What is Demand Forecasting in Logistics? AI-Driven Inventory Planning

Table of Contents

• Understanding Demand Forecasting in Logistics: A Complete Definition
• Why Demand Forecasting Matters for Modern Supply Chains
• Traditional Demand Forecasting Methods and Their Limitations
• How AI is Revolutionizing Demand Forecasting in Logistics
• Implementing AI-Driven Demand Forecasting: A Step-by-Step Framework
• Critical Data Points for Accurate Demand Forecasting
• Real-World Results: Companies That Transformed Their Inventory Planning
• 5 Common Demand Forecasting Mistakes That Cost Businesses Millions
• The Future of Demand Forecasting: Emerging Technologies and Trends
• Frequently Asked Questions

Understanding Demand Forecasting in Logistics: A Complete Definition

The demand forecasting logistics definition refers to the systematic process of predicting future customer demand for products or services using historical data, market analysis, and predictive algorithms to optimize inventory levels, reduce carrying costs, and prevent stockouts across the supply chain. At its core, demand forecasting in logistics transforms raw data into actionable intelligence that drives every decision from procurement to last-mile delivery.

For e-commerce businesses, demand forecasting isn’t just about guessing how many units to order next month. It’s a sophisticated discipline that combines statistical modeling, machine learning algorithms, and domain expertise to answer critical questions: How much inventory should sit in which warehouse? When should you reorder from suppliers? Which products will spike during seasonal peaks? What’s the optimal safety stock level for each SKU?

The traditional demand forecasting logistics definition has evolved dramatically in recent years. What once relied on spreadsheets and gut instinct now leverages artificial intelligence to process millions of data points in real-time. Modern forecasting systems analyze everything from weather patterns and social media sentiment to competitor pricing and macroeconomic indicators.

Consider this: A study by the Institute of Business Forecasting found that companies with advanced demand forecasting capabilities maintain 15% less inventory while achieving 17% higher perfect order rates compared to those using basic methods. The financial impact is staggering—for a business with $50 million in annual revenue, improving forecast accuracy by just 10% can save $500,000 to $1 million annually in reduced carrying costs and lost sales.

The Three Pillars of Effective Demand Forecasting

Understanding the demand forecasting logistics definition requires grasping its three foundational elements:

• Historical Analysis: Examining past sales data, seasonal patterns, and trend lines to identify predictable behaviors. This includes analyzing at least 24-36 months of historical data across multiple dimensions (product, channel, geography, customer segment).
• Market Intelligence: Incorporating external factors like economic indicators, competitor actions, industry trends, and consumer behavior shifts. This prevents the common mistake of assuming the future will mirror the past.
• Predictive Modeling: Applying statistical methods and machine learning algorithms to generate probability-weighted forecasts with confidence intervals. Modern systems don’t just predict a single number—they provide a range of likely outcomes with associated probabilities.

Platforms like ShipPost integrate these three pillars into a unified forecasting engine that continuously learns from actual outcomes, automatically adjusting predictions as new data flows through the system. This closed-loop approach ensures forecast accuracy improves over time rather than degrading as market conditions shift.

Why Demand Forecasting Matters for Modern Supply Chains

The financial consequences of poor demand forecasting are severe and measurable. According to research from the Aberdeen Group, businesses with forecast accuracy below 75% experience stockout rates of 8-12%, while those above 90% accuracy maintain stockouts below 2%. For a mid-sized e-commerce retailer processing 10,000 orders monthly, that difference translates to 600-1,000 lost sales per month—potentially millions in annual revenue.

But the impact extends far beyond lost sales. Excess inventory ties up working capital that could fund growth initiatives. Industry data shows that carrying costs (warehousing, insurance, obsolescence, opportunity cost) typically run 20-30% of inventory value annually. A company holding $5 million in excess inventory pays $1-1.5 million yearly just to store products that aren’t selling.

