Hierarchy Priortization and Dynamic Simulation for Low Volume Production Planning

The manufacturing industry is considered the backbone of multiple global economies. However, the evolving market trends and elevated demand for low volume individualized products have become a challenge to the industry. To address the complexities of low volume production, a robust planning framework is needed to evaluate decision options aligning with business outcomes and organizational objectives. Therefore, a hierarchy based decision making process is eminent to guarantee high confidence in planning from both macro and micro levels. This paper discusses a ground-breaking Hierarchy Based Prioritization (HBP) framework, reinforced by the geometric mean technique and the eigenvalue methodology, as a comprehensive decision architecture that has been designed for dissecting, analyzing, and synthesizing complex decision matrices with real time Digital Twin (DT) data. The framework evaluates a wide range of decision options and prioritizes organizational objectives carefully examining quantitative data, mathematical calculations, qualitative data and human judgement. A case study on carbon fiber manufacturing facility was reviewed considering flexibility, utilization, cost and quality as critical decision making criteria. An unbiased mathematically rigorous decision making protocol highlighting potential inconsistencies was performed through pairwise comparison and consistency check. The findings highlighted the dominant criteria with consistent evaluations using weighted sum, geometric mean and eigen value. Therefore, the criteria are narrowed and assisted in establishing confidence in the decision making process.