"use strict";
/**
 * Workforce Planning Data Simulation
 *
 * Generates realistic hiring forecasts, skill gap analysis, turnover predictions,
 * compensation data, career paths, and diversity metrics for strategic HR planning.
 *
 * PRIVACY & ETHICS: This data is synthetic for planning purposes only.
 * Never use for actual hiring decisions or to bias against protected groups.
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.generateHiringForecast = generateHiringForecast;
exports.generateSkillGapAnalysis = generateSkillGapAnalysis;
exports.generateTurnoverPredictions = generateTurnoverPredictions;
exports.generateCompensationAnalysis = generateCompensationAnalysis;
exports.generateCareerPaths = generateCareerPaths;
exports.generateDiversityMetrics = generateDiversityMetrics;
exports.runAllWorkforcePlanningExamples = runAllWorkforcePlanningExamples;
const index_js_1 = require("../../src/index.js");
/**
 * Generate hiring needs forecasting data
 * Models future workforce requirements based on growth and attrition
 */
async function generateHiringForecast() {
    const synth = (0, index_js_1.createSynth)({
        provider: 'gemini',
        apiKey: process.env.GEMINI_API_KEY
    });
    const forecastSchema = {
        forecastPeriod: { type: 'string', required: true },
        department: { type: 'string', required: true },
        currentHeadcount: { type: 'number', required: true },
        projectedGrowth: { type: 'number', required: true }, // percentage
        expectedAttrition: { type: 'number', required: true }, // percentage
        hiringNeeds: {
            type: 'object',
            required: true,
            properties: {
                newRoles: { type: 'number' },
                backfills: { type: 'number' },
                total: { type: 'number' }
            }
        },
        roleBreakdown: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    role: { type: 'string' },
                    level: { type: 'string', enum: ['junior', 'mid', 'senior', 'lead', 'principal'] },
                    count: { type: 'number' },
                    priority: { type: 'string', enum: ['critical', 'high', 'medium', 'low'] },
                    timeToFill: { type: 'number' }, // days
                    difficulty: { type: 'string', enum: ['easy', 'moderate', 'challenging', 'very_challenging'] }
                }
            }
        },
        budgetRequired: { type: 'number', required: true },
        talentAvailability: {
            type: 'string',
            required: true,
            enum: ['abundant', 'adequate', 'limited', 'scarce']
        },
        competingEmployers: { type: 'number', required: true },
        risks: {
            type: 'array',
            required: true,
            items: { type: 'string' }
        }
    };
    const result = await synth.generateStructured({
        count: 40,
        schema: forecastSchema,
        format: 'json',
        context: `Generate hiring forecast data:
    - Growth: 10-30% annual for growth companies, 0-10% for mature
    - Attrition: 12-18% average (tech industry)
    - Senior roles: 60-90 days to fill, very challenging
    - Junior roles: 30-45 days to fill, moderate difficulty
    - Critical roles: ML engineers, cybersecurity, senior backend
    - Budget: $100K-$180K per engineer, $80K-$130K per designer
    - Talent scarcity: ML/AI scarce, frontend moderate, support abundant
    - Risks: competing offers, remote work expectations, skill gaps
    - Seasonal patterns: Q1 high, Q3 low
    - Include headcount changes month-by-month`
    });
    return result;
}
/**
 * Generate skill gap analysis data
 * Identifies current vs required skills for strategic planning
 */
async function generateSkillGapAnalysis() {
    const synth = (0, index_js_1.createSynth)({
        provider: 'gemini'
    });
    const skillGapSchema = {
        analysisDate: { type: 'string', required: true },
        department: { type: 'string', required: true },
        skillCategory: {
            type: 'string',
            required: true,
            enum: ['technical', 'leadership', 'domain', 'soft_skills', 'tools']
        },
        skills: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    skillName: { type: 'string' },
                    currentLevel: { type: 'number', min: 1, max: 5 },
                    requiredLevel: { type: 'number', min: 1, max: 5 },
                    gap: { type: 'number' },
                    employeesWithSkill: { type: 'number' },
                    employeesNeedingSkill: { type: 'number' },
                    criticality: { type: 'string', enum: ['critical', 'important', 'nice_to_have'] }
                }
            }
        },
        gapImpact: {
            type: 'string',
            required: true,
            enum: ['severe', 'moderate', 'minor', 'minimal']
        },
        closureStrategy: {
            type: 'array',
            required: true,
            items: {
                type: 'string',
                enum: ['training', 'hiring', 'contracting', 'partnering', 'outsourcing']
            }
        },
        timeToClose: { type: 'number', required: true }, // months
        investmentRequired: { type: 'number', required: true }, // dollars
        successProbability: { type: 'number', required: true } // percentage
    };
    const result = await synth.generateStructured({
        count: 100,
        schema: skillGapSchema,
        format: 'json',
        context: `Generate skill gap analysis:
