A Different Commercial
Approach:
Advanced Planning System (APS) Implementation
APS Implementation Methodology (AIM)
Typical Outcomes:
- Higher adoption and planner confidence through fit-for-purpose workflows
- Fewer post-go-live “rescue” efforts due to better upfront design and testing
- Sustained performance through clear governance, training, and KPI ownership.
Demand Planning and Forecasting
How We Improve Accuracy:
- Multivariate forecasting models incorporating drivers like weather, media, marketing, macro signals, pricing, and distribution.
- Forecast value-add (FVA) and bias reduction to ensure human overrides improve, not degrade, performance.
- Segmentation and exception-based workflows so teams focus time where it matters.
What Changes:
- Improved forecast accuracy and stability at the SKU, customer, and channel level.
- Better promotion planning and fewer service failures driven by forecast error.
- Higher trust in the plan because performance is measured and continuously improved.
Inventory and Service Optimization
What We Deploy:
- Segmentation strategies tied to service targets and business value.
- Probabilistic inventory policies and safety stock optimization.
- Automated deployment logic and multi-echelon approaches where appropriate.
- Attach-rate forecasting and service parts planning (when relevant).
- Risk tools to quantify variability, supply disruption, and exposure.
What Changes:
- Reduced excess and obsolete inventory without sacrificing service.
- Clear service policies by product and customer, not one-size-fits-all targets.
- A transparent, data-backed approach to trade-offs that finance and operations both support.
ERP, APS & Systems Integration
We connect APS and planning platforms with SAP ECC and S4, Oracle, JDE, JDA, MES, and other enterprise systems to create a unified planning backbone. The objective is simple: eliminate fragile data handoffs, standardize master data, and enable near real-time visibility so planners and operators can act on the same version of the truth.
Digital Twin & Scenario Planning
We build knowledge-graph digital twins that model the supply chain as it actually operates, including demand volatility, production constraints, logistics capacity, lead-time variability, sustainability and recycling trade-offs, and policy rules.
Long-Term Value, Not Just Go-Live.
We design, implement, support, and run your planning ecosystem end-to-end.
MANAGED SERVICES /AUGMENTED OPERATIONS
Services Include:
- Forecasting-as-a-Service
- Demand Planning-as-a-Service
- Inventory Optimization-as-a-Service
- AI/ML Tuning
- Governance & COE Management
ADVISORY
SERVICES
Services Include:
- Transformation Strategy
- Operating Model Design
- Value Assurance
- Technology Selection
- Roadmap Development
- Site Readiness & COE Setup
Digital Twin Success Examples:
$18B Rolled Aluminum
& Recycling Company
+35%
Productivity
+17%
Supply-Demand Alignment
Foundation
For Autonomous Planning
$9B Sustainable Packaging
Manufacturing Company
+45%
Forecast Accuracy
~20%
Inventory Reduction
>98%
Service & Planning Time Cut By ~50%
Data & Analytics / Machine Learning
We unify data across enterprise platforms into a single planning foundation that enables real-time visibility,
high-quality forecasting inputs, and automated action. This is not analytics for analytics’ sake. We focus on
models that improve decisions and can be embedded into planner workflows.
Capabilities:
Multivariate Forecasting
Cross-Functional Optimization
Operationalizing Algorithms Into Planner
Workflows
Data Visualization/Storyboarding
Parameter Tuning & Data Cleanup
Probabilistic Optimization
Visualization & Narrative Analytics
AI-Driven Decision Automation
We build automation layers that make decisions, not dashboards. Using cost thresholds, business rules, and constraint logic, we enable systems to auto-expedite, reallocate, or adjust orders based on real-time conditions. This reduces planner workload, increases speed-to-response, and ensures exceptions are handled consistently across teams and geographies.
The result is a pragmatic path toward autonomous planning, starting with high-confidence decisions and expanding as performance is validated.
