Therefore, Chief Financial Officer (CFO) can apply a business analytics and intelligence tool to improve data accuracy, make better decisions, and provide greater value [100]. These data can be captured, stored, communicated, aggregated, and analyzed. Data is ruling the world, irrespective of the industry it caters to. We share our knowledge and peer-reveiwed research papers with libraries, scientific and engineering societies, and also work with corporate R&D departments and government entities. One of the main reasons is to make full usage of the data to improve productivity, by providing “the valuable right information, for the right user, at the right time.” In this section, an overview of BDA applications in different companies including manufacturing, finance, and healthcare is provided. BDA can also be applied across the end-to-end supply chain. Some hospitals, like Beth Israel, are using data collected from a cell phone app, from millions of patients, to allow doctors to use evidence-based medicine as opposed to administering several medical/lab tests to all patients who go to the hospital. found that IT capability has positive effect on SCA [69]. The field of Big Data and Big Data Analytics is growing day by day. BDA techniques also are used to identify employees with poor or excellent performance, as well as struggling or unhappy employees. The Big Data analytics is indeed a revolution in the field of Information Technology. Big data specifically refer to large data sets whose size is so large that the quantity can no longer fit into the memory. In today’s world, the manufacturing industry must use advanced data analytic technologies to gain competitive advantage and improve productivity in design, production, sales, and timely product delivery processes. The technological applications of big data comprise of the following companies which … Following are some the examples of Big Data- The New York Stock Exchange generates about one terabyte of new trade data per day. Gupta et al. Big Data is basically a set of data that are so big and complex that the normal data processing system is not able to control the same. Since high volumes of data such as size, weight, origin, and destination are being generated daily for millions of shipments, there is a huge potential for new business creation and operational efficiency and customer experience improvement. Using big data to tighter analysis and integration of these databases, it can improve the efficiency of the distribution and sales process and the continuous monitoring of process and devices. Continuous monitoring of customer behavior, product design, and manufacturing process generated huge data that are considered as big data. 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. SCA can be used to manage suppliers’ performance and supply chain risk [7]. Big data are also collected for melting glaciers, deforestation, and extreme weather through satellite images, weather radar, and terrestrial monitoring devices. However, recent progress in the use of analytics has opened new horizons for managers and researchers. Banking and Securities: For monitoring financial markets through network activity monitors and natural language processors to reduce fraudulent transactions. Comparing descriptive and inferential analyses. At today’s age, fast food is the most popular … The Department of Homeland Security uses Big Data for several different use cases. The culture, politics, environment, and the management team within the organization are very critical factors in decision making. Analytics is a mix of math and statistics to large quantities of data. BDA mean using statistics and math in order to analyze big data. BDA can able to manage and integrate huge sets of diverse data in a complex global supply chain. By progressing BDA, organizations could make better understanding from their customer’s needs, provide suitable service to satisfy their needs, improve sales and income, and penetrate into new markets. Examples include relational data such as employee salary records. 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]. No wonder, there is so much hype for big data, given all of its applications. Modeling and simulation techniques should be used to develop the application of large data, for example, simulation-driven product design. Statistical analysis, simulation, optimization, and techniques are used to supply chain decision making [19]. Security – Since the data is huge in size, keeping it secure is another challenge. With that said, according to Research and Market reports, in 2017 the global Big Data market was worth $32 billion and by 2026 it is expected to reach by $156 billion. Therefore, proposing and applying effective statistical methods are very important, and major attention has been paid to this issue recently. These techniques seek to discover the causes of events and phenomena as well as to predict the future accurately or to fill in the data or information that already does not exist. For instance, the points of sales (POS) data on retailers provide real-time demand data with price information. Many supply chain executives are keen to improve demand forecasting and production planning with big data [45]. Manufacturing companies need to use big data and analytics techniques to grow their manufacturing sector. The authors have been accumulating a lot of data for years. Even proprietary tools now incorporate leading open source technologies and/or support those technologies. found a positive impact of supply chain visibility on SCA [15]. Big data from customer loyalty data, POS, store inventory, local demographics data continues to be gathered by retail and wholesale stores. Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. Designers can identify product features and predict future product trends by continually monitoring the customer behavior and informing the customers’ opinions and needs. 