    - Critical gaps (20%): Cloud architecture, ML/AI, cybersecurity
    - Important gaps (50%): Modern frameworks, data engineering, API design
    - Nice-to-have gaps (30%): Additional languages, tools, certifications
    - Average gap: 1.2 levels
    - Severe impact: blocks strategic initiatives
    - Training: 6-12 months for technical skills
    - Hiring: 3-6 months to close gaps
    - Contracting: immediate but expensive
    - Success probability: Training 70%, Hiring 85%, Contract 95%
    - Investment: $5K-$15K per person for training
    - Include emerging skills (AI, Web3, etc.)`
    });
    return result;
}
/**
 * Generate turnover prediction data
 * Models attrition risk and retention strategies
 */
async function generateTurnoverPredictions() {
    const synth = (0, index_js_1.createSynth)({
        provider: 'gemini'
    });
    const turnoverSchema = {
        employeeId: { type: 'string', required: true },
        predictionDate: { type: 'string', required: true },
        tenure: { type: 'number', required: true }, // years
        role: { type: 'string', required: true },
        level: { type: 'string', required: true },
        flightRiskScore: {
            type: 'number',
            required: true,
            min: 0,
            max: 100
        },
        riskCategory: {
            type: 'string',
            required: true,
            enum: ['low', 'medium', 'high', 'critical']
        },
        riskFactors: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    factor: { type: 'string' },
                    impact: { type: 'number', min: 0, max: 10 }
                }
            }
        },
        retentionActions: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    action: { type: 'string' },
                    effectiveness: { type: 'number', min: 0, max: 100 },
                    cost: { type: 'number' }
                }
            }
        },
        replacementCost: { type: 'number', required: true },
        businessImpact: {
            type: 'string',
            required: true,
            enum: ['critical', 'high', 'moderate', 'low']
        },
        probabilityOfLeaving: { type: 'number', required: true }, // 0-1
        timeframe: {
            type: 'string',
            required: true,
            enum: ['0-3_months', '3-6_months', '6-12_months', '12+_months']
        }
    };
    const result = await synth.generateStructured({
        count: 300,
        schema: turnoverSchema,
        format: 'json',
        context: `Generate turnover prediction data:
    - Overall risk: Low 60%, Medium 25%, High 12%, Critical 3%
    - Risk factors: Compensation (30%), growth (25%), manager (20%), workload (15%), culture (10%)
    - High risk: tenure 1-2 years or 5-7 years, below-market comp, low engagement
    - Retention actions: compensation adjustment (60% effective), promotion (70%), project change (40%)
    - Replacement cost: 1.5-2x annual salary
    - Critical business impact: key person dependencies, unique skills
    - Probability: High risk 60-80%, Medium 30-50%, Low <20%
    - Time patterns: Q1 and post-review periods highest risk
    - Include false positives and negatives for realism`
    });
    return result;
}
/**
 * Generate compensation analysis data
 * Models pay equity, market positioning, and adjustment needs
 */
async function generateCompensationAnalysis() {
    const synth = (0, index_js_1.createSynth)({
        provider: 'gemini'
    });
    const compensationSchema = {
        analysisId: { type: 'string', required: true },
        analysisDate: { type: 'string', required: true },
        jobFamily: { type: 'string', required: true },
        level: { type: 'string', required: true },
        location: { type: 'string', required: true },
        employeeCount: { type: 'number', required: true },
        salaryData: {
            type: 'object',
            required: true,
            properties: {
                min: { type: 'number' },
                max: { type: 'number' },
                median: { type: 'number' },
                mean: { type: 'number' },
                stddev: { type: 'number' }
            }
        },
        marketData: {
            type: 'object',
            required: true,
            properties: {
                p25: { type: 'number' },
                p50: { type: 'number' },
                p75: { type: 'number' },
                p90: { type: 'number' }
            }
        },
        positioning: {
            type: 'string',
            required: true,
            enum: ['below_market', 'at_market', 'above_market']
        },
        equityAnalysis: {
            type: 'object',
            required: true,
            properties: {
                genderPayGap: { type: 'number' }, // percentage
                minorityPayGap: { type: 'number' },
                tenureDisparity: { type: 'number' },
                equitable: { type: 'boolean' }
            }
        },
        adjustmentNeeds: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    employeeId: { type: 'string' },
                    currentSalary: { type: 'number' },
                    recommendedSalary: { type: 'number' },
                    adjustmentPercent: { type: 'number' },
                    reason: { type: 'string' }
                }
            }
        },
        budgetRequired: { type: 'number', required: true },
        complianceStatus: {
            type: 'string',
            required: true,
