Demand Forecasting Software Development

Forecasting models selected per SKU based on demand history length, variability, and pattern -- simple moving average for stable low-volume lines, exponential smoothing for lines with trend, seasonal decomposition for lines with clear annual or weekly cycles, and ensemble methods that combine multiple models where no single approach dominates. Model selection running automatically when a new SKU is onboarded and re-evaluated periodically as demand history accumulates. Forecast horizon configurable to match your supplier lead times -- a 4-week forecast for short-lead suppliers, a 12-week forecast where lead times require earlier commitment. Confidence intervals alongside each point forecast so planners can see the range of likely outcomes rather than a single number that implies false precision. Historical back-testing showing how each model would have performed on past data so the model selection rationale is visible and auditable.

Seasonal index calculation from demand history, identifying recurring uplift and depression patterns by week, month, or quarter and applying them to the baseline forecast automatically. Promotional calendar integration so planned promotions, price changes, and product launches are captured before the event and their demand impact estimated from comparable historical promotions. Event library storing the demand impact of past events -- a specific retailer promotion, an annual trade show, a product recall -- so the impact can be applied to future events of the same type without manual estimation each time. Seasonal profile management per product category, channel, and region where seasonality differs across the business. Override capture recording every manual adjustment a planner makes, with the reason noted, so the adjustments are visible in forecast accuracy reporting and can inform model improvement.

Safety stock calculated per SKU from three inputs: demand variability over the replenishment lead time, lead time variability from supplier data, and target service level set by the planning team per product category. Calculation updated automatically when any input changes -- when lead time data shows a supplier's performance has deteriorated, or when a service level target is revised. Multiple service level targets supported so higher-margin or higher-criticality lines carry more safety stock than lower-priority lines without the planning team managing this manually. Safety stock recommendations presented with the calculation inputs visible so planners can see why the recommendation changed and challenge the inputs if they believe the data is wrong. Historical service level tracking showing actual fill rate against target so the relationship between safety stock setting and service outcome is visible over time.

Replenishment order suggestions generated from the forecast demand, safety stock position, current on-hand inventory, and in-transit stock already ordered -- the suggested order quantity reflecting what is genuinely needed rather than a round number or a minimum order quantity applied without context. Order suggestions grouped by supplier so the planning team can review and approve all lines for a supplier in one step rather than processing individual SKU suggestions separately. Minimum order quantity and order multiple rules applied to each suggestion automatically, with the rounding impact shown so planners can decide whether to round up, round down, or negotiate an exception. Approval workflow capturing the planner's decision on each suggestion -- approve, modify, or defer -- before the order is created. Integration with your ERP or purchasing system so approved replenishment suggestions create purchase orders directly without re-keying.

Forecast accuracy measured at the SKU and aggregate level using MAPE, WMAPE, and bias metrics -- the metrics your operations and finance teams are likely already reporting or want to start reporting. Accuracy reported at multiple levels: by SKU, by product category, by planner, by supplier, and at the total business level, so the planning manager can see where accuracy is strong and where it needs attention. Bias tracking identifying whether the forecast consistently over-predicts or under-predicts specific SKUs or categories, which signals a systematic model problem rather than random error. Accuracy trend over time showing whether the forecasting process is improving, stable, or deteriorating -- particularly useful after model changes, process changes, or significant demand disruptions. Accuracy benchmarking against the previous period and against industry reference points where relevant.

Exception alerts surfacing the SKUs and situations that require planner attention: forecast accuracy below threshold, safety stock below minimum, replenishment suggestion above a set order value, lead time extension flagged by the supplier, or a slow-mover building excess stock beyond a weeks-of-cover limit. Exception queue showing all active exceptions prioritised by impact -- the exceptions that represent the largest stock risk or the largest cost exposure presented first. Exception resolution workflow capturing the planner's response to each exception and the action taken so the exception record is complete and auditable. Configurable exception thresholds per product category so the rules that trigger an exception for a high-value fast mover differ from the rules for a low-value slow mover. Exception trend reporting showing which exception types recur most frequently so the planning team can address root causes rather than treating recurring exceptions as routine.