PredictDelays

The Problem

Tender professionals struggle to accurately forecast project schedule delays during bid preparation, leading to unrealistic timelines and costly contract penalties. Existing approaches rely on manual spreadsheet calculations that fail to systematically incorporate historical performance data with current project risk factors, resulting in inconsistent and overly optimistic predictions.

The Solution

This MCP tool calculates delay probability and duration by applying statistical analysis to historical delay patterns while adjusting for current project conditions. It provides instant, deterministic calculations that an AI assistant can invoke to generate data-driven delay forecasts during tender evaluation and planning phases.

Input Parameters

Functional Requirements (Structured)

FR-001: Base Delay Calculation

• Inputs: historical_delay_rate, project_complexity
• Output: base_delay (number, %)
• Constraint: range [0%, 40%]
• Formula hint: base_delay = historical_delay_rate × complexity_multiplier (1.0 for Low, 1.3 for Medium, 1.7 for High)

FR-002: Risk Adjustment Factor

• Inputs: risk_factor_count
• Output: risk_adjustment (number, %)
• Constraint: range [0%, 25%]
• Formula hint: risk_adjustment = min(25, risk_factor_count × 2.5)

FR-003: Resource Readiness Impact

• Inputs: resource_readiness
• Output: resource_impact (number, %)
• Constraint: range [-10%, 20%]
• Formula hint: resource_impact = ((100 - resource_readiness) / 5) with upper/lower bounds

FR-004: Weather Impact Calculation

• Inputs: weather_sensitivity, project_complexity
• Output: weather_impact (number, %)
• Constraint: range [0%, 15%]
• Formula hint: weather_impact = weather_sensitivity × 0.1 × complexity_factor (1.0/1.²⁄₁.5)

FR-005: Total Predicted Delay

• Inputs: base_delay, risk_adjustment, resource_impact, weather_impact
• Output: total_delay_percentage (number, %)
• Constraint: range [0%, 75%]
• Formula hint: total_delay_percentage = base_delay + risk_adjustment + resource_impact + weather_impact

FR-006: Absolute Delay Days

• Inputs: total_delay_percentage, regulatory_approval_days
• Output: predicted_delay_days (integer, days)
• Constraint: minimum 0 days
• Formula hint: predicted_delay_days = round((regulatory_approval_days × total_delay_percentage/100) + (total_delay_percentage × 2))

Calculation Dependencies

base_delay <- (historical_delay_rate, project_complexity)
risk_adjustment <- (risk_factor_count)
resource_impact <- (resource_readiness)
weather_impact <- (weather_sensitivity, project_complexity)
total_delay_percentage <- (base_delay, risk_adjustment, resource_impact, weather_impact)
predicted_delay_days <- (total_delay_percentage, regulatory_approval_days)

Output Results

Validation Test Cases

Test Case 1: Standard Commercial Construction

Inputs:
  project_complexity: Medium
  historical_delay_rate: 12
  risk_factor_count: 4
  resource_readiness: 85
  regulatory_approval_days: 60
  weather_sensitivity: 40

Expected Outputs:
  base_delay: 15.6
  risk_adjustment: 10.0
  resource_impact: 3.0
  weather_impact: 4.8
  total_delay_percentage: 33.4
  predicted_delay_days: 62

Test Case 2: High-Risk Infrastructure Project

Inputs:
  project_complexity: High
  historical_delay_rate: 25
  risk_factor_count: 8
  resource_readiness: 65
  regulatory_approval_days: 120
  weather_sensitivity: 70

Expected Outputs:
  base_delay: 42.5
  risk_adjustment: 20.0
  resource_impact: 7.0
  weather_impact: 10.5
  total_delay_percentage: 65.0
  predicted_delay_days: 180

Test Case 3: Simple Low-Risk Project

Inputs:
  project_complexity: Low
  historical_delay_rate: 8
  risk_factor_count: 1
  resource_readiness: 95
  regulatory_approval_days: 30
  weather_sensitivity: 10

Expected Outputs:
  base_delay: 8.0
  risk_adjustment: 2.5
  resource_impact: 1.0
  weather_impact: 1.0
  total_delay_percentage: 12.5
  predicted_delay_days: 24

Domain Expertise

• Historical delay rates for construction projects typically range from 5-25% depending on sector
• Regulatory approval processes often account for 20-40% of total project timeline risk
• High-complexity projects exhibit 1.5-2.0x higher delay probabilities than low-complexity equivalents
• Resource readiness below 70% typically adds ≥5% to predicted delays
• Each identified high-risk factor generally contributes 2-3% additional delay probability
• Industry benchmarks suggest contingency planning should cover up to 75% delay scenarios

Who Uses This Tool

Tender managers, bid managers, and project planners in construction, engineering, and infrastructure sectors use this tool during proposal development to generate realistic schedule forecasts. They need quick, consistent delay predictions to develop competitive yet achievable timelines without manual data analysis or complex spreadsheet modeling.