International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Architecting an Order Planner Portal and API for Discrete Manufacturing Efficiency

Author(s) Ravikumar Thinnatti Palanicham
Country United States
Abstract In contemporary discrete manufacturing environments, the efficient allocation of resources and production process optimization are crucial. This paper recommends the architecture of an order planner portal and API to be used for automating the decision-making process concerning the choice of the most suitable manufacturing location for window production. The system combines real-time production schedules, resource availability, plant capacities, and logistical limits to suggest the best plant to fulfill orders. The suggested method utilizes machine learning and rule-based algorithms to maximize plant allocation, offering manufacturers greater operational efficiency, lower costs, and enhanced decision-making.
In the windows manufacturing business, determining where to make a specific order is a complicated decision considering machine availability, transportation expense, order timing, and resource limitations. Automated processes are necessary to enhance speed and accuracy because manual methods are time-consuming and error-prone. The suggested system employs a cloud platform and APIs to integrate manufacturing facilities with enterprise resource planning (ERP) and material requirements planning (MRP) systems for real-time decision-making. The following paper proves that the implementation of an automated order planner system can achieve tremendous improvements in lead times, cost control, and resource optimization in a competitive production scenario.
Keywords Order Planner, API, Discrete Manufacturing, Windows Manufacturing, Plant Location Selection, Manufacturing Efficiency, Production Optimization, Machine Learning, Logistics Optimization
Field Engineering
Published In Volume 5, Issue 1, January-February 2023
Published On 2023-02-08
DOI https://doi.org/10.36948/ijfmr.2023.v05i01.43152
Short DOI https://doi.org/g9gqnk
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