Applying big data analytics to gain insights into operational efficiency and performance.
Role | Deep Tech Used | Impact Vector | Industry | Impact Vector %Benefit |
---|---|---|---|---|
COO | Data Insights | Data | Healthcare & Pharmaceuticals | 36% |
Applying big data analytics to gain insights into operational efficiency and performance involves analyzing large volumes of data from various sources within an organization’s operations. This data-driven approach helps identify patterns, trends, and areas for improvement, ultimately enhancing operational efficiency and performance by optimizing processes, reducing costs, and increasing productivity.
Problem Statement
A multinational pharmaceutical company, manufacturing a diverse range of formulations and active pharmaceutical ingredients (APIs), struggled with suboptimal and manual production planning. Despite using end-to-end planning software, the complexity of their operations—involving 600+ SKUs and 200+ resources—led to impractical plans that couldn’t accommodate intricate factory floor constraints. This resulted in persistent reliance on manual intervention, leading to inefficiencies in resource allocation, production volumes, and inventory management. The plant head faced challenges with complex operational dependencies, changeover issues, and resource availability constraints, necessitating a more sophisticated and automated approach to planning.
Solution
The company implemented an AI-driven constructive scheduling optimization solution. This technology generates highly optimal plans by examining millions of configurations through rapid iterations, all within minutes and without human input. It provides a detailed, minute-by-minute production plan tailored to product and machine specifications, visualized in block-wise and room-wise formats. This visualization facilitates clear communication among block and room in-charges, ensuring efficient coordination. The solution’s strategic scheduling aims to improve Service Level Agreement (SLA) percentages while maximizing the volume of samples analyzed, all without manual intervention.
Results
The implementation of the AI-powered scheduler yielded significant improvements:
The case study demonstrates how advanced AI scheduling can transform complex pharmaceutical manufacturing operations, leading to substantial gains in productivity and resource management.
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