Efficient utilization of supply chain analytics for decision making remains a significant challenge for planners, as critical tasks such as database querying, key performance indicator (KPI) analysis, demand forecasting, and performance diagnosis require heterogeneous expertise spanning data engineering, operations research, and domain knowledge. In this work, we propose an agentic system for supply chain analytics that bridges the gap between business decision-making and technical expertise, where a coordinator agent interprets user intent and delegates sub-tasks to specialized agents. The system supports both exploratory analysis and deterministic workflows, enabling planners to transition between ad hoc questions and structured processes. Domain logic is encapsulated within specialist agents and prompts, yielding a scalable, modular, and auditable design and lowering the cost of functional extension through prompt-centric development. We evaluate the proposed architecture on a test environment that replicates multi-echelon inventory management operations. Results show that our multi-agent design achieves a 90% accuracy, which is competitive with a single agent baseline while reducing input token usage by roughly fourfold, substantially improving scalability and cost-efficiency. Furthermore, we provide case studies to demonstrate interpretable suboptimality detection and automated forecast optimization, illustrating how agentic architectures can effectively combine open-ended exploratory analysis and deterministic supply chain analytics workflows, and provide a practical pathway toward more accessible and extensible decision-support systems.
Figures & tables
Figure 1: Overview of the agentic architecture consisting of the coordinator, open-ended and workflow agents.
Sample Queries
1. What is the delivery lead time between the warehouse and store_01 for June and July 2024?
2. Plot the actual forecast for Item2 in store_01 from December 1, 2024 to January 31, 2025.
3. Explain the demand patterns for Item1 and Item2 at store_01.
4. Which store had the highest demand for Item1 in April 2024?
5. What is the fill rate for Item2 in store_01 from January 12, 2025 to January 30, 2025?
Table 1: Sample User Queries
Model
Setup
Mean Score
Tokens (Rel.)
GPT-4.1
Single-Agent (NR)
0.655±0.027
5.5×
GPT-4.1
Multi-Agent (NR)
0.661±0.018
1.0×
o4-mini
Single-Agent
0.932±0.012
8.4×
o4-mini
Multi-Agent
0.904±0.011
2.5×
Table 2: Performance and Relative Input Token Usage (Lowest = 1 × ). NR denotes non-reasoning models.
Model
Architecture
Fail (%)
Partial (%)
Success (%)
GPT-4.1
Single-Agent
8.4
52.2
39.4
GPT-4.1
Multi-Agent
10.6
46.6
42.8
o4-mini
Single-Agent
1.0
11.6
87.4
o4-mini
Multi-Agent
1.6
16.0
82.4
Table 3: Outcome Distribution Across Configurations
Task Category
o4-mini
GPT-4.1
(Single / Multi)
(Single / Multi)
Analyze or Summarize Results
0.96 / 0.92
0.72 / 0.73
Retrieve Data
0.90 / 0.88
0.55 / 0.54
Visualize Results
0.99 / 0.94
0.50 / 0.47
Calculate Metrics
0.85 / 0.90
0.77 / 0.86
Average
0.94 / 0.91
0.66 / 0.66
Table 4: Performance by Task Category (Mean Score, Single-/Multi-Agent)
Representative Sample of Failure Responses
1. It appears there was an error retrieving the delivery lead time data due to a technical issue with date formatting in the system. The delivery and shipment dates for warehouse to store_01 deliveries for June and July 2024 could not be fetched.
2. There was an error retrieving the data due to a technical issue with date serialization in the query process. This issue relates to how the database handles date fields for output formatting rather than the underlying database structure.
3. The system was unable to retrieve the demand data for Item1 and Item2 at store_01 due to a technical issue with the data source. Specifically, date fields could not be processed and the required file for visualization could not be found.
4. An error occurred while retrieving delivery data for store_01 and store_02, likely because the deliveries table does not exist in the current database or the data source connection is misconfigured.
5. There was an error retrieving inbound shipment data due to a date serialization issue during SQL execution, indicating a technical problem within the tool.
Table 5: Sample Failure Responses
Figure 2: Workflow for store sub-optimality detection.
Figure 3: The agentic system’s user interface with what-if simulation results comparing baseline and optimized forecast for the sub-optimality detection and demand re-forecasting case study.
In today's fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential. Yet most organizations remain data-rich but insight-poor, constrained by the complexity of querying, interpreting, and explaining enterprise-scale information. We present Polaris, a supervisor-led multi-agent framework for conversational enterprise analytics that bridges this gap. Polaris introduces Dynamic Task Coordination (DTC), a decision-theoretic orchestration layer that models agent-task assignment as adaptive bipartite matching, enabling real-time coordination, recovery, and optimization across specialized agents for querying, visualization, and reasoning. By coupling DTC with reason-first, ReAct-style agents, Polaris transforms natural-language queries into coherent analytical workflows that not only retrieve and visualize data but also explain the underlying "why." Evaluation on structured enterprise datasets demonstrates high semantic fidelity and answer relevancy, underscoring the potential of multi-agent orchestration to deliver trustworthy, end-to-end business intelligence at scale.
Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-language interface for this task, but existing methods primarily focus on individual optimization models. Extending them to heterogeneous decision pipelines is challenging because a requirement may admit multiple intervention paths with different downstream effects. We formulate requirement-driven adaptation as the joint selection of an intervention route and an admissible module-level change, and propose a graph-constrained agentic framework in which domain agents expose admissible reformulation interfaces and a central processor searches over bounded intervention paths. Candidates are validated and compared using downstream KPIs. In collaboration with a large retail partner, we evaluate 100 warehouse requirements elicited from practitioner interviews, with GPT, Qwen, and DeepSeek as base LLMs. Relative to direct LLM reformulation, our framework improves correctness and end-to-end success across all three models, raising end-to-end success from 72--76% to 79--83%.
Lei Zheng, Liping Yang, Zihao Li +3
School of Business and Management, Hong Kong University of Science and Technology · School of Management, University of Science and Technology of China · Institute of Operations Research and Analytics, National University of Singapore +1
This paper proposes a multi-agent framework built on CrewAI [1] for conversational business intelligence. Five specialized AI agents operate in a sequential pipeline to process natural language queries, retrieve and analyze data, generate visualizations via the Model Context Protocol (MCP) [2], and deliver actionable insights. The platform features a defense-in-depth security architecture for multi-tenant data isolation and a query parameterization mechanism for transforming conversational insights into reusable dashboard components. Evaluation across 300 end-to-end test cases spanning synthetic and production enterprise datasets demonstrates 95.3% functional accuracy, a mean response latency of 24 seconds, and a response quality score of 4.52/5.0 as assessed by an LLM-as-a-Judge framework, with a 93.0% hallucination-free rate, representing a 22.6 percentage point accuracy improvement and 20.2% quality gain over a single-agent baseline. Cross-model evaluation across four LLM backends and human expert validation confirm architectural generalizability and evaluator reliability. An ablation study confirms that the Data Analysis and Report Aggregation agents are the primary drivers of output quality.
Manoj N M, Vijayakrishna S, Manjunath Srinivas +1
Rakuten India Enterprise Private Limited, Bengaluru, India