The Ripple Effect Across Operations

Demand forecasting influences every aspect of logistics operations:

Operational Area	Impact of Accurate Forecasting	Cost of Inaccuracy
Warehouse Management	Optimized space utilization, reduced handling costs	15-25% higher labor costs, wasted storage capacity
Transportation Planning	Consolidated shipments, negotiated carrier rates	30-40% higher freight costs from expedited shipping
Supplier Relationships	Stable order patterns, volume discounts	Rush order premiums, damaged supplier trust
Customer Experience	98%+ in-stock rates, predictable delivery times	Lost customers, negative reviews, brand damage

The customer experience dimension deserves special attention. Research from Baymard Institute reveals that 69% of online shoppers abandon carts when items are out of stock, and 37% won’t return to a retailer after a stockout experience. In the age of one-click purchasing and same-day delivery expectations, forecast accuracy directly determines whether customers complete purchases or click over to competitors.

Modern logistics platforms recognize these interconnections. When ShipPost generates demand forecasts, the system simultaneously optimizes warehouse allocation, suggests reorder points, and identifies opportunities for shipping consolidation—turning predictions into executable action plans that improve both service levels and margins.

Traditional Demand Forecasting Methods and Their Limitations

Before exploring AI-driven approaches, it’s important to understand traditional forecasting methods and why they’re increasingly inadequate for modern e-commerce operations.

Time Series Analysis

The most common traditional approach involves analyzing historical sales data to identify patterns and project them forward. Methods like moving averages, exponential smoothing, and seasonal decomposition have served businesses for decades. A simple moving average might calculate the average of the last 12 months of sales and use that as next month’s forecast.

The fundamental limitation: These methods assume the future resembles the past and struggle with sudden market shifts, new product introductions, or unprecedented events (like the 2020 pandemic). When Amazon launched Prime Day, traditional time series models failed spectacularly because they had no historical precedent for the sales spike.

Causal Models

More sophisticated traditional approaches attempt to identify cause-and-effect relationships. Regression analysis might correlate sales with factors like advertising spend, economic indicators, or weather patterns. These models work well when relationships are stable and linear.

The problem: Real-world demand drivers rarely follow neat linear relationships. Consumer behavior is influenced by hundreds of interacting variables—social media trends, competitor actions, influencer endorsements, viral moments—that traditional causal models can’t capture. A regression model might show that temperature correlates with ice cream sales, but it won’t predict the TikTok trend that suddenly makes a specific flavor go viral.

Qualitative Forecasting

Many businesses still rely heavily on expert judgment, sales team input, and market research to forecast demand. This “tribal knowledge” approach incorporates insights that quantitative models miss.

The weakness: Human judgment suffers from cognitive biases—recency bias (overweighting recent events), anchoring bias (fixating on initial estimates), and optimism bias (sales teams consistently over-forecasting). Studies show that purely judgmental forecasts are typically 30-50% less accurate than even basic quantitative methods.

Why Traditional Methods Fall Short Today

The e-commerce environment has fundamentally changed in ways that break traditional forecasting assumptions:

• Product Proliferation: Modern retailers manage thousands of SKUs with limited sales history. Traditional methods need 24-36 months of data per SKU—impossible when new products launch monthly.
• Channel Complexity: Customers shop across web, mobile, social commerce, and marketplaces. Traditional models forecast at the aggregate level and miss channel-specific patterns.
• Velocity Variations: Some products sell steadily, others spike unpredictably. Traditional methods optimize for average conditions and fail at extremes.
• External Disruptions: Supply chain shocks, viral trends, and black swan events occur more frequently. Traditional models have no mechanism to incorporate real-time signals.

A 2023 Gartner study found that 73% of supply chain leaders consider their current forecasting capabilities inadequate for modern market conditions. The gap between traditional methods and business needs has never been wider—creating the perfect opportunity for AI-driven alternatives.

How AI is Revolutionizing Demand Forecasting in Logistics

Artificial intelligence transforms the demand forecasting logistics definition from a periodic planning exercise into a continuous, self-improving system that adapts in real-time to changing conditions. The difference isn’t incremental—it’s a fundamental shift in how forecasting works.

Machine Learning Algorithms That Actually Work

Modern AI forecasting employs several complementary approaches:

Gradient Boosting Machines (XGBoost, LightGBM): These ensemble methods combine hundreds of decision trees to capture complex, non-linear relationships in demand data. They excel at handling mixed data types (categorical and numerical) and automatically identify which factors matter most for each product. In practical terms, XGBoost might discover that Product A’s demand correlates strongly with Instagram mentions, while Product B responds to price changes, and Product C follows weather patterns—all without manual feature engineering.

