DataCleaned Consulting for operations-heavy teams

Turn messy operations, supply chain complexity, and AI ideas into decision systems people actually use.

DataCleaned Consulting helps supply chain, warehouse, logistics, and B2B software teams move from scattered reports and vague AI ideas to practical tools, clearer decisions, and executive-ready recommendations.

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Execution-firstGrounded in warehouse operations, network design, and real supply-chain constraints.
Optimization-nativeExperience across SQL, CPLEX, simulation, analytics, and decision-support products.
Human-centered AIAI and automation translated into usable workflows for operators and leaders.
Problem routing

What are you trying to fix?

Start with the problem you recognize. Each path leads to a focused consulting engagement.

01

Our supply chain network needs to be redesigned

Facility footprint, customer assignments, cost-to-serve, service levels, growth, acquisitions, or future-state scenarios.

Go to service
02

Our analytics do not drive action

Too many reports, unclear KPIs, inconsistent definitions, stale dashboards, or no operating rhythm.

Go to service
03

Our warehouse is constantly firefighting

Dock congestion, labor variability, schedule volatility, exception management, or execution visibility.

Go to service
04

We know AI matters, but not where to start

Identify, score, and prioritize AI opportunities before wasting budget on vague pilots.

Go to service
05

Our data is messy and manual

Recurring CSV cleanup, broken joins, inconsistent fields, manual reporting, and unreliable data prep.

Go to service
06

Our AI/product story is too technical

Roadmap clarity, demo narrative, technical positioning, buyer messaging, and GTM translation.

Go to service
Experience behind the work

Built from real supply chain, optimization, AI, and product work.

This is not generic AI strategy. The work is grounded in years of supply chain design, network optimization, warehouse execution, data workflows, and enterprise product leadership.

01

Head of Product at AutoScheduler.AI

02

Co-founder of 3 Tenets Optimization

03

Creator/contributor to Architect, a supply chain network strategy platform

04

Former Chainalytics supply chain design consultant

Built optimization tools at UPS

Hands-on with SQL, CPLEX, Tableau, simulation, analytics, and AI workflows

Experience translating technical systems into executive-ready decisions

The goal is not a prettier report. It is a decision layer operators, leaders, and buyers can trust.

Focused service paths

Pick the path closest to the decision you need to make.

The modules stay expandable because buyers need detail, but the page now starts from outcomes: network decisions, actionable analytics, cleaner data, useful AI pilots, and buyer-ready product strategy.

Path A

Supply Chain Strategy

Compare network scenarios, tune warehouse decisions, and turn supply-chain complexity into executive-ready recommendations.

Typical client journey

01Week 1

Business Question Definition

Clarify the strategic question: facility footprint, customer assignment, mode mix, inventory, growth, or cost-to-serve.

02Weeks 1-3

Data & Baseline Model

Gather demand, lanes, facilities, costs, capacities, constraints, and service rules.

03Weeks 3-6

Scenario Modeling

Use optimization, scenario analysis, geospatial views, and AI-assisted exploration to compare future-state options.

04Weeks 6-8

Executive Decision Package

Translate model outputs into recommendations, savings ranges, risks, and leadership-ready visuals.

What the client receives

Deliverables

  • Current-state network baseline
  • Scenario assumptions log
  • Future-state options with tradeoffs
  • Maps, flows, and cost-to-serve views
  • Executive recommendation deck
Client effortProvide operational data, validate assumptions, and align on business constraints and scenarios.
Best outcomeA decision-ready view of how the supply chain should evolve.

Preview: network scenario tradeoff explorer

Adjust network conditions to simulate centralized vs. distributed tradeoff discussions. This is a simplified preview of the kind of prioritization, modeling, or diagnostic output we can build around your real workflow.

Preview the decision tool
Scenario readout
8%Indicative improvement range
74Distributed fit
34Centralized fit
Distributed model
74
Centralized model
34
Service intensity
55
Likely modeling direction

This case leans toward a more distributed / regionalized network. A real engagement would test multiple scenarios and turn results into maps, flows, and recommendations.

Path B

Supply Chain Analytics & Data

Turn scattered reports, stale dashboards, and unreliable files into trusted analytics and repeatable data workflows.

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Path C

AI Decision Systems & Product Strategy

Identify the first AI pilot worth building, shape useful agent workflows, and translate technical products into buyer-ready stories.

Who this helps

Built for teams where operations, data, and software collide.

The sweet spot is a company with enough complexity to benefit from AI, but a need for a practical path to value.

Warehouses & 3PLs

Reduce manual planning, reporting bottlenecks, dock congestion, and recurring issue firefighting.

Supply Chain Teams

Convert messy operational data into decisions, alerts, dashboards, and repeatable workflows.

B2B SaaS Founders

Clarify product strategy, AI positioning, roadmap tradeoffs, and adoption gaps.

Operations-Heavy Businesses

Create low-risk AI automations for quoting, reporting, customer communication, planning, and cleanup.

Why me

I am currently Head of Product at AutoScheduler.AI, where I bridge AI, optimization, and real-world warehouse execution. Before that, I co-founded 3 Tenets Optimization and helped create Architect.

Head of ProductAutoScheduler.AI3TO co-founderNetwork designCPLEX / SQL / TableauWarehouse AI

1. Diagnose the workflow

Start with the actual job people are trying to do, the data they trust, where work gets delayed, and where decisions break down.

2. Find the wedge

Pick the smallest AI, data, or product intervention that creates visible value without boiling the ocean.

3. Prototype the experience

Design the user flow, prompts, outputs, reporting, and operating process before over-investing in engineering.

4. Translate to adoption

Package the solution with rollout messaging, success metrics, training, and executive communication.

Strategy call

What happens on the strategy call?

In 45 minutes, we will identify whether your problem is best solved through analytics, optimization, AI, process redesign, product strategy, or a focused pilot.

1

Bring one messy workflow, dataset, supply-chain question, or AI idea.

2

We map the business problem, current friction, and decision gap.

3

You leave with the clearest next step, even if that means no project.

Book a 45-min Strategy Call
Next step

Have a messy operational problem worth untangling?

If your team is dealing with scattered reports, unclear decisions, manual planning, AI uncertainty, or supply-chain complexity, start with a focused strategy call.

Book a 45-min Strategy Call

Message me on LinkedIn

Send a short note with the workflow, decision, or operational issue you want to untangle.

Open LinkedIn

Try the data cleaner

Use the existing CSV cleaner as a product preview for the Operational Data Cleanup offer.

Open /data-cleaner