Enterprise Supply Chain Decision Intelligence (ESDI): A Unified Framework for Statistical Intelligence, AI, AI Agents, Procurement, Inventory and Enterprise Decision-Making
Aug 2026· International Journal of Science, Strategic Management and Technology· Vol 02, pp. 1-9· 0 citations
TL;DR
Enterprise Supply Chain Decision Intelligence (ESDI) is proposed as a management discipline that integrates Statistical Intelligence, forecasting science, machine learning, artificial intelligence, AI Agents, inventory science, procurement, warehousing, finance, governance, human expertise and continuous organizational learning.
Abstract
Enterprise planning has progressed from experience-based judgement and spreadsheet calculations to enterprise resource planning, advanced planning systems, statistical forecasting, machine learning, artificial intelligence, digital visibility and emerging AI-agent architectures. Yet organizations across industries continue to experience stock-outs, excess inventory, revenue leakage, procurement delays, working-capital pressure, planner overrides and fragmented decisions. This persistent paradox suggests that forecasting accuracy, although important, is not by itself an adequate measure of enterprise planning performance.
This conceptual article proposes Enterprise Supply Chain Decision Intelligence (ESDI) as a management discipline that integrates Statistical Intelligence, forecasting science, machine learning, artificial intelligence, AI Agents, inventory science, procurement, warehousing, finance, governance, human expertise and continuous organizational learning. The framework does not seek to replace established forecasting methods. Instead, it proposes that demand behaviour should be understood before a forecasting method is selected; forecasts should be evaluated in the context of business decisions; and AI should translate intelligence into explainable, constraint-aware and measurable enterprise action.
The article introduces several proposed constructs: the Statistical Intelligence Layer, the Enterprise Forecast Selection Engine (EFSE), the Decision Cascade Framework, the Enterprise Demand Genome (EDG), the AI Agent Governance Framework and an Enterprise Decision Performance perspective. The EDG is proposed as a dynamic behavioural decision profile that extends static enterprise master data by capturing demand behaviour, lifecycle, variability, seasonality, promotion sensitivity, supply characteristics, margin, inventory policy, forecast confidence, AI confidence and learning history.
The article is intentionally industry neutral. Retail, manufacturing, healthcare, pharmaceuticals, automotive, electronics, consumer goods and distribution are treated as application contexts rather than as the basis of the theory. The contribution is conceptual and requires empirical validation. The central proposition is that organizations increasingly compete not merely through operational efficiency or forecast accuracy, but through the quality, speed, explainability, accountability and continuous improvement of enterprise decisions.
Procurement processes at enterprise level require bona fide validation and risk evaluation for financial compliance, operational transparency, and streamlined approval workflows. In this study, ASAP (Agentic System for Automated Procurement), a decision support system powered by artificial intelligence has been designed to improve procurement approval processes in enterprise ERP spaces. The suggested Architecture gets the information of purchase requisition from SAP S/4HANA using secure OData APIs. It has supplier, quantity, pricing, and delivery schedule details. A deterministic rule-based AI engine analyzes the compliance policy of the organization, supplier indicators of reliability, budget utilization thresholds, and procurement anomalies to assess procurement risks. The system produces interpretable risk scores and generates explainable advisory recommendations that aid the procurement manager and finance controller in an informed approval decision. The tests showed that decisions are taken consistently, audits can be traced well and delays in procurement approval are minimized. The suggested ASAP framework performs transparent and scalable intelligent procurement risk assessment while maintaining the transactional integrity of ERP and adherence to the financial governance policies of the enterprise.
