Systematic Review of Business Process Optimization Techniques Using Data Analytics in Small and Medium Enterprises

@article{1708350,
      author = {Jeffrey Chidera Ogeawuchi, Oyinomomo-emi Emmanuel Akpe, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola},
      title = {Systematic Review of Business Process Optimization Techniques Using Data Analytics in Small and Medium Enterprises},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {4},
      pages = {251-259},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708350.pdf},
      abstract = {This paper presents a systematic review of business process optimization (BPO) techniques using data analytics in small and medium enterprises (SMEs). As SMEs face increasing pressure to improve operational efficiency, adopting data-driven optimization methods such as machine learning, predictive analytics, Lean, Six Sigma, and automation technologies has become essential for their competitiveness and growth. The review highlights the effectiveness of these techniques in enhancing decision-making, streamlining processes, and reducing costs. However, it also identifies several barriers to successful implementation, including resource limitations, data quality issues, and organizational resistance to change. Despite these challenges, the review underscores the significant benefits that SMEs can achieve through strategic investment in data analytics and technological innovation. The paper concludes with actionable recommendations for SMEs looking to adopt or improve BPO strategies, and it suggests areas for future research, particularly in overcoming implementation barriers and exploring new data-driven optimization methods.},
      keywords = {Business Process Optimization, Data Analytics, Small and Medium Enterprises, Lean and Six Sigma, Automation, Predictive Analytics},
      month = {October},
  }