Selected Work
Most of my work has centered on six things: protecting cash, expanding margin, fixing underperformance, creating growth, building better operating systems, and integrating acquired businesses. The stories below are a few examples of what that work looked like in practice.
Build & Grow: Systems Behind the Turnaround
From 2010 to 2012, Pier 1 was rebuilding the business. Part of that work required rebuilding the infrastructure behind how we made merchandising and inventory decisions.
We had 1,000 stores, over 10,000 SKUs, and enormous amounts of data. But much of that information was difficult for merchants and planners to access, analyze, or connect to a decision.
My team partnered with IT to help rebuild the merchandising data environment. We defined the business logic, data structure, terminology, refresh cadence and reporting requirements, then handled testing, training and adoption. The goal was to put reliable data directly into the hands of the people running the business. Our team eventually became the center of excellence for data-cube analytics across Pier 1.
I also led development of two custom planning applications. SKU Level Planning, or SLP, became the forecasting system for every SKU in the assortment and every planner on our 25-person team. Products could be attributed by things like material, color, size, trend and price tier, while weekly forecasts captured sales, units, margin, discounts and seasonal sell-downs. For the first time, we could aggregate thousands of item-level forecasts around virtually any idea we wanted to test.
We then built a new Open-to-Buy platform that connected those SKU decisions back to the company financial plan by group, division, department and class. Core, promotional and clearance demand could be planned independently, making risks visible earlier and giving teams much better tools to scenario-plan purchases, promotions and markdowns.
The last piece was allocation. New functionality allowed us to customize assortments by groups of stores instead of applying one answer across the chain. In lower-volume stores, more disciplined assortment choices ultimately reduced clearance ~30% and improved product selling margins by 3–4 percentage points.
The systems and reporting infrastructure we built during that period remained part of Pier 1’s process for nearly a decade.
When I think back at that time, a few things stand out. Clean, structured data is a prerequisite for advanced analytics. Data has little value if the people making decisions cannot get to it. And the best technology does more than automate a process. It changes what the business can see, how quickly it can react, and where it chooses to allocate capital.
Build & Grow: The $30M Bet
Dining had been one of Pier 1’s most dependable businesses. It was a strategic category that we wanted to "own" which meant floor space in stores and a healthy marketing budget. But the business was struggling. For the first time in years, there were negative comps in dining furniture.
Longstanding furniture collections were declining by double digits, new customer acquisition was down, and we had shown customers many of the same four or five dining collections for years. The team had become increasingly dependent on a core business that was no longer growing.
I was brought in to lead planning for the Furniture division and help figure out what we were missing. I wasn't the merchant, but I had a good eye for product and understood the category. The market was moving toward lighter woods, washed finishes, and transitional furniture, while much of our assortment remained dark and traditional. Internally, there was a belief that “only dark brown sells.” The customer was telling us something different.
Our buyer developed a new whitewashed dining collection called Bradding with our manufacturing partners in Vietnam. Normally, a new table would have been tested in perhaps 50 stores before a broader rollout.
I pushed hard for an all-store launch.
That meant asking the CEO and CFO to approve roughly $500,000 of initial inventory for a collection with no sales history. It also meant solving a difficult floor-space problem: we were launching in January, but four months later stores needed that space for outdoor furniture.
I built the launch plan around that constraint. We mapped the floor set, marketing, promotions, inventory flow, spring sell-down, and exit, with a plan to bring Bradding back permanently in the fall if it worked. We supported the investment with competitive research, customer feedback, and evidence that lighter finishes were gaining ground across the market.
Then we planned it like a top-tier collection and gave it enough inventory and marketing to succeed.
Bradding became the number-one dining collection almost immediately. We moved ahead with the full fall rollout and expanded the concept into bedroom and living room, where those collections also rose toward the top of their categories. Bradding became our largest indoor furniture collection, surpassing $30M in annual sales within a couple years of launch.
The lesson stayed with me. Customers have the only vote that really matters. Our job is to challenge internal assumptions with data, customer feedback, competitive context, and merchant judgment, then build a plan that gives the right idea enough room to work. In this case, the bigger risk was not the $500,000 bet. It was continuing to do what had already stopped working.
$30M Annual Sales | #1 Dining Collection | Expanded into Bedroom & Living Room
Buy & Integrate: 12 Deals in 18 Months
Suma was VC-backed and built to scale quickly. The model depended on deploying capital, acquiring Amazon-native consumer brands, and building enough operating capacity to do it again a few weeks later. Over roughly 18 months, we acquired 12 brands for about $20 million, representing approximately $40 million in annual sales and $5 million in EBITDA.
