[Paper] Agentic AI Framework for Smart Inventory Replenishment

Source: arXiv - 2511.23366v1

Overview

The paper proposes an agentic AI framework that automates the end‑to‑end inventory replenishment loop for mid‑size retail stores. By combining demand forecasting, supplier‑selection optimization, multi‑agent negotiation, and continuous learning, the system aims to cut stock‑outs, lower holding costs, and keep the product mix fresh—issues that affect every retailer from e‑commerce platforms to brick‑and‑mortar chains.

Key Contributions

• Agent‑centric architecture that treats forecasting, supplier selection, and negotiation as autonomous agents that cooperate through a shared knowledge base.
• Hybrid forecasting model blending classical time‑series (ARIMA, exponential smoothing) with lightweight deep learning (LSTM) to handle both regular sales patterns and sudden trend spikes.
• Multi‑objective supplier optimization that balances price, lead‑time, reliability, and sustainability metrics using a Pareto‑front approach.
• Negotiation protocol based on reinforcement‑learning agents that iteratively propose order quantities and prices, learning from supplier responses.
• Continuous learning loop that updates demand models and supplier scores in near‑real time from sales, returns, and market‑trend signals (e.g., social media buzz).
• Prototype deployment in a mid‑scale mart, evaluated on three real‑world and synthetic datasets, showing measurable improvements over baseline heuristics.

Methodology

1. Data Ingestion – POS transactions, inventory logs, supplier catalogs, and external trend feeds (social media, Google Trends) are streamed into a central data lake.
2. Demand Forecasting Agent –
   • Performs feature engineering (seasonality, promotions, holidays).
   • Runs a two‑stage model: a statistical baseline for stability, then an LSTM fine‑tuner for anomaly detection.
3. Supplier Selection Agent –
   • Constructs a candidate set per SKU, scoring each on cost, lead‑time, fill‑rate, and ESG (environmental/social) factors.
   • Uses a multi‑objective evolutionary algorithm to generate a Pareto‑optimal shortlist.
4. Negotiation Agent –
   • Models each supplier as an environment; the agent’s policy (price & quantity offers) is trained via Q‑learning with reward = cost savings – penalty for delayed delivery.
   • Negotiations run asynchronously, allowing parallel talks with multiple suppliers.
5. Learning & Feedback Loop –
   • After each replenishment cycle, actual sales, delivery performance, and margin outcomes are fed back to update the forecasting weights and supplier scores.
6. Evaluation –
   • Benchmarked against three heuristics: (a) simple reorder‑point, (b) EOQ (Economic Order Quantity), and (c) rule‑based seasonal adjustments.
   • Metrics: stock‑out frequency, average inventory holding cost, product‑mix turnover (GMV per SKU).

Results & Findings

The framework consistently outperformed the heuristics across all three test datasets, especially when demand spikes were driven by external trends (e.g., a viral fashion item). The reinforcement‑learning negotiator achieved an average 5 % price improvement over the static supplier contracts used in the baselines.

Practical Implications

• For Retail Tech Vendors – The modular agent design can be wrapped as micro‑services (forecasting, supplier‑scoring, negotiation) and plugged into existing ERP or inventory‑management platforms via REST/gRPC APIs.
• For Developers – The paper’s open‑source prototype (Python, PyTorch, Ray RLlib) provides a ready‑to‑extend codebase for building custom agents (e.g., adding sustainability scores or dynamic pricing feedback).
• Cost Savings – A 28 % reduction in holding costs translates directly into lower working‑capital requirements, a compelling ROI argument for mid‑size chains.
• Risk Mitigation – By continuously learning supplier reliability, the system can proactively reroute orders before a disruption, reducing the likelihood of stock‑outs during supply shocks.
• Product Discovery – Trend‑scanning agents surface high‑margin, fast‑moving items that would otherwise be missed, enabling more agile assortment planning.
• Scalability – The architecture leverages distributed computing (Ray) and can scale from a single store to a regional chain with minimal code changes.

Limitations & Future Work

• Data Dependence – Accurate forecasting hinges on clean, high‑frequency sales data; noisy POS logs can degrade performance.
• Supplier Modeling Simplifications – Real‑world contracts often involve complex clauses (volume rebates, exclusivity) that the current RL negotiator does not capture.
• Explainability – While the agents produce quantitative recommendations, the paper notes a need for better human‑readable explanations to gain trust from merchandisers.
• Scalability Tests – Experiments were limited to a single mid‑scale mart; future work should validate the framework across multi‑store networks and e‑commerce fulfillment centers.
• Integration with Pricing Engines – Coupling replenishment decisions with dynamic pricing could unlock further margin improvements, an avenue the authors plan to explore.

Authors

• Toqeer Ali Syed
• Salman Jan
• Gohar Ali
• Ali Akarma
• Ahmad Ali
• Qurat-ul-Ain Mastoi

Paper Information

• arXiv ID: 2511.23366v1
• Categories: cs.AI, cs.MA
• Published: November 28, 2025
• PDF: Download PDF