Javier Diaz — Founder, SOPITA.GG & BingoBango.ai · VP, Global Supply Chain, Cra-Z-Art

Proof

The book moved. Service held.

Managed book I systemized: >$900M to ~$190M. About ~$710M working capital returned to the business — not a fire sale. Drag the bar, or hit play.

BBB Group · 16 DCs · 2014–2020

Watch ~$710M return to the business.

Working capital on a managed book. Not his money. Not a fire sale. 90th percentile in-stock held while the book compressed.

Inventory on book $900M Start of the book

Method 0% Systems + cadence

Working capital returned $0M Still locked in stock

Method progress

Start · $900M ~$190M on book

Done · ~$710M working capital returned to the business · 90th %ile in-stock held

BBB Group · multi-banner · 16 DCs · 2014–2020 · systems I designed

End state · systems I designed

>$900M→~$190M

≈79% off the managed book · ~$710M working capital returned to the business · 90th %ile in-stock held

BBB Group · multi-banner · 16 DCs · 2014–2020. Systems I designed.

~$710MWorking capital returned

90thIn-stock held

−17%Logistics

18+Systems

$80MObsolete at cost

~150SKUs / mo

Experience

Founder first. Then the operating seat. Same job at every stage: return working capital to the business, hold in-stock, keep the network steady.

What you’re actually buying

Room to lead — exceptions leave your plate; you return to product and people
Capital + service + network — free trapped capital without starving live SKUs; hold service while cost moves
Pace you can hold — multi-country, tariffs, crisis, launch — absorbed by the system, not by you
Systems that stay — 18+ decision systems the team can run without me

Present

Structure first

Product craft · shipping discipline

Founder · SOPITA.GG & BingoBango.ai

SOPITA.GG · BingoBango.ai

Product craft and shipping discipline. Same standard as the operating seat.
Structure first. Swimming harder is not a strategy.

2020 – Present

$80M obsolete at cost

CEO-direct · 6 reports · logistics −17%

Vice President, Global Supply Chain

LaRose Industries, LLC d/b/a Cra-Z-Art · Randolph, NJ · Jacksonville + Lewisburg + import

COVID: repurposed U.S. plant capacity; did not freeze the book; 90th percentile in-stock; ~150 SKUs/month NPI
Tariffs: landed-cost and make-vs-buy design; company 1.5M sq ft target; logistics −17%
Integrations: RoseArt / Rose Moon + Lewisburg pencil factory (2021 Mattel AST); Lexibook; Top Secret Toys (2024); Joker AG / Slimy (2025)
Inventory visibility for an $80M (at cost) obsolete book. S&OP cadence the team can run
CEO-direct. 6 direct reports. End-to-end supply ownership
Stack: Fourth Shift (ERP/MRP), Monday.com, Salsify, Python, Google Cloud SQL / MySQL, Claude Code, GitHub Copilot

2014 – 2020

~$710M WC returned

>$900M → ~$190M · team of 5

Senior Manager, Replenishment

Bed Bath & Beyond Group · Union, NJ · six banners + digital · 16 DCs · team of 5 · company sales >$11B

Inventory planning, demand forecasting, replenishment, and allocation across 16 DCs
Managed book from >$900M to ~$190M. About ~$710M working capital returned to the business by system design, not a purge
18+ forecast, turn, order-efficiency, and JIT methods. Automated buy / move / stop
Store banners and digital on one allocation and replenishment cadence. Team of 5. 90th percentile in-stock held

2006 – 2014

$140M OTB unlocked

95% obsolete eliminated
Foundation chapter

Foundation · Supply & Merchandising Operations

Four seats in sequence. Same early discipline: stock truth, open-to-buy, obsolete out. Headline dollars attach to Baker & Taylor. BBA, Business Management · Fort Hays State University · 2011–2015.

Sr. Planning & Allocation Analyst · Baker & Taylor

2012–2014 · catalog / wholesale scale

Corrected Amazon replenishment failure modes; unlocked $140M open-to-buy
Eliminated 95% of obsolete while protecting depth on live titles

Demand & Supply Planning Manager · Cra-Z-Art

2009–2012 · first tour at the company later run as VP

Inventory, turns, and obsolete on a physical-product book.

Supply Chain Manager · Cherrydale Farms

2008–2009 · seasonal fundraising and commercial food

Physical-product supply for a seasonal fundraising and commercial food book.

Store Analyst · The Children's Place

2006–2008 · specialty retail pace

Specialty retail pace. Early CEO-direct exposure.

Offer · One product

Ninety days. Then a method that stays.

The sprint is the offer. I don’t staff your fire drills. I sit with you, free the book, and encode the method so the team runs it when I’m gone.

You get back

Room to lead

I take the riptides (stockouts, excess, “where is the cash?”) and install cadence. You return to vision, people, product.

You get back

Capital · service · network

Free trapped capital without starving live SKUs. Hold service while cost and multi-country complexity move.

You keep

Systems that stay

It only counts if the team can run it. Parameters on tools you already pay for — not a larger war room.

Sprint 01

Days 1–30 · Map

Capital, service, network, planning gaps. Exceptions still on you. Truth with respect.

Sprint 02

Days 31–60 · Clear

Move what should move. Protect core service and funder SKUs. Light cadence the team can keep.

Sprint 03

Days 61–90 · Encode

Algorithms and automation on your stack. Scoreboard: working capital returned, waste down, hours returned, service held.

The sprint is for you if

Physical product — the mess is real, not a slide
You still carry the hard exceptions yourself
You will share real authority on inventory and planning
You want a method that stays, not a bench of associates

I will decline if

You need someone to own POs every Tuesday
You want a shelf-and-promo politician more than an ops partner
Decks matter more than cash velocity
Decisions stay with a committee that never ships

The first conversation is free. The sprint has a number we set when the book is on the table. A seat after day 90 is a separate conversation — only if the method is holding.

Talk about a 90-day sprint

Builds

Founder who builds

Same ownership standard on products I ship and ops I run.

SOPITA.GG & BingoBango.ai

Founder of SOPITA.GG and BingoBango.ai. Product craft and shipping discipline — the same standard I bring to hard ops.

SOPITA.GGBingoBango.aiBuilder-operator

Decision engines

18+ algorithms and replenishment methods: forecast, turns, order efficiency, JIT — the layer behind ~$710M of managed inventory reduction.

18+ algorithmsForecast · turns · JIT

Automation on the stack you already pay for

I use agents the way I use replenishment engines: to take exceptions off people and encode the method. Fourth Shift, Monday.com, Salsify, Python, and SQL — cadence on the stack you already pay for, not a new war room.

Fourth ShiftMonday.com · SalsifyS&OP

AI · How I actually work

Code. Models. Data.

The bench I use to encode methods and ship product. Agents write. Python and SQL hold the truth.

Factory ERP / MRP

S&OP on the factory system the plants already run.

Fourth Shift (Infor)

Ops / PIM

Cadence and product truth on tools the team already pays for.

Monday.com Salsify

Decision layer

I pair in the editor the way I pair on the floor: Claude Code and GitHub Copilot to encode the method, Python and SQL to make it stick.

Python Google Cloud SQL MySQL Claude Code GitHub Copilot

Live use

Anthropic, NVIDIA, Apple developer-track. Editor tools support the method. Capital, service, and network still lead.

Anthropic NVIDIA Apple developer-track