Journal of Advanced Research in Production and Industrial Engineering

Investigation of Obstacles and Indicators in the Manufacturing Industry: Case Study

Mahendra Pratap Singh — 2026-05-14

Decision Making Methods In Supplier Selection: A Literature And Bibliometric Review

Paramjit Thakur — 2026-05-15

In sustainable supplier selection, social, environmental, and economic factors are all taken into account at the same time. This makes the decision-making process more difficult. A number of strategies have been created to deal with this process, some of which include MCDM approaches. With 486 research articles reviewed in this work, we present an overview of bibliometric analysis and a literature survey of these methods that have been created over the past three decades. Our research helps to identify the most commonly used MCDM techniques and their applications. This paper will help the researchers at the initial level to have an overall view of MCDM methods and their bibliometric review in sustainable supplier selection.

DOI: https://doi.org/10.24321/2456.429X.202602

A Comprehensive Review of Optimization Techniques, Stochastic Models, and Intelligent Systems in Industrial Engineering

Srinivas Prakhya — 2026-04-30

Industrial engineering has undergone a significant transformation with the integration of optimization techniques, stochastic modeling, and intelligent systems, enabling more effective decision-making in complex and uncertain industrial environments. This review presents a comprehensive analysis of classical and modern optimization methods, stochastic models, and intelligent systems used in industrial applications. It highlights recent advancements such as simulation-based optimization, artificial intelligence (AI), machine learning, and hybrid approaches that enhance the adaptability, efficiency, and robustness of industrial systems. The study also examines practical applications in production planning, supply chain management, manufacturing, and logistics. Furthermore, key challenges—including computational complexity, data availability, and methodological integration—are identified, alongside emerging research directions focused on autonomous decision-making, real-time optimization, digital twins, and sustainable industrial systems. This review provides a roadmap for researchers and practitioners seeking to leverage advanced methodologies to design intelligent, resilient, and sustainable industrial operations.

How to cite this article:
Prakhya S, A Comprehensive Review of Optimization Techniques, Stochastic Models, and Intelligent Systems in Industrial Engineering. J Adv Res Prod Ind Engg 2026; 13(1): 8-13.

Statistical Process Control and Reliability Engineering: Contemporary Practices and Emerging Trends

Shaphali Gupta — 2026-04-20

Statistical Process Control (SPC) and Reliability Engineering are foundational elements of quality engineering, playing a crucial role in ensuring the efficiency, consistency, and robustness of industrial systems. SPC utilizes statistical methods to monitor, analyze, and control manufacturing processes, helping to detect variations and maintain product quality. Reliability engineering, on the other hand, focuses on the ability of systems and components to perform their intended functions over time, considering factors such as failure rates, maintenance, and life-cycle performance.
This review provides a comprehensive analysis of both traditional and contemporary practices in SPC and reliability engineering. Key topics include control charts, process capability analysis, reliability modeling, failure prediction, preventive and predictive maintenance, and the integration of intelligent systems for real-time monitoring. The paper also explores emerging trends such as the adoption of Industry 4.0 technologies, machine learning algorithms, digital twins, and advanced analytics, which are transforming how industrial processes are monitored and maintained.

How to cite this artcle:
Gupta S, Statistical Process Control and Reliability Engineering: Contemporary Practices and Emerging Trends. J Adv Res Prod Ind Engg 2026; 13(1): 21-25.

Digital Manufacturing: Integration of E-Business, Automation, and Knowledge Management Systems

Christina Sanchita Shah — 2026-04-20

Digital transformation is fundamentally reshaping modern manufacturing by integrating e-business platforms, automation technologies, and knowledge management systems into industrial operations. This transformation enables smart, agile, and data-driven manufacturing environments that can respond quickly to market demands and operational challenges. This review examines the current state-of-the-art practices, frameworks, and technologies driving digital manufacturing, focusing on three key areas: e-business systems that enhance supply chain integration and collaboration, automation technologies such as robotics, cyber-physical systems, and the Internet of Things (IoT) that improve production efficiency and operational reliability, and knowledge management systems that facilitate organizational learning, decision-making, and continuous improvement.
The paper also discusses emerging trends, including the adoption of digital twins, AI-driven analytics, cloud-based platforms, and real-time monitoring systems, which are transforming traditional manufacturing processes into interconnected and intelligent networks. Furthermore, it addresses the challenges of implementing digital technologies, such as high investment costs, data security concerns, workforce skill gaps, and system interoperability issues. The study identifies opportunities for future research aimed at enhancing digital maturity, sustainability, and resilience in manufacturing operations. Overall, the review highlights how the convergence of e-business, automation, and knowledge management can drive operational excellence, innovation, and competitive advantage in the era of Industry 4.0.

How to cite this article:
Shah C S, Digital Manufacturing: Integration of E-Business, Automation, and Knowledge Management Systems. J Adv Res Prod Ind Engg 2026; 13(1): 26-31.