Builder Program Economics Intelligence
Quantifying True Cost-to-Serve Across a 58-Location, $800M Builder Supply Operation
Industry
Building Materials & Construction
Scale
$800M Revenue
Duration
20 Weeks
Location
Dallas, Texas
Engagement
AI Consulting
Executive Summary
The COO of a 58-location building materials distributor in Dallas ran 72 active builder programs across Texas, Oklahoma, Arkansas, Louisiana, and Mississippi — 4 company-owned truss plants, an installed sales division with 140 subcontractor crews, and a delivery fleet serving production and custom builders. DMSi Agility showed material margin on every program. It didn't show the estimating hours consumed by revision-heavy builders, the truss capacity displaced by programs that ran 18 months past projection, the fill-in delivery costs from superintendents who consistently under-ordered, or the working capital cost of builders who paid in 52 days instead of the contracted 30. We embedded program-level economic intelligence across the operation.
Business Impact
$4.2M
Hidden cost-to-serve variance identified across active programs
3
Builder programs renegotiated based on true economic data
22%
Improvement in estimating capacity allocation to highest-return programs
$1.8M
Working capital recovered through payment term enforcement
The Situation
The distributor had grown from $340M to $800M over 8 years through organic growth and 3 acquisitions that added branches, truss plants, and installed sales operations. The COO managed a vertically integrated business competing for builder programs against Builders FirstSource, US LBM, and regional independents across 5 states.
72 active builder programs, each consuming estimating capacity, truss plant production time, delivery fleet, and installed sales crews — with no unified view of which programs were creating enterprise value and which were destroying it.
Operational complexities:
- A single 400-lot production builder program represented $18-22M in revenue over 24-30 months — committing branch inventory, truss capacity, delivery logistics, and 15-20 installed sales crews for the duration
- Material margin on each program was visible in DMSi Agility, but 6 other cost dimensions — estimating hours, truss utilization, fill-in order frequency, delivery cost per home, installed sales labor efficiency, and payment timing — lived in separate systems or weren’t tracked at all
- The estimating team of 14 people across 4 regional offices was the binding constraint on how many programs the distributor could bid — but capacity was allocated first-come-first-served, not by program return potential
- 3 of 4 truss plants ran at 85-92% capacity. Every new program committed plant time for 18-30 months. A bad commitment didn’t just lose money — it blocked a better program from accessing production
- Builder payment behavior varied from 22 to 58 days across the 72 programs, but finance tracked DSO at the aggregate level — masking $6-8M in excess working capital from 8 builders who consistently paid 20+ days late
- The 3 acquired operations each tracked program economics differently — one in spreadsheets, one in a custom Access database, one not at all
The distributor was making $20M capital commitment decisions — truss capacity, estimating bandwidth, crew allocation — based on material margin and the sales team’s builder relationships. Everything else was invisible.
The Challenge
The COO framed the problem through the distributor’s largest program — a 520-lot community for a national production builder in the Houston suburbs. It appeared profitable at 24% material margin in DMSi.
The builder’s design team had issued 11 floor plan revisions over 14 months, each requiring the estimating team to regenerate framing packages, truss designs, and installed sales scopes for remaining lots. Two estimators spent roughly 40% of their time on this single program — capacity unavailable for 6 other bids the distributor declined during the same period.
The builder’s superintendents consistently under-ordered framing packages by 8-12%, generating fill-in deliveries at 3x the cost-per-board-foot of staged delivery. Payment averaged 48 days — 18 past contract terms — on $1.4M in average receivables.
- Fully-loaded margin on the 520-lot program was 11 points below the material margin the ERP showed
- 14 of 72 active programs were operating at true margins below the distributor’s cost of capital — appearing profitable in DMSi but value-destructive when all costs were included
- Conversely, 9 programs generated returns 40-60% above material margin because their builders ran efficient jobsites, accepted deliveries cleanly, and paid on time
- No system connected estimating hours to a specific program. No system connected truss schedules to program profitability. No system calculated working capital cost of payment behavior against capital deployed
The Solution
We spent 7 weeks in discovery across 4 locations — Dallas headquarters, Houston regional office, San Antonio truss plant, and the installed sales operations center in Fort Worth. The team mapped every cost stream touching a builder program from bid submission to final installed sales invoice.
The discovery revealed that 14 of 72 programs were below cost of capital when fully loaded, and 9 were generating returns far above what material margin suggested. The gap between what DMSi showed and what the programs actually earned was driven by 6 cost dimensions the ERP couldn’t capture.
The system analyzed signals including:
- Estimating hours tracked by program through time-entry integration, revealing which builders consumed disproportionate capacity relative to revenue contribution
- Truss plant production data correlated to program schedules, showing actual capacity utilization and opportunity cost of long-running programs displacing higher-return work
- Fill-in order frequency and cost by program — distinguishing builders whose superintendents managed material efficiently from those generating 3x the delivery cost per home
- Installed sales crew utilization by program — identifying builders whose construction sequencing created idle time versus those who kept crews productively deployed
- Payment behavior tracked at the program level against contracted terms, with working capital cost calculated against the distributor’s cost of capital
- Lumber cost variance between bid date and actual delivery across the program lifecycle
The 520-lot Houston program producing 24% material margin was actually delivering 13% fully-loaded return — below the distributor’s 15% threshold. Three programs declined during the same period would have produced 21-26% fully-loaded returns.
The Challenge
The COO framed the problem through the distributor’s largest program — a 520-lot community for a national production builder in the Houston suburbs. It appeared profitable at 24% material margin in DMSi.
