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Procedia CIRP 79 (2019) 574–579
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12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 18-20 July 2018, Gulf of Naples, Italy Warehouse Design and Operation using Augmented Reality technology: A Papermaking Industry Case Study Dimitris Mourtzis a, *, Vasilios Samothrakis a , Vasilios Zogopoulos a , Ekaterini Vlachou a a Laboratory for Manufacturing Systems and Automation(LMS), Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, 26504, Greece * Corresponding author. Tel: +30 2610 910160; fax: +30-2610-997744. E-mail address: mourtzis@lms.mech.upatras.gr
Abstract
© 2019 The Authors. Published by Elsevier B.V. Peer-revie Z under responsibility of the scienti ¿ c committee of the 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering. Keywords: Warehouse design; Augmented reality; Warehouse simulation; Manufacturing In modern, high competitive markets, efficient warehousing is critical as it accounts for a great part of logistics costs. Companies try to adopt highly adaptive and flexible warehouse design that may support the integration of novel technologies such as Augmented Reality (AR). This paper proposes a framework for warehouse design which minimizes inventory cost while keeping a high degree of service by supporting the integration of an AR warehousing system. The AR system will support the effective management of operations, by providing meaningful information. The proposed methodology is tested and validated in a real-life case study of a papermaking industry.
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Introduction
and also the market that support effective warehouse design, considering different criteria [4] so as to support decision making while offering simulation features that may visualize the functionality of the designed warehouse. Additionally, novel technologies that shape manufacturing have found fertile ground in different fields [5]. Starting from simpler combinations on laser scanners and barcodes, contemporary warehouses try to integrate new technologies to facilitate managing warehouse available stock and effective product localisation, especially in larger facilities [6]. Utilizing wearables, such as smart watches and head-mounted displays, the operators may easily report product input/ output in the warehouse, while also retrieve the position of a stored product [7]. Using advanced visualization techniques, such as Augmented Reality, the operators may intuitively be guided to find a stored product and easily report stock updates [8]. This paper proposes an adaptive methodology for warehouse design while integrating Augmented Reality. Logistics and statistical analysis has been used and the final is designed according to Economic Order Quantity (EOQ) model, aiming to minimize inventory cost. Additionally, an
Warehouses are a key aspect of modern supply chains and play a vital role in the success, or failure, of businesses today, since they: a) provide storage for raw materials, components, work-in-process, and finished goods b) operate as distribution and order fulfilment centres and c) perform localized and value-added warehousing [1]. For these reasons, warehouses have to be highly adaptive to the production rate so as to reach optimal operation level. Inventory management and control is the key to warehouse design and optimization. The main goal of inventory management and control is to optimize three targets: customer service, inventory costs, and operating costs. Service level must be kept at high levels while inventory and operation costs must be minimized [2]. Warehouse design demands a very methodical and well- structured approach due to warehouse complexity. Warehouse design is highly complex as it is a multi-criteria problem, with interconnected functions and parameters. Warehouse simulation is a commonly used approach to improve its design [3]. Novel digital solutions have been proposed in the literature
2212-8271 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering. 10.1016/j.procir.2019.02.097
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