Production Planning Intelligence

How an AI-powered planning engine analyses demand, inventory, and capacity from SAP to generate plain-language recommendations that planners approve in one click.

01 PROBLEM STATEMENT

Calendar Driven Planning is Limiting Your Production Performance

A manufacturer with multiple plants managed production planning through manual SAP data pulls. Planners spent hours reconciling demand against inventory each morning. By the time a plan was ready, demand had shifted. High-velocity SKUs ran into stockouts while slow-moving inventory accumulated. Customer OTIF performance was below target.

02 CURRENT CHALLENGES

What the Manufacturer was Struggling With

Manual Planning Process

Hours of daily effort

Manual SAP data reconciliation required every morning. Planners consumed by routine data pulls instead of decision-making.

No Real Time Demand Signal

Stale data drives decisions

Plans built on stale data. External demand signals not factored into daily planning decisions.

Stockout and Overstock Cycles

Misaligned supply and demand

Stockouts on high-velocity SKUs. Overstock accumulating on slow-movers. Supply and demand chronically misaligned.

Missed OTIF Targets

Customer commitments at risk

Schedule adherence inconsistent. Reactive disruptions impacting customer on-time-in-full performance.

03 SOLUTION OVERVIEW

STAR’s approach – AINE Production Planning Intelligence

STAR Systems deployed AINE Production Planning Intelligence, connecting to SAP PP/APO and SAP MM. The platform enriches signals with Weather and Events API, then uses Google Gemini to generate plain-language recommendations. Recommendations appear in the existing BI dashboard.

AI PATTERN

Demand Signal Analysis + Inventory Optimisation + Natural Language Plan Generation

04 WORKFLOW PROCESS

Step-by-Step: How the AI Generates and Delivers Recommendations

Step 1 (Data Ingestion): SAP PP/APO OData API pulls demand forecasts and capacity. SAP MM provides inventory and BOM context for accurate supply-demand analysis.

Step 2 (Signal Enrichment): Weather and Events API adds external demand signals. Seasonal patterns identified and factored into the planning model automatically.

Step 3 (AI Analysis): Google Gemini analyses combined signals. Supply-demand gaps identified per SKU and production line across all facilities.

Step 4 (Plain-Language Recommendation): Gemini writes a readable recommendation per SKU or production line. Numerical analysis translated into actionable guidance planners can act on immediately.

Step 5 (Planner Review): Recommendations appear in the existing BI dashboard. Planner approves, modifies, or overrides in one click no new tools to learn.

Step 6 (Model Refinement): Acceptance rate tracked per recommendation. STAR reviews and refines seasonal demand parameters quarterly based on outcomes.

05 KEY FEATURES

What the Platform Does

SAP-Native Integration:

Connects to SAP PP/APO via OData API and SAP MM for inventory and BOM context. No changes to existing SAP workflows required.

Real-Time External Signal Enrichment:

Weather and events API incorporated into every planning cycle. Plans reflect real-world demand conditions beyond what SAP data alone can provide.

Gemini Natural Language Recommendations:

Google Gemini translates numerical analysis into plain-language daily recommendations. Planners receive guidance they can read and act on, not raw data.

BI Dashboard Delivery:

Recommendations delivered inside the existing PowerBI or BI tool. No new interface for planners to adopt fits directly into current workflow.

Acceptance Rate Tracking:

System records whether each recommendation was accepted, modified, or overridden. Builds a continuous feedback loop for model improvement.

Quarterly Model Refinement:

STAR reviews and refines seasonal demand parameters quarterly. Model accuracy improves continuously based on real planning outcomes.

06 BUSINESS OUTCOMES

What Changes After Go-Live

Significant

Planner Time Freed Daily — routine generation automated

Reduced

Inventory Carrying Cost through tighter supply-demand alignment

Reduced

Stockout-Driven Lost Sales on high-velocity SKUs

Improved

Customer OTIF through consistent schedule adherence

CFO

Inventory carrying cost reduced through tighter supply-demand alignment.
Stockout-driven lost sales reduced. Working capital improves.

COO

Planner time freed for exception handling and strategic decisions.
Production schedule adherence improves with live data-driven planning.

CEO

Customer OTIF improves. Capacity utilisation improves.
Confident customer commitments backed by real-time planning intelligence.

07 REAL-WORLD SCENARIO

A Day in the Life – Before and After

Before	After
Planner spends hours manually pulling SAP data into spreadsheets each morning before planning can begin.	AI reads SAP data overnight. Recommendations waiting in the BI dashboard when planner arrives.
SKU stockout occurs. Emergency procurement triggered at premium cost. Unable to fulfil customer order.	AI flags inventory risk 3–5 days ahead. Procurement at normal cost. Order fulfilled on time.
Excess stock accumulates on slow-moving SKUs. Carrying cost rises. Write-off eventually required.	Supply-demand alignment tightened. Production adjusted weekly. Carrying cost measurably reduced.
No capacity visibility. Customer commitments made without feasibility check. Disruptions follow.	AI surfaces capacity gaps before commitments are made. Promises aligned to actual capability.

08 ROI AND VALUE JUSTIFICATION

Why the Numbers Work

Value Driver	Indicative Impact	How It Is Realised
Planner productivity	2–3 hours recovered daily per planner	Routine recommendation generation automated. Time redirected to exception handling.
Inventory carrying cost	Measurable reduction	Tighter alignment reduces overstock and eliminates emergency procurement premiums.
Stockout-driven lost sales	Significant reduction on high-velocity SKUs	3–5 day advance signal allows timely procurement before stockout occurs.
Customer OTIF	Consistent improvement	Schedules built on live data reduce reactive disruptions and missed commitments.
Positive ROI timeline	Within 6–9 months of go-live	Time savings and cost reductions exceed platform costs within two quarters.

09 NEXT STEPS

Discovery Call

30-min call to map your SAP landscape, planning process, and current pain points.

Pilot Scoping

We identify 10–20 SKUs or production lines for a 10-week pilot with live SAP integration.

Pilot Delivery

AI recommendations run on your data. Acceptance rate and planning time savings tracked weekly.

Business Case

Inventory savings, planner time recovered, and OTIF improvement measured and presented to leadership.

Schedule a Free Consultation

Production Planning Intelligence