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Big Data challenges for manufacturing; (1: Not at all a challenge; 3: Moderate challenge; 5: Very high challenge), Areas of greatest challenges for manufacturing/production, Building high levels of trust between data scientists who present insights on Big Data and, Determining what data to use for different business decisions, Being able to handle the large volume, velocity and variety of Big Data, Getting business units to share information across organizational silos, Finding the optimal way to organize Big Data activities in a company, Getting functional managers to make decisions based on Big Data, rather than on intuition, Putting the analysis of Big Data in a presentable form for making decisions, Getting top management in a company to approve investments in Big Data and is related investments, Determining what to do with the insights that are created from Big Data, Getting the IT function to recognize that Big Data requires new technologies and new skills, Finding and hiring data scientists who can manage large amounts of structured and, Determining which Big Data technologies to use, Keeping the data in Big Data initiatives secure from external parties, Understanding where in a company people should focus Big Data investments, Reskilling the IT function to be able to use new tools and technologies of Big Data, Keeping the data in Big Data initiatives secure from internal parties, solution based on machine learning (Joseph, managing and using Big Data, etc. statistics), and the applicabil-, ity of prediction analytics to real-world problems. It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. The top three types, blications. As a result, the present data privacy threats, attacks, and solutions were identified. The Big data can help change the way manufacturing processes are carried out. Indeed, as interest in the area began to increase from, 2012 to 2014, the proportion of conference to journal publications rose from 60 % in 2012, to 75 % in 2014. In the R space, even a spatial analysis and visualization can be provided comprehensively. Systematic-Mapping-Study-of-Digitization-and-Analysis-of-Manufacturing-Data-, Predictive maintenance in the Industry 4.0: A systematic literature review, A survey on decision-making based on system reliability in the context of Industry 4.0, Data-driven machine criticality assessment - maintenance decision support for increased productivity, Recent advances on industrial data-driven energy savings: Digital twins and infrastructures, AI-based Decision-making Model for the Development of a Manufacturing Company in the context of Industry 4.0, Big Data and Technology Evolution in the IoT Industry, Privacy and data protection in mobile cloud computing: A systematic mapping study, How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study, A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems, Towards a Process to Guide Big Data Based Decision Support Systems for Business Processes, Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment, Recent advances and trends in predictive manufacturing systems in big data environment, Systematic Mapping Studies in Software Engineering, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Data Quality for Data Science, Predictive Analytics, and Big Data in Supply Chain Management: An Introduction to the Problem and Suggestions for Research and Applications, Data, information and analytics as services, A Machine Learning Supported Solution for Measurement and Verification 2.0 in Industrial Buildings, Data Analytics for Optimising Wind Turbine Performance, Cloud Manufacturing: Innovation in Production, An Exergame Integrated with IoT to Support Remote Rehabilitation, A Survey on IoT (Internet of Things) Emerging Technologies and Its Application, Data Acquisition and Analysis Methods in UAV- based Applications for Precision Agriculture. Network Infrastructures to Network Fabric: Operations and Logistics, and Informatics, Entrepreneurship in the Supply Chain: Using, toolset data mining to accelerate integrated, A big data approach for logistics trajectory, Twitter Analytics: Considering Twitter and, Twitter data for supply chain practice and, abled by Complex Event Processing and Big, The authors would like to thank the Irish Research Council and DePuy Ireland. Machine logs contain data on asset performance. At the same time, advances in computing, storage, communications, and big data technologies are making it possible to store, process, and analyze enormous volumes of data at scale and at speed. To collect research data without disrupting a design learning process, design actions and artifacts are continuously logged as time series by the CAD tool behind the scenes, while students are working on a design challenge. Decision support systems (DSS) are a valuable asset to measure process performance; however, they require a vast amount of process performance data in order to support a valuable analysis with highest precision and accuracy. This is an unsurprising finding as Industry 4.0 is originally a German strategy with supporting strong policy instruments being utilized in Germany to support its implementation. Big data in manufacturing can include productivity data on the amount of product you’re making to all the different measurements you must take for a … The next most significant contributions are frameworks, and platforms, with each type of contribut, of conference to journal papers associated w, present