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. Big data is a mixture of structured, semistructured, and unstructured data gathered by organizations that can be excavated for information and utilized in machine learning projects, predictive modeling, and other advanced analytics applications as many don’t know What is Big Data in this we gonna share some information about Big Data. Licensee IntechOpen. Technology. TIBCO’s Statistica is predictive analytics software for businesses of all sizes, using … Predictive analytics is used to predict purchasing patterns, customer behavior and purchase patterns to identifying and predicting the future trend of sales activities. *Lifetime access to high-quality, self-paced e-learning content. Since 2011 to 2015, Mishra et al. As decision making in organizations has been based on data, organizations must change their strategic capabilities, which affect sustainability. Despite the potential use of big data, many supply chains are unable to harness the power of BDA techniques to generate useful knowledge and insights into available data for their businesses. Free public health data and Google Maps have been used by the University of Florida to create visual data that allows for faster identification and efficient analysis of healthcare information, used in tracking the spread of chronic disease. Following are a few examples of ways big data manage inventory. Another study presents a model for predicting demand for air passenger demand, which uses big data to estimate air passenger demand. Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies. Exchange Commissions or Trading Commissions are using big data analytics to ensure that no illegal trading happens by monitoring the stock market. The reason being … Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. The Barclays Finance Company has widely used big data to support its operations and create and maintain primary competitive advantage. Individual use of Big Data includes route planning to save on fuel and time, for travel arrangements in tourism, etc. 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. Application of analytical techniques in Medical Healthcare System includes image detection, lesion detection, speech recognition, visual recognition, and so on. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations Yichuan Wanga,⁎, LeeAnn Kungb, Terry Anthony Byrda a Raymond J. Harbert College of Business, Auburn University, 405 W. Magnolia Ave., Auburn, AL 36849, USA b Rohrer College of Business, Rowan University, 201 Mullica Hill Road, Glassboro, NJ 08028, USA Given the growing importance of sustainability and BDA, organizations must integrate these two areas to achieve sustainable competitive advantage [78, 80]. Manufacturers need simulation tools to optimize the product development process and increase the creativity, speed the time-to-market product, reduce the production costs, and create the innovation. The following key objectives define the design of inventory control: informing the quantity of goods in warehouse and also the amount of goods needed in the warehouse; facilitating the requisition process to finish in time; automatic recording and backorder serving; minimizing the inventory by analyzing previous purchasing and consumption patterns of the organization; using the automated tools to facilitate management of the inventory, servicing, and purchasing; and. Many parts and processes of the supply chain BDA have been widely used; however, publications regarding data analysis applications in the supply chain remain limited. argue that big data have significant effects on operation management practices [65]. Correct application of prescriptive analytics techniques can lead to optimal and efficient decision making. This is made possible through today’s massive computing power available at a lower cost than ever before. However, combining the big data and analytics makes the different tools that help decision makers to get valuable meaningful insights and turn information into business intelligence. As customers’ preferences and expectations change throughout the product lifetime, designers need tools to predict and measure those preferences and expectations. This model improved the decision making in this production system [23]. Designers can use online behavior and customer purchase record data to predict and understand the customer needs [39]. 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. The Securities Exchange Commission (SEC) is using Big Data to monitor financial market activity. BDA can facilitate the real-time monitoring of supply chain and managing of data that enhance the speed, quality, accuracy, and flexibility of supply chain decision. RFID data provide automated replenishment signal, automated receiving and storing information, and automated checkout data, which inform the real-time inventory status. They apply big data in many areas such as financial crime, treasury, financial crime, risk, intelligence, and finance [103]. In public services, Big Data has an extensive range of applications, including energy exploration, financial market analysis, fraud detection, health-related research, and environmental protection. Prescriptive analytics deals with the question of what should be happening and how to influence it. By accurately anticipating consumer trends based on historical data, real-time data, and future predictions, organizations can put that knowledge to work to become more agile, efficient, and responsive. Data analytics enables manufacturers to accurately determine each person’s activities and tasks through timely and accurate data analysis of each part of the production process and examine entire supply chain in detail. In the production department, a large amount of data is generated by external channels and also by internal networks that contain sensor networks or instrumentation on the production floor. Inventory control is the system that involves requisition process, inventory management, purchase, and physical inventory reconciliation. LLamasoft [24] outlined some examples of where supply chain simulation can be used as follows: predicting the service, testing the inventory policy, analyzing the production capacity, determining the asset utilization, and validating the optimization result. Data science broadly covers statistics, data analytics, data mining, and machine learning for intricately understanding and analyzing ‘Big Data’. of big data analytics and its plans and strategies for the development of big data analytic capabilities, the governmental agencies involved, and some of the particular big