Set Top Runs significantly lower than the total available runs to ensure meaningful comparisons.
Define run boundaries carefullyto capture true production periods—especially for continuous processes.
Use shift changes for historical analysis.
Use campaign-based boundaries (e.g., flavor, color, packaging) where applicable.
If no boundary exists, add a field called production date to the pipeline and use day as a boundary condition.
Align run quantification with the client’s process (e.g., wheel type or unit of production).
Conditions
Choose condition fields that result in a sufficient number of runs per recipe—avoid over-bucketing.
Avoid creating too many conditions, as this can lead to recipes with insufficient data points.
Do not presuppose conditions (e.g., shift, flavor, speed) unless requested—validate run settings first.
Skip product selection during cookbook creation unless products truly differ in outcomes or logic.
Instead, configure product as a condition when appropriate. Instead, configure product as a conditionwhen appropriate.
If there are other significant conditions, use product selection to avoid too many factorial outcomes in conditions.
Use uncontrollable variables (e.g., humidity, wind speed) as conditions or filters, not levers.
Use wisely, or you will end up with too many conditional options due to a factorial approach. Remove any conditions that have minimal impact.
Outcomes
Use top-level KPIs (e.g., OEE) as primary outcomes when deploying cookbook insights to production.
Add secondary KPIs (e.g., quality, efficiency) as 0-weight outcomes if needed for insight.
Ensure outcomes are realistic, production-aligned, and defined with specific, normalized metrics
Do not select metrics which are optimized by switching plant off. (e.g., decrease steam)
Confirm outcome metrics are not better suited as conditions or filters.
Levers
Start broadly, then narrow to 20–30 meaningful leversto avoid confusion.
Consider creating two cookbooks: one for engineers with more levers, and one for operators, where 20–30 levers is typically the maximum they should see.
Use lever insights to identify and retain the most influential levers.
Remove levers that never change, cannot be controlled by operators, or represent duration, counters, or status values.
Levers can be determined if they can be changed typically through conversation with SMEs, PID loop tagging such as PV vs SP, or shape of data
Common effective levers include temperature, pressure, and setpoints.
Levers should be operator-controllable, variable, and directly influential on production outcomes.
Filters
Use filters to exclude unwanted data (e.g., unsafe or noncompliant runs).
Apply filters before or after scoring runs as appropriate to control what data appears in results.
Limits & Alerts
Configure limits to reflect realistic standard deviations, warnings, and alarms.
The SD limits can also be changed, as well as the numerical values. Numerical minima and maxima can be hard-set (e.g., to limit to a new safety parameter).
Adjust limits based on natural process variability, operator and engineer input, and technical documentation or machine manuals.
Spend sufficient time on this—frequent alerts on highly variable fields will quickly erode operator trust in the cookbook.
Well-tuned limits ensure alerts are meaningful and actionable.
Date Range Selection
Limit historical data to periods after major plant updates or overhauls to prevent outdated processes from skewing results.