            enum: ['compliant', 'needs_review', 'non_compliant']
        }
    };
    const result = await synth.generateStructured({
        count: 50,
        schema: compensationSchema,
        format: 'json',
        context: `Generate compensation analysis data:
    - Market positioning: 40% at market, 35% below, 25% above
    - Target: P50-P65 of market for most roles
    - Critical roles: P75-P90
    - Gender pay gap: 2-8% (raw), 0-2% (adjusted for role/tenure)
    - Pay equity: Identify and flag >5% unexplained gaps
    - Adjustment needs: 25% of employees (2-15% increases)
    - Reasons: market adjustment, equity correction, retention risk
    - Budget: 2-4% of total compensation budget
    - Location variance: SF/NYC +30%, Austin +10%, Remote -5%
    - Include both base salary and total compensation
    - Flag compliance issues (pay transparency, equal pay)`
    });
    return result;
}
/**
 * Generate career progression path data
 * Models typical career ladders and advancement timelines
 */
async function generateCareerPaths() {
    const synth = (0, index_js_1.createSynth)({
        provider: 'gemini'
    });
    const careerPathSchema = {
        pathId: { type: 'string', required: true },
        jobFamily: { type: 'string', required: true },
        track: {
            type: 'string',
            required: true,
            enum: ['individual_contributor', 'management', 'technical_leadership']
        },
        levels: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    level: { type: 'string' },
                    title: { type: 'string' },
                    typicalTenure: { type: 'number' }, // years at level
                    keyCompetencies: {
                        type: 'array',
                        items: { type: 'string' }
                    },
                    salaryRange: {
                        type: 'object',
                        properties: {
                            min: { type: 'number' },
                            max: { type: 'number' }
                        }
                    }
                }
            }
        },
        totalCareerLength: { type: 'number', required: true }, // years entry to senior
        advancementRate: {
            type: 'object',
            required: true,
            properties: {
                fast: { type: 'number' }, // years
                typical: { type: 'number' },
                slow: { type: 'number' }
            }
        },
        transitionPoints: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    from: { type: 'string' },
                    to: { type: 'string' },
                    successRate: { type: 'number' }, // percentage
                    requiredDevelopment: {
                        type: 'array',
                        items: { type: 'string' }
                    }
                }
            }
        },
        lateralMoves: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    toJobFamily: { type: 'string' },
                    feasibility: { type: 'string', enum: ['common', 'possible', 'rare'] },
                    skillTransfer: { type: 'number' } // percentage
                }
            }
        }
    };
    const result = await synth.generateStructured({
        count: 30,
        schema: careerPathSchema,
        format: 'json',
        context: `Generate career path data:
    - IC track: Junior (1-2y) → Mid (2-4y) → Senior (3-5y) → Staff (4-6y) → Principal (5+y)
    - Management track: IC → Lead (3-5y) → Manager (2-3y) → Sr Mgr (3-4y) → Director (4-6y)
    - Fast advancement: Top 15%, 50% faster than typical
    - Slow advancement: Bottom 20%, 50% slower than typical
    - IC to management: 40% attempt, 70% succeed
    - Management to IC: 20% return, 90% succeed
    - Lateral moves: Engineering ↔ Product (possible), Sales ↔ Marketing (common)
    - Skill transfer: Same domain 80%, Adjacent 50%, Different 20%
    - Salary growth: 8-12% per promotion, 3-5% annual merit
    - Include diverse paths and non-linear progressions`
    });
    return result;
}
/**
 * Generate workforce diversity metrics
 * Models representation, inclusion, and equity indicators
 */
async function generateDiversityMetrics() {
    const synth = (0, index_js_1.createSynth)({
        provider: 'gemini'
    });
    const diversitySchema = {
        reportingPeriod: { type: 'string', required: true },
        organizationLevel: {
            type: 'string',
            required: true,
            enum: ['company', 'division', 'department', 'team']
        },
        entityName: { type: 'string', required: true },
        headcount: { type: 'number', required: true },
        demographics: {
            type: 'object',
            required: true,
            properties: {
                gender: {
                    type: 'object',
                    properties: {
                        women: { type: 'number' }, // percentage
                        men: { type: 'number' },
                        nonBinary: { type: 'number' },
                        undisclosed: { type: 'number' }
                    }
                },
                ethnicity: {
                    type: 'object',
                    properties: {
                        asian: { type: 'number' },
                        black: { type: 'number' },
                        hispanic: { type: 'number' },
                        white: { type: 'number' },
                        multiracial: { type: 'number' },
