications in customer service reduces the time to market. Nevertheless, the question remains whether or not its adoption in production and logistics will transform the supply chain into a more agile one in developing countries context. Manufacturers are increasingly facing global competition to fulfil incoming orders within limited lead time, and in compliance with the international quality standards, also supporting a customised service. The emergence of industry 4.0 brought many promises to the leading firms. This research paper gives insight information for applying AI algorithms in production cycle then monitoring the production process and subsequently leading to strategic and tactical engineering decisions through the investigation of a number of case studies. The investigation demonstrates that incorporating AI technologies and machine learning opens up fresh perspectives on a variety of topics, including warehousing and logistics management, cooperation, and supply chain management. AI is embraced by business for productivity improvement, given the fact that the more AI adoption rate, the less employment rate and wages will be. Information & Communication Technology alignment model is endorsed in this paper in order to grant fostering of an environment that streamlines, incentivizes and supports AI expansion prior the implementation of any AI practices to ensure its successful and justified investments. The research concludes that the process is extremely challenging in the context of emerging economies which is restricted with low wages rate, inadequate labour skills and insufficient financial resources. |
|---|