in any of the significant contribution types, where expected conference to, journal ratios predictably fall in favour of conferences, but with a healthy distribution. At 26.67 %, the most prominent, ion associated with 17.33 % of all publica-, ith platforms. This is particularly useful in applications where additional metering infrastructure would otherwise need to be installed. To find the useful information from massive amount of data to organizations, we need to analyze the data. Data analysis techniques can be applied to defect tracking and product quality and to improve activities of the product manufacturing process in manufacturing, ... Bioresource Technology 302 (2020) 122847 apply them to business practices to accelerate innovation, drive optimization, and improve business performance (Grover et al., 2018). The set of keywords from different papers were combined together to develop a high-level understanding of the nature and contribution of the research in the topic area. Big data: The next frontier for innovation, competition, and productivity, Data-intensive applications, challenges, techniques and technologies: A survey, Towards a process to guide Big data based decision support, . The uniqueness of this review includes substantive discussions of the rapid certification of the AM components aided by scale models, bidirectional models, cloud based big data, machine learning and digital twins of AM hardware. The approach is carrying out through the impact of the Industry 4.0, Internet of things, big data, virtual reality and additive manufacturing on maintenance. Design/methodology/approach This prescriptive approach to data-driven energy modelling in M&V allows practitioners to identify the optimal model parameters, thus maximising predictive performance. The results are relatively even, with 47.69 % of. sources identified in this study constitute 30. The purpose of this study was to conduct a preliminary trail of the developed Goalie exergame, assessing the viability of such a tool within the rehabilitation environment. The, second most prominent source of research is, Figure 8 illustrates the popularity of res, to the popularity of evaluation and solution research highlighted in Fig. For the cases, where companies deal with hundred thousands of records and hundreds of different parameters, we can offer very effective data analysis solutions, based on machine learning techniques, aiming practically one fundamental goal – accurate forecasting. The contribution of this study is a comprehensive report on the current state of research pertaining to big data technologies in manufacturing, including (a) the type of research being undertaken, (b) the areas in manufacturing where big data research is focused, from each step forms the input for the next step. This provides further evidence for the need for an industry-focused methodology for digitizing and analyzing manufacturing data, which will be developed in future research. The formal methodology of a systematic mapping study was utilized to capture a representative sample of the research area and assess its current state. Examples of potential applications of Big Data in logistics for manufacturers, processes using a CAD tool. filters), with the inte. In, stagnant trend is the lack of focus on prescriptive analytics, which accounted for 3.57 % of, publications in 2014. The tool supports prioritiza-tion and planning of maintenance decisions with a clear goal of increasing productivity. In recent years, the term analytics has become syn-, onymous with big data technologies. The tool is not trustworthy, seldom updated and focuses on individual machines. 68, criteria: a proposal and a discussion. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. As a result, the application in current and future use-cases is discussed. The proposed framework seeks to overcome the issues associated with the complex energy systems in industrial buildings. supporting the realisation of business processes in the Healthcare is considered one of the most important application areas of IoT, offering the potential for enhanced health management. Big Data in manufacturing: A compass for growth Data has long been the essential lifeblood of manufacturing, driving efficiency improvements, reductions in waste, and incremental profit gains. Join ResearchGate to find the people and research you need to help your work. Originality/value Smart manufacturing relies on real-time data from edge computing in automated process control as well as big data that is derived for ongoing analysis and decision making. There are also shown some major influences that big data has over one major segment in the industry (manufacturing) and the challenges that appear. Manufacturing plants generate twice as much data as any other vertical market, according to McKinsey and Company research from the seminal report that launched big data awareness-and hype-back in 2011 (figure 1). However, similar to other indust, systems that support business and manufac, the responsibility of storing increasingly large data sets (i.e. The survey of, ... To carry out this study, we based on the principles of systematic reviews to achieve reproducibility and high-quality results. The big data era has only just emerged, but the practice of advanced analytics is grounded in years of mathematical research and scientific application. In the asset-intensive manufacturing industry, equipment breakdown and scheduled maintenance are a regular feature. big data