# Why Output Metrics Can Miss Problems in Complex Production Systems

DevFeed: [Why Output Metrics Can Miss Problems in Complex Production Systems](<https://devfeed.tech/articles/what-you-measure-28516.md>)

Original publisher: [Read original article](<https://thedailywtf.com/articles/what-you-measure>)

Author: Remy Porter

Published: 2026-09-02T06:30:00Z

Content type: opinion

Language: en

Sources: [The Daily WTF](<https://devfeed.tech/sources/the-daily-wtf.md>)

Topics: [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [robot sense of touch](<https://devfeed.tech/topics/robot-sense-of-touch.md>), [Embedded Software Dev](<https://devfeed.tech/topics/embedded-software-dev.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [databases](<https://devfeed.tech/tags/databases.md>), [embedded](<https://devfeed.tech/tags/embedded.md>), [feature-articles](<https://devfeed.tech/tags/feature-articles.md>), [manufacturing](<https://devfeed.tech/tags/manufacturing.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [widget](<https://devfeed.tech/tags/widget.md>)

## AI overview

This commentary examines a metrics-driven manufacturing team whose automated production line combines robotics, embedded firmware, web-based monitoring tools, and PLC code. It argues that tracking output and limited performance metrics does not adequately explain how such a complex system behaves or why bottlenecks occur.

## Source excerpt

Rachel joined a new team which was proudly "metrics driven". When she first met with her boss, Zane, he explained his thinking. "We need to be data-driven to make good decisions, right? We're a manufacturing company. We make widgets. At the end of the day, we need to make the most widgets for the lowest cost of goods sold. So we track that, and that feeds into every decision." The team oversaw an automated production line, which meant the software was a mix of robotics, embedded firmware, high-level web based monitoring tools, and thickets of dreaded PLC code. And because you can't build an entire factory for test purposes, they only way they could test real-world scales with real-world data was to roll changes out to production. They could simulate, they could run tests on subsets of the system, but a change in the production line software couldn't truly be validated until it rolled out into the real world. Rachel's first task on the new team involved making some changes to their metrics dashboard. It was viewed as a good way to get her feet wet with the new team. As it turned out, the metrics dashboard was a Google Sheet, with a complex series of formulas that involved multi-level INDEX functions- essentially querying the spreadsheets like they were a database. Why not use an actual database? Oh, they did -- six actually -- but the company obeyed Remy's Law of Requirements Gathering: "no matter what the requirements the users ask for, what they really wanted was Excel". The database data was pulled into the spreadsheet for reporting. Now, a complicated sheet pulling in data from not one, but six different databases, they must have a pretty complex model to explain how changes to their software would impact productivity. And since they needed to model the software to make predictions about how it'd behave in production, that model must be extremely useful. Of course it wasn't. The only metrics they tracked were output metrics, variations on "widgets produced per unit