Neural Networks and Deep Learning: Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks process sequential data to identify temporal dependencies that traditional methods miss. These architectures recognize that today’s sales might depend not just on yesterday’s numbers, but on patterns from three weeks ago that repeat cyclically. For seasonal products, LSTMs automatically learn multi-level seasonality (daily, weekly, monthly, yearly) without manual specification.

Ensemble Forecasting: Rather than relying on a single algorithm, sophisticated systems generate predictions from multiple models and combine them using weighted averaging or meta-learning. This approach reduces the risk that any single model’s weaknesses dominate the forecast. Research shows ensemble methods improve accuracy by 15-30% compared to individual models.

The Data Advantage: Processing Signals Traditional Methods Ignore

AI systems ingest and process data sources that would overwhelm traditional forecasting:

• Real-time web traffic and search behavior: Spikes in product page views or search queries often precede sales increases by days or weeks. AI models incorporate these leading indicators automatically.
• Social media sentiment and mentions: Natural language processing analyzes millions of social posts to detect emerging trends, brand sentiment shifts, and viral moments that drive unexpected demand.
• Competitor pricing and inventory levels: Web scraping and marketplace APIs provide continuous competitive intelligence that informs demand predictions.
• Weather forecasts and local events: Hyperlocal data enables location-specific predictions—forecasting umbrella demand in Seattle differently than Miami.
• Economic indicators and consumer confidence: Macro factors influence purchasing behavior, especially for discretionary categories.

The key insight: AI doesn’t just process more data—it identifies which data matters for which products under which conditions. A human analyst might manage 10-20 variables; AI systems routinely optimize across 500+ features, automatically selecting the most predictive combinations.

Continuous Learning and Adaptation

Perhaps the most transformative aspect of AI forecasting is continuous learning. Traditional forecasts are static—generated monthly or quarterly and unchanged until the next planning cycle. AI systems update predictions continuously as new data arrives.

When actual sales come in, the system calculates forecast error, identifies which model assumptions were wrong, and adjusts future predictions accordingly. This closed-loop learning means forecast accuracy improves over time rather than degrading. Businesses using AI forecasting typically see 5-10% accuracy improvements in the first year as models learn their specific demand patterns.

Platforms like ShipPost implement this continuous learning architecture, automatically retraining models as new data flows through the system. The result: forecasts that adapt to your business’s unique characteristics without requiring data science expertise or manual model tuning.

Implementing AI-Driven Demand Forecasting: A Step-by-Step Framework

Moving from traditional forecasting to AI-driven approaches requires a structured implementation process. Based on successful deployments across hundreds of e-commerce businesses, here’s the framework that delivers results:

Phase 1: Data Foundation (Weeks 1-4)

Audit your current data landscape. Most businesses discover their data is messier than expected. Product identifiers change over time, historical sales include returns and cancellations inconsistently, promotional periods aren’t tagged properly. Spend the first two weeks cleaning and standardizing historical data.

Minimum requirements:

• 24 months of daily sales history (36 months preferred)
• Product master data with consistent SKU identifiers
• Pricing history including promotional periods
• Inventory levels and stockout dates
• Order fulfillment data (which warehouse, which carrier)

Establish data pipelines. AI forecasting requires continuous data feeds, not periodic exports. Set up automated connections between your e-commerce platform, warehouse management system, and forecasting engine. Most modern platforms offer APIs—use them. Manual data exports introduce delays and errors that degrade forecast quality.

Phase 2: Baseline Establishment (Weeks 5-8)

Measure current forecast accuracy. You can’t improve what you don’t measure. Calculate your baseline forecast accuracy using Mean Absolute Percentage Error (MAPE) or Weighted Mean Absolute Percentage Error (WMAPE) across product categories. Most businesses discover their actual accuracy is 20-30% worse than they assumed.

The formula: MAPE = (1/n) × Σ |Actual – Forecast| / Actual × 100

Industry benchmarks suggest good forecasting achieves 80-85% accuracy (MAPE of 15-20%) for established products. New products and long-tail SKUs typically run 60-70% accuracy.

Identify high-impact products. Not all forecasting errors cost equally. A 50% error on a slow-moving item with $10 margin matters less than a 10% error on your bestseller with $100,000 monthly revenue. Apply ABC analysis to focus initial efforts where accuracy improvements deliver maximum financial impact.