data applications it is pursuing. This chapter tries to demonstrate some of the most fundamental and recent applications of BDA within the SCM and also notice some of these techniques in SCM that are critical for managers. Srinivasan and Swink noted that supply chain visibility is a prerequisite for building data analytic capability and vice versa [68]. Many parts and processes of the supply chain BDA have been widely used; however, publications regarding data analysis applications in strategic sourcing and inventory management are still limited. Currently, this magnitude is usually used for data analytics and mining on the terabyte level. Several cities all over the world have employed predictive analysis in predicting areas that would likely witness a surge in crime with the use of geographical data and historical data. Big Data Analytics and Its Applications.pdf. Song et al. When designing a supply chain, the following steps must be followed: (1) define the long-term strategic targets; (2) define the project scope; (3) determine the form of analyses to be done; (4) the tools that will be used must be determined; and (5) finally, project completion, the best design. As the volume of data has grown, the need to revamp the tools has used for analyzing it. For example, in a research, a parallel statistical algorithm is presented to do a sophisticated statistical analysis of big data. Approximately, manufacturing industry stores 2 exabytes of new data in 2010 [89]. Login to your personal dashboard for more detailed statistics on your publications. The Big Data also allows for better customer retention from insurance companies. The supply chain is the number of firms from raw material suppliers to producer/central organization, wholesalers, retailers, customers, and end users. Gunasekaran et al. In another study, we have used big data to share transportation capacity in order to improve the efficiency of urban healthcare services [63]. Big Data Providers in this industry include Alstom Siemens ABB and Cloudera. Now, this analytics mainly deals with the huge amount of data examination, analyze the same to fetch and understand the critical pattern and other different aspects. However, the present book chapter indicates the benefits of big data application in extracting new insights and creating new forms of value in ways that have influenced supply chain relationships. Source: Big Data in the Healthcare Sector Revolutionizing the Management of Laborious Tasks. In the past, organizations faced laborious processes that took several weeks to gather internal and structural data from the operations and transactions of the company and its partners. 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. For instance, IoT can provide real-time telemetry data by the real-time monitoring of supply chain to reveal the details of production processes. And the need to utilize this Big Data efficiently data has brought data science and data analytics tools to the forefront. On a governmental level, the Office of Educational Technology in the U. S. Department of Education is using Big Data to develop analytics to help correct course students who are going astray while using online Big Data courses. Progressive organization: The dynamic changes in markets and the emergence of advanced data management and analysis technologies as well as “boundary-less” paradigm make organizations to abandon traditional BI analytic methods and governance structures and use new advanced techniques. The results indicated that the number of articles in the field of BDA has increased [14]. This new technologies and trends are emerging that will change the rules of supply chain design and management [56]. Since in production lines and factories, various electronic devices, digital machineries, and sensors are used, and a huge amount of data is generated. Schmitz Cargobull, a German truck body and trailer maker, uses sensor data, telecommunication, and BDA to monitor cargo weight and temperatures, routes, and maintenance of its trailers to minimize their usage breakdown [94]. We are IntechOpen, the world's leading publisher of Open Access books. An Australian university with over 26000 students has deployed a Learning and Management System that tracks, among other things, when a student logs onto the system, how much time is spent on different pages in the system, as well as the overall progress of a student over time. Second, the authors paid to the role of statistical analysis, simulation, and optimization in supply chain analytics. Evaluating the size of the market opportunity. Selecting the optimal supply chain design and appropriate planning, the company will achieve a significant competitive advantage. Many researchers have applied various techniques of BDA across different industries including the healthcare finance/banking and manufacturing. The real challenge will lie in solving these minute hassles and in developing better products reaching a new level in the product design as a whole. Some applications of Big Data by governments, private organizations, and individuals include: Source: Using Big Data in the Transport Sector. Data is a very valuable asset in the world today. Deutsche Bank has set up a Data Lab that provides internal data, analytics consultancy, test-out business idea, and technology support to other division and business function [104]. By Saeid Sadeghi Darvazeh, Iman Raeesi Vanani and Farzaneh Mansouri Musolu, Submitted: July 28th 2019Reviewed: August 29th 2019Published: March 25th 2020, Home > Books > New Trends in the Use of Artificial Intelligence for the Industry 4.0. Other industries such as hospitality, technology, energy, and other service industry will also take advantage of BDA techniques. A schematic view of the design process is shown in Figure 2 . Brief introduction to this section that descibes Open Access especially from an IntechOpen perspective, Want to get in touch? In today’s competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. With more collaborative teams across the globe, it is essential for an organization to have a structured process around development for the end-users. Other big data initiatives were to monitor inhaler usage and reduce the risk of the asthma attack and cancer [106]. The need for Big Data Analytics springs from all data that is created at breakneck speeds on the Internet. