- MySales enforces Shelf Term limits to prevent orders from exceeding what can realistically sell before expiration, eliminating manual guesswork for perishable categories.
- Write-off forecasting operates in two horizons -- D1 (losses before next delivery) and D1D2 (losses between the next two deliveries) -- and feeds directly into the order formula.
- The system automatically compensates for anticipated spoilage by increasing order quantities, while simultaneously capping orders at shelf-life-safe maximums.
- Shelf Term values default from product master data but can be overridden per SKU and store for precise local control.
Perishable goods represent one of the highest-risk categories in retail replenishment. Order too much, and product expires on the shelf. Order too little, and lost sales erode margin.
MySales resolves this tension through automated shelf-life accounting and write-off forecasting -- ensuring every order reflects both the realistic sales window and the expected spoilage rate for each SKU at each location.
How MySales Accounts for Shelf-Life
MySales enforces shelf-life constraints through a single, powerful parameter: Shelf Term.
Shelf Term defines the maximum number of days of forward demand that inventory should cover. It directly governs how much stock the system will allow for any given product, preventing accumulation beyond what can sell before expiration.
The calculation is straightforward:
Maximum Stock = Average Daily Forecast x Shelf Term (in days)
The Shelf Term constraint is not static. MySales recalculates the permissible maximum stock dynamically, incorporating seasonal demand shifts and active promotional forecasts. During a promotional peak, the allowable maximum rises accordingly -- the system does not artificially suppress orders when genuine demand justifies higher volumes.
By default, Shelf Term values are inherited from product master data. However, they can be manually overridden at the SKU-store level through the Replenishment > Item Parameters screen, giving category managers precise control where local conditions differ from the default.
The system also enforces a minimum order of one package per SKU, even when the forecast approaches zero. This prevents complete stock-outs on slow-moving items that still require shelf presence.
Understanding D1 and D1D2 Write-Offs
MySales forecasts product losses -- write-offs -- across two distinct time horizons, each serving a different role in the order calculation:
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D1 Write-Offs represent the forecast of product losses that will occur between now and the next delivery date (D1). These are units currently in stock that are expected to expire or be disposed of before the next shipment arrives.
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D1D2 Write-Offs represent the forecast of losses during the period between the next delivery (D1) and the subsequent delivery (D2). These account for spoilage of both existing and newly received stock during that window.
This dual-horizon approach ensures the system has full visibility into expected losses across the entire replenishment cycle.
How Write-Offs Drive the Order Formula
Write-offs are not informational -- they are computational. Both categories feed directly into the order calculation, but through different mechanisms.
D1 write-offs reduce the projected stock at D1. When the system estimates how much inventory will be available when the next delivery arrives, it subtracts expected losses. Lower projected stock means a higher required order.
D1D2 write-offs are added directly to the order formula as incremental demand. The system treats anticipated spoilage during the next sales period as volume that must be replaced, increasing the order accordingly.
These two mechanisms create a balanced system. D1 write-offs ensure the system does not overestimate available inventory, while D1D2 write-offs ensure the system orders enough to cover both real consumer demand and expected losses. The result: orders that are neither inflated by stale stock assumptions nor deflated by ignoring spoilage.
Worked Example: Order Calculation with Shelf-Life and Write-Offs
A retail store needs to place an order for a perishable dairy product. The following parameters are known at the time of order calculation.
Input Data:
| Parameter | Value |
|---|---|
| D1D2 forecast (until next delivery) | 100 units |
| D1D2 write-offs | 10 units |
| Safety stock | 15 units |
| Presentation stock | 5 units |
| Current stock (Stock O1) | 90 units |
| Forecast to D1 (Forecast O1D1) | 50 units |
| Write-offs to D1 (Write-offs O1D1) | 5 units |
| In-transit supply (In-transit O1D1) | 10 units |
| Shelf Term | 14 days |
| Average daily forecast | 10 units/day |
Step 1 -- Calculate available stock at D1:
Stock D1 = Stock O1 - Forecast O1D1 - Write-offs O1D1 + In-transit O1D1 = 90 - 50 - 5 + 10 = 45 units
The system projects that 45 units will be available when the next delivery arrives, after accounting for expected sales, spoilage, and inbound shipments.
Step 2 -- Calculate the shelf-life limit:
Shelf-life limit = Average daily forecast x Shelf Term = 10 x 14 = 140 units
This is the absolute ceiling -- the system will never order beyond this quantity regardless of other formula outputs.
Step 3 -- Calculate the order:
Order = D1D2 forecast + D1D2 write-offs + Safety stock + Presentation stock - Stock D1 = 100 + 10 + 15 + 5 - 45 = 85 units
The calculated order of 85 units falls below the shelf-life limit of 140 units, so the constraint does not apply. The final order stands at 85 units. Had the calculation yielded a result above 140, the system would have capped the order at 140 units to prevent shelf-life risk.
Configuring Shelf Term
The Shelf Term parameter is managed through the Replenishment > Item Parameters screen. Users can locate the relevant SKU and store combination, then set the desired number of days in the Shelf Term column -- for example, 14 days for a fresh dairy product or 3 days for a short-life prepared meal.
Once saved, the manual value overrides the default from product master data for that specific SKU-store combination. All other locations continue using the master data default. This granular control is particularly valuable for stores with atypical turnover patterns, such as low-traffic locations where the same product requires a shorter Shelf Term to avoid waste.
Monitoring Write-Off Forecasts
Write-off data is visible at the order line level through the Order > Lines screen. For any SKU and store combination, two dedicated columns display the system's loss forecasts:
- Write-off D1 shows the projected losses before the next delivery, which reduce the system's estimate of available stock.
- Write-off D1D2 shows the projected losses between the next two deliveries, which are added as incremental volume in the order formula.
Unusually high write-off forecasts for a given SKU may indicate a systemic issue -- poor rotation practices at the store level, supplier quality problems, or a Shelf Term that no longer reflects actual product behavior. Reviewing the Order > Lines screen regularly allows replenishment managers to spot these signals early and take corrective action before losses compound.
Business Impact
Automated shelf-life accounting and write-off forecasting deliver measurable results across three dimensions.
Waste reduction. By capping orders at shelf-life-safe maximums, MySales prevents the accumulation of inventory that cannot sell before expiration. Retailers implementing this logic typically see significant reductions in perishable write-offs, particularly in categories with short shelf lives such as dairy, bakery, and prepared foods.
Availability improvement. The write-off compensation mechanism ensures that anticipated spoilage does not create unexpected gaps on the shelf. The system proactively orders replacement volume, maintaining target service levels even in high-waste categories.
Operational efficiency. Store-level and category managers no longer need to manually adjust orders based on intuition about shelf life and spoilage rates. The system encodes this logic systematically, freeing teams to focus on exception management and strategic decisions rather than routine order corrections.
The combination of shelf-life constraints and write-off forecasting transforms perishable replenishment from a high-risk, judgment-dependent process into a controlled, data-driven operation.