                        other: { type: 'number' },
                        undisclosed: { type: 'number' }
                    }
                },
                age: {
                    type: 'object',
                    properties: {
                        under30: { type: 'number' },
                        age30to40: { type: 'number' },
                        age40to50: { type: 'number' },
                        over50: { type: 'number' }
                    }
                }
            }
        },
        representation: {
            type: 'object',
            required: true,
            properties: {
                leadership: {
                    type: 'object',
                    properties: {
                        women: { type: 'number' },
                        minorities: { type: 'number' }
                    }
                },
                technical: {
                    type: 'object',
                    properties: {
                        women: { type: 'number' },
                        minorities: { type: 'number' }
                    }
                },
                hiring: {
                    type: 'object',
                    properties: {
                        women: { type: 'number' },
                        minorities: { type: 'number' }
                    }
                },
                promotions: {
                    type: 'object',
                    properties: {
                        women: { type: 'number' },
                        minorities: { type: 'number' }
                    }
                }
            }
        },
        inclusionMetrics: {
            type: 'object',
            required: true,
            properties: {
                belongingScore: { type: 'number', min: 0, max: 100 },
                psychologicalSafety: { type: 'number', min: 0, max: 100 },
                fairTreatment: { type: 'number', min: 0, max: 100 },
                voiceHeard: { type: 'number', min: 0, max: 100 }
            }
        },
        trends: {
            type: 'object',
            required: true,
            properties: {
                direction: { type: 'string', enum: ['improving', 'stable', 'declining'] },
                yearOverYearChange: { type: 'number' } // percentage points
            }
        },
        goals: {
            type: 'array',
            required: true,
            items: {
                type: 'object',
                properties: {
                    metric: { type: 'string' },
                    current: { type: 'number' },
                    target: { type: 'number' },
                    deadline: { type: 'string' }
                }
            }
        }
    };
    const result = await synth.generateStructured({
        count: 60,
        schema: diversitySchema,
        format: 'json',
        context: `Generate diversity metrics (based on tech industry benchmarks):
    - Overall women: 25-35% (improving +1-2% annually)
    - Technical women: 20-30%
    - Leadership women: 25-40%
    - Underrepresented minorities: 15-25%
    - Age distribution: 40% under 30, 35% 30-40, 20% 40-50, 5% over 50
    - Hiring: Should meet or exceed current representation
    - Promotions: Within ±3% of representation (equity indicator)
    - Inclusion scores: 70-85 range, correlates with diversity
    - Belonging: Higher in diverse teams (+10-15 points)
    - Pipeline challenge: Narrowing at senior levels
    - Goals: 40% women in tech by 2025 (aspirational)
    - Include intersectional data where appropriate`
    });
    return result;
}
/**
 * Run all workforce planning examples
 */
async function runAllWorkforcePlanningExamples() {
    console.log('=== Workforce Planning Simulation Examples ===\n');
    console.log('1. Generating Hiring Forecasts...');
    const hiring = await generateHiringForecast();
    console.log(`Generated ${hiring.data.length} hiring forecast records`);
    console.log('Sample:', JSON.stringify(hiring.data[0], null, 2));
    console.log('\n2. Generating Skill Gap Analysis...');
    const skills = await generateSkillGapAnalysis();
    console.log(`Generated ${skills.data.length} skill gap records`);
    console.log('Sample:', JSON.stringify(skills.data[0], null, 2));
    console.log('\n3. Generating Turnover Predictions...');
    const turnover = await generateTurnoverPredictions();
    console.log(`Generated ${turnover.data.length} turnover prediction records`);
    console.log('Sample:', JSON.stringify(turnover.data[0], null, 2));
    console.log('\n4. Generating Compensation Analysis...');
    const compensation = await generateCompensationAnalysis();
    console.log(`Generated ${compensation.data.length} compensation analysis records`);
    console.log('Sample:', JSON.stringify(compensation.data[0], null, 2));
    console.log('\n5. Generating Career Paths...');
    const careers = await generateCareerPaths();
    console.log(`Generated ${careers.data.length} career path records`);
    console.log('Sample:', JSON.stringify(careers.data[0], null, 2));
    console.log('\n6. Generating Diversity Metrics...');
    const diversity = await generateDiversityMetrics();
    console.log(`Generated ${diversity.data.length} diversity metric records`);
    console.log('Sample:', JSON.stringify(diversity.data[0], null, 2));
}
// Uncomment to run
// runAllWorkforcePlanningExamples().catch(console.error);
//# sourceMappingURL=workforce-planning.js.map