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with … The benefits of using BDA in supply chains are listed below. They utilized a big data approach to acquire data and manage their quality [17]. In a different use case of the use of Big Data in education, it is also used to measure teacher’s effectiveness to ensure a pleasant experience for both students and teachers. Wang et al. “Big data” in the healthcare industry include all data related to well-being and patient healthcare. Maintaining the sustainable competitive advantage and enhancing the efficiency are important goals of financial institutions. In today’s competitive environment, the use of simulators to produce innovative products is considered a challenge. Today, due to the high volume of data generated from various sources such as sensors, scanners, GPS, and RFID tags, as well as due to integrating business judgment and fusing multiple data sources, powerful techniques are needed to quickly and timely analyze these data and provide real-time insights for a timely and accurate decision making. Therefore, BDA can be used to build intelligent shop floor logistic system in factories [54, 90]. Data analysis techniques can also be used to predict customer demands and tastes. conducted a systematic literature review to investigate the application of BDA in SCM areas. Several research studies indicated the big data applications in various sectors such as financial services sector, marketing, bank industry, insurance industry, logistics, and manufacturing [6]. Data analysis techniques can also be used in financial markets to examine the market volatility and calculate VPIN [101]. This ability enables manufacturers to identify bottlenecks and reveal poorly performing processes and components. A huge amount of data also creates from design and manufacturing engineering process in the form of CAM and CAE models, CAD, process performance data, product failure data, internet transaction, and so on. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. Businesses optimize their processes by tracking and analyzing their supply chain delivery routes and combine that data with live traffic updates. Others use machine data to optimize the service cycles of their equipment and predict potential faults. By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. Applying Cloud Technologies to selecting vendors is making a big impact. Big Data Providers in this industry include CSC, Aspen Technology, Invensys, and Pentaho. The healthcare sector has access to huge amounts of data but has been plagued by failures in utilizing the data to curb the cost of rising healthcare and by inefficient systems that stifle faster and better healthcare benefits across the board. Although sustainable SCM has been discussed in corporate offices for some time, actually implementing the sustainability phenomenon in the extended supply chain has proved difficult [73]. Big data create different capabilities in the supply chain that provides networks with greater data accuracy, insights, and clarity and also create a greater e-contextual intelligence shared across the supply chains. Traditional statistical methods are no longer responsive because the massive data lead to noise accumulation, heterogeneity, and so on. To fully understand the impact and application of BDA, we first need to have a clear understanding of what it actually is. Analytics without big data is simply mathematical and statistical tools and applications. While the primary goal for most organizations is to enhance customer experience, other goals include cost reduction, better-targeted marketing, and making existing processes more efficient. Today’s progressed analytical technologies empower us to extract knowledge from all kinds of data. In this article, I shall examine ten industry verticals that are using Big Data, industry-specific challenges that these industries face, and how Big Data solves these challenges. Deep learning techniques can also be used to accurately predict customers’ demand and their preferences and expectations. It is an obvious fact that BDA can support all supply chain activities and processes and create a supply chain strategies/agiler logistics. Supply chain network design project involves determining supply chain physical configuration that affects most business units or functional areas within a company. Vertical industry expertise is key to utilizing Big Data effectively and efficiently. Big data without analytics are just lots of data. The use of Data analytics by the companies is enhancing every year. BDA is also used to support risk management and regulatory reporting activities [99]. In governments, the most significant challenges are the integration and interoperability of Big Data across different government departments and affiliated organizations. Using BDA techniques can provide accurate information on organizational spending patterns that help manage supplier relationships [28]. Such data are used to comprehensively study global climate change and assign specific causality [21]. BDA have been used to gain competitive advantage and provide new services in logistics [61]. Already using Big Data solutions. In the next section, the authors explore the literature related to supply chain risk management. More empowered engineering: Traditionally, engineers rely on marketers, customer visits, or their own best guesses to design the competitive products. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc. Features of descriptive, predictive and prescriptive analytics. Supplier data provide important data about suppliers and ordering processes that can help the supplier risk management and better coordination with supplier processes. In the health industry, a large amount of data is generated to control and monitor the various processes of treatment, protection, and management of patients’ medical records, regulatory requirements, and compliance. Through massive data from digital channels and social media, real-time monitoring of claims throughout the claims cycle has been used to provide insights. Companies can extract intelligence out of these huge amounts of data. Few scholars have addressed this issue that to achieve strategic and competitive advantages, BDA and sustainability must be integrated [78, 80]. Big Data Technology and Applications in Intelligent Transportation . 