Decision Intelligence for Food Manufacturing
Your plant is a system.
Small decisions move millions.
SmallInput provides decision intelligence for your plant. Test operational decisions before implementing them, using a model built from how your plant actually runs.
The decisions your plant makes every day — scheduling, sequencing, changeovers — are where the real capacity lives.
Typical result: 8–22% hidden capacity unlocked without capital investment.
The Problem
Plants spend millions fixing the wrong bottleneck.
Plants add equipment because they believe it will increase throughput. Without a system model, the constraint is often somewhere else entirely. So the investment moves the bottleneck instead of removing it.
Without a system model, plants frequently:
Misidentify the constraint
The bottleneck is rarely where intuition says it is.
Move bottlenecks instead of removing them
Equipment purchases that shift the constraint, not eliminate it.
Add equipment that doesn’t increase throughput
Capital spent on non-constraints produces no system improvement.
Stabilize schedules with excess inventory
Buffer stock compensates for poor system understanding.
The result is reactive operations and misallocated capital.
Core Philosophy
Small Input → Massive Output
In systems with interacting constraints, small changes in decisions can produce outsized improvements in throughput, reliability, and capital efficiency.
The leverage is rarely in the equipment. The leverage is in the system.
PLANT SYSTEMS
MES • ERP • spreadsheets • floor observation
OPERATIONAL SIGNALS
Unified event stream from plant operations
BEHAVIORAL PLANT MODEL
Lines, equipment, buffers, constraints — how the plant is connected
DECISION SIMULATION
Test operational and capital decisions
ECONOMIC IMPACT
Throughput, schedule stability, capital efficiency
COMPOUNDING ADVANTAGE
Intelligence that deepens with every decision
PLANT SYSTEMS
MES • ERP • spreadsheets • floor observation
OPERATIONAL SIGNALS
Unified event stream from plant operations
BEHAVIORAL PLANT MODEL
Lines, equipment, buffers, constraints — how the plant is connected
DECISION SIMULATION
Test operational and capital decisions
ECONOMIC IMPACT
Throughput, schedule stability, capital efficiency
COMPOUNDING ADVANTAGE
Intelligence that deepens with every decision
The Decision Engine
Model your plant. Simulate decisions.
See consequences.
Fulcrum reconstructs how your plant actually behaves, then tests operational and capital decisions against that system model.
Instead of asking “What should we do?” you can ask “What actually happens if we do this?”
Decision Engine
Typical Outcomes
8–22%
Hidden capacity found
effective throughput improvement
$1–5M
Capital redirected
equipment spend avoided or optimized
2–3×
Schedule stability
improvement in plan adherence
< 1 hr
Decision turnaround
from question to modeled answer
Every decision compounds. Over time, Fulcrum accumulates knowledge about your plant — true constraints, scheduling leverage points, capital allocation opportunities — creating compounding operational advantage.
Examples
Real decisions, modeled.
Every plant faces these questions. Fulcrum answers them with system-level simulation — before capital is committed.
Decision Under Evaluation
1 / 12Should we add a second filler to Line 3?
Current State
With Second Filler
Fulcrum Insight
Adding a filler moves the constraint to packaging. A scheduling change to the existing filler achieves 80% of the throughput gain at zero capital cost.
Verdict
Scheduling change preferred — $0 capital
Decision Under Evaluation
2 / 12Can we reduce changeover time on Line 2 by 30%?
Current State
With SMED Improvement
Fulcrum Insight
Changeover reduction unlocks 16% throughput but exposes a sanitation bottleneck during peak scheduling windows. Sequence the sanitation shifts first.
Verdict
+$1.9M throughput value — phased rollout
Decision Under Evaluation
3 / 12Should we run a third shift on the packaging line?
Current State
With Third Shift
Fulcrum Insight
Third shift gains only 8% throughput while destabilizing the schedule. The real constraint is upstream sanitation timing, not packaging capacity.
Verdict
Decline — fix upstream sanitation first
Decision Under Evaluation
4 / 12What happens if demand increases 20% next quarter?
Current State
At +20% Demand
Fulcrum Insight
Current constraint absorbs the load but schedule stability collapses. Constraint migrates between two lines, causing cascading delays. Preemptive buffer strategy needed.
Verdict
Buffer strategy + scheduling rules — $0 capital
Decision Under Evaluation
5 / 12Should we invest in automated QA inspection?
