Overview

AUTO-REPR-070, Evaluate Mobile Service Feasibility, ranks candidate mobile repair or maintenance service models by market demand, startup cost, margin potential, technician availability, service scope fit, and operational risk.

Decision ID

AUTO-REPR-070

Decision Name

Evaluate Mobile Service Feasibility

Decision Preparation Model

auto-repair-evaluate-mobile-service-feasib-auto-repr-070 version 1.0.0

Default Profile

balanced

Default Scenario

standard

Catalog

DKR-AUTO-RUNTIME-001, active Decision Catalog runtime catalog

Understanding This Decision

Evaluate Mobile Service Feasibility helps an auto repair business compare possible mobile service models before investing in vehicles, technicians, scheduling capacity, and operating processes.

Criteria

Criterion IDs are intentionally stable machine identifiers. Display labels are for users; request values should be keyed by canonical criterionId.

Data Preparation Guide

Constraint Processing

This decision includes a catalog-defined hard constraint. Decision Catalog returns the constraint with the decision detail, and Decision Service evaluates it before criteria-based ranking.

{
  "constraintSummary": {
    "definedConstraintCount": 1,
    "activeConstraintCount": 1,
    "eligibleOptionCount": 2,
    "excludedOptionCount": 1,
    "eligibleOptions": [
      "option-1",
      "option-2"
    ],
    "excludedOptions": [
      {
        "optionId": "option-3",
        "reasons": [
          "Operational risk score is above the maximum threshold of 40."
        ]
      }
    ]
  }
}

const constraints = decisionDetail.constraints || [];
if (constraints.length > 0) {
  // Decision Service applies catalog constraints before ranking.
  // Excluded candidates appear in response.constraintSummary.excludedOptions.
}

const summary = response.constraintSummary;
const excluded = response.decisionResult?.excludedOptions || [];

Excluded candidates do not participate in ranking because hard constraints are evaluated before scoring. For AUTO-REPR-070, option-3 is excluded from the showcase sample because its operational risk score is above the catalog threshold.

Decision Preparation Model

The published Decision Preparation Model validates and transforms illustrative Business Data into the canonical criteria required by AUTO-REPR-070 before Decision Service applies ranking.

Profile ID

auto-repair-evaluate-mobile-service-feasib-auto-repr-070

Version

1.0.0

Options Path

$.repairOrders

Option ID Path

$.optionId

Display Name Path

$.name

{
  "requestContext": {
    "sourceSystem": "automotive-profile-factory",
    "correlationId": "factory-auto-repr-070"
  },
  "repairOrders": [
    {
      "optionId": "REPAIR-070-01",
      "name": "Evaluate Mobile Service Feasibility Option 1",
      "assessment": {
        "marketDemandScore": 81.0,
        "marginPotentialScore": 65.0,
        "technicianAvailabilityScore": 56.0
      },
      "financial": {
        "startupCost": 50000.0
      },
      "business": {
        "performance": {
          "serviceScopeFit": 43.0
        }
      },
      "risk": {
        "operationalRiskScore": 36.0
      }
    },
    {
      "optionId": "REPAIR-070-02",
      "name": "Evaluate Mobile Service Feasibility Option 2",
      "assessment": {
        "marketDemandScore": 49.0,
        "marginPotentialScore": 34.0,
        "technicianAvailabilityScore": 42.0
      },
      "financial": {
        "startupCost": 147500.0
      },
      "business": {
        "performance": {
          "serviceScopeFit": 28.0
        }
      },
      "risk": {
        "operationalRiskScore": 34.0
      }
    }
  ]
}

{
  "optionId": "REPAIR-070-01",
  "name": "Evaluate Mobile Service Feasibility Option 1",
  "values": {
    "market_demand_score": 81.0,
    "startup_cost": 50000.0,
    "margin_potential_score": 65.0,
    "technician_availability_score": 56.0,
    "service_scope_fit": 43.0,
    "operational_risk_score": 36.0
  }
}

Profiles and Scenarios

This example selects a Profile for evaluation emphasis and a Scenario for operating context. Discover both from the selected Decision Catalog definition.

Prepared Input

For Prepared Criteria Mode, send decisionId, selected profile/scenario IDs, and option values keyed by canonical criterion ID. The showcase request includes two eligible mobile service models and one excluded model so constraint handling is visible.

{
  "decisionId": "AUTO-REPR-070",
  "mappingProfileId": "auto-repair-evaluate-mobile-service-feasib-auto-repr-070",
  "mappingProfileVersion": "1.0.0",
  "profileId": "balanced",
  "scenarioId": "standard",
  "algorithm": "TOPSIS",
  "weightStrategy": "Expert",
  "runSensitivity": false,
  "requestContext": {
    "correlationId": "auto-repr-070-demo-001"
  },
  "options": [
    {
      "optionId": "option-1",
      "name": "Mobile Battery Replacement",
      "values": {
        "market_demand_score": 88,
        "startup_cost": 450,
        "margin_potential_score": 88,
        "technician_availability_score": 88,
        "service_scope_fit": 88,
        "operational_risk_score": 20
      }
    },
    {
      "optionId": "option-2",
      "name": "Mobile Tire Service",
      "values": {
        "market_demand_score": 81,
        "startup_cost": 750,
        "margin_potential_score": 81,
        "technician_availability_score": 81,
        "service_scope_fit": 81,
        "operational_risk_score": 35
      }
    },
    {
      "optionId": "option-3",
      "name": "Mobile Maintenance Van",
      "values": {
        "market_demand_score": 74,
        "startup_cost": 1100,
        "margin_potential_score": 74,
        "technician_availability_score": 74,
        "service_scope_fit": 74,
        "operational_risk_score": 50
      }
    }
  ]
}