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. One of the earliest adopters is the financial sector. Increased customer service satisfaction: The access to real-time data and the ability to timely analyze these data provide operational managers with the ability to match their inventory levels with customer orders and tastes, which will increase customer satisfaction. As a simple definition, big data refer to large quantity of data. In one study, a model was presented to predict the electric vehicle charging demand that used weather data and historical real-world traffic data. They can come in the form of radio-frequency identification (RFID), global positioning system (GPS), point-of-sale (POS), or they can be in the frame of Twitter feeds, Instagram, Facebook, call centers, or customer blogs. Choi et al. On the technical side, there are challenges to integrating data from different sources on different platforms and from different vendors that were not designed to work with one another. Recently, BDA techniques have been used for product design and development, which lead to the production of new products according to customer preferences. BDA allow to identify new market trends and determine root causes of issues, failures, and defects. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. 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]. What should be the shipment strategy for each retail location? The purpose of supply chain design is to design a network of members that can meet the long-term strategic targets of the company. Banks and financial service organizations using big data and analytical techniques gain valuable knowledge and insights that can be used in continuous monitoring of client behavior in real time, predict their wants and needs, and provide the exact resource and service according to customer’s requests and needs. The economics of data is based on the idea that data value can be extracted through the use of analytics. According to the report of US Congress in August 2012, big data are defined as “large volumes of high velocity, complex, and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.” Big data in healthcare encompass such characteristics as high-dimensional, variety, heterogeneous, velocity, generally unstructured, poorly annotated, and, with respect specifically to healthcare, veracity. Logistic organizations, given the high volume of widely dispersed data generated across different operations, systems, and geographic regions, need advanced systems to manage these enormous data, as well as skilled professionals who can analyze these data, and extract valuable insights and knowledge into them in order to apply them in their planning and decisions. Based on SCOR supply chain model, Souza explored the opportunities for applying BDA in SCM [8]. Despite the pressing need to integrate data analysis with sustainability and supply chain measures, little progress has been made so far [81]. However, big data could provide volumes of reliable feedback that none of those channels offer. Predictive maintenance of equipment is an immediate segment in this sector ripe for growth. Reduced costs by migrating to the cloud: A Software-as-a-Service (SaaS) approach to IT management means that the cloud-based nature of big data reduces hardware and maintenance costs. Swafford et al. Social media is used for customer prospecting, customer retention, promotion of products, and more. The objective is to select supply partner that can adapt to the future challenges from big data. In a more complex global supply chain, BDA techniques can help supply chain managers to predict external future events and adopt a proactive against them. Data science (DS) is defined as a process of transforming observed world reality data into comprehensible information for decision making [34]. With BDA, manufacturers can discover new information and identify patterns that enable them to improve processes, increase supply chain efficiency, and identify variables that affect production. 3D printing is an innovative technology that makes possible to create a physical object from a digital model. Big data in healthcare are critical due to the various types of data that have been emerging in modern biomedical including omics, electronic health records, sensor data and text, and imaging, which are complex, heterogeneous, high-dimensional, generally unstructured, and poorly annotated. Supplier relationship management involves establishing discipline in strategic planning and managing all interactions with organizations’ suppliers in order to reduce the risk of failure and maximize the value of these interactions. More importantly, however, where do you stand when it comes to Big Data? Data analytics can predict customers’ preferences and needs by examining customer behavior, which can drive creativity and innovation in business services [48]. Given the high volume of orders and massive flow, huge data sets and methods for timely analysis are needed to manage and maintain them. This is mainly because electronic data is unavailable, inadequate, or unusable. As one doctrine, product developers can achieve a perpetual enhancement of their products and services based on real-life use, work, and failure data. Since consumers expect rich media on-demand in different formats and a variety of devices, some Big Data challenges in the communications, media, and entertainment industry include: Organizations in this industry simultaneously analyze customer data along with behavioral data to create detailed customer profiles that can be used to: A case in point is the Wimbledon Championships (YouTube Video) that leverages Big Data to deliver detailed sentiment analysis on the tennis matches to TV, mobile, and web users in real-time. BDA have many important applications across the end-to-end supply chain. Big Data Providers in this industry include First Retail, First Insight, Fujitsu, Infor, Epicor, and Vistex. Some studies have used big data analysis to predict natural disasters to take preventive action against them, and simulation has been used reduce the