A Stakeholder Aware Decision Support System for Production Planning in Make-to-Order Manufacturing: A RAG Enabled Generative AI Approach

Articles in Press

Document Type : Research Article

Authors

Mozhde Rahjoo ¹

Farzad Haghighi Rad ²

Amir-Reza Abtahi ²

¹ Department of Operations Management and Information Technology, Faculty of Management, Kharazmi University, Tehran, Iran.

² Department of Operations Management and Information Technology, Faculty of Management, University of Kharazmi, Tehran, Iran

10.22067/jstinp.2026.98779.1206

Abstract

Make-to-order (MTO) manufacturing involves production planning decisions under uncertain orders, incomplete operational information, and conflicting stakeholder priorities. To address the need for traceable and defensible cross-functional alignment, this study develops a stakeholder aware, RAG enabled decision support system (DSS) operationalized through a mixed-methods industrial case study. The system integrates empirical data from 200 orders, 279 downtime events, and 400 quality control, nonconformity, and optical testing records. Rather than relying on probabilistic models for execution calculations, the architecture enforces deterministic release gates to evaluate organizational release readiness prior to optimization. Retrospective evaluation reveals that the gates classified organizational release status, releasing 170 orders and placing 30 orders on HOLD due strictly to quality control (QC) and optical time-domain reflectometer (OTDR) violations. For the organizationally releasable orders, the engine evaluates two benchmark policies (BASE_FIFO and BASE_EDD) together with three generated scenarios (PROFIT, EFFICIENCY, and CONSENSUS) using the implemented E2 stakeholder-weighted evaluation model. The objective weights were obtained through direct preference elicitation (production efficiency = 0.4688, profit = 0.2997, and value added = 0.2314). The baseline policy (BASE_EDD) benchmark achieved the highest E2 stakeholder-weighted score of 0.5664 and maintained an OTD rate of 90.0% for the evaluated dataset. By restricting the local Large Language Model (Llama-3-8B) strictly to an explainable AI (XAI) interaction layer, the system generates source-grounded justifications without altering deterministic data. The primary academic contribution of this research is the structural separation of organizational release control from multi-objective scenario evaluation, providing a verifiable socio-technical framework for complex MTO environments.

Keywords

Make-to-Order Manufacturing

Systems Thinking

Decision Support System

Retrieval Augmented Generation

Generative AI

Stakeholder Alignment

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