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Demand Forecasting for Retail

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Precise location-level forecasts
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Fewer markdowns by selling more at full price
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Optimized inventory turnover
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Seasonal trend forecasting

Discover Demand Forecasting in Action

View More Case Studies
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Imagine a world where your warehouse operates at maximum efficiency, reducing costs, and increasing customer satisfaction. With Increff’s regional utilization capabilities, you can turn this vision into a reality. Delve into our captivating success story, featuring a renowned furniture and home decor brand in India, who joined forces with Increff to revolutionize their operations. Through this collaboration, they experienced a multitude of benefits: Efficient inventory allocation at appropriate locations Optimized warehouse inventory by – style ranking Reduced logistics expenses Enhanced delivery speed Lowered the chances of unfulfilled orders
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BESTSELLER, one of the leading casual wear brands of Denmark with 500+ retail outlets and selling in multiple retail formats across India, was experiencing difficulty in analyzing individual store demand, leading to imprecise inventory distribution. Allocating international-sized products to the domestic market was a challenge The manual practice of allocating inventory through Excel was extremely time-consuming leading to inefficiency within the team, loss of sales opportunities at some locations, and blockage of working capital at others The brand was looking for a solution that would allow them to analyze store capacity, revenue targets, and stock cover in a single view and facilitate optimum allocation across all stores. Objective: Increase ROS by precisely analyzing region-wise store-specific, category, and product demand to distribute inventory mirroring actual demand Allocate and utilize bottom wear sizes without any wastages or understocking for 2D size combinations, an International sizing system unknown to Indian retailers Automate inventory allocation to avoid human decision-making errors and free up resource time for market trends analysis, market visits, etc. thus increasing productivity in other areas Optimize allocation of fringe sizes in Shop-in-shop stores to avoid overstocking in stores where certain sizes absolutely do not sell Allow manual intervention in defining key sizes in new allocation for certain specific Store-Category combinations Solutions: Increff Allocation & Replenishment Solution was implemented to analyze store capacity, revenue target, & stock cover all at once, and to improve stock distribution & health through demand-based allocation. Using in-built algorithms, the tool was able to analyze true customer demand served at every store format, with the correct approach considering all business constraints (different approaches for EBO, SIS, etc), and suggest intelligent inventory allocation for 2D sizes. As per the tool analysis, the brand was able to place the right stock in the required quantity, at the right location, and at the correct size curve. It also facilitated fresh season allocation and mid-season replenishments/replacements with inventory redistribution to increase revenue, reduce inventory holding and optimize sales. Considering the uniqueness of each store, the tool was able to predict store-wise demand based on the historical performance of attributes (category, MRP bucket, color, gender, etc). Other solutions: Accurate fringe size measurement to enable fringe size allocation based on true demand since fringe size buys are limited in quantity Maintain style health with 2D size measurements and solve for brokenness while replenishing stocks contingent on the presence of Warehouse inventory. This is also tailored in such a way that for 2D sizes if one of the key sizes is out of stock then the child SKUs for the same will be allocated. Optimal management of key size input/override designed to handle continuity checks when odd/fringe sizes are in between key sizes in Replenishment, as well as to override key sizes detected by the tool in specific requirements. Impact: Implementing Increff Allocation & Replenishment Solution resulted in: Increase in ROS – By rectifying wrong allocation i.e stock getting distributed to location/region where it was not required while the store which had the demand was deprived of the same. Increase in Efficiency – Reduction in resource time spent on MS excel led to better utilization of time and an increase in overall team efficiency. Higher conversion – Increase in sales as a result of a reduction in stock brokenness and improvement of stock health by implementing replacement suggestions for SKUs in 2D sizes. ~100% accurate analysis of store capacity, revenue targets, stock cover & store DNA by eliminating human errors.
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Optimized inventory through the smart assortment planning for different sales channels. One of India’s biggest celebrity-endorsed fashion brands reduced overstocking by defining display norms as per the actual sales patterns. Using Increff Merchandising solution, it was able to identify and bet more on top performers at each store and exit buying of non-performing attribute groups. It also redefined stock requirements to optimize initial launch requirements across stores. Objective: To optimize inventory for one of India’s biggest high-growth celebrity-endorsed fashion brands. Solution: Smart Assortment plan for 4 different channels (~200 stores) was created Optimized the buying to exit the non-performing attribute groups and bet more on top performers at each individual store Reduced overstocking in bottom stores by defining display norms as per the actual sales patterns. Business benefits: 13% increase in full price sell-through 25% reduction in width at a channel level 12% improvement in ASP.
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Unified Fulfillment
Fulfill orders across D2C, e-commerce, quick commerce, marketplaces, wholesale, and offline retail from a single WMS platform.
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End-to-End Operations
Manage high SKU variety, large inventory volumes, all order types, and channel-specific needs with robust receiving, putaway, picking, packing, dispatch, and returns workflows.
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Effortless Scalability
Easily scale to multiple fulfillment centers, dark stores, mother hubs, and regional warehouses from a single WMS instance.
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Marketplace Excellence
Boost performance on every marketplace with seamless fulfillment, top seller ratings, and zero penalties.

