AI Use Case: Supply Chain Consultants Using Excel To Identify Redundant Distribution Nodes for Retail Clients

For supply chain consultants working with retail clients, Excel-based workflows augmented by AI can reveal redundant distribution nodes, cut transport costs, and improve service levels. This practical page shows how to connect data, choose the right tools, and implement a repeatable process without heavy custom development.

Direct Answer

Use an Excel-based network model augmented by AI to identify nodes that add little incremental value. In practice, you pull orders, shipments, and cost data from your ERP or TMS, apply clustering and routing insights, and generate scenarios that consolidate or reroute nodes. Off-the-shelf automation handles data refresh and reporting, while targeted GenAI can surface explanations and recommended action plans. The result is a leaner network with lower costs and clearer execution steps.

Current setup

Manual Excel models and static charts that struggle to keep data fresh.
Data spread across silos (ERP, TMS, spreadsheets, and emails) with inconsistent formats.
Limited scenario planning and no formal decision-tracking or audit trail.
Reliance on gut feel or single-point metrics rather than network-wide optimization.
Low collaboration across stakeholders, slowing consensus on node eliminations or rerouting.
Occasional use of CSV exports for ad-hoc analyses, but no automated refreshes.

Where relevant, this approach aligns with other AI use cases such as AI use case for retail stores using Square POS to identify purchasing patterns and optimize staff scheduling and AI use case for franchise consultants using regional demographic data to identify ideal locations for expansion.

What off the shelf tools can do

Data integration and automation: connect Zapier or Make to pull data from your ERP or TMS and push results into Excel or Google Sheets.
Collaborative data work: use Google Sheets or Airtable for shared data models with version history.
AI-assisted analysis in familiar apps: leverage Microsoft Copilot in Excel to suggest groupings, scoring, and scenario options.
Prompted insights and narrative: bring in ChatGPT or Claude to explain why a node is flagged and propose follow-up questions for clients.
Documentation and briefs: Notion or Notion for keeping the methodology and approvals in one place.
Communication: share findings via Slack or Microsoft Teams.
Notes on data sources: link to your financial or accounting data in Xero or other ERP-adjacent systems when relevant.
Visualization and reporting: embed findings in Power BI or Excel dashboards for client reviews.

Where custom GenAI may be needed

Custom clustering and network-reduction logic tailored to client constraints (service levels, capacity, and constraint satisfaction).
Proprietary scoring models to rate redundancy, risk exposure, and marginal saving potential per node.
Complex routing optimization tied to cost functions, time windows, and service commitments.
Prompts and explainability layers that translate AI outputs into client-ready actions and rationale.
Secure integration with ERP/TMS data schemas and role-based access controls for sensitive information.

How to implement this use case

Define objective and data requirements: identify which nodes to assess, what costs to include (transport, handling, dwell time), and desired service targets.
Ingest and normalize data: pull orders, shipments, and cost data from ERP/TMS into Excel or a data model in Google Sheets or Airtable.
Prepare the network model: map nodes, routes, capacities, and demand, then clean inconsistencies and standardize fields.
Apply AI-assisted analysis: use Copilot in Excel or a GenAI tool to cluster nodes, identify overlap, and simulate consolidations or reroutes.
Run scenarios and create recommendations: compare total cost, service impact, and implementation effort for each option; document rationale.
Automate refresh and reporting: set up data refresh pipelines with Zapier or Make and deliver client-ready dashboards or briefings.

Tooling comparison

Aspect	Off-the-shelf automation	Custom GenAI	Human review
Data preparation	Automated data pulls; repeatable transforms	Custom data schemas and validation rules	Quality checks and governance
Insight generation	Pattern spotting with built-in analytics	Tailored clustering, scoring, and scenario logic	Interpretation and client storytelling
Speed	Fast, repeatable runs	Longer setup, fast runs once in place	Slower, but adds context
Maintenance	Low to moderate	Ongoing tuning and data governance	Ongoing oversight and approvals
Decision confidence	Transparent if data is clean	Higher with explainability prompts	Final validation and client buy-in

Risks and safeguards

Privacy and data security: limit sensitive data exposure and enforce access controls.
Data quality: establish data sovereignty, versioning, and validation checks.
Hallucination risk: require human-in-the-loop review for AI-generated conclusions.
Access control: role-based permissions for who can run analyses and publish results.
Auditability: document data sources, assumptions, and decisions for each recommendation.

Expected benefit

Lower total transport and handling costs through node consolidation or rerouting.
Improved service levels by reducing transit times and variance.
Faster scenario testing and decision-making for clients.
A repeatable, auditable process that scales across multiple retailers.
Clear ownership and traceability for optimization recommendations.

FAQ

What data do I need?

Order histories, shipment records, costs (transport, handling, warehousing), node locations, capacities, and service targets. Data should cover at least 12–24 months for meaningful patterns.

Can this be done in Excel?

Yes. Start with a network model in Excel and use AI-assisted features (via Copilot) to surface clusters and scenarios. For larger datasets, consider integrating with Google Sheets or Airtable and automating refreshes with Zapier or Make.

Do I need custom GenAI?

Not for a basic consolidation analysis. Custom GenAI helps when your client has unique routing constraints or requires explainable recommendations tailored to internal rules.

How do I validate results?

Cross-check with historical outcomes of past node changes, run backtests on prior periods, and obtain client sign-off on the rationale and risk assessment.

How often should I refresh data?

Schedule monthly refreshes for ongoing optimization, with quarterly reviews to capture structural changes in demand or capacity.

AI Use Case for Supply Chain Consultants Using Excel To Identify Redundant Distribution Nodes for Retail Clients