Execute

curl -X POST "https://dde.vinquery.com/api/v1/decide" \
  -H "Authorization: Bearer ${DECISIOQ_TOKEN}" \
  -H "Content-Type: application/json" \
  -H "X-Correlation-Id: auto-repr-070-demo-001" \
  --data @auto-repr-070-execute.json

Decision Service retrieves authoritative criteria, constraints, and validation metadata from Decision Catalog, executes the deterministic ranking, and returns the decision result plus execution metadata.

Interpret the Result

The successful response includes both the deterministic decisionResult and a top-level explanation object. The explanation adds context but does not select, rerank, recalculate, or override the ranking.

{
  "success": true,
  "decisionType": "AUTO-REPR-070",
  "decisionResult": {
    "winner": "option-1",
    "ranking": [
      {
        "optionId": "option-1",
        "name": "Mobile Battery Replacement",
        "score": 0.91
      },
      {
        "optionId": "option-2",
        "name": "Mobile Tire Service",
        "score": 0.84
      }
    ],
    "excludedOptions": [
      {
        "optionId": "option-3",
        "reasons": [
          "Operational risk score is above the maximum threshold of 40."
        ]
      }
    ]
  },
  "explanation": {
      "summary": "The selected mobile service model provided the strongest overall fit.",
      "whyRecommended": "It combined strong availability and quality with acceptable cost.",
      "keyDrivers": [],
      "tradeoffs": [],
      "competitors": [],
      "sensitivitySummary": "Sensitivity analysis was not included in this response.",
      "scenarioSummary": "The standard scenario was selected.",
      "risks": [],
      "nextSteps": [],
      "assumptions": []
  },
  "constraintSummary": {
    "definedConstraintCount": 1,
    "activeConstraintCount": 1,
    "eligibleOptionCount": 2,
    "excludedOptionCount": 1
  },
  "requestContext": {
    "correlationId": "auto-repr-070-demo-001"
  }
}

Sensitivity Analysis

runSensitivity is an optional execution flag supported by both Business Data Mode and Prepared Criteria Mode. Set it to true when the client wants recommendation-stability information in the same response. The sensitivity engine analyzes the prepared criteria produced by either input path, perturbs criterion weights by controlled factors, and reports whether the winner remains stable.

{
  "sensitivityResult": {
    "stableWinner": true,
    "winner": "option-1",
    "mostSensitiveCriterion": "startup_cost",
    "confidence": 95,
    "criterionImpacts": {
      "startup_cost": 0.1842
    },
    "winnerChangeCounts": {
      "startup_cost": 0
    }
  }
}

Use sensitivity output to decide whether a recommendation is robust enough for automation or should be reviewed by a person.

Explanation of Decision Result

Every successful response includes a provider-neutral top-level explanation object. It explains the already-finalized deterministic result and does not change the ranking.

{
  "explanation": {
      "summary": "The selected mobile service model provided the strongest overall fit.",
      "whyRecommended": "It combined strong availability and quality with acceptable cost.",
      "keyDrivers": [],
      "tradeoffs": [],
      "competitors": [],
      "sensitivitySummary": "Sensitivity analysis was not included in this response.",
      "scenarioSummary": "The standard scenario was selected.",
      "risks": [],
      "nextSteps": [],
      "assumptions": []
  }
}

Tracing and Logs

Use request identifiers to connect client, catalog, and execution activity during support or integration testing.

X-Request-Id

Optional client-supplied request ID. If omitted, the server generates one.

X-Correlation-Id

Optional client correlation value propagated from Decision Service to Decision Catalog.

requestContext.correlationId

Optional payload value echoed in the response and used for tracing.

configurationUsed

The authoritative effective weight strategy, ranking algorithm, profile, scenario, sensitivity setting, and source for each value.

diagnostics

Safe execution counts returned only when responseOptions.includeDiagnostics is true.

Code Examples

These examples demonstrate the current DecisioQ flow:

1. Request jwtToken from https://identity.vinquery.com/connect/token.
2. Load AUTO-REPR-070 metadata from https://dks.vinquery.com/decisioncatalog/decisions/AUTO-REPR-070.
3. Execute a Prepared Criteria Mode at https://dde.vinquery.com/api/v1/decide.

Set these environment variables before running any companion example:

DECISIOQ_CLIENT_ID
DECISIOQ_CLIENT_SECRET
DECISIOQ_AUDIENCE=vinquery:api:decisioq

Optional:
DECISIOQ_IDENTITY_URL=https://identity.vinquery.com/connect/token
DECISIOQ_DKS_URL=https://dks.vinquery.com
DECISIOQ_DDE_URL=https://dde.vinquery.com

Download the source files directly:

Troubleshooting

Production Checklist