Pricing & Profitability
Gross Margin by DNet Pricing Policy & Buy Recommendation Engine
Pricing policy engine that turns Dealer Net, landed cost, deductions, margin targets, and DNet bucket rules into buy, hold, review, and exception recommendations.
Pricing policy command view
Gross Margin DNet Engine
DNet buckets, margin guardrails, cost deductions, action scoring, and buy recommendations
Validate
Inputs
Bucket
DNet ranges
Calculate
Margins
Score
Action logic
Recommend
Buy output
Policy controls
Recommendation preview
policy output| Part | DNet | Target | Net | Action |
|---|---|---|---|---|
| 100-4412 | $84.20 | 60% | 52% | Buy |
| 225-0198 | $142.50 | 60% | 47% | Review |
| 300-7710 | $39.80 | 55% | 50% | Hold |
| 410-2250 | $72.30 | 60% | 38% | Do Not Buy |
Recommendation outputs
Business problem
Procurement decisions needed margin-aware rules, Dealer Net guardrails, cost deductions, and structured recommendation outputs. Without a policy engine, buy decisions could be based on cost alone without enough visibility into gross margin, net margin, and profitability risk.
The process needed a repeatable way to evaluate whether a part could meet margin requirements after deductions and then translate that evaluation into a clear action recommendation.
System built
Built a pricing policy engine with validation, DNet bucket modeling, gross and net margin targets, cost deductions, .99 rounding, action scoring, and recommendation logic for buy, hold, review, and exception handling.
The system turns pricing rules into a controlled decision layer that helps procurement review profitability before committing to purchasing action.
Pricing policy signals
Signals reviewed
The engine evaluates price, cost, deduction, margin, bucket, and scoring signals before producing a recommendation.
Recommendation flow
How it works
Validate
Check source pricing fields, cost inputs, DNet values, and required columns before policy logic runs.
The engine starts with a QA-first validation layer so pricing decisions are based on usable source data.
Bucket
Group parts into DNet pricing buckets so margin rules can be applied consistently by price range.
Bucket modeling creates a structured pricing policy instead of treating every part as a one-off decision.
Calculate
Apply margin targets, cost deductions, transport, commission, overhead, and rounding rules.
The calculation layer converts raw price and cost data into gross margin, net margin, expected profit, and policy thresholds.
Score
Evaluate profitability, buy risk, pricing fit, and exception conditions to generate an action score.
The scoring layer helps separate strong buying opportunities from hold, review, or do-not-buy situations.
Recommend
Produce buy, hold, review, and exception outputs that procurement can use for decision support.
The final recommendations turn margin policy into an actionable output that can support purchasing workflows.
Policy layers
What the engine coordinates
Validation layer
Checks DNet, List, landed cost, required columns, and numeric fields before recommendation logic begins.
Policy layer
Applies DNet bucket rules, gross margin targets, net margin targets, deductions, and rounding behavior.
Scoring layer
Evaluates margin quality, profit potential, risk flags, and exception conditions for recommendation output.
Output layer
Generates action-ready files for buy, hold, review, do-not-buy, and exception review decisions.
Impact signals
What the policy engine improved
Margin guardrails for gross and net profitability decisions
Buy / hold / review recommendations with action scoring
DNet bucket modeling for structured pricing policy
Cost deductions for commission, transport, and overhead
Exception review outputs for risky or incomplete recommendations
Operational value
Procurement recommendations with margin discipline
Margin-aware buying
Helps procurement decisions account for gross and net margin targets instead of only focusing on availability or cost.
Cleaner policy execution
Turns pricing rules into repeatable logic that can be applied consistently across large part lists.
Better exception handling
Flags records that need review instead of letting weak or incomplete pricing data drive automatic decisions.
Action-ready outputs
Produces recommendation files that can support buy, hold, review, or do-not-buy workflows.
Why this project matters
Pricing policy turned into a repeatable decision engine.
This project shows how procurement decisions can become more disciplined when margin rules are converted into a structured engine. DNet buckets, deductions, targets, action scoring, and recommendation outputs create a clearer path from price data to buy decisions.
The value is not just calculating margin. The value is converting profitability policy into decisions that buyers can review, explain, and act on.
Confidentiality note
Visuals and descriptions are sanitized conceptual representations. They do not expose private company data, customer records, credentials, raw exports, internal pricing, operational screenshots, or proprietary source files.