Phase 3: Model Development and Testing (Weeks 9-16)

Start with automated solutions. Unless you have in-house data science resources, use platforms that provide pre-built forecasting models. Building custom ML models from scratch takes 6-12 months and requires specialized expertise. Modern logistics platforms offer production-ready AI forecasting that delivers 80% of custom model performance at 10% of the cost and time.

Run parallel forecasting. For the first 2-3 months, generate both traditional and AI forecasts without changing inventory decisions. This parallel run provides risk-free validation of AI accuracy and builds organizational confidence before committing to automated decisions.

Test with controlled SKU sets. Rather than switching your entire catalog at once, select 50-100 representative SKUs for initial AI forecasting. Choose a mix of fast-movers, seasonal items, and new products. Monitor performance weekly and expand gradually as results validate the approach.

Phase 4: Operationalization (Weeks 17-24)

Integrate forecasts into purchasing workflows. AI predictions only create value when they drive actual decisions. Connect forecasting outputs directly to purchase order generation, safety stock calculations, and warehouse allocation rules. The goal: minimize human intervention in routine replenishment while maintaining oversight for exceptions.

Establish exception management processes. AI handles the 80% of forecasting that’s routine; humans should focus on the 20% that’s exceptional. Define clear rules for when forecasts require human review—new product launches, promotional events, supplier disruptions, anomalous demand signals.

Train your team. Successful AI adoption requires helping planners and buyers understand when to trust the system and when to override it. The best implementations combine AI’s pattern recognition with human judgment about market context the model can’t see.

Using ShipPost’s intelligent automation, businesses typically complete this implementation framework in 4-6 months, achieving 15-25% forecast accuracy improvements and 10-20% inventory reductions while maintaining or improving service levels.

Critical Data Points for Accurate Demand Forecasting

The quality of demand forecasting depends directly on the breadth and accuracy of input data. AI models can’t compensate for missing or incorrect information—garbage in, garbage out remains true even with sophisticated algorithms.

Essential Internal Data

Transactional sales data forms the foundation. Capture not just what sold, but when, through which channel, at what price, with which promotions. Daily granularity is minimum; hourly data enables better short-term forecasting for fast-moving items. Include both completed sales and cancelled orders—cancellation patterns often signal inventory or pricing issues.

Inventory positions across locations reveal stockout impacts. If a product shows zero sales for three days, was that because demand disappeared or because you were out of stock? AI models need to distinguish between genuine demand drops and supply constraints. Track inventory at the warehouse-SKU-day level.

Product attributes and relationships enable sophisticated cross-product forecasting. Capture product hierarchies (category, subcategory, brand), attributes (color, size, material), and substitutability relationships. When forecasting demand for a specific shoe size, models should consider demand patterns for other sizes of the same style.

Pricing and promotional history with clear date ranges and discount depths. Tag promotional types (percentage off, dollar off, buy-one-get-one, bundle deals) because each affects demand differently. Include planned future promotions so models can predict promotional lifts.

External Data Sources That Improve Accuracy

Competitive intelligence matters more than most businesses realize. Competitor stockouts often drive demand spikes as customers seek alternatives. Competitor price cuts can suppress your demand. Web scraping or third-party services can provide this data, though it requires careful interpretation.

Economic and demographic data at relevant geographic levels. For businesses serving specific regions, local unemployment rates, housing starts, or population growth correlate with demand better than national averages. Government sources provide this data freely, but it requires geocoding your customer base.

Weather data and forecasts influence demand for obvious categories (snow blowers, air conditioners) and surprising ones (beer sales correlate with temperature, online shopping increases during rainy weekends). Historical weather data is free from NOAA; forecasts require paid APIs.

Social media and search trends provide leading indicators. Google Trends data is free and can predict demand shifts 2-4 weeks ahead for trending products. Social listening tools (paid) track brand mentions, sentiment, and emerging topics that drive unexpected demand.

Data Quality Standards

More important than data volume is data quality. Implement these standards:

• Consistency: Product identifiers, customer IDs, and location codes must remain stable over time. Changing SKU numbering systems breaks historical analysis.
• Completeness: Missing data points reduce forecast accuracy more than you’d expect. A single missing day in sales history can throw off seasonality detection.
• Timeliness: Stale data produces stale forecasts. Establish real-time or near-real-time data feeds rather than nightly batch updates.
• Accuracy: Validate data against known ground truth. If your system shows 1,000 units sold but only 900 shipped, investigate the discrepancy.