in, of strong research themes that makes a depth fir, Figure 1 provides a visual workflow of the s, in this study. architecture of ANN classifier was chosen in a series of Due to high data variability in service remanufacturing of armature windings in rotary machines, data abstraction for intelligent automation and analytics leads to increased operational productivity and new insights into market dynamics. Indeed, the next most, prominent research outputs after theory are frameworks and platforms. Thus, being aware of the impacts that a local failure can impose on the entire company has significant weight in the decision-making process. With an aggressive push towards “Internet of Things”, data has become more accessible and ubiquitous, contributing to the big data environment. A new wave of inexpensive electronic sensors, microprocessors, and other components enables more automation in factories, and vast amounts of data to be collected along the way. Maintenance 4.0 will contribute to a circular and sustainable economy. In section 2, the research method-, e presented and future areas of research are, ture the current state of the research relat-, g. Compared with other secondary research, while sacrificing depth . Findings Key simulation results can then be associated with CAD geometry and, for example, processes using machine learning algorithms for both supervised and unsupervised learning. More to the point, if a particular digita, the study, there is a realistic chance that the, indexed by another source that is being used, or indeed, be discovered by following the, references from each papers in the study (e.g. This work also serves as a concise guideline for researchers and industrialists who are looking to implement advanced energy-saving systems. As research efforts progress through the process, the outcome. can provide an understanding of the types of problems being addressed. It can be a critical tool for realizing improvements in yield, particularly in any manufacturing environment in which process complexity, process variability, and capacity restraints are present. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. The U.S. and E.U. Section 6 concludes the paper and provides future research avenues. These issues include design and manufacturing data, Big Data benefits and impacts and its applications and opportunities. But today, a new breed of Big Data analytics is taking over manufacturing and providing a totally new dimension to the value of research and trend To cope and/or to take advantage of these changes, we are in need of finding new and more efficient ways to collect, store, transform, share, utilize and dispose data, information and analytics. Existing literature is dominant with theoretical study and conceptual research, such as the development of frameworks or architectures on the adoption and implementation of BDA in manufacturing and SCM. Finally, the snowballing method [13, 14] wa, the references from each of these publications, with each reference being screened to, ascertain if the research should be added to the study. This paper also concentrates on application of Big Data in Data Mining. systems involving maintenance workers are based on Artificial Drawing on a systematic review and case study findings, this paper presents an interpretive framework that analyses the definitional perspectives and the applications of big data. Main and candidate search terms for big data in manufacturing, Year-on-year publication growth for big data in manufacturing, Comparison of publications in conferences and journals, All figure content in this area was uploaded by Peter O'Donovan, All content in this area was uploaded by Peter O'Donovan on Sep 13, 2015, , Kevin Leahy, Ken Bruton and Dominic T. J. O, The manufacturing industry is currently in the midst of a data-driven, which promises to transform traditional manufacturing facilities in to highly, optimised smart manufacturing facilities. Industry 4.0 is collaborating directly for the technological revolution. At present, smart manufacturing is driven by big data through three steps, which are association, forecast and control . Publications that, exclusion criteria (i.e. undertaken, (b) the areas in manufacturing where big data research is focused, and (c) the outputs from these big data research efforts. Data insights into customer movements, promotions and competitive offerings give useful information with regards to customer trends. We introduce a Business Process Improvement methodology for overcoming this limitation by integrating process improvement with big data based DSSs. The maintenance problems are well exemplified by this tool in industrial practice. The top three, What type of research is being undertaken in the area of big data in, earch type by year. The main contribution of this article is to highlight how reliability can be used to support different types of strategic decisions in the context of Industry 4.0. The influence of each factor was quantitatively estimated through linear regression analysis. remove those that do not focus on, and contribute to, the area of big data in, All of the publications in the study were, sions were chosen to provide different perspectives on the, the area, while also building a data set that could be used to answer each of the re-. We observed that surveys and tutorials about Industry 4.0 focus mainly on addressing data analytics and machine learning methods to change production procedures, so not comprising predictive