GSK’s Journey to Enhanced Supply Chain Antifragility
The Challenge: Navigating Volatility in Pharma & Life Sciences Manufacturing
Global pharmaceutical manufacturers seek to optimise production and capacity planning. Challenges such as changeover downtime, CAPEX tied up in production lines, and the need to rapidly scale or reconfigure capacity to meet fluctuating demand and forecasts are common. In a sector where agility and resilience are paramount, these pain points directly impact operational efficiency, financial performance, and the ability to achieve critical sustainability goals.
GSK, a global biopharmaceutical leader with a vast manufacturing footprint, encountered a specific, yet broadly relevant, challenge at its Irvine site. While a clear correlation existed between batch production schedules and energy consumption, the unpredictable nature of machine usage made accurate energy forecasting incredibly difficult. GSK sought greater foresight to understand the true financial and environmental impact of its production planning decisions and to strategically integrate renewable energy sources. This scenario is a microcosm of the larger need for supply chain antifragility in the pharma sector – the ability not just to withstand disruption, but to improve and adapt.
The Solution: Predictive Analytics & Simulation for Antifragile Operations
Decision Lab partnered with GSK to address these complexities by developing a cutting-edge hybrid model. This innovative solution seamlessly integrated machine learning (ML) with advanced simulation, creating a powerful decision-support tool that connects production planning directly with energy consumption forecasting. This approach empowered GSK to achieve smarter, more sustainable, and antifragile operations.
Key Components of the Decision Lab Solution:
- Machine Learning (ML) for Precision Forecasting:
- A Python-based model meticulously analysed historical energy data, establishing a precise baseline for consumption under various production plans.
- A Random Forest Regressor was employed to predict electricity and steam usage with high accuracy, taking into account machine schedules and their inherent variability.
- Simulation for What-If Scenario Planning:
- Outputs from the ML model were fed into a dynamic AnyLogic simulation environment . This allowed GSK to virtually assess the impact of introducing additional renewable energy sources, such as wind and solar, into the site’s energy infrastructure. The simulation established a real-time link between production cycle plans and energy usage.
- The model facilitated extensive “what-if” scenario testing, enabling GSK to explore alternative manufacturing plans and simulate unforeseen operational disruptions or system failures, using Monte Carlo simulations to account for inherent variability and uncertainty.
- Detailed tracking of machine utilisation and energy consumption identified inefficiencies and provided actionable insights for energy reduction. The model incorporated variables such as electricity, steam, gas usage, and renewable energy generation for a holistic view.
- AnyLogic’s robust capabilities and seamless Python integration via Pypeline were crucial, enabling real-time data exchange and dynamic, accurate modelling.
You can read about the project from a simulation point of view in the AnyLogic case study Enhancing Energy Efficiency at GSK with Predictive Analytics in Manufacturing.
The Impact: Building a Resilient, Sustainable, and Cost-Efficient Future
The implementation of Decision Lab’s predictive analytics and simulation solution provided GSK with significant strategic advantages, directly contributing to a more antifragile supply chain and addressing core manufacturing pain points:
- Enhanced Energy Consumption Forecasting: Achieved highly accurate predictions for energy usage, enabling better budgeting and proactive planning.
- Optimised Renewable Energy Integration: Data-driven insights guided strategic investments in solar and wind energy, accelerating progress towards sustainability goals.
- Streamlined Production Plans: The ability to link production schedules with energy impact allowed for optimising manufacturing plans, leading to reduced energy costs and environmental footprint.
- Reduced Operational Costs and Emissions: Through intelligent planning and renewable energy adoption, GSK realised potential cost savings and significant reductions in greenhouse gas emissions.
- Increased Operational Resilience: The simulation capabilities allowed GSK to proactively evaluate and prepare for potential system failures and disruptions, minimising downtime and energy waste. This directly mitigates the risk of CAPEX being tied up in underutilised or vulnerable lines.
- Improved Capacity Planning Agility: By understanding the energy implications of different production plans, GSK gained greater flexibility in ramping up or changing capacity to meet demand, addressing a key pain point for global pharma manufacturers.
This successful collaboration with GSK underscores Decision Lab’s expertise in delivering intelligent, data-driven solutions that not only meet immediate operational needs but also build a foundation for long-term supply chain antifragility in the demanding Pharma & Life Sciences sector. It provides a scalable blueprint for other mid-to-large global pharma manufacturers striving for optimised production, sustainable capacity planning, and enhanced resilience in an ever-evolving market.
Want to learn how Decision Lab can help your organisation build an antifragile supply chain? Contact us today to discuss your specific challenges in production and capacity planning.