effects of these environmental hazards [83]. This analytics can be categorized into descriptive, predictive, and prescriptive analytics [7, 8]. Source: Presented at Everis by Wilson Lucas (note that the diagram shows potential Big Data opportunities). A single Jet engine can generate … Supply chain design is a strategic decision, which includes all decisions regarding the selection of partners of the supply chain and defines company policies and programs to achieve long-term strategic targets. Many research studies pointed to the application of BDA in the areas of transportation, and logistics. Fifth, the authors presented some insight into future application of BDA in supply chain, and lastly, the book chapter ends with the conclusion, some managerial implications, and recommendations for future research. Retail traders, Big banks, hedge funds, and other so-called ‘big boys’ in the financial markets use Big Data for trade analytics used in high-frequency trading, pre-trade decision-support analytics, sentiment measurement, Predictive Analytics, etc. Zhao et al. As big data analytics increases its momentum, the focus is on open-source tools that help break down and analyze data. Nowadays, this is facilitated the implementation of the concept of (run-time) data-driven design. The importance of big data lies in how an organization is using the collected data and not in how much data they have been able to collect. There is substantial real spending on Big Data. To date our community has made over 100 million downloads. This majorly involves applying various data mining algorithms on the given set of data, which will then aid them in better decision making. This has resulted in the number of scholarly articles on this topic, which has risen precipitously in recent years. Contact our London head office or media team here. Supporting the creation of sustainability in SCM. This has seemed to work in major cities such as Chicago, London, Los Angeles, etc. Analyzing big data can optimize efficiency in many different industries. Barbosa et al. In order to achieve sustainable competitive advantage and stay afloat in the industry, these institutions must continually use big data and appropriate analytic techniques into their business strategy. Analytics – In the case of Big Data, most of the time we are unaware of the kind of data we are dealing with, so analyzing that data is even more difficult. Big data is finding usage in almost all industries today. Supply chain analytics (SCA) means using BDA techniques in order to extracting hidden valuable knowledge from supply chain [7]. 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. Your Complete Guide To The Top Big Data Tools, An In-depth Guide To Becoming A Big Data Expert, Big Data in the Healthcare Sector Revolutionizing the Management of Laborious Tasks. Big data is analyzed from various government agencies and is used to protect the country. 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. Gunasekaran et al. improving the financial control of the inventory through a timely and regular checkup of the inventory balances with the physical counts. Designers still face many challenges and should consider many limitations. Big Data Providers in this industry include Digital Reasoning, Socrata, and HP. This allows for a faster response, which has led to more rapid treatment and less death. Pervasive analytics: An open and adaptive framework is needed to integrate seamlessly the different insights into an organization and to apply them effectively. Their findings show that big data could provide all the necessary information about penalty cost data and service level; therefore, it is a very powerful tool for complex distribution network design [30]. Modeling and simulation help developer to run the “what-if” analysis under different system configuration and complexity [22]. This data, derived from customer loyalty cards, POS scanners, RFID, etc. Big data reduce healthcare costs and also improve the accuracy, speed, quality, and effectiveness of healthcare systems. IBM, in partnership with Cloudera, provides the platform and analytic solutions needed to … In designing the supply chain network, it is important to determine the customer satisfaction and supply chain efficiency. 2. Organizations will become knowledge-based organizations that utilize powerful horizontal platform and supportive tools that are in line with associated security, next-gen data sets, and business semantic policies. 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]. Built by scientists, for scientists. For example, big data can provide accurate information on the return on investment (ROI) of any investment and in-depth analysis of potential supplier. *Address all correspondence to:, 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. The prospects of big data analytics are important and the benefits for data-driven organizations are significant determinants for competitiveness and innovation performance. Source: Supply Chain Talent of the Future. Lack of enough information about customers’ preferences and expectations is an important issue in the product design process. Big data appear completely in different kinds of data. In the current years, BDA practices have been extensively reported. There are Big Data solutions that make the analysis of big data easy and efficient. Some studies have investigated the applied techniques of BDA in the production area. This algorithm uses specific methods such as Mann-Whitney U testing, conjugate gradient, and ordinary least squares to model and compare the densities and big data distribution squares [2]. Nowadays, there are several simulation software that allow to evaluate the performance of a system before its creation. The applications of data analytics are broad. BDA have important applications across the end-to-end supply chain. further argue that supply chain disruptions have negative effects, and agile supply chain enablers were progressively used with the aid of big data and business analytics to achieve better competitive results [66, 67]. Other challenges related to Big Data include the exclusion of patients from the decision-making process and the use of data from different readily available sensors. As Big Data continues to permeate our day-to-day lives, there has been a significant shift of focus from the hype surrounding it to finding real value in its use. The Food and Drug Administration (FDA) is using Big Data to detect and study patterns of food-related illnesses and diseases. From a practical point of view, staff and institutions have to learn new data management and analysis tools. