AI-Driven Management of Out-of-Stock, Preorder and Backorder Scenarios in E-Commerce: A Comprehensive Analysis

Abstract

Out-of-stock, preorder and backorder situations in e-commerce are significant challenges, impacting customer satisfaction and revenue. This paper examines how e-commerce giants - Amazon, Walmart, Shein, Temu, Target, Best Buy and eBay- leverage AI-driven strategies for inventory management and customer engagement. It also explores how AI-driven inventory management can align with sustainability goals, such as reducing overproduction and optimizing supply chain efficiency. Focusing on technical solutions, SME strategies, global perspectives and customer experience, the paper provides actionable insights into managing inventory challenges effectively.

Keywords: Out-of-Stock, Preorder, Backorder, E-commerce, Artificial Intelligence, Inventory Management, Small Business Strategies, Machine Learning, Customer Experience, Predictive Analytics, Sustainability, Emerging Markets

1. Introduction
Inventory challenges such as Out-of-Stock, preorder and backorder scenarios disrupt the customer experience and damage brand loyalty. Addressing these requires advanced tools like predictive analytics, real-time inventory management and customer-centric communication strategies. AI has become pivotal in improving supply chain efficiency and ensuring sustainability in operations. This paper explores AI’s role in mitigating these challenges while integrating sustainability goals, offering insights into reducing environmental impact through better inventory management.

2. AI-Driven Strategies for Out-of-Stock, Preorder and Backorder Management
2.1. Technical solutions
Out-of-stock management

• Predictive analytics: Time-series forecasting models like ARIMA and advanced techniques such as Long Short-Term Memory (LSTM) networks forecast demand spikes, reducing Out-of-Stock situations.
• Real-time inventory management: IoT-enabled systems and computer vision ensure accurate stock tracking and automated replenishment.

Preorder management

• Demand forecasting: Learning models analyze historical sales and real-time customer data to estimate preorder quantities accurately.
• Recommendation systems: AI recommends alternative products to customers hesitant to wait for preorders.

Backorder management

• Dynamic pricing: Reinforcement learning models optimize pricing for backordered items, balancing demand and profitability.
• Customer communication: AI-driven chatbots provide real-time updates and offer compensation or alternative products to maintain customer satisfaction.

2.2. Sustainability and AI-driven out-of-stock management

Reducing overproduction

• AI demand forecasting minimizes overproduction by accurately predicting customer demand, reducing unsold inventory that often ends up in landfills.
• Example: Shein uses AI to align production volumes with real-time demand, reducing surplus inventory by 15%¹.

Optimizing supply chain efficiency

• AI models can optimize logistics, reducing emissions associated with unnecessary transportation and warehousing inefficiencies.
• Example: Amazon’s use of route optimization algorithms has cut last-mile delivery emissions by 20%².

Lowering energy consumption in warehousing

• Real-time inventory tracking reduces the need for frequent manual checks, lowering energy consumption in warehouses.

Sustainable packaging integration

• AI-powered systems identify opportunities to use eco-friendly packaging for preorders and backorders, reducing environmental waste (Figure 1).
• Example: Walmart’s AI systems recommend minimal packaging options to reduce waste and shipping weight³.
  
  

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Figure 1: Walmart's AI-powered waste management solution³.

Circular economy support

• AI can facilitate returns and recycling programs, ensuring that unsold or returned items are reintegrated into the supply chain or repurposed (Figure 2).
• Example: Best Buy uses AI to identify products eligible for refurbishment or recycling, reducing waste from electronic goods⁴.
  
  

Figure 2: Best Buy's Recycling Offers generated through AI⁵.
2.3. Global perspectives

Emerging market challenges

• Regions like Southeast Asia and Latin America face unique obstacles, such as fragmented supply chains and limited infrastructure. AI must adapt to these constraints using lightweight, data-sparse models.

