Peak Planning

Inventory Modeling Use Case

1. Executive Summary

The Challenge

Companies with seasonal demand patterns face critical inventory planning challenges during peak periods:

2-3% Cost Reduction
50-70% Markdown Reduction
5-10% Sales Increase

2. Decision Framework

Decision Framework: Peak Planning

Peak Planning Decision Matrix

When to Use Peak Planning

Seasonal demand patterns with predictable peaks. High demand variability between peak and off-peak periods. Capacity constraints during peak periods. Need to balance inventory costs with service levels. Planning for promotional events or product launches. Managing holiday or seasonal inventory buildup.

Alternative Approaches

Steady demand patterns: Constant inventory levels may be sufficient. High carrying costs: Consider just-in-time or 3PL for peak periods. Limited storage capacity: Use 3PL or temporary facilities. Short peak periods: May be more cost-effective to accept stockouts. High demand uncertainty: Focus on flexibility over optimization.

3. Strategy

Peak Planning Optimization Methodology

Using inventory modeling software, we developed optimized peak planning strategy:

Optimized Peak Planning Strategy

Inventory Build Timeline

Month Action Inventory Level Build Strategy Service Level Target
August Begin slow build 1.2x normal Start with long lead time items 96%
September Accelerate build 1.5x normal Build core SKUs 96%
October Peak build 2.2x normal Build all peak SKUs 95%
November Final build 2.8x normal Final adjustments 95%
December Peak period 2.5x normal Sell through 95%
January Aggressive clearance 1.0x normal Clear excess quickly 96%

Key Improvements

4. Use Case Example: Seasonal Retailer

Company Profile

Current State Analysis

Month Normal Demand Peak Demand Inventory Build Service Level Markdowns
January-September $15M/month — Normal levels 96% Low
October $15M $25M Start building 94% Low
November $15M $40M Peak build 91% Low
December $15M $52M Peak inventory 88% Low
January $15M $8M Excess inventory 98% High (30%)

Key Issues Identified

4.5 How Our Software Helps

Plan for seasonal demand peaks with confidence. Model inventory buildup, capacity constraints, and 3PL integration across multiple time periods to optimize peak season operations.

Seasonal Demand Visualization

Seasonal Demand Visualization

Time series chart showing demand patterns across 12 months. Identify peak periods, demand variability, and capacity constraints by time period.

Multi-Period Optimization

Multi-Period Optimization

Optimize inventory and capacity across all time periods simultaneously. See how inventory builds up before peak and depletes after, with optimal timing.

Capacity Utilization Analysis

Capacity Utilization Analysis

View capacity utilization by period to identify when facilities reach limits. Plan for 3PL integration or temporary capacity expansion during peak periods.

Peak Period Recommendations

Peak Period Recommendations

Automated recommendations for when to add 3PL capacity, build inventory, and scale back. Optimize costs while maintaining service during peak demand.

Key Software Features

5. Implementation Roadmap

Phase 1: Historical Analysis (Months 1-2)

Analyze 3-5 years of historical peak season data. Identify demand patterns by SKU and category. Calculate stockout costs and markdown costs. Model demand variability and forecast accuracy.

Phase 2: Optimization Modeling (Month 2-3)

Develop peak demand forecasts by SKU. Calculate optimal build quantities and timing. Model clearance strategies and pricing. Validate with business stakeholders.

Phase 3: Pre-Peak Preparation (Months 3-6)

Begin inventory builds per optimized plan. Monitor build progress vs. plan. Adjust as needed based on early demand signals. Prepare clearance plans for post-peak.

Phase 4: Peak Execution (Peak Season)

Monitor service levels and inventory levels daily. Make real-time adjustments as needed. Track performance vs. plan. Begin clearance planning for slow movers.

Phase 5: Post-Peak Clearance (Post-Peak)

Execute aggressive clearance strategy. Monitor markdown effectiveness. Return to normal inventory levels quickly. Capture lessons learned for next year.

Key Success Factors

  • Start planning early (6+ months before peak)
  • Use data-driven forecasts, not intuition
  • Monitor and adjust throughout peak season
  • Execute aggressive clearance to minimize markdowns
  • Learn and improve each year

Key Success Factors

  • Start planning early (6+ months before peak)
  • Use data-driven forecasts, not intuition
  • Monitor and adjust throughout peak season
  • Execute aggressive clearance to minimize markdowns
  • Learn and improve each year

Best Practices

  • Early Planning: Start peak planning 6+ months before peak season. Early planning enables better execution.
  • Data-Driven Forecasting: Use statistical forecasting models, not rules of thumb. Historical patterns inform future peaks.
  • Phased Build Strategy: Build inventory gradually, starting with long lead time items. Avoid last-minute rushes.
  • Real-Time Monitoring: Monitor demand and inventory daily during peak. Make adjustments quickly as patterns emerge.
  • Aggressive Clearance: Clear excess inventory quickly post-peak. Better to take markdowns early than hold inventory.
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