By Damilola Fatunmise
Nsisong Louis Eyo-Udo is an experienced business development leader with a strong background in supply chain management, transport logistics, and data analytics. He holds an MSc in International Business with Data Analytics from Ulster University, UK, and a B.Tech. in Transport Management Technology, specializing in Maritime Management, from the Federal University of Technology, Owerri, Nigeria.
With over 13 years of professional experience, Mr. Eyo-Udo has demonstrated expertise in streamlining supply chain operations, optimizing transport logistics, and leveraging technology to enhance business performance. In his recent role as Business Development Manager at E-Ranch Autocare, he led initiatives that reduced lead times by 15%, cut operational costs by 10%, and improved procurement practices, achieving a 12% reduction in procurement costs.
He is passionate about using data analytics and AI to drive business growth, having co-authored multiple publications on AI-driven strategies for entrepreneurial success and the impact of the Fourth Industrial Revolution. In addition to his role in business development, he is a member of several professional organizations, including the Nigerian Institution of Professional Engineers and the Institute of Management Consultants. His extensive knowledge and hands-on experience make him a leader in integrating technology into business and supply chain management.
You’ve had a diverse career spanning business development, fleet management, and supply chain optimization. To start off, could you share a specific challenge you faced in your role as Business Development Manager at E-Ranch Autocare, and how you overcame it?
One of the biggest challenges I faced was streamlining our supply chain when we had unpredictable demand spikes. This was especially challenging because we had to balance customer expectations with operational efficiency. To tackle this, I used historical data and predictive analytics to better forecast demand, and worked closely with suppliers to improve lead times. We introduced buffer stock strategies that helped mitigate shortages during peak times. It wasn’t perfect at first, but with constant adjustments and data monitoring, we significantly reduced stockouts and improved customer satisfaction.
That’s interesting. In your role, you optimized transport routes, reducing shipping times by 18%. Could you describe a real-world example of how route optimization improved your operations? Was there any unexpected outcome or lesson learned?
Absolutely. One of the projects I led involved optimizing routes for our fleet delivering to urban and rural areas. Using route optimization software, we analyzed traffic patterns, delivery windows, and vehicle capacity. One unexpected benefit was that we discovered some of our routes were taking longer than necessary due to inefficient loading times at certain depots. We adjusted scheduling to spread out the load times, which reduced delivery delays. Another lesson learned was the importance of real-time data; during high traffic periods, even the best-planned routes can become irrelevant, so we invested in GPS and real-time tracking, which made the optimization process much more adaptive.
With your background in transport management and your focus on data analytics, I’m curious: What was the most surprising way you saw data improve day-to-day operations in your supply chain or fleet management?
The most surprising aspect was how data could uncover inefficiencies that weren’t immediately obvious. For example, we used data on fuel consumption, maintenance schedules, and even driver behavior, and realized that some of our vehicles had frequent minor breakdowns due to poor driving habits—something we hadn’t anticipated. By addressing this with targeted driver training and using telematics to monitor habits, we cut our maintenance costs by 15%. Data truly opened our eyes to small daily decisions that were costing us big.
That’s a great example of how data can reveal hidden opportunities. As a fleet manager, you likely faced pressure to reduce costs while maintaining quality service. What’s one specific instance where you had to make a tough decision about balancing cost reduction with service quality?
One tough decision I had to make was related to vehicle maintenance. We had an aging fleet, and we needed to balance the costs of maintaining older vehicles versus investing in new ones. Initially, it seemed like maintaining the old vehicles would save us money, but after running the numbers, we realized that frequent repairs and downtime were causing us to lose more in the long run, not to mention the impact on customer satisfaction. I made the call to invest in newer, more fuel-efficient vehicles, which were more expensive upfront but ultimately saved us on maintenance costs and improved reliability.
You mentioned earlier that you leveraged ERP systems for better supply chain visibility. In real-life operations, what were the biggest challenges you faced while implementing or managing such systems, and how did you manage them?
The biggest challenge with ERP implementation was ensuring that the system was fully integrated across all departments. Often, different teams were using separate systems, so aligning everything was a real hurdle. We had data silos, which made it difficult to get a full picture of the operations. The key was involving stakeholders from every department—procurement, logistics, sales, etc.—in the decision-making and implementation process, so they could understand the value of the system. It wasn’t easy, and there were bumps along the way, but constant training and a phased implementation plan helped smooth out the transition. The result was a much more cohesive workflow and far greater transparency across the supply chain.
You’ve had success in using data analytics to improve efficiency. Can you share a real-world example where you used data to solve a problem you didn’t initially know existed, or where data led to a significant improvement in an unexpected area?
Certainly. One example that stands out was when we were tracking order fulfillment times, and the data revealed a bottleneck in our warehouse—not in the shipping or delivery part, but in how we were picking and packing orders. We hadn’t anticipated this, but by analyzing the data from our warehouse management system, we noticed that certain products were taking significantly longer to locate, which led to delays in shipment. After reorganizing the warehouse layout based on picking frequency and using data to optimize staff shifts, we were able to reduce order fulfillment time by 20%. It was a real “aha” moment—sometimes the root cause of inefficiencies is not where you expect it to be.
Looking back at your time working with fleets, was there a specific technology or tool you wish you had implemented sooner to improve fleet efficiency or reduce costs?
Yes, I wish I had implemented telematics and fleet management software much sooner. Initially, we relied on manual logs and basic GPS tracking. But once we upgraded to a full telematics system, it gave us real-time data on driver behavior, fuel usage, vehicle diagnostics, and even location. This allowed us to make immediate adjustments, such as rerouting vehicles or addressing maintenance issues before they became costly problems. The ability to monitor fleet health in real-time drastically improved operational efficiency and helped us avoid costly repairs or breakdowns.
You’ve mentioned a number of technology-driven improvements, which obviously required data and constant monitoring. When you think about the future of transport and supply chains, what real-life trend or innovation excites you the most and why?
I’m particularly excited about the rise of autonomous vehicles and drones in logistics. While we’re still a few years away from widespread adoption, the potential for reducing delivery costs and improving speed is huge. Imagine a future where freight transportation can be fully automated, with drones handling last-mile deliveries and autonomous trucks transporting goods across long distances. It could reduce labor costs, minimize human error, and cut down on fuel consumption. The challenge will be integrating this with existing infrastructure and regulatory frameworks, but the possibilities are fascinating.
Finally, if you could give one piece of advice to businesses today who are looking to integrate data analytics into their supply chain operations, what would it be?
I would say start with the basics: collect the right data and focus on the areas that matter most to your business. Don’t get overwhelmed by trying to analyze everything at once. Prioritize key metrics—whether that’s inventory turnover, transportation costs, or supplier performance—and use data to improve those first. Also, ensure that your team is trained and aligned with the tools you’re using. Data analytics is only as powerful as the people who use it, so fostering a data-driven culture within your organization is crucial for success.
I hope the insights can help others navigate the complexities of modern supply chains.