Job tracking vs. production intelligence: what to implement on the shop floor first

Digitalisation · 5 min read · 13.08.2026

The production director walks onto the shop floor on a Tuesday morning. He asks the operator: "How many pieces did we actually produce yesterday on CNC 3?" The answer is familiar. "About two hundred... I can check the paper." After five minutes of searching, the number turns out to be 187. An hour later, the same number is verified in the finished goods warehouse. It comes out to 203. The discrepancy between the paper on the shop floor and the actual data plagues every production director.

At Foreast, we see a recurring scenario in our projects. A company buys an MES system for 150,000 euros. The implementation team installs the modules. A year later, only the logging of job start and end times is actually being used. The rest of the system functions like an office no one enters. Production still runs on paper and intuition. Just with more expensive software on the server.

Job tracking: a cheap way to get hard data

Job tracking is an electronic replacement for paper job tickets. Instead of paper, the operator logs the start and end of an operation into a terminal. It could be a tablet by the machine. A QR code reader. A simple app on a company phone.

Job tracking gives you the real start and end time of every job on every machine. It records the actual length of downtimes, not supervisor estimates that come eight hours after the fact. It provides identification of the operator, material, and tool. It allows direct comparison of planned time with actual time.

A metalworking company with 80 employees that we worked with implemented job tracking on three key machines. The investment was 4,500 euros. Within two months, they found that the actual time to produce a batch was 22% longer than the standard indicated. The operators weren't slow. The standards simply didn't include times for program changes and tool changes.

That is the added value of job tracking. It might not immediately tell you the exact reason. But it shows you that it's happening — and that's enough to start asking the right questions.

Production intelligence: when is the right time

Production intelligence goes a step further. It analyzes patterns. It connects data from machines, ERP, and quality control. It provides predictions. When a machine is highly likely to fail. Which job will be delayed. Where a bottleneck is occurring on the shop floor.

In projects within the automotive supply chain, we see a clear boundary. Production intelligence brings real ROI only if three conditions are met. First: Job tracking has been functioning for at least six months. Operators are actually using it. Not just when an audit comes around. Second: Machine data like temperatures, vibrations, and cycles are captured automatically. Without manual input from the shop floor. Third: Management is capable of making tough decisions based on data. Even when that data contradicts years of proven intuition.

If you don't meet all three, production intelligence is a premature investment. The system will generate prettier dashboards. But the data underneath will be inaccurate, or people will simply ignore it.

We know of a company that spent 280,000 euros on a predictive maintenance platform. A year later, they had prettier charts than before. However, the number of unplanned downtimes didn't decrease. They lacked reliable basic data and the discipline to record it. At Foreast, we often say on the shop floor: there's no point in measuring to the micron if we don't know where zero is.

Before you start analyzing data, make sure you are collecting it correctly. Before you start collecting data, make sure you know which questions you need answered.

How to do it: four steps in a month

Implementing job tracking doesn't depend on technological complexity. It depends on discipline and strict selection of what you truly need to measure. The process we recommend at Foreast is always the same.

1. Select two to three key machines. Look for those that form the production bottleneck. Or generate the highest added value. On one of our past projects, we selected a press, a CNC machining center, and a welding robot. Together, they covered 68% of the average production throughput.

2. Define the minimum data set. Operation start. Operation end. Reason for downtime. Operator ID. Four fields. The more fields you require from the operator, the lower the completion rate. This applies universally on the shop floor.

3. Choose hardware that operators will accept. A tablet on the wall by the machine with a touch interface. One click to start. One click to end. If an operator needs more than 15 seconds to log an event, the system will start to be systematically bypassed.

4. Build a daily routine. At 14:00 every day, the supervisor opens the dashboard. They see where production is actually happening and where machines are idle. Five minutes a day gives the operator more control than any monthly report generated in the office.

We have applied this process in companies ranging from 40 to 300 employees. The average implementation on three machines took 18 to 25 working days. The investment paid off in an average of four months. Uncovering hidden downtimes that nobody had tracked before helped.

Topics of digital security and cyber resilience are legitimate. However, without basic discipline in recording production data, every additional layer of technology is more of a risk than an opportunity.

If you are deciding between simple job tracking and full-fledged production intelligence, the answer is: both, but in the right order. Job tracking first. Get the initial data. Verify that it is correct. Let the team get used to production being measured. Once you have six months of real numbers, you will know exactly what you need from production intelligence. And what is unnecessary ballast. We will advise you on the selection of the first step that makes sense for your shop floor.