Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach.

Area of Science:

• Operations Research
• Industrial Engineering
• Management Science

Background:

• Manufacturing inefficiencies lead to significant global financial losses.
• A robust Performance Measurement System (PMS) is crucial for addressing efficiency challenges.
• Multi-stage manufacturing systems require advanced analytical tools for performance evaluation.

Purpose of the Study:

• To introduce a novel Performance Measurement System (PMS) integrating Network Data Envelopment Analysis (NDEA).
• To address efficiency challenges in multi-stage manufacturing systems.
• To provide a framework for pinpointing sources of inefficiency and guiding operational improvements.

Main Methods:

• A case study approach was employed within the pharmaceutical industry.
• Network Data Envelopment Analysis (NDEA) served as the primary analytical instrument.
• The study focused on the production processes of intravenous (IV) sets to evaluate performance across diverse production stages.

Main Results:

• The NDEA-based PMS provides actionable insights into specific process inefficiencies on the shop floor.
• NDEA effectively disaggregates interconnected processes and quantifies efficiency measures.
• A trade-off exists between model complexity and discrimination power as the number of stages increases in NDEA models.

Conclusions:

• The integrated NDEA-based PMS offers a novel analytical framework for multi-stage manufacturing systems.
• This research bridges theoretical modeling with practical implementation in performance measurement.
• The study provides managers with actionable guidance for optimizing operations and highlights NDEA as a valuable tool for continuous improvement.