How We Work
From the plant floor to a decision you can trust.
SmallInput is forward-deployed decision intelligence for food manufacturing. We build a behavioral model of how your plant actually runs, then test operational and capital decisions against it using Theory of Constraints, so you can see the consequence before you commit the spend.
What We Model
The constraint is rarely where intuition says it is.
Most plant capacity is decided every day, not on the capital line. We model the operating decisions where throughput, reliability, and capital efficiency actually move.
Scheduling & sequencing
The order you run SKUs sets your changeover burden, your sanitation cascades, and how much capacity the schedule quietly gives away.
Changeovers
Where setup time concentrates, and which sequencing or staging changes recover the most run time without new equipment.
Capital & equipment
Whether a proposed purchase removes the constraint or just moves it. Tested against the system before the spend is committed.
Line balancing & staffing
How labor and line speeds interact under real demand, and where the true bottleneck sits versus where intuition says it is.
The Engagement
Outpost → Instrument → Twin → Sync
Forward-deployed engineers construct the model from direct observation and operational data. The result is a behavioral twin of the plant that stays 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 the dependencies the process diagrams do not 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 distributions
Throughput baselines
Twin
System model & 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) the model updates. Decisions compound. Every operational choice turns into accumulated advantage.
Deliverables
Continuous model refinement
Decision-impact tracking
Pattern recognition
Compounding insight
What You Get
Capacity you already own, without the capital.
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
Ranges reflect typical engagement outcomes and depend on plant complexity and data availability.
See where your plant's capacity actually lives.
The goal is simple: find the decisions that increase throughput without moving capital.