Abstract
Managing distributed and delocalized production is one of the essential characteristics to address in the recent market globalization in the manufacturing industry. In this environment, heterogeneous enterprise applications, either manufacturing or supply chain management, either inside a single enterprise or among network enterprises, require sharing information. Thus, information management has become a significant driver for management and product development in networked enterprises. This article describes an information systems framework for the global product development environment. Several applications involved in manufacturing enterprises may refer to the knowledge, which embeds in it, appropriately storing all its technical data based on a standard model for product development purposes. Then it identifies critical categories of big data analytics applications for the key businesses of the supply chain operations reference (SCOR) model. Finally, the article presents a big data framework for supply chain management (SCM).
TopIntroduction
As more and more manufacturing business operational activity is digitized, a new source of data and ever-cheaper equipment combine (known as software system technology) to usher manufacturers into a new business world in which vast amounts of digitized data exist on virtually any topic of interest to regular operation. Web-based ordering, online shopping, digital communication, and instrumented machinery generate torrents of data as a by-product of their day in day out operations. Each of these is now a dynamic data creator. The data available are often unstructured, not organized in a database, and unwieldy, but there is a massive amount of unwanted information in it, simply waiting to be released. Besides, analytics brought rigorous decision-making techniques, and big data is more straightforward and robust. In this way, SST, particularly data analytics, significantly impacts the manufacturing industry. However, manufacturing professionals have been slow to exploit the full potential of SST. Instead of using SST to maximize productivity and revenue-generation ability, SSTs have been used mainly for enterprise resource planning (e.g., accounting, inventory management, human resource management) purposes within the manufacturing industry. As a result, the manufacturing industry has not yet exploited data analytics-based SST as an effective tool.
In addition, the advantage of globalization has simulated different initiatives in global product manufacturing and marketing business activities. For example, in the 1980s, the “quick response” strategy was developed to maintain a competitive advantage (Porter, 1985) for the domestic manufacturing of products. Technological innovations have made fast electronic communication a global phenomenon (Pal, 2022), and the rapid acquisition of technical skills in various countries has meant that many professional tasks could be outsourced (quality control, raw materials purchasing, sample making). Researchers (Gereffi, 1999) (Pal & Yasar, 2020) identified some of the trends for the manufacturing business. Also, the globalization trends have continued, and the radical social reform idea of making more from fewer resources (known as Gandhian Engineering) (Prahalad & Mashelkar, 2010) has become the business rule in today’s global market. Also, operational planning – and appropriate information system (IS) – drives the whole business, where customers play a pivotal role.
With the technological advances, manufacturing companies regularly employ data mining techniques to explore the contents of data warehouses looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that allow companies to serve their customers better. This way, manufacturing organizations rely on business processes related data to formulate strategy and succeed under value-based reimbursement models. The new paradigm requires data-driven insights that can help operational managers reduce unnecessary variation in business and make more informed service-line decisions across the enterprise. In this way, intelligent data processing plays a key role. This chapter presents some of these issues identifying in particular: (i) the concept of big data, (ii) data gathering, (iii) data processing, and (iv) the broader research dilemmas. Hence, the central theme of this chapter is to expose the reader to some of the more interesting insights into how data and information systems (IS) to help run manufacturing supply chain management.
Evolution in computer processing power and storage capacity has enabled organizations to develop data-rich IS for daily operations, and therefore, there has been tremendous growth for data stored. In addition, business data collection itself has progressed from the transcription of paper-based records via manual data-entry processes to the use of smart cards, mobile phones (Location Data, GPS), Internet of Things (IoT) (e.g., radio frequency identification (RFID) tags, sensors), webcasting and Internet users' mouse clicks. In turn, this data generation has generated a need for new techniques and technologies that can transform these data into appealing and valuable information and knowledge.
Key Terms in this Chapter
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.
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.
Big Data Analytics: 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.
Virtual Reality: 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.
Augmented Reality: It is a modern technology that involves the overlay of computer graphics on real-world applications.
Internet of Things: 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.
Supply Chain Management: A supply chain consists of a network of key business processes and facilities involving end-users and suppliers that provide products, services, and information.
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.