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Improving Order Accuracy by Integrating Sales and Inventory Data – A Unified Data Synchronization Framework for Hospitality Supply Operations

Author(s) Kishan Patel
Country United States
Abstract Hospitality supply distributors frequently experience order inaccuracies due to disconnected sales and inventory systems, delayed stock updates, manual order verification processes, and inconsistent commu-nication between warehouse and sales teams. These operational inefficiencies often result in incorrect shipments, duplicate orders, backorders, delayed fulfillment, and reduced customer satisfaction. This paper presents an expanded, data-driven framework for improving order accuracy by integrating sales and inventory data into a unified operational environment. The proposed framework combines central-ized inventory visibility, automated stock synchronization, real-time order validation, inventory reserva-tion logic, exception management, and KPI-driven governance to reduce fulfillment errors and improve service reliability. An Order Accuracy Performance Index (OAPI) is introduced to measure synchroniza-tion quality across order accuracy rate, mismatch frequency, fulfillment delay rate, manual correction workload, synchronization latency, and customer complaint intensity. Simulation results using a hospital-ity supply operation processing approximately 42,000 annual order lines show that system integration increases order accuracy from 89.1% to 97.2%, reduces inventory mismatch incidents by 55-65% across major product categories, decreases average fulfillment processing time from 3.6 days to 2.1 days, and lowers order-related customer complaints over successive operating quarters. The framework is designed for repeatable deployment across hospitality suppliers managing large SKU portfolios, project-driven mo-tel purchasing, and multi-department order execution workflows.
Keywords order accuracy, sales integration, inventory synchronization, hospitality supply chain, ware-house analytics, ERP integration, fulfillment optimization, business intelligence.
Field Engineering
Published In Volume 7, Issue 6, June 2026
Published On 2026-06-03
DOI https://doi.org/10.70528/IJLRP.v7.i6.2269
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