Current State
With Automated QA
Fulcrum Insight
Automated QA removes the primary variability source, improving both throughput and stability. The investment pays back in 4 months through reduced hold time alone.
Verdict
+$2.8M value — 4-month payback
Decision Under Evaluation
6 / 12Can we consolidate from 4 lines to 3?
Current State
3-Line Configuration
Fulcrum Insight
Consolidation loses 7% throughput and creates a dangerous single-point constraint at Line 2 packaging. The labor savings do not offset the throughput loss.
Verdict
Decline — throughput loss exceeds savings
Decision Under Evaluation
7 / 12Should we change the sanitation schedule from daily to per-shift?
Current State
Per-Shift Sanitation
Fulcrum Insight
Per-shift sanitation reduces each downtime event by 40%, smoothing the production rhythm. Changeover becomes the new constraint, addressable through sequence optimization.
Verdict
+$1.6M value — operational change only
Decision Under Evaluation
8 / 12What if we add a buffer tank between Lines 2 and 3?
Current State
With Buffer Tank
Fulcrum Insight
Buffer decouples the lines, improving stability by 28%. Throughput gains are modest but schedule predictability transforms planning reliability.
Verdict
+$1.1M value + planning reliability
Decision Under Evaluation
9 / 12Should we switch to a pull-based scheduling system?
Current State
Pull-Based Scheduling
Fulcrum Insight
Pull scheduling trades 4% throughput for 38% stability improvement. WIP drops by half. The system becomes predictable, enabling accurate delivery commitments.
Verdict
Adopt — stability gains outweigh throughput trade
Decision Under Evaluation
10 / 12Can we run mixed-size products on Line 4 simultaneously?
Current State
Mixed-Size Capability
Fulcrum Insight
Mixed-size capability eliminates 60% of changeovers. The labeler becomes the new constraint but is addressable with a $45K calibration upgrade — 3-week payback.
Verdict
+$2.1M value — $45K upgrade enables it
Decision Under Evaluation
11 / 12What is the impact of reducing batch size by 50%?
Current State
Half Batch Size
Fulcrum Insight
Smaller batches double changeover frequency, losing 14% throughput. But inventory carrying cost drops $800K/yr. Net positive only if changeover time is first reduced below 12 minutes.
Verdict
Conditional — reduce changeover first, then batch size
Decision Under Evaluation
12 / 12Should we add predictive maintenance to Line 1?
Current State
With Predictive Maintenance
Fulcrum Insight
Predictive maintenance cuts unplanned downtime by 70%, shifting the constraint to packaging. Combined with existing scheduling improvements, total system throughput increases 8%.
Verdict
+$1.4M value — complements scheduling changes
Deployment
Forward-deployed engineers. Embedded at the plant.
SmallInput engineers construct the system model from direct observation and operational data. The result is a behavioral twin of the plant that remains connected as operations evolve.
Outpost
Forward-deployed discovery1–2 days on-site
An engineer embeds at the plant with CAD files, basic production data, and a structured discovery framework. Walking the lines, observing changeovers, mapping dependencies the process diagrams don't show.
Deliverables
Zone and line mapping
Dependency observations
Constraint hypotheses
Data gap identification
Instrument
Targeted signal capture1–3 weeks
Where data gaps exist, we deploy lightweight tracking — not a sensor buildout. Targeted measurement of the specific operational events the model needs: changeover durations, downtime patterns, throughput variability, sanitation cascades.
Deliverables
Operational event streams
Changeover timing data
Downtime frequency distributions
Throughput variability baselines
Twin
System model and simulation2–4 weeks
Signals become a working model of the plant. Not a CAD replica. A behavioral twin that captures how the system actually operates — interaction effects, penalty structures, constraint behavior under load. Scenarios are tested against it.
Deliverables
Behavioral plant model
Constraint identification
Scenario simulations
Decision recommendations
Sync
Intelligence layerOngoing
The model stays connected. As the plant changes — new SKUs, equipment modifications, seasonal demand shifts — the model updates. Decisions compound. The intelligence layer deepens over time, turning every operational choice into accumulated advantage.
Deliverables
Continuous model refinement
Decision impact tracking
Pattern recognition
Compounding operational insight
Get Started
See where your plant's capacity actually lives.
The goal is simple: find the decisions that increase throughput without moving capital.