In 2025, global supply chains are under constant pressure—from demand fluctuations and transportation disruptions to geopolitical instability and rising customer expectations. The need for predictive, intelligent, and automated decision-making is no longer optional—it’s critical.
At Splisys, we help forward-thinking enterprises leverage SAP’s embedded machine learning (ML) capabilities to transform supply chains from reactive cost centers into data-driven growth engines. This blog explores how ML algorithms in logistics and demand forecasting, combined with SAP’s technology stack, are optimizing supply chains across industries like Oil & Gas, Manufacturing, Retail, and Utilities.
Traditional supply chains rely heavily on static rules, human intuition, and outdated ERP reports. While this may have sufficed in the past, today’s supply chains need:
Machine learning delivers exactly that.
Gain real-time demand insights and reduce forecasting errors by up to 65%.
According to recent studies:
SAP continues to infuse machine learning throughout its intelligent suite, with powerful applications in IBP, BTP, and SAP S/4HANA.
Here’s how SAP drives ML-powered supply chain value:
SAP IBP leverages time-series ML models to account for seasonality, market volatility, weather patterns, and promotional activities—drastically improving forecast reliability.
Client Example (Retail – Asia Pacific)
Forecasting error reduced from 28% to 9.5% within 3 months using ML-augmented SAP IBP simulations. Inventory overstock was reduced by 17%.
SAP’s ML algorithms evaluate thousands of SKUs across multiple locations in real-time—calculating optimal reorder points, safety stock levels, and replenishment windows.
Client Example (Manufacturing – Europe)
Automated stock threshold decision-making in SAP S/4HANA using embedded machine learning algorithms. Result: 26% reduction in working capital tied to raw materials.
With SAP Transportation Management (TM) integrated with SAP BTP ML models, businesses can dynamically re-route shipments, predict delays, and optimize carrier selection based on historical patterns.
Client Example (Oil & Gas – Middle East)
Using predictive shipment delay models, a large fuel supplier reduced downstream distribution delays by 31% during peak demand seasons.
ML helps detect abnormal order behaviors, supply disruptions, or fraud. SAP’s embedded analytics alert supply chain leaders in real-time for immediate intervention.
Client Example (Utilities – North America)
ML-powered SAP alerts identified data anomalies in meter equipment delivery delays—saving $1.2M in SLA penalties over a 12-month cycle.
We don’t just plug in models—we bring a consulting-led approach that aligns data science with business priorities.
Our supply chain optimization services include:
Time-to-value matters. In most cases, our clients begin seeing performance improvements within the first 60–90 days of implementation.
| Industry | Use Case | Outcome |
| Oil & Gas | Predictive fuel demand forecasting | Forecast accuracy ↑ 33%, transport delays ↓ 22% |
| Manufacturing | ML-based inventory alerts | Inventory cost ↓ 19%, fulfillment rate ↑ 11% |
| Retail | AI for store-wise replenishment | Out-of-stock ↓ 27%, excess stock ↓ 16% |
| Utilities | Predictive supplier reliability | SLA compliance ↑ 21%, operational disputes ↓ 31% |
Today, ML isn’t a pilot—it’s a performance accelerator. With SAP’s intelligent capabilities and Splisys’ execution expertise, organizations are eliminating inefficiencies, reducing manual decisions, and creating resilient supply chains.
If you’re looking to make your supply chain faster, smarter, and more adaptable—now is the time to act.
Splisys enables faster, data-driven logistics through embedded ML models in SAP. Our clients see results in as little as 60 days.
Q1. Do I need to be on SAP S/4HANA to use ML in supply chain?
Not necessarily. While S/4HANA offers the most seamless experience, SAP BTP allows side-by-side ML model deployments that integrate with ECC as well.
Q2. What kind of data is needed for accurate ML forecasting?
Historical transaction data, demand patterns, supplier logs, weather inputs, and external market trends. SAP platforms consolidate this into structured inputs.
Q3. How long does it take to deploy an ML-powered forecasting solution?
A working prototype can go live within just 6 to 8 weeks. Full-scale rollout with integrations takes 12–16 weeks depending on system complexity.
Q4. Can Splisys help with proof-of-concept (PoC) before full deployment?
Absolutely. Our digital innovation lab model helps validate ML use cases in sandbox environments before full-scale SAP deployment.
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