There was no standard acquisition. Some businesses were built around a single hero product; others had portfolios approaching 1,000 SKUs. We bought across CPG, wellness, home décor, apparel, automotive accessories, textiles and several other consumer categories. Supply chains ranged from straightforward imports out of China and Taiwan to more complicated domestic component sourcing and contract manufacturing.
That variety created an obvious problem. We needed to move quickly, but every deal came with a unique set of operating risks. Moving fast only worked if we could get good at identifying risks before close and absorbing the business without slowing down the next acquisition.
My role focused primarily on that operating side of the deal. Each business was rebuilt at the SKU level to pressure-test the M&A pro forma, understand inventory requirements and assess where growth might realistically come from. At the same time, the supply chain was evaluated for supplier risk, landed-cost exposure, logistics dependencies and potential sourcing upside. Inventory was valued based on what we actually intended to sell, rather than simply accepting the value sitting on the seller’s balance sheet.
As the pace increased, M&A, Finance, Marketing and Operations built a common playbook for diligence and onboarding. Data collection became standardized. Supplier and purchase-order information moved into common formats. Inventory, contracts, systems, logistics and founder transition all had defined owners and timelines. The target was to have each brand operating independently of the seller within 45 days of close.
The playbook became better with every acquisition because every acquisition exposed something new. A difficult 3PL transition made direct conversations with critical logistics partners a requirement before future deals. Questionable import practices led to more scrutiny around landed costs and customs compliance. Supplier relationships, hidden operating dependencies and other issues that were difficult to see in a P&L became things we deliberately looked for before committing capital.
By the later acquisitions, our team was moving faster while taking fewer unknowns into close. Just as importantly, the capabilities built during those 18 months became the foundation for a large merger that came later.
The bigger lesson, though, came from watching the brands perform over time. Some difficult integrations became great businesses. Some easy integrations turned out to be overvalued assets.
Execution can protect the downside, expose risk and accelerate a transition. But it cannot fix the wrong asset at the wrong price. The quality of the business, the durability of its demand, and the terms of the deal ultimately matter most.
12 Single-Asset Deals | ~$40M Revenue Acquired | 45-Day Onboarding
Buy & Integrate: The Merger
When Suma merged with D1 Brands, the company more than doubled overnight.
D1 brought $60 million in revenue, 20+ brands, and over 50 employees and contractors into our business that was already trending at $40 million. The deal moved quickly. D1 was short on cash, diligence was compressed, and we knew we were taking on operating complexity that we would have to work through after close.
There was also a hard deadline. D1 managed inventory and purchasing through a heavily customized NetSuite environment that cost more than $300,000 a year and would disappear roughly 100 days after close. Replicating that setup inside our own NetSuite instance would have taken 12–18 months and brought along processes and limitations we did not want. So we built a custom solution that could scale with the newco.
Before close, I mapped the operating model in Google Sheets: purchase orders, production milestones, payment terms, freight bookings, landed-cost estimates, invoices, and inventory valuation. Our data engineer then turned that model into a custom Retool application designed around how we actually wanted the combined company to operate.
About 45 days after close, the new system was running. It supported roughly 5,000 SKUs, more than 100 suppliers, 200–300 open purchase orders, 30–50 inbound shipments at any given time, and approximately $25 million in annual purchases. We ran D1 through a full monthly cycle in parallel with NetSuite before cutting over, including month-end close and inventory reconciliation. The systems worked, but that was only half the integration.
Roughly 30 D1 associates moved into my organization. We reorganized the team around Planning, Purchasing, Logistics, Sourcing, and Inventory Control, clarified ownership, and introduced a common weekly forecast that connected Sales, Marketing, Operations, inventory, and financial targets. Most of the people offered roles stayed, even as responsibilities changed.
The entire transition was completed in under 90 days with no material interruption to purchasing, supplier payments, inbound freight, inventory visibility, or fulfillment.
Not everything we inherited was clean. There were messy entity structures, incomplete cost history, legacy Amazon-account issues, and parts of the portfolio that would eventually require significant restructuring. But by the end of the integration, we had one operating model, one forecast, clearer accountability, and a new supply chain infrastructure built to support the company as it continued to scale.
The lesson I carried forward was that integration is not about preserving legacy processes. The hard part is deciding what deserves to come with you, what needs to change, and building enough trust and structure to make the transition a success without interrupting daily operations.
~$100M Newco Revenue | 20 Brands Onboarded | 90-Day Integration
Improve the Economics: Cash is King
When Suma merged with D1 Brands, the incoming portfolio was generating roughly $60 million in annual sales and carrying about $20 million of inventory.
That immediately became one of the most important operating issues in the combined company. Some of the inventory was productive and needed to stay in stock. Some was simply too deep. And some belonged to products that no longer deserved another dollar of investment.
The first job was to separate those things.
