PerciHub – Business Suite

Ingests sales history, open orders and seasonal patterns to auto-predict demand per SKU. Auto-generates Planned Independent Requirements (PIRs) in S/4HANA, reducing planner intervention significantly.

Senses real-time work centre load from SAP production orders and shopfloor feeds. Proactively shifts lower-priority batches to remove bottlenecks before they occur.

Optimised sequencing of production orders using constraint-based AI for throughput and on-time delivery. Dynamic real-time rescheduling when materials are delayed, machines go down, or priority orders are inserted

Automatic matching of actual inspection results against planned quality characteristics in SAP QM. AI classifies usage decisions — Accept, Rework, or Reject — without manual planner sign-off, based on deviation scoring.

Sensor data from CNC machines, routers, and assembly lines fed to ML models detecting wear and failure patterns. SAP PM maintenance notifications auto-raised before failure occurs, shifting from reactive to predictive maintenance.

Sales and customer service teams guided by AI to valid, margin-positive configurations across a high-volume SKU range. Surfaces historical order data to recommend best-selling finishes, sizes, and hardware combinations per customer segment.

End-to-end batch lineage tracked in SAP — from raw material lot through blending and filling to finished goods and customer delivery. AI flags forward and backward traceability gaps before a recall scenario can escalate.

ML models predict remaining shelf life based on actual storage conditions, temperature, and humidity sensor data per batch. Proactive alerts when batches approach minimum remaining shelf life thresholds before they reach distribution.

In-process and final inspection results auto-matched against recipe-level quality plans in SAP QM for every batch. AI recommends Accept, Conditional Release, or Reject based on spec deviation scoring — removing manual bottlenecks.

Analyses historical batch records to identify raw material substitution opportunities without compromising performance. Models what-if scenarios — substitute supplier, alternate formulation grade, adjusted mixing parameters.

Correlates process parameters — temperature, mixing time, pH, humidity — with yield variance across production batches. Identifies root causes of scrap at batch level: operator, raw material lot, equipment condition, or process deviation.

Continuously monitors batch formulations against REACH, CLP, and regional chemical regulations as they change. AI drafts Safety Data Sheet (SDS) updates automatically when formulations change, accelerating regulatory sign-off.

Integrates subscription data, website traffic, dietary trend signals, and social sentiment to predict weekly meal demand per SKU. Forecast drives MRP automatically, triggering procurement of fresh ingredients with correct perishable lead times.

Real-time shelf life tracking across all incoming ingredients — auto-alerts when items approach use-by thresholds. AI recommends menu substitutions when ingredients are near expiry to avoid waste without disrupting the production schedule.

Actual versus planned nutritional values auto-validated using live weighing data at every portion and stage. Nutritional labelling auto-generated from batch actuals for regulatory compliance and customer-facing information.

Schedules meal production in reverse from delivery date, respecting cold-chain windows and shelf-life constraints. AI sequences recipes across shifts to minimise allergen cross-contamination risk with intelligent line-cleaning rules.

AI clusters delivery routes by postcode density, time-window commitments, and cold-chain distance constraints. Cold-chain breach risk scores assigned to each delivery slot based on weather forecasts, route length, and vehicle type.

ML models score every subscriber by churn risk based on engagement patterns, skip history, and meal ratings. AI-triggered interventions — personalised menu suggestions and targeted offers — to re-engage at-risk customers.

Integrates satellite imagery, weather data, and soil sensor readings to forecast crop yield per field zone ahead of harvest season. SAP production planning auto-adjusted based on forecast crop availability per zone and variety.

ML models trained on commodity price cycles, export demand trends, and domestic consumption patterns. AI-driven procurement strategy recommendations — forward contracts versus spot buying versus buffer stock holding.

Sensor data from milling, evaporation, and crystallisation stages fed continuously to AI optimisation models. AI recommends optimal temperature set-points and centrifuge parameters per incoming cane quality grade.

IoT sensors track house temperature, humidity, CO₂ levels, and water consumption per flock in real time. ML models detect early health deviation patterns 48–72 hours before clinical signs appear – enabling early intervention.

Lab test results auto-matched against batch quality plans in SAP QM. AI usage decision engine: Accept, Rework, or Recall trigger based on pathogen test deviations and HACCP limits.

Supplier risk scoring based on delivery performance history, price volatility, weather events, and geopolitical signals. Proactive purchase requisition creation when AI detects supply risk breaching threshold, before shortage occurs.