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CNFANS Spreadsheet: Mastering Seasonal Shipping Cost Trends

2025-12-18

In global trade, shipping costs are rarely static. They ebb and flow with market demand, fuel prices, and crucially, seasonal cycles. For businesses, anticipating these fluctuations is key to budgeting and maintaining competitiveness. The CNFANS Freight Spreadsheet provides a structured framework to track these trends and turn historical data into actionable forecasts.

The Power of Quarterly Freight Analysis

Breaking down the year into quarters (Q1: Jan-Mar, Q2: Apr-Jun, Q3: Jul-Sep, Q4: Oct-Dec) creates a clear lens for viewing cost patterns. Each quarter often carries its own logistical signature:

  • Q1:
  • Q2:
  • Q3:Peak Season
  • Q4:

How to Track with the CNFANS Spreadsheet

Your CNFANS spreadsheet is more than a ledger; it's a forecasting tool. Here’s a step-by-step method:

Step 1: Consistent Data Entry

For every shipment, log the final freight cost, ship date (which determines its quarter), route (e.g., Shanghai to LA), and service type (e.g., Air Express, FCL Ocean).

Step 2: Calculate Quarterly Averages

At the end of each quarter, use formulas to calculate the average cost per major lane and service.

=AVERAGEIFS(Cost_Column, Date_Column, ">=Q1 Start", Date_Column, "<=Q1 End", Route_Column, "Specific Route")

Step 3: Visualize the Trends

Create a line chart comparing the quarterly average costs across two or more years. This visualization makes the seasonal spike in Q3 immediately apparent and reveals if the baseline cost is rising year-over-year.

Step 4: Compare and Anticipate

This is the critical action phase. When planning for Q3 2024, look back at the rate increases from Q2 to Q3 in 2023 and 2022. For example:

Route Q2 2023 Avg. Q3 2023 Avg. % Increase Forecast Implication for Q3 2024
Shanghai to Hamburg (Ocean) $2,800 /container $4,500 /container +60% Budget at least a 50-65% premium over Q2 2024 rates.

Pro Tips for Accurate Forecasting

Segment Your Data:

Note Anomalies:

Factor in Macro Trends:

Conclusion

By systematically comparing quarterly freight averages