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Analytics in Life Sciences: Supply Chain Management

We have already looked at New Product Development and Manufacturing and Quality in this Analytics in Life Sciences blog series. Let’s continue with how analytics can improve management and risk mitigation throughout the life sciences supply chain. Supply chains have evolved from local models to global networks, introducing more risks and potential for network disruption. A global presence also means complying with standards in local markets related to good manufacturing, distribution, and serialization practices. The emergence of advanced analytical tools are enabling more effective supply chain management and risk mitigation.

Demand & Supply Planning Visibility

Demand volatility, and forecast accuracy remain significant challenges. The use of historical data and scenario simulation with predictive analytics can at least offer planners a better outlook for contingency planning.  Simulation success, however, requires the combined involvement of Marketing, Sales, and Finance departments to arrive at quality forecasts. Fortune 100 pharmaceutical companies continue to invest in forecasting software to provide a single system for  demand visibility. Though for demand to be appropriately fulfilled, planner visibility alone is not sufficient. Supply chain bottlenecks and constraints can have devastating outcomes to public health. Having a system that synchronizes demand forecasts, multisite capacity plans, and production schedules facilitates decision-making. For life sciences companies that have invested in ERP systems, this functionality is apparent, though can continue to be augmented with historical data. The combined result is now more accurate analytical tools for what-if scenario planning – complete with scenario comparisons against high-level KPIs and operational constraints – which are required to support and streamline effective S&OP meetings. Other industries are implementing a collaborative platform with ERP to allow end-to-end visibility across internal and external supply chain stakeholders. We believe that this is something the life sciences industry can also leverage to respond swiftly to unforeseen changes. Oftentimes, this is a centralized team (e.g., “control towers” or “central command and control”) that can monitor the global network of internal facilities and external vendors and alert the broader organization when there is a supply disruption due to significant reactor downtimes, departure from vendors’ promised dates, changes in launch sequence and timing, delays in site readiness, and of course, forecast variation.

Sourcing Optimization

Visibility to the operations of suppliers and external partners also offers an opportunity to realize new insights. As companies diversify product portfolios and the number of required vendors increases, the costs and risks associated with bringing on new ones must be controlled, and decisions regarding from whom and where materials are sourced must be streamlined. Analytical tools can clearly compute the total cost of ownership versus emphasis on price alone. Along with improvements in supply chain practices and negotiations, this systematic and fact-based approach ensures an optimal supply base and can improve companies’ total value proposition. In addition, to drive sourcing optimization, companies can review supplier performance by examining financial data, vendor capabilities and relevant organizational objectives. The typical life sciences organization does not regularly switch suppliers due to the rigorous vendor requalification requirements. However, the results can not only compare risk factors between already-qualified primary and secondary suppliers, but also recommend suppliers of certain materials during product development, engineering and process qualification campaigns.

Logistics Optimization

Demand volatility and a growing number of suppliers and logistics partners offer the opportunity for logistics optimization. Logistics optimization is particularly important for life sciences organizations balancing perfect service levels with inventory months-on-hand targets, shelf life, and replenishment time considerations. Companies have traditionally invested in inventory as a buffer to cover for forecast inaccuracy, but investment in technology and analytics can both improve accuracy and optimize the response to variance. For optimal cold chain management, it is important that logistics partners are conducting scenario-based analyses using predictive analytics, factoring in elements such as route optimization, fleet sizing and load planning. For large companies, having a central team to monitor and to refine these network designs can mean significant savings. Refinement every five to ten years though is no longer adequate; a proactive approach to continuously monitor sensitivity signals that trigger redesign activity based on changes in the market, the volume and maturity of respective products, and the cost elements in transportation, manufacturing and warehousing is a new necessity. Lastly, no supply chain is truly effective if it does not protect consumers from subpar or counterfeit product. Traceability tools with the capacity to compare unique identifiers against those in a national database can be powerful in the identification and prevention of counterfeit drugs and product diversion. Analytics, combined with a strong reporting platform, are a key enabler of a compliant end-to-end supply chain. This visibility will enable supply chain agility while  protecting the corporate brand, as well as the safety of consumers. In the next post, we will continue this series to discuss how analytics can further influence market intelligence efforts.

Tags: Analytics, Supply Chain Technology