Advanced forecasting platforms handle many data quality issues automatically—identifying outliers, interpolating missing values, and normalizing formats—but starting with clean data accelerates time-to-value significantly.

Real-World Results: Companies That Transformed Their Inventory Planning

Abstract forecasting improvements mean little without concrete business outcomes. Here are three detailed case studies showing measurable results from AI-driven demand forecasting:

Mid-Market Fashion Retailer: 32% Inventory Reduction

A $45 million annual revenue fashion retailer struggled with seasonal inventory management. Their traditional forecasting relied on last year’s sales adjusted by category growth rates—a method that consistently over-ordered slow sellers and under-ordered trending items.

The Challenge: Fashion’s short product lifecycles (8-12 weeks) meant forecasting errors had no second chances. Overstock went to clearance at 40-60% discounts; stockouts lost sales permanently to competitors.

The Implementation: They deployed AI forecasting that incorporated social media trend analysis, competitor inventory tracking, and weather forecasts. The system analyzed Instagram hashtags, fashion blogger mentions, and TikTok engagement to identify emerging trends before they appeared in sales data.

Results After 12 Months:

• Forecast accuracy improved from 67% to 89% (MAPE reduction from 33% to 11%)
• Average inventory value decreased 32% ($4.2 million reduction)
• Stockout rate dropped from 9% to 2%
• Gross margin increased 4.3 percentage points due to fewer markdowns
• Working capital freed up funded expansion into two new product categories

The retailer’s CFO noted that reducing inventory by $4.2 million while improving in-stock rates was “impossible with our old methods—we assumed we had to choose between excess inventory or stockouts.”

Consumer Electronics Distributor: $2.1M Annual Savings

A consumer electronics distributor with 8,000 SKUs across three warehouses faced constant firefighting—expedited freight to cover stockouts, aged inventory write-offs, and customer complaints about delivery delays.

The Challenge: Electronics demand is notoriously volatile, driven by product launches, price wars, and rapid obsolescence. Their spreadsheet-based forecasting couldn’t keep pace with daily market shifts.

The Implementation: AI forecasting integrated with their warehouse management system to optimize inventory positioning. The system predicted not just how much to order, but which warehouse should hold which quantities based on regional demand patterns and shipping costs.

Results After 18 Months:

• Forecast accuracy improved from 71% to 93%
• Expedited freight costs decreased 67% ($890,000 annual savings)
• Obsolescence write-offs reduced 54% ($720,000 annual savings)
• Average order fulfillment time decreased from 3.2 days to 1.8 days
• Customer satisfaction scores increased from 78% to 91%
• Total documented savings: $2.1 million annually

The operations director emphasized that “AI didn’t just improve our forecasts—it eliminated the chaos. Our team stopped spending 60% of their time on emergency fixes and started focusing on strategic improvements.”

Health and Beauty Brand: 3x New Product Success Rate

A health and beauty brand launching 40-50 new SKUs annually struggled with new product forecasting. Traditional methods required historical sales data they didn’t have, leading to conservative initial orders that missed opportunities or aggressive orders that created excess inventory.

The Challenge: New products represented 35% of annual revenue but consumed disproportionate management attention and created 60% of inventory write-offs.

The Implementation: AI models used similar-product analysis, comparing new launches to historical products with comparable attributes (category, price point, target demographic, marketing spend). Social media pre-launch buzz and early sales velocity informed rapid forecast adjustments.

Results After 24 Months:

• New product forecast accuracy improved from 52% to 78%
• Percentage of new products achieving profitability targets increased from 31% to 89%
• New product inventory write-offs decreased 71%
• Time to profitability for new SKUs decreased from 8.3 months to 4.1 months
• Overall new product revenue increased 47% with same launch volume

The brand’s product development team noted that better forecasting enabled them to “double down on winners faster and cut losers earlier—we’re no longer guessing which products will succeed.”

These results aren’t outliers. Businesses implementing AI-driven forecasting through platforms like ShipPost typically achieve 20-40% inventory reductions, 15-30% forecast accuracy improvements, and 10-25% service level gains within the first year.

