ArticleF1000Research2025
Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach.
Article in F1000Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 authors.
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Abstract
Background: Manufacturing inefficiencies result in substantial financial losses for global industries. The present study introduces a robust Performance Measurement System (PMS) incorporating Network Data Envelopment Analysis (NDEA) to address efficiency challenges in multi-stage manufacturing systems. Methods: The study employs a case study approach within the pharmaceutical industry to reveal the pragmatic application of NDEA, which serves as the primary analytical instrument for evaluating performance across diverse production stages. Focusing on the production processes of intravenous (IV) sets, the research aims to highlight how NDEA disaggregates interconnected processes and quantify efficiency measures to pinpoint sources of inefficiencies in particular production stages and actionable insights for operational improvement. The analysis also explores the trade-off between model complexity and discrimination power as the number of stages increases. Results: First, the NDEA-based PMS provides insights to address specific process inefficiencies on the shop floor, providing strategic insights for process improvement. Second, despite its power in pinpointing the source of inefficiency, modelling a process-based PMS faces a challenge as increasing the number of stages in the model presents a trade-off between the accuracy and discrimination power of the NDEA model. Conclusions: This study holds significance within the broader field of performance measurement and efficiency analysis by bridging theoretical modelling and practical implementation. It advances existing knowledge through the integration of NDEA into a process-based PMS, offering a novel analytical framework for multi-stage manufacturing systems. By examining the trade-off between model complexity and discrimination power, this research contributes new methodological insights and extends the applicability of NDEA in real-world industrial settings. The framework offers managers actionable guidance for optimizing multi-stage manufacturing operations and contributes novel insights into the methodological behaviour of NDEA. Ultimately, this work strengthens the linkage between performance measurement theory and industrial practice, positioning NDEA as a valuable tool for continuous improvement in manufacturing systems.
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