That curiosity shaped how I studied mathematics, learned Chinese, worked in high-tech engineering — and how I now approach business problems. The questions stay simple: What is happening? Why? What matters? What is missing?

For a small or mid-sized business, the first step is rarely choosing an algorithm. It is understanding the decision, the process, the evidence and the people who know the work.

That is why FeiShe values explanation as much as the solution. A result should be clear enough to question, validate and use. Mathematics, Data Science, Operations Research and Simulation make that understanding more precise — they do not replace it.

Understand the decision before choosing the method.

Data Science is a way to learn from evidence: describe what happened, find patterns, quantify relationships, forecast, test ideas and understand uncertainty.

For smaller businesses, that often means using the data already available — no large platform or complex AI required.

Forecasts can feed Operations Research models — and a model can show which data is worth collecting next.

Data is evidence. Its value lies in what it helps a business understand and decide.

Operations Research helps when a business must choose between workable alternatives, not just describe what happened. The model makes decisions, objectives, constraints and trade-offs explicit.

Data Science supplies forecasts; simulation tests how a decision holds up when reality varies. The aim is not to replace judgment, but to give judgment a clearer structure.

A good model does not hide the decision. It makes it easier to see.

Many decisions depend on what changes over time: demand rises and falls, queues form, capacity runs short, and a small change in one place has effects elsewhere.

Simulation offers a safe place for what-if questions before committing time, money or people. What if demand grows? Where does a bottleneck appear? How much capacity is really needed? Which assumptions matter most?

For smaller businesses this matters, because experimenting in the real operation is expensive. Simulation compares scenarios, exposes side effects and makes uncertainty visible — without pretending to predict the future.

For FeiShe, simulation is a core method, not an extra after optimization.

Explore change before reality becomes the test.

A model, forecast or simulation can be technically correct and still fail if nobody understands what it means for the decision.

That is why FeiShe puts unusual weight on explanation: the reasoning, assumptions and limits should be clear enough for people to question the work, not just accept it.

The final form depends on the problem — a standalone application, a forecasting workflow, an optimization model, a simulation, a dashboard, or a mix. These tools can work on their own or alongside AI tools where that adds real value.

The goal: leave the business with better understanding, not just another technical output.

Explain the reasoning, not only the result.