Boosting Life Sciences Supply Chain Efficiency with Cost-to-Serve Frameworks and AI/ML

The life sciences industry is grappling with unprecedented operational cost pressures, driven by a confluence of global challenges. The lingering effects of COVID-19 exacerbated resource and material scarcity, while inflation has compounded cost pressures across the global value chain. Additionally, the Inflation Reduction Act (IRA), signed into law in August 2022, has introduced stringent cost controls and price caps, further intensifying the financial strain on the industry. On top of these challenges, ESG requirements and Scope 3 emissions control mandates are increasingly influencing corporate strategies and investor expectations.

In response to these multifaceted pressures, life sciences leaders are reimagining their end-to-end supply chains. They are prioritizing a comprehensive understanding of costs at every node, striving to create a dynamic network that is both flexible and cost-efficient.

A pivotal tool in this transformation is the cost-to-serve framework. This approach provides critical visibility into the supply chain, allowing businesses to pinpoint cost drivers, identify revenue leakages, and uncover operational inefficiencies. Through this framework, companies can also conduct regression testing across various scenarios, using artificial intelligence (AI) and machine learning (ML) to develop predictive models and drive cost optimization.

At Bristlecone, we partner with our life sciences clients to implement this transformative cost-to-serve framework, leveraging our cutting-edge AI/ML tools and accelerators. Our approach begins with achieving granular spend visibility by standardizing data from ERP systems and other sources, integrating diverse costs such as shipment, duty, tariffs, and warehouse expenses into a global spend data cube.

Our AI/ML accelerators are designed to account for a wide array of variables, including product costs, market fluctuations, weather impacts, and regulatory changes. This powerful platform enables ‘what-if’ scenario analysis and predictive modeling, empowering businesses to identify spend anomalies, compare freight rates, optimize transportation modes, and explore global tax and duty opportunities. Moreover, these powerful tools assess supplier and network efficiency through the lens of ESG and Scope 3 carbon emissions goals, ensuring that sustainability is woven into the fabric of supply chain management.

One of our recent successes involved assisting a life sciences client in analyzing $1 billion of spending data using our cost-to-serve framework. By deploying our AI/ML accelerators, we helped identify multiple cost anomalies, streamline the process of generating spot quotes, capture shipment rate discrepancies, and empowered them to negotiate more favorable rates. Ultimately, this platform optimized their overall supply chain costs by approximately 5%. Additionally, we developed a CO2 emissions model that provided critical visibility into ESG and Scope 3 supplier emissions, offering further opportunities for optimization.

As the industry continues to face mounting supply chain challenges and navigate operational pressures, the demand for innovative strategies and rigorous cost controls has never been greater. By harnessing the power of AI/ML within a proven cost-to-serve framework, businesses can achieve significant cost savings while advancing their sustainability goals.