I use data, mathematics and business analysis to help businesses make better decisions, especially when resources are limited, choices compete, and the stakes are real.
The decision problem
These are not questions intuition answers well. They involve constraints, trade-offs, and interdependencies. They can be modelled, tested, and optimized.
What I solve
What should you buy, how much should you buy, and how should limited capital be allocated?
What should you produce when materials, labour, machinery and demand are constrained?
How many people do you need, when should they work, and should you hire or increase existing capacity?
How to allocate limited resources; money, time, people, across multiple competing uses or locations.
Finding what's working, what isn't, and why, using data to separate signal from noise.
Structuring a complex or high-stakes decision, identifying the trade-offs, running the scenarios, and making the choice defensible.
The process
Understand the business problem and what you're trying to decide.
Identify the relevant numbers, constraints, resources and alternatives.
Examine the available options and determine what the numbers support.
Find the strongest feasible course of action.
Give you the recommendation, the reasoning behind it, and what the result means for the business.
Cases
The situation
A manufacturer that produces about 120,000 units every month realises that the percentage of defective products has risen to around 8%. At that rate, approximately 9,600 units are being rejected every month, and at an estimated cost of ₦85 per defective unit, that represents roughly ₦816,000 in direct monthly waste before considering rework, delays, customer complaints and other consequences.
The decision
Which of the four observable factors was causing these defects: machine speed, temperature, pressure or sealing time?
| Option | Cost |
|---|---|
Recommendation
Recommended operating point: 70 RPM, 150°C, 7 bar, and 0.7 seconds
9,600 defective units reduced to 843 defective units per month. At ₦85 of direct cost per defective unit, that represents approximately: ₦744,371 in projected monthly savings.
Consider a Nigerian woven-packaging manufacturer producing four types of bags: Standard 50kg rice sacks Printed 50kg rice sacks Fertilizer bags Animal-feed bags Each product earns a different contribution per bag. Each also consumes different amounts of the factory's scarce resources. The management problem is therefore not simply: “Which product makes the most money?” but, “Given the resources we have available, what combination of products produces the greatest total contribution?”
A PP woven sack manufacturer had to decide how much to produce for September. The forecast was only the beginning of the analysis.
A customer promotion experiment revealed that the biggest discount wasn't the most profitable option.
A furniture manufacturer makes ₦20,000 per shelf. I recommend producing zero of them.
Background
I studied Industrial Mathematics at the University of Benin — operations research, optimisation, probability, statistics, and systems modelling.
I also spent five years running and expanding a local business: managing sales, purchasing, pricing, inventory, and finance with real capital on the line.
That combination raised a question I kept coming back to: what if the decisions being made by feel could actually be modelled and solved? Not as a theoretical exercise but as a practical answer to a specific business problem.
Get in touch
Don't worry about which package fits. Send me the problem, I'll determine the appropriate scope before we begin.
I typically respond within 24–48 hours.