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM). Organizations need to be able to manage their huge data and extract the knowledge and insight contained in these data and then use them in all their business processes and decision making. In recent times, huge amounts of data from location-based social networks and high-speed data from telecoms have affected travel behavior. Stages in Big Data Analytics. Improved operational efficiency: Due to the possibility of continuous monitoring and analysis of operational data by operational managers and better access to metrics, efficiency has improved, and bottlenecks have been removed. Mechanical engineers have the opportunity for product insights that were never possible before. By Alejandro Sánchez-Sotano, Alberto Cerezo-Narváez, Francisco Abad-Fraga, Andrés Pastor-Fernández and Jorge Salguero-Gómez. Prescriptive analytics guides alternative decision based on predictive and descriptive analytics using descriptive and predictive analytics, simulation, mathematical optimization, or multicriteria decision-making techniques. Generally, most organizations have several goals for adopting Big Data projects. applied RFID-enabled big data to support shop floor logistic planning and scheduling [53]. Big Data Providers in this industry include Qualcomm and Manhattan Associates. A number of large companies have used data analytics to optimize production and inventory. Since, sufficient resources with analytic capabilities become the biggest challenges for many today’s supply chain. As another categorization, big data consist of numerical data, image data, voice, text, and discourse. 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. identify the influential and prominent researchers and articles with most citations carried out a bibliographic analysis of big data. 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. Bean reported that 70% of global financial service organization thought BDA was important and 63% has applied big data in their organizations [97]. A battery of tests can be efficient, but it can also be expensive and usually ineffective. Statistica. Though numerous data analytic (software) tools and packages have been developed for extracting product-associated data, exploiting data analytic methods and tools in product enhancement is still in a rather premature stage [43]. In recent times, data breaches have also made enhanced security an important goal that Big Data projects seek to incorporate. At the end of the 2-day course, participants will be able to: Gain an overview of business applications of big data and analytics techniques; Gain real-world insights into various applications of big data analytics and how it can be used to fuel better decision-making within an organisation/ business For example, informing the social media and news about exchange rate movement and disasters affects the supply chain. This granular data is being used to analyze the consumption of utilities better, which allows for improved customer feedback and better control of utilities use. The importance of using BDA techniques in SCM is true to an extent that organizations will not stand a chance of success in today’s competitive markets. [66] and [67] argue that big data and predictive analytics have positive effects on supply chain performance and organizational performance [67, 68]. Because manufacturers have to continually drive their operational efficiencies, meet the cost, require the time-to-market product, and predict the customer preferences. In New York’s Big Show retail trade conference in 2014, companies like Microsoft, Cisco, and IBM pitched the need for the retail industry to utilize Big Data for analytics and other uses, including: Social media use also has a lot of potential use and continues to be slowly but surely adopted, especially by brick and mortar stores. In the era of big data, we need new processing models to process these information assets. Strategic resources and supplier relationship management (SRM) are the success factors of organizations, which focus on relationship management and collaboration. They can be structured, semi-structured, or fully unstructured. For example, currently, BDA techniques have applied in the retail supply chains to observe customer behaviors by accurately predicting the customer tastes and preferences. Raytheon Corp manufacturing company has develop smart factories through the powerful capacity of handling huge data that collect from various sources including instruments, sensors, CAD models, Internet transactions, digital records, and simulations that enable the company in real-time control of multiple activities of the production process [92]. The summary of the challenges and features of the three types of analytics is shown in Table 1 . This report is intended to provide an initial baseline description of China’s efforts Repositioning existing services and products to utilize Big Data, or, Collecting, analyzing, and utilizing consumer insights, Leveraging mobile and social media content, Understanding patterns of real-time, media content usage, Create content for different target audiences, Optimized staffing through data from shopping patterns, local events, and so on, Governments use of Big Data: traffic control, route planning, intelligent transport systems, congestion management (by predicting traffic conditions), Private-sector use of Big Data in transport: revenue management, technological enhancements, logistics and for competitive advantage (by consolidating shipments and optimizing freight movement). The underutilization of this information prevents the improved quality of products, energy efficiency, reliability, and better profit margins. Intelligent transportation is an emerging trending topic in the frontier of world transportation development. Gupta et al. However, literature on the application of BDA for supply chain sustainability has been much less explored. In descriptive statistics, past data are used to describe or summarize the feature of a phenomenon; it uses either graphs or tables or numerical calculations. The use of optimization techniques supports supply chain planning and also increases the accuracy of planning but presents the large-scale optimization challenge [7]. Bort reported on combating influenza based on flu report by providing near real-time view [105]. Developing new services and products that will utilize Big Data. That is in part because engineers will increasingly design sensors and communication technology into their products. Though Big data and analytics are still in their initial growth stage, their importance cannot be undervalued. One of the major concerns of adaptable product manufacturers is ensuring that these products conform to their customers’ preferences. 