Data Scientist Charts New Path for Secure, Scalable Supply Chains with Big Data Framework

In a world where supply chains are increasingly powered by data, a Nigerian data scientist is drawing attention for developing a strategic solution to one of the biggest challenges facing modern logistics: how to manage the massive, fast-growing volume of data without compromising security, accuracy, or operational stability.

Olufunmilayo Ogunwole, a seasoned data scientist with deep roots in supply chain systems, has authored a research paper that is already being described by local experts as a blueprint for building resilient, data-secure, and scalable supply chain operations in Nigeria and beyond.

The paper, titled “Enhancing Risk Management in Big Data Systems: A Framework for Secure and Scalable Investments,” was published earlier this year in the International Journal of Multidisciplinary Comprehensive Research. It lays out a structured, practical model for managing the security, scalability, and compliance risks that come with big data systems—particularly those operating in logistics, inventory management, procurement, and distribution networks.

Unlike theoretical models often built in isolation from industry realities, Ms. Ogunwole’s framework emerges from years of experience working inside supply chain operations across Nigeria. With a background in systems engineering and over a decade of managing and analyzing complex logistics environments, she brings hands-on understanding to the problem.

At SAKL Nigeria, where she serves as Manager of Data Science and Analytics, Ms. Ogunwole leads nationwide spare parts operations across multiple warehouses, introducing analytics-driven dashboards that achieved over 99.9% inventory accuracy. She streamlines reporting processes, develops automation for data capture, and trained teams across the value chain to act on insights in real time.

Before that, at Krones LCS West Africa, she boosted sales forecast accuracy to around 98%, transforming sales and operational planning for beverage manufacturing clients. There, she introduced predictive analytics that helped synchronize materials management, sales planning, and warehouse operations—critical elements in any supply chain.

These experiences form the bedrock of the risk management framework, which targets four central problem areas: Security Vulnerabilities – Data breaches, system infiltration, and poor access control.
Compliance Failures – Breach of data privacy regulations such as Nigeria Data Protection Regulation (NDPR). Operational Inefficiencies – Fragmented systems, duplicated or corrupt data, and poor data visibility. Scalability Barriers – Infrastructure that cannot keep up with the volume, variety, or velocity of expanding data.

“We often think data risks are just IT problems,” Ms. Ogunwole said. “But in supply chain, these risks can lead to wrong forecasting, overstocking, delivery delays, or even regulatory penalties. That’s millions lost in a blink.”

Ms. Ogunwole’s proposed model is built on four pillars, all tailored to the supply chain sector: AI-Powered Risk Detection

Using machine learning algorithms, the system monitors data streams across the supply chain—orders, inventory movements, payment cycles—for anomalies. For example, a sudden spike in product returns or delivery delays can trigger an alert for closer inspection.

Zero-Trust Security Mode

This approach assumes no actor—whether inside or outside the organization—should be trusted automatically. Every access request to data or systems must be authenticated and authorized, reducing the risk of insider threats or leaking supplier information. Automated Compliance Management.

Especially in large retail and logistics systems, compliance with NDPR, procurement regulations, and industry policies is often manual and inconsistent. The framework embeds compliance rules directly into data flows, flagging violations in real-time.

Scalable Infrastructure Design

By leveraging cloud technologies and distributed processing, the framework helps companies scale their data systems as they grow—without performance lag or system failures. This is vital for Nigerian SMEs venturing into e-commerce and regional trade.

Supply chain systems in Nigeria face unique hurdles: poor infrastructure, multiple regulatory jurisdictions, fragmented data sources, and limited investment in cybersecurity. Ms. Ogunwole’s framework meets these challenges head-on by offering low-barrier implementation paths that even mid-sized businesses can adopt.

Take the example of agro-logistics companies coordinating the movement of tomatoes from Kano to Port Harcourt. Without accurate data visibility, products are lost to spoilage, trucks delayed due to lack of coordination, and market prices fluctuate unnecessarily. Ogunwole’s model enables real-time tracking, alerts for bottlenecks, and safe sharing of data between producers, logistics providers, and retailers.

Or consider a manufacturing firm sourcing parts locally and abroad. By embedding the framework, procurement officers can automate compliance checks, flag unreliable vendors based on performance data, and use anomaly detection to identify fake invoices or misreported stock.

“If Nigeria must industrialize sustainably,” said Engr. Chinedu Mbakwe, a logistics advisor to the Federal Ministry of Trade, “we need supply chains that are smart, secure, and responsive. This framework gives us that edge.”

While rooted in Nigerian realities, the research has broader applications. Across Africa, Southeast Asia, and Latin America, growing economies face similar challenges: digitizing without adequate safeguards. Ms. Ogunwole’s paper speaks directly to these regions, providing a scalable model for secure digital transformation.

Global supply chain networks—with vendors and data partners across continents—also stand to benefit. In the wake of disruptions caused by COVID-19, more organizations are reevaluating how they handle operational risk. Ms. Ogunwole’s work could influence how international players evaluate third-party data security, standardize compliance, and plan for resilient growth.

Ms. Ogunwole is already engaging with Nigerian businesses and research groups to adapt the framework into sector-specific templates—for pharmaceuticals, FMCGs, agro-processors, and logistics platforms. She advocates for national investment in data security awareness, alongside support for tools like predictive analytics, automation, and real-time reporting in supply chains.