How Increff Forecasts True Demand

  • Designed for Fashion & Retail Demand
  • Self-Learning Algorithms
  • Seasonal, Event-Driven & Scenario Planning
  • From Forecast to Profitable Action

Designed for Fashion & Retail Demand

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  • True ROS™ (Rate of Sale): Predicts a daily true rate of sale for each POS–style, computed only on days a style was genuinely live and available.
  • Liquidation & noise cleanup: Strips out discount-driven and clearance noise so forecasts reflect real, full-price demand.
  • Potential demand identification: Estimates demand for stores/styles where an item was never listed, using attribute-group-level rate of sale.
  • Attribute-based prediction: Models demand across up to 17 levels of fashion attributes instead of the usual 5.

Self-Learning Algorithms

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  • Patent-pending algorithms: Consumes only raw data and generate intelligent inputs automatically, protected by US & India patents.
  • 100+ configurable algorithms: Adapts to each brand's business rules and constraints.
  • Seasonality & recency handling: Handles seasonality, festivity, and recency, plus full-price and discounted sales behavior.
  • Lifecycle awareness: Applies a Freshness Index and lifecycle logic for new launches vs. core styles.
  • Constraint-aware planning: Accounts for operating constraints like planogram, MOQ, lead time, and safety stock.

Seasonal, Event-Driven & Scenario Planning

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  • Event-aware forecasting: Integrates sales calendars, holidays, and promotional events to ramp inventory up before events and down afterward.
  • Multiple scenario planning: Blazing-fast algorithm runs let planners test and compare demand scenarios in seconds.
  • Forecast accuracy validation: Back-test on historical periods (e.g. forecast H1 and compare against actual sales) to prove accuracy before rollout.

From Forecast to Profitable Action

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  • Assortment & buying: Converts demand forecasts into ideal buy plans and store-level assortments, identifying NOOS/bestsellers and core styles.
  • Allocation & replenishment: Places the right inventory in the right depth, at the right location and time, with automated reordering.
  • Reorder forecasting: Projects forward closing inventory using current ROS, lead time, safety stock, and open orders to prevent over- or duplicateordering.
  • Regional utilization: Forecasts true regional demand down to the zip-code level to optimize multi-warehouse distribution.
  • Markdown optimization: Uses demand and sell-through signals to time and size markdowns and protect margins.

Frequently Asked Questions

What is demand forecasting in retail?

Demand forecasting in retail is the practice of predicting how much of each product customers will actually buy, at a specific location and time, so that buying, allocation, replenishment, and markdown decisions are based on real demand rather than guesswork. It uses historical sales, seasonality, events, and product attributes to project future demand at the store and SKU level.

What is true demand, and how is it different from historical sales?

True demand is the quantity customers would have bought if a product had been fully available, whereas historical sales only capture what was actually sold. Historical sales are distorted by stockouts, broken size sets, and discount-driven liquidation, so forecasting on raw sales repeats past mistakes. True demand cleans out that noise to reveal genuine, full-price demand.

What is Rate of Sale (ROS), and what makes true ROS™ different?

Rate of Sale (ROS) is the pace at which a product sells over a given period. True ROS™ measures a daily rate of sale for each store–style using only the days a style was genuinely live and available with a minimum representation of pivotal sizes, so the rate is not diluted by stockout or broken-size days. This produces a far more accurate base for forecasting.

How does demand forecasting reduce markdowns and improve full-price sellthrough?

By predicting true demand at the store and SKU level, demand forecasting places the right depth of the right products where they will sell at full price, reducing overstock that later needs discounting. Better initial buys and allocation mean fewer markdowns and higher full-price sell-through — brands typically see a lift in full-price sell-through and improved margins.

How does Increff forecast demand at the store–SKU level?

Increff uses patent-pending, self-learning algorithms that consume raw sales data and automatically clean out stockout, brokenness, and liquidation noise. It forecasts every store individually — no forced clustering — across up to 17 levels of fashion attributes, while accounting for seasonality, events, and operating constraints such as planogram, MOQ, lead time, and safety stock.