3D printing is any of various processes in which material is joined or solidified under computer control to create a three-dimensional object [57]. Submission Deadline: 31 March 2020 IEEE Access invites manuscript submissions in the area of Big Data Technology and Applications in Intelligent Transportation.. If designers continuously monitor customer behavior and access up-to-date information on customer preferences, they can design products that meet customer preferences and expectations. Big data are a powerful tool for solving supply chain issues and driving supply chains ahead. Here is a list of the top segments using big data to give you an idea of its application and scope. With the help of big data, an automated inventory control system can be designed [60]. 1. Due to the high volume of financial transactions and activities, the application of big data and analytic techniques is very necessary and important in most of the financial organizations such as asset management, insurance companies, banks, and capital market. Furthermore, for the supply chain to be sustainable, the potential risks disrupting operations must be identified and predicted. Statistical multivariate techniques are also used for supply chain monitoring to effectively manage the flow of materials and minimize the risk of unintended situation [20]. Regarding this purpose, first, the authors defined the key concepts of BDA and its role in predicting the future. Big Data Analytics and Its Applications in Supply Chain Management, New Trends in the Use of Artificial Intelligence for the Industry 4.0, Luis Romeral Martínez, Roque A. Osornio Rios and Miguel Delgado Prieto, IntechOpen, DOI: 10.5772/intechopen.89426. Big data can be used to population health management and preventive care as a new application of Huge Data in the future [106]. 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]. The term ‘Data Analytics’ is not a simple one as it appears to be. Today’s organizations must use methods to analyze high volumes of data to gain insights and knowledge in order to achieve the three dimensions of environmental, social, and economic sustainability [82]. In utility companies, the use of Big Data also allows for better asset and workforce management, which is useful for recognizing errors and correcting them as soon as possible before complete failure is experienced. Banking and Securities. Applying this framework to identify supply chain risk enables real-time risk management monitoring, decision support, and emergency planning. Basically, Big Data Analytics is largely used by companies to facilitate their growth and development. That may lead to more participants and disciplines involved in the product development cycle early on. Companies use big data to better understand and target customers by bringing together data from their own transactions as well as social media data and even weather predictions. Data analysis techniques can be used to analyze the data, extract the relationships between them, and predict the optimal rate of inventory ordering [7]. Hadoop, Spark and NoSQL databases are the winners here. Big Data Providers in this industry include Recombinant Data, Humedica, Explorys, and Cerner. The data generated from IoT devices turns out to be of value only if it gets subjected to analysis, which brings data analytics into the picture. Some of the crucial scenarios that prescriptive analytics allows companies to answer include in the following: What kind of an offer should make to each end-user? It can also be seamlessly integrated to existing systems with a minimum of expense. Data were collected from 205 manufacturing companies, and using structural equation modeling based on partial least square was analyzed. Lack of personalized services, lack of personalized pricing, and the lack of targeted services to new segments and specific market segments are some of the main challenges. There are only two publications in the field of BDA applications in the inventory management in Perish or Publish Software. Supply chain visibility is a desired organizational capability to mitigate risk resulting from supply chain disruptions [70]. Corporations are increasingly interested in using BDA in their sustainable efforts, which in turn give them a strategic edge [75]. Optimization techniques by extracting the insights and knowledge of the enormous data generated by complex systems that include multiple factors and constraints such as capacity and route can analyze multiple objectives such as demand fulfillment and cost reduction. Big Data Implementation in the Fast-Food Industry. recommended BDA as one of the most important factors affecting organizational performance [5]. For example, as a predictive tool, simulation can help the manufacturers to predict the need for machines and additional equipment based on customer order forecast and learning from other historical data such as cycle time, throughput, and delivery performance. Third, the authors had a review on application of BDA in supply chain management areas. Despite the high potential of using massive data in healthcare, there are many challenges, for example, improving the available platform to better support the easy friendly package, a menu driven, data processing, and more real times. 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need for big data analytics and its applications

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