maintenance methods and their organization. in the title, abstract or meta-data section of the document. This is the conversion of information from an analogue format to a digital format. Given the results from the other, The primary search results were filtered using a set of inclusion and exclusion criteria, to identify the most relevant research for the study. Much of the hype surrounding big data revolves around the ways in which it can increase a manufacturer’s profits. Wang et al. of manufacturing where Artificial Intelligence (AI) wa, To classify the type of contribution made b, method known as keywording  was chosen. Rather predictably, due, research efforts in 2012 possessed a strong, ing 60 % of the papers published. Furthermore, this work proposes to standardize and modularize industrial data infrastructure for smart energy savings. Findings — The study reveals 65% of the articles published between the year 2015 to 2019. implementation of activities and investments aimed at from predictive maintenance, to real-time diagnostics. The Big Data foundation is composed of two major systems. snowballin, As specified in the research methodology, there was an issue with constructing an ap-. without exclusively investigating the area. An overview on opportunities to healthcare, technology etc. It was found that the uncertainty introduced in the energy modelling stage can be greatly reduced through the use of advanced machine learning algorithms. These huge vol-umes (terabytes) of data can be processed and analyzed to gain insight into systems. The applications included in the report are predictive maintenance, budget monitoring, product lifecycle management, field activity management, and others. in the area of big data in manufacturing. Manufacturing data examples (IC, 2014), Benefits and Impacts of Big Data in Design. However, in today's volatile and complex businesses, local decisions are no longer sufficient; it is necessary to analyze the organization entirely. There exists an unresolved gap between the data science experts and the manufacturing process experts in the industry. In the oil and gas sector, big data facilitates decision-making. The results of the suitability assessment were used to guide the development of a machine learning supported methodology for energy savings verification. With this much data comes a corresponding opportunity for improvement, to the tune of $50 billion in the upstream oil and gas industry alone (figure 2). Big Data provides business intelligence that can improve the efficiency of operations … Therefore, the guid-, ile also providing the results needed to answer, The intention of this question is to illustrate the interest in the research area, The intention of this question is to high, The intention of this question is to understand the type of contributions and, ese outputs may vary greatly and range from. Clancy is with the Civil Engineering. Research publications have systematically been selected with a focus on the adoption of I4.0-ET to provide collective insights through theoretical synthesis into fields and sub-fields. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. At present, research interest in the, high, which is clearly illustrated by the year-on-. In: Conference on ENTERprise information systems towards, vol 00., p 2212. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing. ublications relating to big data in manufac-, nly includes research published in January, ns by journal and conference. information system architectures, to analyti. Section 4 describes our findings, and section 5 compares our findings to the literature. This paper gives an introduction to Hadoop and its components. By answering t, Furthermore, the classification of the differe. Depending on those guidelines a segmentation tool called PatSeg is developed based on a combination of text mining techniques. Big Data challenges in Smart Manufacturing 10 1.Introduction pathways towards the realisation of the vision described for each of the personas, while considering different key aspects such as Platform characteristics, Data, Skills, Security, Regulation, business models, etc.. as depicted here in Figure 1. The integration of the concepts, as mentioned earlier, set the base for the development of the PdM area. However, investigating the anomaly further is not warrante, this study given that it is not critical to answering the research question. This paper discusses our efforts in curating a large Computer Aided Design (CAD) data set with desired variety and validity for automotive body structural compositions. This paper aims at illustrating the role of Big Data analytics in supporting world-class sustainable manufacturing (WCSM). Big data; Manufacturing; Smart manufacturing; Industry 4.0; Big data. Present and future work consists of an M&V framework that utilises the modelling methodology and evolves the process to a real-time, automated state. the reduction of waste and the increase of output yielded. This level of maturity, commonly referred to as M&V 2.0, is already achievable in the more simplistic fields of residential and commercial buildings. Opportunities for future research are identified considering the gaps in knowledge in modeling. The majority of analytics focus on, predictive analytics, with a minority focused. creasing distribution and balance in the area. Predictive analytics use big data to predict system behavior and trends. While such data sets already exist for financial, sales and business