Create a demand forecast using sell-in and sell-through numbers
To plan for future inventory, we would need to know what is our sell-through data (what's sold to end customers) and then roll that forecast up to determine the sel in-figures (what retailers or suppliers should order)
Why Forecast with Sell-Through Data?
- Actual Consumer Demand:
Sell-through represents real sales, giving you a true picture of what customers are buying. - Accurate Insights:
Unlike sell-in numbers—which can be influenced by ordering practices—sell-through data reflects genuine market behavior. - Informed Decisions:
Forecasts based on sell-through help prevent overstocking or stockouts, aligning your inventory with customer needs.
Turning Demand into Manufacturing Orders
1. Analyze Historical Sell-Through Data
- Collect Past Sales:
Review historical sell-through data to identify patterns and trends. - Identify Seasonality and Promotions:
Note that periods of high or low demand are influenced by seasons or special events.
2. Forecast Future Sell-Through
- Project Future Sales:
Use historical trends to estimate future sell-through. Ask yourself: “What will customers likely buy next month or quarter?” - Consider Market Changes:
Factor in any upcoming promotions, economic shifts, or market trends that could affect consumer behavior.
3. Adjust for Buffer and Safety Stock
- Safety Stock:
Include a cushion for unexpected demand spikes or supply delays. - Buffer Orders:
Ensure your sell in orders account for the safety stock needed to maintain service levels.
4. Roll Up to Sell In
- Convert Demand to Orders:
Once you have your sell through forecast, translate that into the number of units that need to be ordered from suppliers. - Align with Inventory Policies:
Adjust the raw forecast based on lead times, minimum order quantities, and supplier agreements.
5. Communicate and Collaborate
- Share Forecasts:
Work with sales, marketing, and procurement teams to ensure everyone is aligned. - Continuous Review:
Regularly update forecasts with the latest sell-through data to keep orders accurate and timely.
Challenges in Rolling Up Forecasts from Sell-Through to Sell-In
Data Quality & Timeliness
- Inconsistent Data:
Sell through data may come from different sources or systems that record sales at different times, leading to discrepancies. - Delayed Reporting:
If sales data isn’t updated regularly, your forecast may be based on outdated information, reducing accuracy.
Timing and Lead Time Differences
- Lag Between Sales and Orders:
Sell-through data reflects past consumer behavior, while sell-in orders need to account for future demand. This time gap can cause forecasting errors. - Variable Lead Times:
Different products or suppliers have varying lead times. Adjusting your forecast to account for these differences can be challenging
Demand Variability and External Factors
- Seasonality and Promotions:
Sales can spike during certain seasons or promotions. Accurately rolling up these peaks into sell in orders requires careful adjustment. - Market Fluctuations:
Economic changes or competitive actions can suddenly alter consumer behavior, making it harder to translate sell through trends into reliable sell in figures.
Aggregation and Granularity Issues
- Multiple Channels and Regions:
When you combine data from various sales channels or geographic regions, maintaining consistency across your forecast becomes more difficult. - SKU-Level Variability:
Individual products might have unique sales patterns that don’t translate well when aggregated into a single forecast.
Collaboration and Communication
- Cross-Functional Alignment:
Ensuring that sales, marketing, demand planning, and procurement teams share the same data and assumptions is critical. Misalignment can lead to overstocking or stockouts. - Supplier Constraints:
Supplier minimum order quantities (MOQ) or production limits may restrict how accurately you can roll up your forecast into sell in orders.
Example of how to determine manufacturing order based on sell-through data
Below is an example of how you might structure a table to roll up your monthly sell through forecasts into a sell in requirement and ultimately determine your manufacturing quantity. In this example, we use a three-month period (Q1) with sample numbers:
| Month | Sell Through Forecast (units) |
|---|---|
| January | 1,000 |
| February | 1,200 |
| March | 1,100 |
| Q1 Total | 3,300 |
Roll-Up Calculation from Sell-Through to Manufacturing
| Step/Metric | Calculation | Example Value (units) |
|---|---|---|
| Total Sell Through Forecast (Q1) | Sum of monthly forecasts | 3,300 |
| Safety Stock (10% of Q1 Forecast) | 10% × 3,300 | 330 |
| Total Forecast Demand | Sell Through Forecast + Safety Stock | 3,300 + 330 = 3,630 |
| Sell In Requirement | Total Forecast Demand + Additional Buffer* | 3,630 + 0 = 3,630 |
| Current Inventory | Given (e.g., current stock on hand) | 2000 |
| Manufacturing Quantity Needed | Sell In Requirement − Current Inventory | 3,630 − 2000 = 1,630 |
* In this example, we assume no additional buffer beyond the safety stock.
| Month | Sell‑Through Forecast (units) |
Safety Stock (10%) |
Sell‑In Forecast (Demand) |
Starting Inventory (units) |
Shortfall / Manufacturing Order Request (units) |
Order Placement Date |
|---|---|---|---|---|---|---|
| January | 800 | 80 | 880 | 2,000 | 0 | – |
| February | 850 | 85 | 935 | 2,000 - 880 = 1,120 | 0 | – |
| March | 900 | 90 | 990 | 1,120 - 935 = 185 | 990 - 185 = 805 | January 11–12 |
| April | 950 | 95 | 1,045 | 0* | 1,045 | January 11–12 |
| May | 1,000 | 100 | 1,100 | 0 | 1,100 | January 11–12 |
| June | 1,050 | 105 | 1,155 | 0 | 1,155 | January 11–12 |
| July | 1,100 | 110 | 1,210 | 0 | 1,210 | January 11–12 |
| August | 1,150 | 115 | 1,265 | 0 | 1,265 | January 11–12 |
| September | 1,200 | 120 | 1,320 | 0 | 1,320 | January 11–12 |
| October | 1,250 | 125 | 1,375 | 0 | 1,375 | January 11–12 |
| November | 1,300 | 130 | 1,430 | 0 | 1,430 | January 11–12 |
| December | 1,350 | 135 | 1,485 | 0 | 1,485 | January 11–12 |
| Annual Totals | 12,900 | 1,290 | 14,190 | – | 12,190 | – |
*Note: After February, the starting inventory is fully depleted.
Assumptions made for this example
- Starting Inventory: There are 2,000 units available at the beginning of January.
- Consumption Pattern: Inventory is consumed following a FIFO (First In, First Out) approach.
- Lead Time: There is a 60‑day lead time from order placement to when new stock arrives.
- Order Time: New orders must be placed 60 days before the anticipated stockout date to ensure timely delivery.
- Inventory Depletion Calculation: It is assumed that consumption is uniform within each month (e.g., for March, 900 units are spread evenly over the month, allowing an estimation of the stockout date
- Single Order Assumption: The analysis assumes that a single manufacturing order is placed to cover all shortfalls (i.e., one order to cover March's shortfall plus the remaining demand from April to December)
Next Steps
Build your demand forecast using your sell-in and sell-through numbers!
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