# Dry Cleaning & Laundry Group Model See how customer demand, service mix, pricing, and plant costs shape a dry cleaner. - Canonical: https://finamodel.com/templates/dry-cleaning - Excel download: https://finamodel.com/templates/dry-cleaning.xlsx - Category: Consumer - Model type: Operating model - Difficulty: Intermediate - Audiences: Investors & analysts, Founders & operators, Dry cleaner owners and operators, Search-fund and PE buyers, Consumer and services investors, Lenders and analysts - Tags: dry-cleaning, laundry, garment-care, rollup, dcf ## Overview This model helps you understand a dry-cleaning business across garment care, alterations, collection and delivery, and commercial accounts. It links customer orders and service mix to labour, utilities, equipment, vehicles, and shop costs. Use it to assess an existing operation, a new location, or a pickup-and-delivery expansion. Test order volume, pricing, staffing, and capital needs to see their impact on profitability and cash flow. ## What's included - Store inputs: Year-1 stores, new stores per year, orders per store, production staff per store, average ticket - Utilisation: Year-1 utilisation with an annual ramp and a practical ceiling - Service mix: dry cleaning, laundered shirts, wash-and-fold, alterations, and specialty shares, per-tier price indices and net margins - Ancillary: route members per store and annual route fee, retail spend per order, price escalation - Cost structure: production and counter-and-delivery FTEs and wage with benefits and wage growth; utilities and energy as % of revenue; facilities, marketing and SG&A as % of gross profit; depreciation (% of revenue); tax - Capital and working capital: maintenance capex %, store build-out cost per store, NWC % of revenue growth, base-year revenue - Valuation: WACC, terminal growth, net debt, shares outstanding - Operations sheet: store roll-forward, utilisation ramp, orders per store, total orders, staff headcount, orders per production FTE - Revenue sheet: five service tiers, service revenue, route memberships, retail and sundries, total revenue - P&L sheet: revenue to net income with product and supplies cost, labour, utilities and overhead, margins, identity check - FCF sheet: NOPAT, depreciation add-back, capex, change in NWC, unlevered FCF, discount factor, PV - Valuation sheet: sum of PV, terminal value, enterprise value, equity value, value per share, implied EV/EBITDA - Dashboard with stores, orders, utilisation, revenue per store and average ticket, EBITDA margin, EV, per share, and revenue mix ## How the Dry Cleaning Financial Model Works This dry cleaning financial model projects a multi-location dry cleaning and laundry group over seven years. It connects store openings, capacity utilisation, service mix and pricing to a full P&L, cash flow and valuation. The article explains the operating drivers, calculation flow, key outputs and practical applications so you can evaluate the template. ### Operating drivers: stores, utilisation and service mix The model starts with a store roll-forward: opening stores plus new stores equals closing stores. Closing stores drive production and counter headcount, order volume and route memberships. - Each store supports a mature order count, multiplied by a utilisation factor to get effective orders per store. Utilisation starts at a Year 1 input, ramps annually and is capped at a practical ceiling. - Total orders is the key volume driver. Revenue splits across dry cleaning, laundered shirts, wash-and-fold, alterations and specialty services, each with a share, an average ticket and a per-tier price index. Route memberships and retail attach layer on top. This structure lets you test how new stores, utilisation ramp and service mix interact. ### Calculation flow from orders to EBITDA Orders times tier share times average ticket times price index, escalated at a menu step-up, builds service revenue by tier. Product and supplies cost is each tier's revenue times one minus its net margin. - Revenue less this direct cost gives gross profit. Production and counter/delivery labour are headcount-driven: FTEs per store times wage, loaded for benefits and wage growth. - Utilities and energy are a percent of revenue, while facilities, marketing and corporate SG&A are percentages of gross profit. These costs stack to EBITDA, then depreciation leads to EBIT, tax on positive EBIT and net income. The model also checks the P&L identity. ### Outputs: P&L, free cash flow and valuation The P&L outputs include revenue, gross profit, gross margin, EBITDA, EBITDA margin, EBIT, net income and net margin. The free cash flow bridge starts with EBIT, calculates unlevered tax and NOPAT, adds depreciation, subtracts maintenance capex and store build-out capex, and subtracts the change in working capital. - Working capital is a light call because dry cleaning is a prepaid, low-inventory business. Unlevered free cash flow is discounted at WACC. - Valuation sums the present value of explicit cash flows and a Gordon-growth terminal value to enterprise value, then subtracts net debt for equity value and value per share. The dashboard summarises