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A pack of salad sitting on the refrigerated shelf has a shelf life of just a day or two. If it doesn't sell today, it's headed for disposal tomorrow. Under this unforgiving time constraint, fresh food inventory management ranks as one of the toughest challenges in the retail industry. Globally, roughly 30–40% of all food produced never reaches consumers and ends up as waste — and the economic toll is staggering.
In the U.S. market alone, food-related economic losses amount to approximately $382 billion annually, with direct losses at the retail stage reaching about $161 billion. Korea is no exception. According to analysis by the Korea Rural Economic Institute (KREI), domestic agricultural food waste has increased by 15.8% over the past five years, with annual economic costs reaching approximately 20 trillion KRW. The fact that a significant share of this loss originates from the fresh food category only underscores the severity of the problem.
So why does so much food end up wasted? One fundamental cause is inaccurate demand forecasting. If you could know exactly how much would sell tomorrow, you'd order precisely that amount. But in practice, reading "tomorrow's demand" accurately is anything but simple. Seasonal shifts, sudden weather events, promotions, holiday periods, competitor pricing — a web of variables interacts in complex ways. This is precisely where AI-powered demand forecasting systems become essential.
For decades, the retail industry's go-to demand forecasting tools have been traditional statistical models like ARIMA (Autoregressive Integrated Moving Average) and exponential smoothing. These models analyze trends and recurring patterns in historical sales data to project future demand, and they've delivered reasonable performance for products with relatively stable demand patterns.
Fresh food, however, is a different story. When a heatwave hits, demand for watermelons and cold beverages spikes overnight. When a sudden downpour strikes, vegetable sales drop off a cliff. And families planning a weekend barbecue can change their buying plans entirely based on a single weather forecast.
Extended models like ARIMAX that incorporate external variables do exist, but traditional statistical models fundamentally struggle to learn from diverse external variables — temperature, weather, events — all at once, and they can't effectively handle the nonlinear ways fresh food demand responds to these factors. The result is an inability to react in time when demand shifts sharply, leading to a recurring cycle of stockouts and overstock.
What makes fresh food especially unforgiving is that, unlike frozen or processed goods, leftover inventory can't simply be carried over to next week. Even a small forecasting error translates into outsized cost impact. Frozen dumplings that don't sell this week can still move next week, but a fresh salad becomes unsellable within days. In the fresh food category, even a modest dip in forecast accuracy can trigger significant losses.
State-of-the-art AI and machine learning algorithms are breaking through these limitations by capturing external variables and nonlinear relationships that traditional models miss.
Tree-based machine learning methods like XGBoost and LightGBM calculate complex interactions across diverse external data — weather, day of week, events — to effectively predict nonlinear patterns such as temperature-driven shifts in beverage demand. Time-series deep learning architectures like LSTM and DeepAR learn long-term, recurring patterns from sequential data — such as the surge in premium beef demand during Korea's Chuseok season — and factor them into forecasts. Transformer-based models like PatchTST and TST jointly learn correlations across multiple product categories, enabling cross-category demand forecasting and improving early-stage accuracy for new products by leveraging data from similar items.
Research from Tilburg University puts this technological advantage into concrete figures. The following table compares forecast accuracy between traditional time-series models and general-purpose machine learning models at both national and store levels.
A particularly noteworthy finding is that XGBoost's RMSE improved from 0.13 to 0.09 when external variables were integrated. This quantifies just how critical contextual data like weather and day of week is for fresh food demand forecasting.
One of the key reasons AI demand forecasting outperforms conventional methods is its ability to leverage contextual data. Rather than looking at historical sales numbers in isolation, it analyzes the external factors that explain why those numbers turned out the way they did.
A one-degree rise in temperature visibly boosts demand for ice cream and cold noodles, while a sudden rainstorm causes demand for outdoor cooking ingredients to plummet. As noted earlier, fresh food inventory can't be carried over — so when weather forecasts miss the mark, the risk exposure is particularly severe. Fresh meat prepared for weekend sales that goes unsold due to a weather shift becomes a straight write-off. AI systems integrate with meteorological data in real time and automatically propose inventory adjustments for different weather scenarios.
Holiday seasons like Chuseok and Lunar New Year are the periods with the widest demand swings for fresh food. Floating holidays — whose dates shift each year — make simple year-over-year comparisons unreliable. Consumer behavior patterns differ significantly depending on whether a holiday falls on a Friday versus a Sunday. Advanced AI systems learn these day-of-week effects and factor in demand differences between urban-center stores and residential-area stores, calculating optimized order quantities by store type.
When a retailer runs a discount on a specific product, sales of that item increase. But at the same time, demand for similarly priced alternatives drops — the cannibalization effect — while complementary products see a lift. Discount pork belly, for example, and demand for lettuce wraps rises in tandem. AI forecasting models learn these intra-category causal relationships and adjust order quantities to maximize profitability across the entire category.
For a deeper look at the cannibalization effect that retailers need to watch, refer to the articles below.
👉🏻 The Cannibalization Effect Retailers Have Been Missing — and How AI Solves It
👉🏻 Overcoming Cannibalization in Retail with AI
India's large-format grocery chain More Retail Ltd (MRL) classified tens of thousands of products by revenue contribution and forecasting difficulty, then concentrated AI deployment on the hardest-to-predict, highest-revenue segment. After adopting Amazon Forecast's DeepAR+ algorithm, demand forecast accuracy jumped from 24% to 76%, fresh food waste dropped by 30%, inventory availability rose from 80% to 90%, and gross profit increased by 25%.
