Stats & Data
Working with SAP eventually means you need an eye for data, too.
Notes from studying statistics and data analysis alongside SAP work.
My first semester as a junior transfer student, spent on assignments and finals
A recap of my first semester as a junior transfer student at the Korea National Open University — from assignment formats to how finals actually worked, and what I learned by fumbling through it
Making SAP data work properly in Excel: from Power Query to pivot tables
Why SAP exports don't behave in Excel out of the box, and the practical workflow of cleaning them up with Power Query and analyzing them with pivot tables.
Inventory variance: when the numbers check out but the floor doesn't
When SAP inventory and physical count don't match, here's how to read the variance statistically and track down the cause in practice.
Correlation is not causation: what data tells you, and what it doesn't
From the meaning of the correlation coefficient r to the difference between correlation and causation and common mistakes made in practice — a statistics concept note.
The mean trap: mistaking a KPI number for a target actually met
A look at the distortion that creeps in when a KPI is managed by a single mean value, through real cases of quote lead time and on-time delivery rate, and how to read data properly.
Descriptive statistics: the first thing you do when you face a new dataset
From mean, median, and variance to standard deviation and quartiles — the core concepts of descriptive statistics, explained for beginners with practical context.
Passing the ADsP: a working adult's 2-week study plan
I took the ADsP exam to learn to read SAP data with evidence instead of gut feeling. A round-47 passer shares a 2-week study plan for working adults, plus AI-generated mock exams.
Why I went back to school: wanting to say in data what I used to say by gut feeling
Why a production management practitioner working with SAP and MES transferred into a statistics and data science program at the Korea National Open University. A plain account of wanting to speak about production metrics in data instead of gut feeling.