By: Lekan Onanuga
When most people think of supply chains, they picture trucks, warehouses, and inventory spreadsheets. For Olubunmi Anifowose, the picture is far more analytical: a complex, data-rich ecosystem where predictive intelligence, optimisation models, and strategic analytics determine whether businesses thrive or collapse under uncertainty. At the intersection of business analytics and supply chain execution, Anifowose has built a career focused on turning operational data into foresight.
Her signature contribution is CHAINALYTICA™, an intelligent business analytics framework she developed after repeatedly encountering the same challenge across organisations: supply chains generating vast volumes of data but lacking the analytical structure to convert that data into timely, decision-ready insight. “Data alone doesn’t optimise supply chains,” Anifowose has observed. “What matters is how intelligently that data is translated into decisions.” That philosophy underpins CHAINALYTICA™, which integrates predictive analytics, demand intelligence, inventory optimisation, logistics performance modeling, and sustainability-aligned metrics into a unified decision-support system.
Unlike traditional reporting tools that explain what already happened, CHAINALYTICA™ is designed to anticipate what comes next. By applying advanced analytical modeling and scenario analysis, the framework enables organisations to forecast demand volatility, identify bottlenecks before they escalate, optimise supplier performance, and balance cost, speed, and resilience in real time.
In practice, the analytical approaches embodied in CHAINALYTICA™ have delivered measurable enterprise results. organisations applying these methods have recorded 15–30% improvements in demand forecast accuracy, resulting in 20–35% reductions in excess inventory and improved working-capital efficiency. Logistics and fulfillment operations achieved 10–25% reductions in order cycle times, while analytics-driven process optimisation produced 12–28% decreases in operational inefficiencies, including warehousing overhead and expediting costs. At the executive level, Anifowose’s work enabled a shift from retrospective reporting to forward-looking scenario planning, strengthening decision accuracy, cross-functional alignment, and resilience against supply disruptions.
Anifowose’s path to this innovation was shaped by years of hands-on experience as a business analyst and supply chain optimisation professional, working across complex enterprise environments. Her career reflects a consistent focus on using analytics to improve operational performance, enhance forecasting accuracy, and align supply chain execution with broader business strategy. Colleagues describe her as a practitioner who understands both the technical foundations of analytics and the real-world constraints of supply chain operations.
That dual fluency is evident in CHAINALYTICA™’s design. Rather than remaining an abstract academic model, the framework is execution-oriented, built to support procurement, inventory management, logistics, and executive planning teams within a single analytical architecture. By consolidating fragmented data streams into a coherent intelligence layer, CHAINALYTICA™ enables leaders to act proactively rather than reactively in increasingly volatile supply environments.
Beyond her technical contributions, Anifowose has emerged as a respected voice on the evolving role of analytics in global supply chains. She advocates for analytics systems that guide decisions under uncertainty, rather than simply measure past performance. Her work reflects a broader shift in the field, as supply chains become strategic assets driven by data intelligence rather than back-office operations.
Peers also highlight her commitment to mentorship and professional development, particularly in helping analysts and operations teams move beyond descriptive dashboards toward predictive and prescriptive thinking. Through applied innovation and leadership, Anifowose has influenced how organisations approach data literacy, analytics governance, and cross-functional collaboration within supply chain environments.
For Anifowose, the future of supply chain optimisation lies in intelligent systems that continuously learn. Her ongoing work focuses on refining CHAINALYTICA™’s analytical models, expanding its applicability across industries, and ensuring that advanced business analytics remains accessible to organisations without massive data science budgets. “Resilient supply chains,” she notes, “belong to organisations that can see forward, not just backward.”
