With over 25 years of experience leading supply chain operations across Latin America and Africa, Fernando Figiacone has witnessed firsthand the shift from reactive to predictive supply chain management. Now equipped with executive training from MIT, Stanford, and Cambridge in Supply Chain and Generative AI, he brings a forward-thinking perspective on how advanced analytics is transforming procurement, logistics, and manufacturing. In this Executive Insight, Fernando explores how data accuracy, predictive modelling, and cross-functional alignment are driving smarter, more resilient supply chains in an increasingly volatile world.
How are you leveraging supply chain analytics to predict future challenges?
Advanced analytics allows organisations to shift from reactive to proactive decision-making. In my experience, it has been instrumental in identifying inefficiencies on manufacturing lines, optimising logistics routes in real-time, and enhancing procurement decisions by combining internal cost models with external market trends.
But beyond operational improvements, the real power of analytics lies in its ability to build trust in data-driven decisions. When teams see tangible results, not just theoretical benefits, analytics become embedded in the culture. It’s no longer just a tool, but a mindset that drives more efficient, informed, and confident decision-making across the entire supply chain.
What supply chain analytics data do you prioritise for accurate predictions?
Beyond price and delivery times, which are always essential,I prioritise consistency, responsiveness, and strategic alignment. Key metrics include on-time-in-full (OTIF), lead time variability, quality defect rates, and how quickly suppliers respond to corrective actions.
But it’s equally important to go beyond the metrics. A supplier might perform well on paper but struggle during moments of disruption. That’s why I also assess agility, digital readiness, and willingness to collaborate. In today’s volatile environment, these qualitative factors are just as critical as quantitative ones for ensuring long-term performance and resilience.
How has data analytics helped you mitigate risks in your supply chain?
In today’s uncertain environment, supply security has become a top priority. Data analytics is essential, not just for reacting to risks, but for anticipating and mitigating them through structured, informed decision-making.
For example, analytics enables us to identify vulnerabilities in critical materials and proactively develop alternative suppliers. By combining historical performance data, quality metrics, and financial indicators, we can quickly and objectively evaluate new sources, often in close collaboration with Manufacturing teams.
Analytics also plays a key role in working capital management. By simulating different inventory and payment scenarios, we can strike the right balance between ensuring supply continuity and maintaining cash flow discipline.
Ultimately, analytics has become a strategic enabler. It allows us to detect risks earlier, collaborate more effectively with suppliers, and build resilient, data-driven partnerships.
How do you ensure data accuracy and reliability in your procurement process?
Ensuring data accuracy in procurement begins with establishing clear ownership and governance across key functions, typically Procurement, Finance, and IT. It’s essential that master data is aligned across systems to maintain consistency and support a single version of the truth.
Simplicity also plays a vital role. By streamlining data entry, automating inputs wherever possible, and embedding validation rules into procurement workflows, we reduce errors and significantly improve data reliability.
Transparency is another key factor. When teams understand how their data influences supplier evaluations, negotiations, and planning decisions, accountability naturally increases, and so does data quality over time.
Crucially, organisations must recognise that the effectiveness of advanced analytics depends on the quality of the data feeding it. Too often, businesses rush to apply data science tools without first clearly defining the problem or ensuring the underlying data is accurate and complete. Investing in data quality is just as important as investing in analytics capabilities, it’s the foundation for trustworthy insights and smarter decisions.
Can you share examples of how predictive analytics has improved supply chain outcomes?
There are numerous use cases across the supply chain, spanning Manufacturing, Logistics, and Procurement, where predictive analytics has delivered measurable results.
In Procurement, demand forecasting models have been instrumental in anticipating material shortages, particularly for critical packaging and raw materials. This foresight enables teams to adjust order volumes, activate alternative suppliers, or renegotiate terms before disruptions occur.
In Logistics, predictive models that incorporate traffic, weather, and historical performance data help optimise delivery routes, reduce delays, and improve service reliability. Meanwhile, in Manufacturing, predictive maintenance algorithms detect early signs of equipment failure, significantly reducing unplanned downtime and extending the lifecycle of key assets.
These improvements go far beyond efficiency gains, they directly enhance supply chain resilience, cost optimisation, and service levels to customers.
