Production Analytics
See what your batch records add up to: failure rate, duration, and what is ready.
Your team already logs every batch in FourFoxes. Production Analytics adds those records up: failed-batch rate by product and year, what is ready to package, what is still in progress, and median days from start to packaging. No spreadsheet rebuild at month-end.
The basics
What is production analytics for food manufacturers?
Production analytics in food manufacturing turns batch records and production data into a clear view of how production is performing: how many batches passed or failed quality, what is ready to package, what is still in progress, and how long runs take from start to finish. For small producers, the value is seeing that picture at a glance, the moment it matters, instead of rebuilding it in a spreadsheet after the fact.
The problem
You can see today's batches. Can you see the pattern across all of them?
When you are running two or three batches a day across shifts, the thing that bites you is usually not the one bad batch you caught. It is the quality problem creeping across a single product line that nobody spotted until it showed up in three runs.
- The data that would show the pattern is already captured on the floor. It just sits inside individual batch records until someone adds it up.
- A failed batch you noticed is a fact. A failure rate climbing on one product across the year is a warning, and it is the one that is easy to miss.
- Knowing what is ready to package and what is still in progress should not require walking the floor and asking everyone.
How it works
Four views, built from the records you already keep
Spot quality risk by product, not just by batch.
The quality breakdown shows failed-batch rate for every product and year, color-coded so a climbing failure rate stands out before it spreads across runs. Sort by highest failure rate and the products that need attention surface first.
Know how long production really takes.
Median production duration, from fermentation start to packaging, tracked per product type. So your lead times are based on what actually happened on the floor, not a guess from memory.
See what is ready, and what is still in progress.
Ready batches are eligible to package now. In-progress batches are still in production. Both sit in the same summary, so you know what is moving without asking the floor.
Your production data is already here. The analysis is not extra work.
Because batch records, CCP logs, and ingredient lots all live in FourFoxes, the view is built from data captured during normal production, not assembled separately at month-end.
What your team can answer without rebuilding a spreadsheet
Which products are failing more often, what is ready to package, and how long runs really take.
Questions
Frequently asked questions
What does production analytics show for a small food producer?
It summarizes how production is performing: failed-batch rate overall and by product, how many batches are ready to package, how many are still in progress, and median production duration from start to packaging. It is one summary a supervisor can scan in a minute and decide what needs attention first.
How do you track batch failure rates across products?
Failure rate is the share of batches for a product that were marked with a failed quality condition. Viewing it by product and year, color-coded and sorted by highest rate, makes a climbing failure rate on one product line visible before it spreads across multiple runs.
How is this different from a spreadsheet for tracking production?
A spreadsheet shows what someone enters and updates by hand, usually after the shift. FourFoxes builds the same summary from batch records, CCP checks, and ingredient lots captured during normal production, so the view is current without anyone reconciling it at month-end.
Your batch records already hold the answers. See what they show.
See batch quality, what is ready to package, and how long production takes, all in the system where your records already live. 14 days free. No credit card required.