Case Study
Data-Driven Quoting and Multi-Dimensional Profit Visibility for a Growing SMB Manufacturer
Client Overview
A small manufacturer specializing in custom-machined components faced growing pressure to quote competitively while protecting margins. Each job varied in materials, machine time, setup requirements, and labor complexity, making accurate quoting and profitability tracking increasingly difficult. Quotes were often based on experience and rough historical averages rather than precise cost data, leaving the company vulnerable to underpricing complex jobs or overpricing simpler ones.
Business Challenge
The company’s quoting process relied on spreadsheets and tribal knowledge. Estimators would add a standard markup, but actual job costs frequently deviated 15–40% from the quote.
Lack of Visibility into Source Data: No systematic way to compare quoted price vs. actual labor, material, machine hours, and outside services.
Inefficient Profitability Reports: Profitability reports took 2–3 weeks to compile in Excel and were only done quarterly—if at all.
Gaps in Business Insights: Leadership could not answer basic questions with confidence:
Which customers are truly profitable?
Which machines or product families lose money?
Which jobs are being chronically under-quoted?
The result was a mix of lost margin on “winner” jobs, surprise losses on “loser” jobs, and growing frustration that the company was working harder but not necessarily smarter.
Solution
The company engaged Lasso to build a lightweight, high-impact analytics layer on top of their existing systems (QuickBooks, JobBOSS, and machine monitoring logs). This analytics layer leveraged the latest in data storage & processing technology while still being simple to implement and use. It ingested data from the company’s existing source systems, utilized automated pipelines to aggregate and transform data and output profitability calculations across jobs, customers, and products.
Quoted-vs-Actual Dashboard
Real-time comparison for every job: quoted price, actual cost (broken into labor, material, machine, subcontract, and overhead), and variance.
Color-coded alerts when actuals exceed quote by >8%.
Drill-down to individual time tickets, purchase orders, and machine run times.
Multi-Dimensional Profit Cube
Profit calculated and visualized across four dimensions simultaneously: – Job – Product family / part number – Machine / work center – Customer
Contribution margin, fully absorbed profit, and contribution per machine-hour metrics.
Quoting Intelligence Module
Historical cost database that auto-populates estimators’ quoting templates with actual average costs by part family, material, and machine.
“What-if” simulator showing margin impact of different markup scenarios.
Outcomes
Job-Level Profitability Visibility: The company can now see true profit performance for every job. By comparing quoted estimates to actual labor, material, and machine costs, managers quickly identify overruns, uncover cost drivers, and take corrective action before margins erode.
Customer & Product Margin Insights: Profitability is now measurable across customers and product lines. Leadership can identify high-margin relationships, reevaluate underperforming accounts, and prioritize the products and customers that drive sustainable growth.
Improved Quote Accuracy: With historical cost data and variance analysis built into the quoting process, estimates are grounded in real production performance. This reduces underquoting, protects margins, and increases confidence when bidding competitive work.
Faster, More Efficient Quoting: Automated cost calculations and standardized pricing inputs significantly reduce manual effort. Estimators can generate accurate quotes in less time, enabling faster response to customers and greater throughput without additional overhead.
Proven Results
Lasso Case Studies
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