The techniques MRL applied along the way are worth highlighting. They flagged days where sales hit zero due to stockouts so the system wouldn't misinterpret them as "zero demand" days. They also configured the system to output probabilistic forecasts across confidence intervals rather than a single average, allowing stores to flexibly adjust order levels based on local conditions.
Grocery retailers in Europe that adopted RELEX, a leading SCM solution, achieved an average 40%+ reduction in fresh food waste. RELEX's core emphasis is on the importance of day-level forecasting. Monday demand and Saturday demand are fundamentally different, and given the short shelf life of perishables, weekly forecasting simply can't deliver the granularity needed for precise inventory control. RELEX's system integrates with automated replenishment workflows, automatically requesting AI-calculated optimal quantities from distribution centers — maintaining availability above 99% while minimizing waste.
Market Kurly built its proprietary demand forecasting system, nicknamed "Demungi" (literally, "the data-fetching puppy"), from its earliest days. Given the dawn delivery business model — where product must already be staged at the fulfillment center before customers even place orders — forecast accuracy is directly tied to business survival.
By continuously refining machine learning models that analyze not just historical sales but also add-to-cart behavior, site dwell time, weather, and seasonal factors, Market Kurly maintains a fresh food waste rate below 1% — compared to the roughly 3% average at major hypermarkets. The company has recently pushed this down to approximately 0.5%, and this operational efficiency improvement has been cited as a key factor in its first quarterly profit after ten years of operation.
Coupang uses "pre-positioning" technology that predicts which products customers will order and stages them at regional fulfillment centers in advance. AI analyzes order data in real time and calculates optimal loading positions within delivery trucks in a matter of seconds.
Coupang's improvement in cost of goods sold is the result of multiple factors working together, including logistics infrastructure expansion and economies of scale. Among these, demand forecast-driven inventory optimization and logistics efficiency are recognized as having played a meaningful role in improving the company's cost structure.
The convenience store industry — managing high-SKU, low-volume data across tens of thousands of stores nationwide — is one of the sectors where AI efficiency gains are most visible.
CU's smart ordering system analyzes store-level sales patterns to automatically calculate optimal stock levels. Since deployment, store operators' ordering time has dropped from an average of 30 minutes to 5 minutes, and fresh food waste costs have fallen by 10–20%.
GS25's AI auto-ordering focuses on weather-sensitive product categories, pushing suggested quantity accuracy above 90% and reducing waste volumes by approximately 30%.
AI model performance is directly proportional to the quality of input data. When POS systems, warehouse management systems (WMS), and external weather data are siloed, the quality of data available for training degrades. Building a data integration and cleansing pipeline should therefore be the first step in any AI deployment.
If the store-level ordering manager doesn't trust AI-recommended quantities, the system's ROI is cut in half. Dashboards that explain the reasoning behind AI outputs are critical. For example, showing "Tomorrow's temperature is expected to rise 5°C and a local festival is scheduled, so we've increased the estimate by 20% above baseline" dramatically improves frontline adoption.
Rather than applying AI across all product categories at once, it's more effective to start with a pilot focused on high-waste categories — bakery, salads, fresh fish — validate the results, and then expand scope incrementally. This approach reduces initial investment risk while naturally building organization-wide AI adoption.
As outlined above, deploying an AI demand forecasting system involves a complex set of challenges spanning data integration, model selection, and field-level application. Deepflow, developed by ImpactiveAI, is an AI-powered demand and price forecasting solution designed to address these challenges in an integrated way.
Deepflow delivers accurate 6–12 month SKU-level sales and shipment volume forecasts, powered by over 200 deep learning and machine learning models and 72 patented technologies. Its data augmentation capabilities are particularly noteworthy — enabling stable forecast performance across manufacturing, retail, and food industries even in data-scarce environments.
Want to assess your company's current fresh food inventory management maturity and see the tangible impact of AI adoption? ImpactiveAI offers PoC (Proof of Concept) projects for its Deepflow solution, giving enterprises the opportunity to validate AI demand forecasting accuracy using their own data.
ImpactiveAI provides a complimentary PoC that uses your actual sales data to quickly verify whether AI demand forecasting delivers production-ready accuracy. The process builds time-series forecasting models from product master data and sales/shipment history, and delivers a report covering forecast accuracy, product-level forecast graphs, and trend explanations — so you can assess feasibility before committing. Customized strategy consulting helps develop optimal ordering and inventory management strategies tailored to the characteristics of each fresh food category, delivering actionable insights ready for immediate field application. For organizations requiring more rigorous validation, a paid PoC incorporating promotional data, external variables (weather, macroeconomic indicators), and inventory status enables deeper analysis and further accuracy refinement.
Still absorbing losses from inaccurate forecasts? Contact ImpactiveAI today and take the first step toward building a data-driven, next-generation fresh food inventory management system.
As Market Kurly's 0.5% waste rate and MRL's 25% gross profit lift demonstrate, AI-powered demand forecasting is no longer theoretical — it's producing proven results. In perishable food management, even incremental improvements in forecast accuracy translate directly into cost savings and profitability gains.
At the same time, this technology delivers environmental value by reducing greenhouse gas emissions through less food waste. The ability to simultaneously achieve cost reduction and sustainability goals means adopting an AI demand forecasting system is no longer a matter of choice — it's a matter of competitiveness.
The true leaders of tomorrow's retail industry won't be the companies that simply ship more products faster. They'll be the ones that leverage data to accurately read future demand and operate supply chains with zero unnecessary waste.
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