stores, orders, utilisation, revenue per store, average ticket, revenue, EBITDA, EBITDA margin, enterprise value and value per share. ### Practical use and analytical flexibility Use the model to assess an existing operation, a new location or a pickup-and-delivery expansion. Flex orders per store, utilisation ramp, service mix, route attach and pricing to see how blended revenue per order, gross profit, EBITDA and enterprise value respond. - The model makes per-tier price indices and net margins explicit, so you can analyse the trade-off between high-ticket specialty work and low-ticket laundered shirts. Because labour sits in operating expense, gross margin is not the headline profitability measure; EBITDA margin is more meaningful. - Changing new store counts shows the capital call from build-out. The discounted cash flow ties operating assumptions to a valuation, helping you evaluate growth strategies. All monetary figures are in dollars, with stores, orders and FTEs as counts. ## The service mix sets the ticket, labour sets the margin Because the five service shares sum to one, service revenue is total orders times the average ticket, and the per-tier price index governs the blend of tickets rather than the size of the order. The model makes store count, orders per store, a utilisation ramp, and the dry-cleaning, shirts, wash-and-fold, alterations and specialty mix explicit, so total orders, average ticket and the blended gross margin are transparent operating metrics an analyst can flex against the labour-heavy cost stack rather than a top-down growth rate. ## Designed for one-edit responsiveness Every input, the store pipeline, orders per store, the utilisation ramp, the service mix and its indices, per-tier margins, the full cost stack, capex, working capital, and the WACC, is a named-range cell. Edit one and the operations build, revenue, P&L, free-cash-flow bridge, valuation, and dashboard all recompute. No formula rewrites are needed to test a mix, labour, or expansion scenario. ## An unlevered DCF, not an EBITDA shortcut A cleaner builds out a plant of boilers, presses and dry-clean machines and refreshes that equipment over time, so the model bridges to unlevered free cash flow and discounts it at a defensive WACC with a below-normal Gordon-growth terminal value that reflects the secular casual-dress headwind. Enterprise value bridges through net debt to equity value and a per-share figure, and the implied EV/EBITDA falls out as a sanity check against where dry-cleaning platforms change hands. ## Workbook structure ### Cover Workbook overview, sheet legend, units, and tab-colour key. - Title and scope framing - Sheet-by-sheet purpose summary - Units and tab-colour legend ### Assumptions Every driver in one sheet: stores, utilisation, service mix, costs, capital, valuation. - Year-1 stores, new stores per year, orders per store, production staff per store, average ticket - Utilisation with an annual ramp and a practical ceiling - Service shares, per-tier price indices and net margins, route and retail inputs, price escalation - Production and counter-and-delivery comp and wage, utilities, the percent-of-gross-profit overhead lines, depreciation, tax - Maintenance capex, build-out cost per store, NWC, base-year revenue - WACC, terminal growth, net debt, shares ### Operations Stores, orders, utilisation, and staff. - Opening plus new stores equals closing stores - Utilisation ramps from a Year-1 input, capped at a ceiling - Orders per store equal mature orders times utilisation - Total orders equal closing stores times orders per store - Production and counter-and-delivery headcount equal closing stores times per-store FTE - Orders per production FTE as a productivity metric ### Revenue Revenue by service tier and ancillary. - Each tier equals total orders times average ticket times tier share times price index times escalation - Service revenue subtotal - Route memberships equal closing stores times members per store times annual route fee - Retail and sundries equal total orders times a retail spend per order - Total revenue ### P&L Revenue to net income. - Revenue from the Revenue sheet - Product and supplies cost as the inverse of the per-tier net margin - Gross profit and gross margin - Production and counter-and-delivery labour by headcount, utilities as a percent of revenue, the percent-of-gross-profit overhead stack - EBITDA, depreciation, EBIT, tax on positive EBIT, net income, margins, identity check ### FCF Unlevered free cash flow bridge. - EBIT and unlevered tax from the P&L - NOPAT equals EBIT less unlevered tax - Add back depreciation - Maintenance capex on revenue and build-out capex on new stores - Change in net working capital on revenue growth - Unlevered free cash flow - Discount factor and PV of UFCF ### Valuation Discounted cash flow. - Sum of PV of explicit UFCF - Gordon-growth terminal value and its PV - Enterprise value - Less net debt to equity value - Shares outstanding and value per share - Implied EV/EBITDA ### Dashboard Headline metrics and revenue mix. - Stores, orders, utilisation, revenue per store and average ticket - Revenue and EBITDA - EBITDA margin - Enterprise