Abstract
Big data analytics has played an enormous role in supply chain operations in recent years. Today, web-applications, social media, intelligent machines, sensors, mobile phones, and other innovative information technology devices generate big data in supply chain operations. These data often provide new digitized services that improve supply chain performance. In this operating environment, heterogeneous enterprise applications, manufacturing processes, or supply chain management, either inside a single enterprise or among network enterprises, require sharing of data-driven information. Thus, data management and its analytical interpretation have become significant drivers for management, product development, and provision of relevant services in network enterprises that function as mediators in effective and sustainable supply chain operations. This chapter reviews the effect of big data analytics on the supply chain operation literature, highlights how modern supply chains can manage sustainability, and presents a conceptual information system architecture.
TopIntroduction
There is considerable awareness and concern for environmental, social, and economic effects on apparel (e.g., textile products, foot ware) production and consumption (Pal, 2023). It has increased pressure from consumer organizations, social, and environmental advocacy groups for apparel-based business organizations and policymakers to develop sustainable supply chain management (Pal, 2022). The present apparel products consumption pattern and ever-increasing population pose concerns about apparel production and serving supply chain management systems (Pal, 2018).
The requirement for sustainable development is widely recognized by regulative bodies, business organizations, and consumers and can be defined as development that meets the current requirements without compromising future generations' capabilities to meet their needs (Pal, 2021). It thus requires a global view of development, emphasizing the relationships between environmental improvement and social equitability through sustainable economic growth and performance (Coyle, 2023) (Stern, 2007). In this way, enterprises' supply chain managers consider including well-positioned to support sustainable development at this critical juncture due to their wide-ranging impacts and influences. However, most of the recent research considers the sustainability aspect of the supply chain by considering social, economic, and environmental elements for regular operations (Pal, 2023). The effect of big data on Lean, Agile, Resilient, and Green (LARG) related issues is attracting significant promise for industrial supply chain research (Chun Wu, 2003) (Dora et al., 2016) (Baramichai et al., 2007) (Cabral et al., 2012) (Srivastava, 2007) in recent decades. It is also highlighted that the LARG concepts-based supply chain operations assist in lowering production lead-time and transportation time, improving data-driven integration levels, and achieving effective information exchange (Alqudah et al., 2020). Lean supply chain interconnects the interdependent partners to eliminate waste through techniques such as value chain analysis (Taylor & Pettit, 2009) (Dey et al., 2019). Manufacturing companies in developing economies (i.e., India, Indonesia, Malaysia) have already started the implementation of LARG practices, and it turned out to be a crucial benchmarking tool to support their heterogeneous data integration applications (Chavez et al., 2020) (Wong et al., 2018).
Key Terms in this Chapter
Virtual Reality (VR): It is a term used for computer-generated three-dimension (3D) environments that allow the user to enter and interact with synthetic environments. The users can immerse themselves to varying degrees in the artificial computer world, which may either be a simulation of some form of reality or a complex phenomenon.
Neural Network: Neural network is an information processing paradigm inspired by how biological nervous systems, such as the brain, process information. It uses a classification mechanism that is modelled after the brain and operates by modifying the input through weights to determine what it should output.
Radio Frequency Identification (RFID): This wireless technology is used to identify tagged objects in certain vicinities. Generally, it has three main components: a tag, a reader, and a back end. A tag uses the open air to transmit data via a radio frequency (RF) signal. However, it is also weak in computational capability. Finally, RFID automates information collection regarding an individual object's location and actions.
Internet of Things (IoT): The Internet of things (IoT) is the inter-networking of physical devices, vehicles (also referred to as “connected devices” and “smart devices”), buildings, and other items; embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data.
Decision Making Systems: A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities, typically ranking, sorting, or choosing from among alternatives. Decision support systems can be either fully computerized, human-powered or a combination of both.
Augmented Reality (AR): It is a modern technology that involves the overlay of computer graphics on real-world applications.
Big Data Analytics (BDA): Analytics is the discovery, interpretation, and visualization of meaningful patterns in Big Data. In order to do this, analytics use data classification and clustering mechanisms.
Supply Chain Management (SCM): A supply chain consists of a network of key business processes and facilities involving end-users and suppliers that provide products, services, and information.