A conceptual framework for data-driven business optimization: Enhancing operational efficiency and strategic growth in U.S. small enterprises DOI Creative Commons

Enuma Ezeife,

May Equitozia Eyeregba,

Chukwunweike Mokogwu

и другие.

Magna Scientia Advanced Research and Reviews, Год журнала: 2024, Номер 12(2), С. 182 - 197

Опубликована: Ноя. 30, 2024

Small enterprises play a vital role in the U.S. economy, yet many face significant challenges optimizing operations and achieving sustainable growth due to resource constraints market dynamics. This study proposes conceptual framework for data-driven business optimization, aimed at enhancing operational efficiency fostering strategic small enterprises. Leveraging advancements data analytics, integrates predictive modeling, real-time processing, machine learning algorithms enable informed decision-making proactive strategy formulation. The emphasizes three core components: · Data Acquisition Management, which involves collecting structured unstructured from internal external sources build robust repository; Analytics Insights Generation, utilizing advanced tools identify patterns, forecast trends, detect inefficiencies; Strategic Implementation, applying insights streamline workflows, reduce costs, capitalize on emerging opportunities. By aligning strategies with organizational goals, ensures systematic approach addressing unlocking new avenues. Furthermore, this explores of technologies, including artificial intelligence Internet Things (IoT) devices, scalability adaptability framework. It also examines adopting approaches, such as limited technological expertise privacy concerns, offering practical recommendations overcome these barriers. Case studies successful implementations diverse sectors—retail, manufacturing, services—highlight framework's potential deliver measurable outcomes, increased productivity, improved customer satisfaction, competitive advantage. research provides actionable into leveraging asset, innovation, resilience rapidly evolving landscape.

Язык: Английский

A conceptual framework for data-driven business optimization: Enhancing operational efficiency and strategic growth in U.S. small enterprises DOI Creative Commons

Enuma Ezeife,

May Equitozia Eyeregba,

Chukwunweike Mokogwu

и другие.

Magna Scientia Advanced Research and Reviews, Год журнала: 2024, Номер 12(2), С. 182 - 197

Опубликована: Ноя. 30, 2024

Small enterprises play a vital role in the U.S. economy, yet many face significant challenges optimizing operations and achieving sustainable growth due to resource constraints market dynamics. This study proposes conceptual framework for data-driven business optimization, aimed at enhancing operational efficiency fostering strategic small enterprises. Leveraging advancements data analytics, integrates predictive modeling, real-time processing, machine learning algorithms enable informed decision-making proactive strategy formulation. The emphasizes three core components: · Data Acquisition Management, which involves collecting structured unstructured from internal external sources build robust repository; Analytics Insights Generation, utilizing advanced tools identify patterns, forecast trends, detect inefficiencies; Strategic Implementation, applying insights streamline workflows, reduce costs, capitalize on emerging opportunities. By aligning strategies with organizational goals, ensures systematic approach addressing unlocking new avenues. Furthermore, this explores of technologies, including artificial intelligence Internet Things (IoT) devices, scalability adaptability framework. It also examines adopting approaches, such as limited technological expertise privacy concerns, offering practical recommendations overcome these barriers. Case studies successful implementations diverse sectors—retail, manufacturing, services—highlight framework's potential deliver measurable outcomes, increased productivity, improved customer satisfaction, competitive advantage. research provides actionable into leveraging asset, innovation, resilience rapidly evolving landscape.

Язык: Английский

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