value and value per share - Revenue mix across service, route, and retail ## Features - **The service mix sets the ticket, labour sets the margin:** Because the five service shares sum to one, service revenue is total orders times the average ticket, and the per-tier price index governs the blend of tickets rather than the size of the order. The model makes store count, orders per store, a utilisation ramp, and the dry-cleaning, shirts, wash-and-fold, alterations and specialty mix explicit, so an analyst can flex the mix and watch a blended revenue per order, gross profit and EBITDA move together, while the labour-heavy opex stack, not cost of goods, sets the EBITDA margin. - **A high gross margin, but a labour- and energy-heavy cost stack:** Dry cleaning consumables, poly, hangers, solvent and detergent, are cheap, so the blended gross margin runs in the low eighties. The real cost is people and energy: production pressers and cleaners and counter-and-delivery staff are built bottom-up from FTEs per store, wage, benefits and wage inflation, and an energy-intensive utilities line for boilers, presses and water is charged as a percent of revenue. Because labour sits in operating expense rather than cost of revenue, the EBITDA margin lands in the high teens and expands as utilisation and price escalation fill fixed headcount. - **An unlevered DCF, not an EBITDA shortcut:** A cleaner builds out a plant of boilers, presses and dry-clean machines and refreshes that equipment over time, but collects on pickup and holds almost no inventory, so the model bridges EBITDA to cash through NOPAT, depreciation, maintenance and build-out capex, and only a light change in working capital, then discounts the unlevered free-cash-flow stream at a defensive WACC with a below-normal terminal growth that reflects the secular casual-dress headwind on garment volumes. ## Use cases - **Intrinsic valuation:** Set the store pipeline, utilisation ramp, service mix, the cost stack, and a WACC, and read the enterprise value, equity value, value per share, and implied EV/EBITDA. Sense-check the multiple against where dry-cleaning platforms and route businesses change hands. - **Roll-up and pipeline planning:** Flex new stores per year and the build-out cost per plant to see how the de novo and tuck-in pipeline consumes cash and lifts order volume, and watch revenue per store and the EBITDA margin respond as the group scales. - **Mix, labour and route stress test:** Shift the mix toward higher-ticket dry cleaning and specialty, flex production FTEs per store and wage inflation, or grow the pickup-and-delivery route membership, and read the gross-margin, EBITDA-margin and valuation impact on a business where labour productivity is decisive. ## Frequently asked questions ### What is a dry cleaning financial model? A dry cleaning financial model captures the seven-year operating economics and intrinsic value of a multi-location dry cleaning and laundry group that offers garment dry cleaning, laundered shirts, wash-and-fold laundry, alterations and repairs, and specialty and preservation work, plus a pickup-and-delivery route membership. It rolls a store count forward, converts a capacity-utilisation ramp into total orders, splits an average ticket across five service tiers each at its own price index and net margin, charges the labour- and energy-heavy cost stack to EBITDA, and discounts an unlevered free-cash-flow stream to enterprise value, equity value, and value per share. ### How is dry cleaning revenue built? Revenue is driven by the store estate and its utilisation: total orders equal closing stores times orders per store times a utilisation factor that ramps to a ceiling, and service revenue is total orders times an average ticket split across a five-tier service mix that sums to one, with a per-tier price index setting each ticket. Store-driven route memberships and an order-driven retail attach layer on to total revenue. ### Why does the gross margin run so high? The consumables that touch a garment, poly, hangers, solvent and detergent, are inexpensive, so each service tier carries a high net margin and the blended gross margin runs in the low eighties. The meaningful profitability line is EBITDA, because the pressers, cleaners, spotters and counter staff who do the work sit in operating expense rather than cost of revenue, alongside an energy-intensive utilities line for the plant. ### Why an unlevered DCF instead of an EBITDA multiple? A cleaner still builds out and equips each plant and refreshes that equipment, so EBITDA overstates cash. The model bridges to unlevered free cash flow, NOPAT plus depreciation, less maintenance and build-out capex, less a light change in working capital for a prepaid, low-inventory cash business, and discounts it at a WACC, then adds a below-normal Gordon-growth terminal value. The implied EV/EBITDA falls out as a sanity check rather than as the valuation input. ## Related templates - [Laundromat Model](https://finamodel.com/templates/laundromat) - [Retail Store Model](https://finamodel.com/templates/retail-store) - [Coffee Shop Chain Model](https://finamodel.com/templates/coffee-shop)