AI-NATIVE FOOD PROCESSING INTELLIGENCE
More Value From Every Production Run.
Yieldstem Analytics is the intelligence layer for modern food processing. By connecting production data, material flows, and predictive analytics, we help manufacturers understand yield performance, investigate losses, and identify opportunities to recover more value from their operations.
Apples received
24,000 kg
Finished puree
15,480 kg
Example production run.
RAW MATERIALS
What entered production.
24,000 kg
Apples received
- Raw-material lot
- L-88
- Feed solids
- 13.0 °Bx
- Line
- 2
Run AP-2417. Example production run.
PROCESSING OPERATIONS
What happened during processing.
Still in the process
15,480 kg
- Sorting rejects720 kg
- Pomace, sold3,120 kg
- Evaporated water3,600 kg
- QA and line clearance600 kg
- Unresolved480 kg
- Finished puree15,480 kg
YIELD INTELLIGENCE
What came out, and which conditions shaped the result.
- Expected yield
- 66.2%65.1 to 67.3%
- Actual yield
- 64.5%15,480 kg
- Process deviation
- -1.7 ptsBelow expected range
- Reconciled mass
- 98.0%480 kg unresolved
- Throughput
- 3.0 t/h8 h run
- Quality status
- In spec15.8 °Bx, target 15.5 to 16.5
OPERATIONAL DECISIONS
Operational decisions.
- 1
Inspect the finisher
Pomace ran above comparable runs. Check screen condition and paddle clearance.
- 2
Confirm tank T-4 closing level
No level was recorded at run close. This may account for part of the unresolved mass.
- 3
Weigh the deviation
Worth $186 to $526 on this run, if recoverable.
Proposed for review by the responsible process engineer. Documented losses total 1,320 kg, or 5.5% of input.
THE CHALLENGE
Production Creates Data. Understanding Loss Requires More.
Every processing operation generates information about material intake, production output, quality, equipment performance, and operating conditions. Yet these records often remain distributed across systems, spreadsheets, departments, and facilities.
When that information is disconnected, manufacturers can see what they produced without always understanding why performance changed, where value was lost, or which operational conditions contributed to the outcome.
Yieldstem is built to bring these signals into a consistent operating picture—making production performance easier to measure, investigate, and improve.
Six records, one production run
Weighbridge ticket
WB-30114
24.0 t net
ERP goods receipt
GR 88/A
52,911 lb
Paper batch sheet
Lot 0088
Line 2, start 06:10
QC lab export
LOT-88
13.0 °Bx
Filler count, spreadsheet
L88 drums
1,290 × 12 kg
Inventory adjustment
ADJ-7702
Pomace bin +3.12 t
One reconciled production record
Run AP-2417Apple puree, Line 2
| Field | Value | Source |
|---|---|---|
| Raw-material lot | L-88 | 4 identifiers resolved |
| Input, net | 24,000 kg | Weighbridge, ERP |
| Feed solids | 13.0 °Bx | QC lab |
| Finished product | 15,480 kg | Filler count |
| Saleable byproduct | 3,120 kg | Inventory |
| Unresolved | 480 kg | After all known streams |
THE YIELDSTEM PLATFORM
A Connected Intelligence Layer for Processing Performance.
Yieldstem brings together the operational records that describe how raw materials move through food-processing operations. On that foundation, the platform develops a clearer understanding of yield, material recovery, process variation, and financial performance.
01
Production Data Foundation
Connect and standardize information from material receipts, production records, quality systems, inventory, and existing operational software.
Produces: Attributed, validated production records
02
Yield & Material Analytics
Reconcile material inputs and outputs, calculate production yield, evaluate byproduct recovery, and make performance comparable across batches and production lines.
Produces: A closed material balance for each run
03
Predictive Intelligence
Use historical production patterns and relevant material characteristics to estimate expected output, detect abnormal performance, and highlight emerging production risks.
Produces: Expected ranges and flagged deviations
04
Performance Decision Intelligence
Translate analytical findings into prioritized investigations, financial assessments, and evidence-backed improvement opportunities.
Produces: Ranked investigations with their evidence
OPERATING INTELLIGENCE
From Material Input to Measured Outcome.
01
Connect
Bring together production, material, quality, and operational records.
02
Reconcile
Establish a consistent view of material movements and production performance.
03
Understand
Compare actual outcomes against expected performance and comparable production conditions.
04
Investigate
Identify deviations, associated factors, and potential financial consequences.
05
Improve
Support operational decisions and track whether implemented changes produce measurable results.
Yieldstem is designed to support the knowledge of manufacturing professionals, not replace their judgement. The people who run the line decide what a finding means and what, if anything, should change.
BUILT FOR FOOD PROCESSING
Different Processes. One Need for Better Production Intelligence.
Fruit & Vegetable Processing
How much of today's preparation loss was set by the raw material, and how much by the line?
Juice & Puree Production
Did extraction fall, or did the fruit carry less to extract?
Grain & Milling Operations
Is extraction changing, or is the moisture basis changing?
Food Ingredient Manufacturing
Which input lots, through which intermediates, produced this finished lot?
Frozen Food Processing
How much prepared material reached a saleable pack at the right grade?
Multi-Facility Manufacturers
Are two sites actually measuring the same thing?
THE ECONOMICS OF PRODUCTION
Small Yield Differences. Meaningful Business Consequences.
In high-volume processing operations, relatively small differences in material recovery can have significant commercial implications. The value of better production intelligence lies in understanding which differences are expected, which can be improved, and what those improvements are worth.
Additional saleable output
144 t per year
Gross value of that difference
$185,760
- 0.10 pts$37,152
- 0.25 pts$92,880
- 0.50 pts$185,760
- 1.00 pts$371,520
- 1.50 pts$557,280
Difference × annual tonnage × (product value − byproduct value). Default inputs are examples. Costs that change with output, and whether any part of a difference can actually be recovered, are not included.
Improved material accountability
A closed balance for each run, with unresolved mass sized and separated from documented loss.
Better visibility into production costs
Cost per saleable tonne that responds to the yield of the run, instead of a standard applied after the fact.
Faster investigation of performance deviations
The evidence for a deviation assembled while the lot, the shift and the equipment condition can still be checked.
More informed production and operational planning
Output ranges for planned lots, so purchasing, scheduling and commitments rest on realistic expectations.
Understand More of What Happens Inside Production.
Every processing operation has a different material flow, production environment, and performance objective. Tell us about yours, and let’s explore where better production intelligence could create value.