2026, Vol. 8, Issue 1, Part A

Computational statistics plays a pivotal role in optimizing processes in industrial engineering. With the growing complexity of manufacturing systems and the demand for precision in decision-making, leveraging statistical techniques for optimization has become crucial. This paper explores the applications of computational statistics in industrial engineering optimization, focusing on how these methods are used to improve efficiency, reduce costs, and enhance quality control in industrial operations. Techniques such as statistical modeling, simulation, machine learning, and regression analysis are discussed in the context of solving practical engineering problems, from production scheduling to supply chain management.

The main objective of this review is to present a comprehensive overview of various computational statistical methods used in industrial engineering. The paper highlights case studies that demonstrate the effectiveness of these techniques in real-world scenarios, such as optimizing inventory systems, improving production throughput, and enhancing product quality. Furthermore, the paper discusses the integration of computational statistics with other fields, such as artificial intelligence and data mining, to further optimize industrial processes.

By reviewing the current literature, this paper also identifies key challenges faced by practitioners when applying computational statistics in industrial engineering. These challenges include the complexity of data, the need for advanced algorithms, and the integration of these methods with existing industrial systems. Additionally, the research suggests potential areas for future research to overcome these barriers and improve the adoption of computational statistics in industrial engineering optimization.

This paper aims to contribute to the body of knowledge by presenting a detailed analysis of how computational statistics can be utilized to optimize industrial processes, improve decision-making, and support sustainable growth in manufacturing.