The builder’s design team had issued 11 floor plan revisions over 14 months, each requiring the estimating team to regenerate framing packages, truss designs, and installed sales scopes for remaining lots. Two estimators spent roughly 40% of their time on this single program — capacity unavailable for 6 other bids the distributor declined during the same period.
The builder’s superintendents consistently under-ordered framing packages by 8-12%, generating fill-in deliveries at 3x the cost-per-board-foot of staged delivery. Payment averaged 48 days — 18 past contract terms — on $1.4M in average receivables.
- Fully-loaded margin on the 520-lot program was 11 points below the material margin the ERP showed
- 14 of 72 active programs were operating at true margins below the distributor’s cost of capital — appearing profitable in DMSi but value-destructive when all costs were included
- Conversely, 9 programs generated returns 40-60% above material margin because their builders ran efficient jobsites, accepted deliveries cleanly, and paid on time
- No system connected estimating hours to a specific program. No system connected truss schedules to program profitability. No system calculated working capital cost of payment behavior against capital deployed
The Solution
We spent 7 weeks in discovery across 4 locations — Dallas headquarters, Houston regional office, San Antonio truss plant, and the installed sales operations center in Fort Worth. The team mapped every cost stream touching a builder program from bid submission to final installed sales invoice.
The discovery revealed that 14 of 72 programs were below cost of capital when fully loaded, and 9 were generating returns far above what material margin suggested. The gap between what DMSi showed and what the programs actually earned was driven by 6 cost dimensions the ERP couldn’t capture.
The system analyzed signals including:
- Estimating hours tracked by program through time-entry integration, revealing which builders consumed disproportionate capacity relative to revenue contribution
- Truss plant production data correlated to program schedules, showing actual capacity utilization and opportunity cost of long-running programs displacing higher-return work
- Fill-in order frequency and cost by program — distinguishing builders whose superintendents managed material efficiently from those generating 3x the delivery cost per home
- Installed sales crew utilization by program — identifying builders whose construction sequencing created idle time versus those who kept crews productively deployed
- Payment behavior tracked at the program level against contracted terms, with working capital cost calculated against the distributor’s cost of capital
- Lumber cost variance between bid date and actual delivery across the program lifecycle
The 520-lot Houston program producing 24% material margin was actually delivering 13% fully-loaded return — below the distributor’s 15% threshold. Three programs declined during the same period would have produced 21-26% fully-loaded returns.
Implementation
Deployment occurred over a Month 1-2 – Month 6-7 period.
Program-Level Economic Model
Unified cost-to-serve calculation integrating material margin, estimating hours, truss utilization, delivery cost, fill-in frequency, crew efficiency, and working capital into a single program profitability view.
Estimating Capacity Allocation Intelligence
Program bids scored by projected fully-loaded return, enabling the COO to allocate the 14-person team to highest-return opportunities rather than processing bids in arrival order.
Truss Plant Capacity Optimization
Production scheduling linked to program economics so plants prioritized programs with highest return per production hour, not just highest volume.
Builder Behavior Scoring
Every active builder scored on 6 operational dimensions — revision frequency, fill-in rate, payment timing, delivery efficiency, crew coordination, and scope change frequency.
DMSi Agility Integration
Program economic intelligence surfaced within existing ERP workflow, with monthly profitability reviews replacing the quarterly spreadsheet exercise.
Program-Level Economic Model
Unified cost-to-serve calculation integrating material margin, estimating hours, truss utilization, delivery cost, fill-in frequency, crew efficiency, and working capital into a single program profitability view.
Estimating Capacity Allocation Intelligence
Program bids scored by projected fully-loaded return, enabling the COO to allocate the 14-person team to highest-return opportunities rather than processing bids in arrival order.
Truss Plant Capacity Optimization
Production scheduling linked to program economics so plants prioritized programs with highest return per production hour, not just highest volume.
Builder Behavior Scoring
Every active builder scored on 6 operational dimensions — revision frequency, fill-in rate, payment timing, delivery efficiency, crew coordination, and scope change frequency.
DMSi Agility Integration
Program economic intelligence surfaced within existing ERP workflow, with monthly profitability reviews replacing the quarterly spreadsheet exercise.
Strategic Impact
Portfolio-Level Capital Reallocation
The COO renegotiated terms on 3 programs operating below cost of capital — one accepted revised payment terms, one accepted a pricing adjustment, one the distributor exited at end of phase. Estimated annual impact exceeded $1.6M in recovered margin and freed truss capacity for 2 higher-return programs the sales team had previously been unable to accommodate.
Estimating as a Strategic Asset
Estimating capacity was allocated based on projected program return rather than sales team relationships or bid arrival order. The 14-person team’s output didn’t increase — but the value of what they produced did. Programs bid in the first 6 months post-deployment carried average fully-loaded margins 340 basis points above programs bid in the prior 6 months.
Enterprise Visibility Across Acquired Operations
The 3 acquired operations — each tracking program economics differently or not at all — were unified into a single profitability framework. The COO described it as the first time in 3 years she could compare a program in Little Rock against a program in Baton Rouge on the same economic basis.
Key Takeaway
At $800M with 72 active builder programs, the question isn’t which programs are profitable on material margin — nearly all of them are. The question is which programs are earning a return on the estimating capacity, truss plant time, delivery fleet, installed sales crews, and working capital they consume. This distributor wasn’t losing money on bad programs. She was making less than she should have been — across every program, every branch, every truss plant — because capital allocation decisions worth $20M+ were made with 30% of the relevant information. The other 70% existed in the operation. It just wasn’t unified, quantified, or available at the moment the commitment was being made.
