monte-carlo-simulator
Version:
Business decision framework with Monte Carlo risk analysis - instant via npx
126 lines (107 loc) • 4.42 kB
YAML
name: Software Investment ROI Analysis
category: Technology Investment
description: Comprehensive ROI analysis for software investments including productivity gains, cost savings, automation benefits, and risk-adjusted returns
version: 1.0.0
tags: [software-investment, roi, productivity, automation, cost-savings, technology]
parameters:
- key: initialInvestment
label: Software Investment ($)
type: number
default: 250000
min: 10000
max: 10000000
step: 10000
description: Total software purchase, implementation, and training costs
- key: affectedEmployees
label: Employees Affected
type: number
default: 50
min: 5
max: 10000
step: 5
description: Number of employees who will use or benefit from the software
- key: averageEmployeeCost
label: Average Employee Cost ($/year)
type: number
default: 120000
min: 50000
max: 300000
step: 5000
description: Fully-loaded annual cost per affected employee
- key: productivityGain
label: Productivity Gain (%)
type: number
default: 15.0
min: 2.0
max: 50.0
step: 1.0
description: Expected productivity improvement per affected employee
- key: implementationMonths
label: Implementation Timeline (months)
type: number
default: 6
min: 1
max: 24
step: 1
description: Time required for full deployment and adoption
- key: adoptionRate
label: User Adoption Rate (%)
type: number
default: 85.0
min: 50.0
max: 100.0
step: 5.0
description: Expected percentage of users who will fully adopt the software
outputs:
- key: annualProductivitySavings
label: Annual Productivity Savings ($)
description: Annual savings from improved employee productivity
- key: annualMaintenanceCost
label: Annual Maintenance Cost ($)
description: Ongoing annual costs for software maintenance and support
- key: netAnnualBenefit
label: Net Annual Benefit ($)
description: Annual benefits minus maintenance costs
- key: simpleROI
label: Simple ROI (%)
description: First-year return on investment percentage
- key: paybackMonths
label: Payback Period (months)
description: Time to recover initial investment
- key: threeYearNPV
label: 3-Year NPV ($)
description: Net present value over three years (10% discount rate)
- key: implementationRisk
label: Implementation Risk Score (%)
description: Risk adjustment factor based on timeline and complexity
simulation:
logic: |
// Calculate annual productivity savings
const annualEmployeeCost = averageEmployeeCost
const productivitySavingPerEmployee = annualEmployeeCost * (productivityGain / 100)
const totalProductivitySavings = affectedEmployees * productivitySavingPerEmployee * (adoptionRate / 100)
// Add implementation risk and execution variance
const implementationRisk = Math.max(0.5, 1.0 - (implementationMonths - 6) * 0.05) // Longer implementations have more risk
const executionVariance = 0.7 + random() * 0.6 // ±30% execution variance
const realizedSavings = totalProductivitySavings * implementationRisk * executionVariance
// Calculate maintenance costs (20% of initial investment annually)
const annualMaintenanceCost = initialInvestment * 0.20
const netAnnualBenefit = realizedSavings - annualMaintenanceCost
// ROI calculations
const simpleROI = initialInvestment > 0 ? (netAnnualBenefit / initialInvestment) * 100 : 0
const paybackMonths = netAnnualBenefit > 0 ? (initialInvestment / (netAnnualBenefit / 12)) : 999
// 3-year NPV (10% discount rate)
const discountRate = 0.10
const year1NPV = netAnnualBenefit / (1 + discountRate)
const year2NPV = netAnnualBenefit / Math.pow(1 + discountRate, 2)
const year3NPV = netAnnualBenefit / Math.pow(1 + discountRate, 3)
const threeYearNPV = year1NPV + year2NPV + year3NPV - initialInvestment
return {
annualProductivitySavings: Math.round(realizedSavings),
annualMaintenanceCost: Math.round(annualMaintenanceCost),
netAnnualBenefit: Math.round(netAnnualBenefit),
simpleROI: Math.round(simpleROI * 10) / 10,
paybackMonths: Math.min(Math.round(paybackMonths * 10) / 10, 99.9),
threeYearNPV: Math.round(threeYearNPV),
implementationRisk: Math.round(implementationRisk * 100)
}