A NEO DIGITAL INDUSTRIES INITIATIVE · UNITED STATES
Find where AI creates real value.
Identify high-value AI opportunities across demand, inventory, production, procurement and distribution—and keep deterministic planning where reliability, reproducibility and auditability matter.
A focused working session for supply chain leaders. No generic AI pitch and no obligation to buy software.
Use AI for judgment. Keep calculation deterministic.
Most “AI for supply chain” conversations start with a model and look for a problem. We start with planning decisions, identify the work that language models can improve, and preserve the engines that must remain dependable.
- —Capture unstructured business context
- —Triage and prioritize exceptions
- —Explain why the plan changed
- —Translate intent into scenarios
- —Draft supplier and stakeholder communications
- —Support demand consensus and decision narratives
- —Replace statistical demand forecasting
- —Calculate safety stock inside an LLM
- —Solve finite-capacity scheduling
- —Run MRP or optimization
- —Guess a plan without constraints
- —Delegate calculations that must be reproducible and auditable
One planning cycle. Two distinct layers.
We examine every step through the same lens: what requires calculation, and what benefits from context, language and orchestration?
Demand
Deterministic engine
Statistical or ML baseline, segmentation, error and FVA
AI layer
Market context, assumption records and consensus preparation
Inventory
Deterministic engine
Coverage, safety stock, service level and rebalancing
AI layer
Policy explanation and exception prioritization
Production
Deterministic engine
Master plan, finite capacity, sequencing and constraints
AI layer
Scenario translation, delay explanation and tacit-constraint capture
Procurement
Deterministic engine
MRP, netting, pegging and rescheduling exceptions
AI layer
Email and contract reading, impact triage and communication drafts
Distribution
Deterministic engine
DRP, allocation and network transfers
AI layer
Allocation rationale and decision narratives for commercial teams
Supply Chain AI Opportunity Diagnostic
A structured, no-cost working session to understand your planning environment, map opportunities and identify the most credible next moves.
- 01
Understand
Your planning process, systems, data, constraints and the decisions creating the most friction.
- 02
Map
Potential use cases across the planning cycle, separated into generative and deterministic capabilities.
- 03
Prioritize
Opportunities by business impact, feasibility, dependencies and time to value.
What you can expect
- →Initial AI opportunity map
- →Impact × feasibility view
- →Generative × deterministic classification
- →Build, Buy or Hybrid direction
- →Data and process prerequisites
- →Candidate use cases for a 90-day roadmap
Contact NEO directly to discuss your planning context and schedule the free working session.
Build, Buy—or draw a better boundary.
The real question is rarely whether to build or buy an entire solution. It is which capabilities should remain yours, which components are mature enough to purchase, and where a vendor boundary creates avoidable dependency.
The framework evaluates
- 01Five-year total cost
- 02Speed of business-rule change
- 03Execution and key-person risk
- 04Data and planning-process maturity
- 05Long-term ownership after go-live
An online program is being developed.
The curriculum behind this initiative covers foundations, Build or Buy, practical use cases and reusable playbooks across the end-to-end planning cycle. The international online edition is not open for enrollment yet.
More details · Program outline
Foundations
Where AI creates value in planning, how Claude fits into daily work, and the boundary between generative interpretation and deterministic calculation.
- →Claude across desktop, code, spreadsheets, presentations and API
- →Generative AI versus statistics, optimization, solvers and ML
- →Solution architecture, entry points and agent harnesses
Build or Buy
A practical framework for deciding what to build, what to purchase and where the vendor boundary should sit.
- →Five-year economics and execution risk
- →Business-rule flexibility and data maturity
- →Build, Buy, Hybrid and NO-GO outcomes
Planning use cases
End-to-end applications built on deterministic planning capabilities with an AI layer above them.
- →Demand and inventory
- →Production and procurement
- →Distribution and cross-functional decision support
Resources
Reusable assets that help teams move from concepts to repeatable planning workflows.
- →Planning-cycle playbooks
- →Composable AI skills and agents
- →Tested prompts with defined inputs and outputs
Planning expertise before AI became a headline.
Claude for Supply Chain is an education and thought-leadership initiative within NEO Digital Industries, the primary company behind this work in the United States.
NEO brings more than two decades of experience in Advanced Planning & Scheduling, S&OP/IBP, industrial operations and digital transformation. That background shapes a practical view of AI: language models add context and leverage, while deterministic planning engines protect operational rigor.
- 20+ years in supply chain planning
- Projects across the US, Brazil and Europe
- Consulting, implementation and product-building experience
- Real-world work in complex industrial operations
Questions before we start.
Yes. The initial session is free and has no obligation to purchase software or services. If there is a strong fit for deeper work, NEO may propose a separate scope afterward.
Supply chain, operations, planning, technology and transformation leaders who are evaluating AI initiatives or struggling to prioritize them.
No. Understanding data and process readiness is part of the diagnostic. A credible result may be to address prerequisites before building an AI use case.
No. NEO has implementation and product experience, but the diagnostic explicitly considers Build, Buy and Hybrid paths and makes dependencies visible.
We review your context, contact you by email and schedule a working session when the challenge fits the scope of the diagnostic.