Adoption of Data-Driven Automation Techniques to Create Smart Key Performance Indicators for Business Optimization

Key performance indicators (KPIs) are crucial for managing business performance and optimization strategies. However, traditional KPIs are inflexible and cannot adapt to changes in staff, business units, functions, and processes. To address this issue, this paper proposes a method that combines statistics, machine learning (ML), and artificial intelligence (AI) to augment traditional KPIs with the flexibility of data-driven automation (DDA) techniques. This study builds a model that takes traditional KPIs generated by an integrated ecosystem as input data and assesses the suitability and correlation of the data using statistical techniques, such as Bartlett’s test of sphericity and the Kaiser–Meyer–Olkin (KMO) test of sampling adequacy. The model then employs exploratory Factor Analysis (FA) techniques to identify correlations and patterns, prioritize KPIs, and automatically generate smart KPIs for business optimization. The model is designed to adapt automatically by creating new KPIs as the business evolves and data change. A case study evaluation validates this approach, showing that DDA techniques can effectively create smart KPIs for business optimization. This approach provides a flexible and adaptable way to manage business performance and optimization strategies, enabling organizations to stay ahead of the competition and achieve their goals.