Arun Saravanan S, A. R. Kumar, Vijay Kumar J et al.· 2026 7th International Confe...· 0 citations
Artificial intelligence is increasingly embedded in management information systems to support strategic decisions involving forecasting, resource allocation, market intelligence, risk assessment, supply-chain resilience, financial control, cybersecurity, human-resource analytics and organizational governance. Strategic decision-making differs from routine operational automation because it involves uncertainty, long-term consequences, value-laden trade-offs, reputational exposure, regulatory obligations and human accountability. In such settings, predictive accuracy alone is insufficient. Managers require explanations that clarify why an AI system recommends a course of action, what evidence and assumptions shaped the recommendation, how reliable the output is under changing conditions and who remains accountable when algorithmic advice influences organizational outcomes. Explainable artificial intelligence (XAI) has therefore become a critical capability for transforming black-box machine-learning outputs into decision-relevant, contestable and auditable knowledge. This review critically examines XAI for strategic decision-making in management information systems, focusing on transparency, trust calibration and governance frameworks. The paper synthesizes interpretable modelling, post-hoc local explanations, feature attribution, counterfactual explanations, surrogate models, causal explanation and human-centred explanation interfaces. It further evaluates how these approaches influence managerial trust, decision quality, accountability, compliance and organizational learning. The review argues that XAI should not be treated as a technical add-on to predictive modelling; it must be embedded across the complete decision lifecycle through data governance, model documentation, explanation quality controls, stakeholder participation, human oversight and continuous monitoring. A conceptual XAI-GovMIS framework is proposed to connect data governance, model transparency, explanation design, human-AI interaction, strategic decision accountability and responsible AI governance. The paper concludes by identifying unresolved research gaps, including explanation overload, performative transparency, overtrust, weak empirical validation, causal insufficiency, cross-functional accountability gaps and the need for sector-specific governance models for AI-enabled management information systems.
Sampath Kini, Shital Ajit Dumbre, Sunita Kumar et al.· Journal of Intelligent Decis...· 0 citations
An Autonomous Decision Assurance Layer (ADAL) is proposed for AI-driven enterprise analytics environments that bridges data governance, multi-agent AI, human-in-the-loop oversight, responsible AI controls, and executive decision intelligence.
Choudhry Bilal Mazhar· International Journal for Re...· 0 citations
The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights, and organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage.
Peter Stone· Research Journal in Business...· 0 citations
Supply chain uncertainty has increased the complexity of inventory decision-making due to demand fluctuations, supplier disruptions, and rapidly changing operational conditions. Although artificial intelligence (AI) has demonstrated strong potential in improving supply chain analytics, existing approaches often investigate prediction, explainability, optimization, and human involvement as separate components. This study proposes a Human-Centered Artificial Intelligence (HCAI) framework for adaptive inventory decision support under supply chain uncertainty by integrating AI-based demand prediction, Explainable Artificial Intelligence (XAI), adaptive inventory optimization, and human decision support within a unified framework. The Design Science Research Methodology (DSRM) was adopted to develop and evaluate the proposed framework using empirical inventory data collected from food distributors and rice warehouses in Pidie Regency and Bireuen Regency, Aceh, Indonesia. Forecasting models, including ARIMA, Exponential Smoothing, Random Forest, LSTM, and XGBoost, were evaluated using RMSE, MAE, and MAPE, with the best-performing model subsequently integrated with SHAP-based explanations and a Mixed-Integer Programming (MIP) optimization model. The evaluation results showed that XGBoost achieved the highest forecasting performance with a MAPE of 7.40%. The proposed framework reduced total inventory cost by 22.7%, improved service level from 88.5% to 96.8%, and decreased stockout rate from 11.5% to 3.2% compared with the conventional inventory policy. Furthermore, user evaluation involving 20 inventory practitioners indicated positive acceptance of the Human Decision Support Layer, with high scores for perceived usefulness, trust, and decision confidence. These findings demonstrate that integrating predictive intelligence, explainable AI, adaptive optimization, and human oversight enable transparent and adaptive inventory decision support under uncertain supply chain conditions. This study contributes to advancing Human-Centered AI applications in supply chain management by providing an integrated framework that bridges AI capability with human operational expertise
A. D. Kuraesin· International Journal of Eng...· 0 citations
The integration of Enterprise Resource Planning (ERP) systems with Artificial Intelligence (AI) and data analytics-based predictive maintenance is a pillar of industrial digital transformation in the industry 4.0 and 5.0 era. However, a comprehensive understanding of integration patterns, AI methods, benefits, and challenges remains scattered across the literature. This study conducts a Systematic Literature Review (SLR) using the PRISMA protocol to map the current state of ERP integration with AI-based predictive maintenance. A search was performed on the Scopus database, yielding 59 initial articles that were reduced to 10 final open-access articles published between 2024 and 2026. The results show that the dominant AI methods are Long Short-Term Memory (LSTM) and ensemble learning (XGBoost, Random Forest), often combined with Digital Twin technology. ERP integration patterns range from decision support to full bidirectional integration via OPC-UA and REST API protocols. Key benefits include up to 40% improvement in mean time to failure (MTTF), a 30% reduction in maintenance costs, and a return on investment (ROI) of 42.5%. The main challenges include data quality, legacy system interoperability, cybersecurity, and limited multi-site validation.