There are many scopes for advancement in the application of appropriate analytic techniques in this area. Data analysis techniques can be applied to defect tracking and product quality and to improve activities of the product manufacturing process in manufacturing [91]. The IT infrastructure of cloud computing will enable new approaches for concurrent CAD design and system engineering principles combining mechanical, electrical, and software in product development. RFID data provide automated replenishment signal, automated receiving and storing information, and automated checkout data, which inform the real-time inventory status. Utilize a wide range of data from news, social media, weather data (SNEW), and events as well as direct data inputs from multiple static and dynamic data points provide the capability to predict and proactively plan all supply chain activities. Security – Since the data is huge in size, keeping it secure is another challenge. Inventory control is the system that involves requisition process, inventory management, purchase, and physical inventory reconciliation. For a long time, managers and researchers have used statistical and operational research techniques in order to solving supply and demand balancing problems [8, 9]. Industry influencers, academicians, and other prominent stakeholders certainly agree that Big Data has become a big game-changer in most, if not all, types of modern industries over the last few years. Collecting, managing such huge data, and applying new analytical methods to gain insights and useful information and then apply them to decisions can reduce uncertainty [32]. The optimization technique is a powerful tool for supply chain data analytics [25]. Statistical techniques cannot be used to predict the future with 100% accuracy. Hence, mutual coordination and cooperation between different supply chain units must be established, use BDA techniques to link these units, and exist an ability to share and access data and information throughout the entire supply chain. In the next section, the authors explore the literature related to supply chain risk management. The study of big data is persistently advanced and extended, and the most properties of big data are presently extended into “5 V” concept containing variety, verification/veracity, velocity, volume, and value [3, 4]. Establishing close relationships with key suppliers and enhancing collaboration with them are an important factor in discovering and creating new value and reducing the risk of failure in SRM. Forth, the authors provided a brief information about application of BDA in different types of supply chain. In the era of big data, we need new processing models to process these information assets. According to Technavio, costs of big data technology in the global financial industry will grow by 26% from 2015 to 2019, which suggests the importance of big data in this industry [98]. The technological applications of big data comprise of the following companies which … The effective and appropriate use of big data sources and techniques resulted in enormous improvements in processes of supply chain: Building agile or responsive supply chains through predicting and gaining a better understanding of the market trends and customer expectations and preferences. *Address all correspondence to: [email protected], New Trends in the Use of Artificial Intelligence for the Industry 4.0, Edited by Luis Romeral Martínez, Roque A. Osornio Rios and Miguel Delgado Prieto. Another study applied policy-driven big data to support and improve sustainability measures in various operations. This industry also heavily relies on Big Data for risk analytics, including; anti-money laundering, demand enterprise risk management, "Know Your Customer," and fraud mitigation. BDA is also used to support risk management and regulatory reporting activities [99]. BDA is applied to all transactions and activities of the financial service industry, including forecasting and creating new services and products, algorithmic trading and analytics, organizational intelligence (such as employee collaboration), and algorithmic trading and analytics. During the delivery process, GPS data provide real-time inventory location data and help in finding optimal routes and reducing inventory lead times and fulfillment [110]. big data (infographic): Big data is a term for the voluminous and ever-increasing amount of structured, unstructured and semi-structured data being created -- data that would take too much time and cost too much money to load into relational databases for analysis. The different potential advantages that can be achieved utilizing data-supported decision making have incited academicians and researchers to pay attention to the possible integration of big data in SCM. For example, big data can provide accurate information on the return on investment (ROI) of any investment and in-depth analysis of potential supplier. Imagine, for example, a bike fork that captures force measurements or a utility cabinet that transmits internal temperature readings. From a technical point of view, a significant challenge in the education industry is to incorporate Big Data from different sources and vendors and to utilize it on platforms that were not designed for the varying data. There are only two publications in the field of BDA applications in the inventory management in Perish or Publish Software. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Other industries such as hospitality, technology, energy, and other service industry will also take advantage of BDA techniques. Given the high volume of orders and massive flow, huge data sets and methods for timely analysis are needed to manage and maintain them. In a survey conducted by Marketforce challenges identified by professionals in the insurance industry include underutilization of data gathered by loss adjusters and a hunger for better insight. Despite the importance of big data in today’s world, many organizations overlook the importance of using big data for their organizational performance. Applying Cloud Technologies to selecting vendors is making a big impact. Our team is growing all the time, so we’re always on the lookout for smart people who want to help us reshape the world of scientific publishing. Big data from customer loyalty data, POS, store inventory, local demographics data continues to be gathered by retail and wholesale stores. Big Data Implementation in the Fast-Food Industry. have used BDA techniques to predict demand and production levels in manufacturing companies [55]. However, reducing costs by driving down excessive inventory, both staged and in-transit, proactively responding to inbound and outbound events and sharing assets has become critical in today’s supply chain environment. This data, derived from customer loyalty cards, POS scanners, RFID, etc. In another study, we have used big data to share transportation capacity in order to improve the efficiency of urban healthcare services [63]. In a study, fuzzy synthetic evaluation and analytical hierarchy process (AHP) were used to supplier evaluation and selection, given the high capacity of big data processing as one of the evaluated factors has been used [29]. Big Data Analytics and Its Applications.pdf. In most places, transport demand models are still based on poorly understood new social media structures. Toyota also uses vehicle big data collected from connected car platform to create new business and service such as adding security and safety service and to create mobility service, traffic information service, and feedback to design [95]. Teacher’s performance can be fine-tuned and measured against student numbers, subject matter, student demographics, student aspirations, behavioral classification, and several other variables. Applying BDA to product design enables the designer to be constantly aware of customer preferences and expectations that lead to produce a product according to their needs and preferences [32]. Obamacare has also utilized Big Data in a variety of ways. PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc. Communications and Media: For real-time reportag… The results indicated that BDA techniques usually use the predictive and prescriptive approaches rather than descriptive approach [10]. Challenges of Big Data Analytics. Design process from data science view [32]. The recent developments of data analytics and application of data analytics tools have opened up a new path for generating knowledge for product enhancement and achieving their objectives [42]. They apply big data in many areas such as financial crime, treasury, financial crime, risk, intelligence, and finance [103]. 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