Localized strategies

• Adapting AI for cultural shopping behaviors, such as festive season peaks in India or cash-on-delivery preferences in Southeast Asia, enhances effectiveness.

Examples:

Blinkit (India): Reduced delivery delays by 20% during peak demand using AI-driven demand forecasting and route optimization⁶.

Mercado libre (Brazil): Improved customer retention by integrating multilingual AI chatbots to handle backorders and preorders efficiently (Figure 3).

Figure 3: Multilingual AI chatbot⁷.

3. Case Studies: E-Commerce Leaders
3.1. Amazon

Focus: AI-powered demand forecasting, dynamic pricing and route optimization⁸.

Outcome: Reduced Out-of-Stock by 25%, $200M increase in Prime Day sales and 20% lower emissions in last-mile delivery.

Sustainability Impact: Enhanced operational efficiency aligns with carbon footprint reduction goals.

3.2. Walmart

Focus: Efficient tracking and eco-friendly packaging solutions⁹.

Outcome: Regional fulfillment accuracy improved while eco-friendly initiatives reduced packaging waste significantly.

3.3. Shein

Focus: Preorder prediction and real-time trend analysis.

Outcome: Overproduction reduced by 15%, aligning inventory with demand.

Sustainability Impact: Reduced waste in fast fashion production processes.

3.4. Temu

Focus: Demand forecasting and dynamic inventory management¹⁰.

Outcome: Enhanced turnover rates and reduced stockouts.

Sustainability Impact: Streamlined logistics reduce transportation emissions (Table 1).

Table 1: Competitive Analysis of Customer Experience and Sustainability¹¹

4. Conclusion

AI-driven solutions are revolutionizing inventory management in e-commerce by addressing Out-of-Stock, preorder and backorder challenges effectively. Integrating sustainability goals into these strategies not only enhances operational efficiency but also aligns with environmental priorities. Companies that prioritize both customer satisfaction and sustainability will emerge as leaders in the evolving e-commerce landscape.

5. Future Directions

AI for carbon neutrality: Develop models that optimize inventory and logistics for minimal environmental impact.

Circular economy expansion: Use AI to identify opportunities for recycling, refurbishing or repurposing unsold items.

Localized innovations: Adapt AI systems to account for regional sustainability challenges, such as waste reduction in emerging markets.

6. References

1. https://www.sustainably-chic.com/blog/ai-helping-shein-become-the-worlds-fastest-polluter
2. https://www.supplychaindive.com/news/amazon-deploys-new-delivery-route-algorithm-condor/630747/
3. Kirti S. "How Walmart plans to use AI to reduce waste," Walmart GlobalTech, 2024.
4. https://www.yahoo.com/news/best-buy-ultra-easy-way110011795.html?guccounter=1&guce_referrer=aHR0cHM6Ly9lZGdlc2VydmljZXMuYmluZy5jb20v&guce_referrer_sig=AQAAAI7kUbnmCgT7MBwPC0MDAdNh1x_35O5czKEc7oJnXWzBxUMzSgp99cXuI7C1zOlDqlda4y5j0PLGPVd6d_UBcfUWnSccTf3zye8
5. https://www.bestbuy.com/site/services/recycling/pcmcat149900050025.c?id=pcmcat149900050025&intl=nosplash
6. https://wiserfeed.in/blinkit-vs-zepto-vs-instamart-the-quick-commerce-revolution-in-india/
7. https://www.smatbot.com/blog/multilingual-chatbots/
8. https://www.forbes.com/sites/amazonwebservices/2021/12/03/predicting-the-future-of-demand-how-amazon-is-reinventing-forecasting-with-machine-learning/
9. Robinson A. "Optimizing Walmart's Supply Chain for Maximum Efficiency," ShipScience, 2024.
10. https://pondviewconsulting.com/the-rise-of-temu-their-supply-chain-competitive-advantage/
11. https://trurating.com/blog/retail-competitor-analysis/