We rebuilt the portfolio at the SKU level, distinguishing core products from inventory that needed to exit and challenging future purchases against actual demand. Forecasting, Marketing, and Purchasing began working from a common view of what we expected to sell, how much inventory those sales required, and when additional cash commitments were actually necessary.
But inventory levels were only part of the problem. The timing of cash mattered just as much.
Many supplier relationships required substantial deposits when orders were placed and full payment before goods shipped. We went back through the supplier base and renegotiated those terms, ultimately moving much of the business from 30% deposits and 70% pre-shipment payments to zero deposits and payment 60 days after shipment.
The combination changed the working-capital model. Less cash was tied up in inventory, new commitments were made more deliberately, and the company held onto its cash much longer before products were available for sale.
Over time, inventory across the portfolio was reduced by roughly 50%, while improved supplier terms unlocked approximately $4.5 million in working capital that was previously tied up in prepaid inventory.
The lesson was bigger than inventory management. In an inventory-heavy business, cash is the result of dozens of operating decisions made long before Finance sees the impact. Forecasts, assortment choices, purchase quantities, supplier terms, and markdown decisions all compete for the same capital.
The goal was never simply to buy less. It was to become much more deliberate about what deserved the company’s cash.
~$60M Portfolio | ~50% Inventory Reduction | ~$4.5M Cash Unlocked
Improve the Economics: Global Sourcing
One of D1 Brands’ largest products was doing more than $10 million in annual sales and less than 5% EBITDA. It was a great product with strong demand. But the unit economics were terrible. Advertising was necessary to defend its position on Amazon, there was only so much room to raise retail prices, and most of the remaining operating costs were relatively fixed.
If we wanted to materially improve profitability, product cost had to change. We started with that SKU because the scale made the opportunity meaningful. Rather than treating the incumbent factory price as the starting point, we worked backward from the economics of the business. What price could the customer support? What margin did we need? What level of advertising was required to remain competitive? Those answers told us what the product needed to cost.
From there, the sourcing team documented specifications, benchmarked alternative manufacturers, and quoted the product across a broader supplier base. The goal was not simply to find the cheapest factory. We needed the same product quality, reliable production capacity, acceptable lead times, and a cost structure that made the business materially better.
The project reduced unit cost by roughly 30%. We could now compete at a healthy margin. More important, it gave us a model we could repeat.
We began working through the broader portfolio the same way, prioritizing products where sourcing could have the greatest impact on contribution margin. Existing factory costs were no longer treated as fixed. Each product was evaluated against what its economics should support, then sourcing worked backward toward that target.
The program ultimately became a meaningful part of a broader effort that improved company EBITDA by roughly three percentage points.
We carried the approach forward. Sourcing works best when it is connected directly to the P&L. Retail price, advertising, product cost, freight, and margin are all part of the same equation. When one of those levers is constrained, sometimes the answer is not another promotion or a higher price. Sometimes you have to rebuild the economics upstream.
$10M+ Hero SKU | ~30% Lower Unit Cost | ~3 pts EBITDA Improvement
Reshape the Business: Exit Readiness
Suma had a problem that shows up in a lot of complicated businesses: the historical financials were accurate, but they did not tell the most useful story about the company.
At the portfolio level, brand EBITDA was roughly 15%. That number blended together strong brands and profitable SKUs with products that were losing money, absorbing working capital, or no longer deserved additional investment. The company had real underlying value, but it was difficult to see through the noise.
At the same time, debt covenants limited how aggressively we could restructure the portfolio. We could see opportunities to exit products, release inventory, reduce complexity, and concentrate capital behind the strongest parts of the business, but we needed to demonstrate what that future state would actually look like before taking action.
I rebuilt the portfolio from the SKU level up. Every product was classified based on its role in the go-forward business. From there, the model connected those decisions to sales, margin, inventory, purchasing, cash flow, and the cost structure required to support the remaining portfolio.
A much different picture emerged. The go-forward business supported brand EBITDA above 20%. More importantly, the model showed that roughly $5 million of cash could be extracted from unproductive inventory as those products were sold down and purchases were redirected toward the core business.
But the model alone was not enough. The work had to make the opportunity understandable to management, lenders, and potential buyers. The data became the basis for telling a much cleaner story about what the company could become, why the restructuring made economic sense, and how it could be executed without creating unnecessary risk.
The plan was ultimately approved and executed. More than $5 million in cash was recovered from the exiting businesses. The portfolio now operates under new ownership, sales and profitability have improved, and the company is investing in new products again.
The lesson stayed with me. Data does more than measure a business. Used well, it can uncover value that is easy to miss and help make the case for doing something about it.
20%+ go-forward Brand EBITDA | $5M+ cash recovered | Go-Forward Proforma P&L
Reshape the Business: The Apparel Turnaround
Coming Soon...