const demandForecast = {
timeHorizon: {
years: 3,
periods: 'quarterly'
},
baseScenario: {
revenuegrowth: [10, 12, 15],
productivityImprovement: [2, 3, 3]
},
departmentModels: [
{
department: 'Engineering',
driver: 'product-roadmap',
currentHeadcount: 100,
projectedGrowth: [15, 20, 25]
},
{
department: 'Sales',
driver: 'revenue-ratio',
revenuePerSalesperson: 1000000,
projectedRevenue: [50000000, 60000000, 75000000]
},
{
department: 'Customer Success',
driver: 'customer-ratio',
customersPerCSM: 50,
projectedCustomers: [500, 650, 850]
}
],
assumptions: {
attrition: 15,
internalMobility: 10,
leadTime: 90
}
};
const gapAnalysis = {
planning Period: '2026-2028',
scope: 'critical-skills',
skills: [
{
name: 'Machine Learning',
currentSupply: 10,
futuredemand: { y1: 15, y2: 25, y3: 40 },
internalPipeline: 3,
externalAvailability: 'scarce'
},
{
name: 'Cloud Architecture',
currentSupply: 20,
futureDemand: { y1: 25, y2: 30, y3: 35 },
internalPipeline: 5,
externalAvailability: 'moderate'
}
],
strategies: {
build: { timeToReady: 18, costPerPerson: 25000 },
buy: { timeToHire: 4, costPerHire: 50000 },
borrow: { availability: 'contractors', premiumRate: 1.5 }
}
};
