Ideas made useful.
I write about how work moves through factories, service teams and projects, and the math, software and AI that help it move better. Topics include industrial engineering (how work and workplaces are designed), simulation, optimization (finding the best plan), networks, data and AI. Some pieces pair with a hands-on lab. Search, or filter by topic.
How Gamification Can Transform Learning
Games can make kids want to practice, or turn learning into a slot machine. Here’s what dopamine really does, when rewards help and when they backfire, and how that shaped my early-math game.
Network Science for Engineers: Stop Treating Connections Like a List
A list of tasks and people can hide one link that splits a plant or project in two if it breaks. A map of the connections shows it.
Constraint Thinking: Why Keeping Every Station Busy Can Hurt the Whole System
One slow step caps how much a line can make. Keeping every other step busy often just piles up unfinished work and makes orders take longer.
CP-SAT for Manufacturing Scheduling: Finding a Plan That Follows Every Rule
A factory schedule has to follow thousands of rules at once, from the order of steps to deadlines. A program called CP-SAT searches for plans that do.
When Straight Lines Aren’t Enough: Nonlinear Optimization in Engineering
Costs, delays and lots of other real things curve, jump or affect each other. Treat them as straight lines and you can end up picking the wrong best choice.
Simio vs. SimPy: Choosing a Simulation Tool for Engineering Work
Picking a simulation tool is less about building on screen versus writing code. It’s more about who will use the model, how long it will last and what it must connect to.
Why AI Coding Agents Change the SimPy vs. Simio Decision
AI coding agents can write much of a Python simulation for me. So more of my effort goes into defining the problem and checking that the model really works.
Little’s Law: A Simple Rule for Work, Output and Time That’s Easy to Misuse
On average, the amount of unfinished work equals the output rate times the time each item spends inside. It’s a handy check, but only if all three are measured the same way.
Simulation vs. Optimization: Find the Best Plan, Then Test How It Holds Up
Optimization finds the best plan for the numbers you give it. Simulation tests how that plan holds up when real days get messy. They work best as a team.
Knowledge Graphs for Factories: Connecting Work, Resources and Evidence
Some factory questions, like which jobs a late part will hold up, depend on how records connect across many systems. A knowledge graph stores those connections directly.
AI for Industrial Engineering: Build Tools, Not Chatbots for Show
The most useful AI often lives inside one clear task. You know what it’s allowed to read, what it’s allowed to do and how its answer gets checked.
Erlang C vs. Little’s Law: Staffing a Waiting Line Without a Full Simulation
Two classic formulas give a quick first guess at how many people it takes to answer most requests on time. You can use them before anyone builds a detailed simulation.
From Vibe Coding to Engineering: How I Work With AI Coding Agents in Kiro
AI coding agents get more reliable with a clear written plan, the right background and tools, automatic checks, and reviewed lessons carried between sessions. Longer and longer prompts don’t get you there.
Process Stability: Telling Real Change from Everyday Noise
Every process wobbles. Here’s how to tell a real change from everyday noise, and how my Process Stability Lab does it.
Factorial Experiments: Why You Should Test Settings Together, Not One at a Time
A sealing machine makes weak seals, and three settings are the suspects. Testing every combination shows which settings matter. It also catches pairs that work together, which changing one at a time can miss.
Work Measurement: Stopwatch Times, Practice and Forgetting
How a stopwatch reading turns into a planned time per job and a crew size. Plus how practice makes people faster, and how time away takes some of that back.
Factory Flow: Capacity, Variation, Rework and the Moving Bottleneck
Why the slowest station sets the pace for a whole production line. And why speeding up the others, or starting more work than it can handle, doesn’t help.
Scheduling: Rules of Thumb vs Checking Every Order
Seven jobs share one machine. Which order leaves the fewest hours late? And how do quick rules of thumb stack up against checking every possible order?
Reliability and Layout: Breakdowns, Repairs and When a Backup Machine Helps
Machines break and get fixed. How often does the whole line run? When does a backup machine help? And what changes when one cause can stop both?
Network Resilience: Single Points of Failure and Targeted Attacks
Which sites and links can a network not afford to lose? How the lab finds them, and what its numbers can and can’t tell you about real operations.
How Many Staff Keep Waits Short? Erlang C, Overload and Little’s Law
Why a crew with spare time can still keep people waiting too long. And how the lab finds the fewest staff who answer 90% of calls on time.
Simulating a Waiting Line: How It Is Built and How It Is Checked
How the lab plays out every customer at random. Plus the checks that show the moving picture is a real working model, not just decoration.