Warehouse Management Systems: Comparison of Two Pittsburgh-Based Manufacturing Firms

Warehouse Management Systems: Comparison of Two Pittsburgh-Based Manufacturing Firms

Copyright: © 2025 |Pages: 17
DOI: 10.4018/978-1-6684-7366-5.ch026
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In this article, a qualitative business mini-case study concerning issues surrounding warehouse management systems and supply chain management techniques are implemented at two very different Pittsburgh-area companies. The increasing importance of supply chains as it relates to selection and management of real-time data to improve its overall efficiency and proactiveness are briefly highlighted through the literature. This chapter showcases some of these techniques developing into integrated management tools. It is hoped as business firms enter into deeper manufacturer/supplier relationships. These relationships will change from being skeptical and hostile to being mutually cooperative and dependent on one another.
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Warehouse Management Systems and Supply Chain Management Technologies

A warehouse management system (WMS) is an integral part of supply chain management. It enables a warehousing operation to gather data about items as they are received, monitor inventory, and facilitate picking, packing, and shipping processes. A WMS should be able to support information visibility between the warehouse and procurement and logistics operations. This improves accuracy, quality, and productivity for the entire supply chain (Chaturvedi & Chakrabarti, 2018; Clarke & Clarke, 2014; Constantinos & Oyon, 2010; Shah, et al., 2008). Beginning with automatic identification and data capture (AIDC), the need for paperwork and manual processes is reduced; WMS makes possible the automation of warehouse operations, reducing time and effort of staff (Biswas & Sarker, 2008; Browning & Heath, 2009). Vendor-managed inventory (VMI) can optimize inventory levels in the warehouse, while approaches such as just-in-time (JIT), digital twins, and or cross-docking can almost eliminate costly storage of warehouse inventory entirely. A number of these applications and/or technologies are discussed in the next sections.


Automatic information and data capture (AIDC) can be defined as a systematic data capture that more traditionally was manual inputted by notation or keyboard. AIDC inherently reduces input error by reducing human interaction with the data (Baxter & Hirschhauser, 2004; Beldona & Tsatsoulis, 2010; Bergé, 2016; Blanchard, 2014; Smith & Offodile, 2008). Digital twins couples with AIC and blockchain technologies (Bhurjee, et al., 2018; Casino, et al., 2019; Tseng, et al., 2018; Wang, et al., 2019; Weking, et al., 2020), provide a very promising future in logistics and supply chain technologies in terms of enhanced efficiencies and economies of scale in the short-term future. The purpose is obtaining accurate and reliable identification of physical objects in real time and in meaningful detail. Its simplest form might involve affixing a barcode to an item and then scanning that barcode to enter its number and corresponding data into an information system. More advanced than traditional barcodes are RFID (radio frequency identification) tags, which can contain more data and can transmit that data with no direct line-of-sight between tag and reader (Brandon-Jones, 2017; Casadesus & de Castro, 2005). AIDC technologies are an essential first step for a modern warehouse management system (WMS) (Basu & Nair, 2012; Smith, 2005; Yang & Park, 2011).

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