5 Common Demand Forecasting Mistakes That Cost Businesses Millions

Even with AI-powered tools, businesses make predictable mistakes that undermine forecasting effectiveness. Avoid these costly errors:

Mistake 1: Optimizing for Average Accuracy Instead of Business Outcomes

Many businesses obsess over improving overall forecast accuracy without considering that different errors have different costs. A 20% forecast error on a $5 product with 10% margin costs $1 in lost profit. A 20% error on a $500 product with 40% margin costs $40.

The Fix: Implement weighted accuracy metrics that prioritize high-value products. Optimize for profit impact, not statistical elegance. Some AI platforms allow you to specify custom loss functions that penalize high-value errors more heavily than low-value ones.

Mistake 2: Ignoring Forecast Bias

Forecast accuracy measures absolute error, but bias measures systematic over- or under-forecasting. A forecast that’s consistently 10% high has the same MAPE as one that’s randomly ±10%, but the business impacts differ dramatically. Consistent over-forecasting creates perpetual excess inventory; under-forecasting creates chronic stockouts.

The Fix: Track forecast bias separately using Mean Percentage Error (MPE). If MPE is significantly positive or negative, investigate root causes. Sales teams often introduce upward bias (“sandbagging” forecasts to look good when they beat them). AI models can develop bias if training data includes promotional periods without proper tagging.

Mistake 3: Treating All Products the Same

Applying identical forecasting methods across your entire catalog ignores fundamental differences in demand patterns. Fast-moving staples follow different patterns than seasonal items, which differ from fashion products, which differ from new launches.

The Fix: Segment your catalog by demand characteristics and apply appropriate forecasting approaches to each segment:

Product Type	Demand Pattern	Best Forecasting Approach
Core staples	Steady, predictable	Time series with trend adjustment
Seasonal items	Annual cycles	Seasonal decomposition + ML
Fashion/trend	Short lifecycle, volatile	Similar-product analysis + social signals
New products	No history	Analogous product modeling + early velocity
Long-tail/slow movers	Intermittent, lumpy	Probability-based methods

Mistake 4: Over-Relying on Automation Without Human Oversight

AI forecasting works remarkably well for routine situations, but algorithms don’t read industry news, attend trade shows, or understand strategic decisions. Blindly following automated forecasts during market disruptions or major business changes leads to poor outcomes.

The Fix: Establish clear governance for forecast overrides. Define scenarios requiring human review (new product launches, promotional events, supplier changes, market disruptions). Track override accuracy—if humans consistently improve on AI forecasts in certain situations, incorporate that feedback into model training.

Mistake 5: Neglecting Forecast Value of Information

Businesses often invest heavily in improving forecasts for products where accuracy matters little. If a product has a 90-day lead time and stable demand, improving forecast accuracy from 80% to 90% adds minimal value—you have plenty of time to react. Conversely, for products with 7-day lead times and volatile demand, accuracy improvements are extremely valuable.

The Fix: Calculate the “forecast value of information” for different product segments considering lead times, demand volatility, and profit margins. Focus improvement efforts where better forecasts create the most value. This might mean accepting 70% accuracy for slow-moving items while pushing for 95% accuracy on fast-moving, high-margin products with short lead times.

The Future of Demand Forecasting: Emerging Technologies and Trends

Demand forecasting continues evolving rapidly. Understanding emerging trends helps businesses prepare for the next generation of capabilities:

Probabilistic Forecasting Becomes Standard

Traditional forecasting generates point estimates—”we’ll sell 1,000 units next month.” This single number provides no information about uncertainty. Will actual sales be 900-1,100 (low uncertainty) or 500-1,500 (high uncertainty)?

Modern AI systems generate probability distributions showing the full range of likely outcomes. Instead of “1,000 units,” you get “70% probability of 900-1,100 units, 95% probability of 750-1,250 units.” This probabilistic approach enables risk-based inventory decisions—holding more safety stock for uncertain products, less for predictable ones.

By 2026, probabilistic forecasting will become table stakes for competitive logistics operations. Businesses still using point estimates will struggle to optimize inventory across thousands of SKUs.

Real-Time Forecast Updates

Current forecasting typically updates daily or weekly. Emerging systems update continuously as new signals arrive—a social media spike, a competitor stockout, a weather forecast change. This real-time responsiveness enables dynamic inventory allocation and pricing.

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