Exploring Interconnections Between Machine Learning and Operations Strategy

EasyChair Preprint 4718

Abstract

Within the data science and artificial intelligence fields of study, machine learning have supported performance improvement in medicine, manufacturing, law and even sport environments. The purpose of this paper is to investigate how machine learning has been used as a tool to improve the assertiveness of decision-making, providing competitive advantages in the wide field of operations management. This exploratory research analyzes the content of five machine-learning studies, relating each of them to Slack’s strategy pillars: cost, speed, quality, flexibility and dependability. Research design was limited to Scopus’ papers published exclusively in high impact journals. Results emphasize the important role of machine learning in organizational competitive advantages and limitations are used to address further research suggestions, extending the present investigation with a more extensive bibliographic portfolio analysis. The contribution of this paper is a matrix analysis of how machine-learning projects indirectly contribute to at least three strategy dimensions simultaneously. Complementarily, an illustration was built for a better comprehension of the interrelationships among the strategic pillars reinforced by the analyzed studies.

Keyphrases: Artificial Intelligence, Operations, Operations Strategy, Slack, Strategy, decision making, machine learning

@booklet{EasyChair:4718,
  author    = {Thais C. Pfutzenreuter and Nathália R. G. Chamie and Sergio E. Gouvea da Costa and Edson Pinheiro de Lima},
  title     = {Exploring Interconnections Between Machine Learning and Operations Strategy},
  howpublished = {EasyChair Preprint 4718},
  year      = {EasyChair, 2020}}