applications, this is not the case for engineering product design data. To this end, collaborative schemes based on industry-research-education-government alliances must be fostered. Thus, reading and analyzing patent documents can be complex and time consuming. In production, combining several emerged technologies such as cloud computing, service-oriented technologies, and the Internet of Things, a new manufacturing system is introduced. Analytics: The real-world use of big data in manufacturing . We also review popular data analysis methods of remotely sensed imagery and discuss the outcomes of each method and its potential application in the farming operations. To do so, an established set of guidelines is exploited for defining the segments in the description part of the patent text. Purpose — This research aims to evaluate the current adoption of Industry 4.0, enabling technologies (I4.0-ET) in the manufacturing and supply chain management (SCM) context. Table 6 provides a summary of each type of, research contribution. inent classifications in both sets of result, based research, and theory-based contributions. Supply chain management. Results of a pilot study in a high school engineering class, in which students solved a solar urban design challenge, suggest that these data can be used to measure the level of student engagement, reveal the gender differences in design behaviors, and distinguish the iterative and non-iterative cycles in a design process. supply chain. Although different concepts of biorefinery are currently under development, further research and improvement are still required to obtain environmentally friendly and economically feasible commercial scale biorefineries. These databases were chosen collectively by all researchers involved in the, study, and were deemed a relevant source of t, transformed to the native syntax of each databa, to journal and conference publications based on the assumption that these publications are, more likely to be peer-reviewed than other sources, such as white papers and book, number of publications returned using the primary search string. Based on component, it is bifurcated into software and services. Using Best Tools - In manufacturing, Big Data in manufacturing has enabled organizations to look beyond just revenue generation and focus on the actual business. In 2016, Forbes reported that 68% of manufacturers are already investing in data analytics. KG was similarly used in maximizing the output current in an optoelectronic device. The literature suggested future research directions and highlighted one specific gap in the area. RQ4: What type of analytics are being used in the area of big data in manufacturing? s of energy management systems has led to a vast quantity of energy data becoming available. KB was responsible for the initial classification, of analytics associated with each publication, and contributed to the decisions relating to other classifications. Greatest benefit areas for manufacturing/operations; (1: No benefits; 3: Moderate benefits; 5: Very high benefits), Areas of greatest benefit for manufacturing/operations, Supplier/supplier component/parts defect tracking, Collecting supplier performance data to inform contract negotiations, Simulation and testing of new manufacturing processes, Enable mass-customization in manufacturing, Fig. This correlation may be a result, lopment of short research papers for confer-, Popularity of research contribution by year, a of big data technologies in manufacturing is, year exponential growth in publications over, Areas of manufacturing with significant research contributions, this study to be the most mature type of re-, s, is associated with the same number of publi-, ronological terms, the exponential growth, What type of contributions are being made to the area of big data in, attributed to the presence of theories and, y, the process of systematic mapping is not. The article also suggests that the emerging tools being developed to process and manage the Big Data generated by myriads of sensors and other devices can lead to the next scientific, technological, and management revolutions. Cyber-Physical System-based manufacturing and service innovations are two inevitable trends and challenges for manufacturing industries. The contribution of this study is a comprehensive report on the current state of research pertaining to big data technologies in manufacturing, including (a) the type of research being undertaken, (b) the areas in manufacturing where big data research is focused, and (c) the outputs from these big data research efforts. Analysis and interpretation: For analyzing the big data in AM, high performance computing (HPC) clusters, ... We are now inundated with massive amounts of data resulting from emerging computer technologies, scientific tools, the Internet of things, etc., and it can be expected that this data avalanche will continue unabated . In this study, big data on customers’ experience with front loading washers, represented by reviews and ratings on the BestBuy website, were collected and used to analyze the relationship between the customers’ experience and the associated satisfaction by using text analytics. This article introduces GBDIL and HGC-IA, and describes a common reference architecture for developing, deploying, and operating big data solutions that leverage Hitachi's innovative analytics technologies. Very large data storages, known as big data, contain an increasing mass of different types of homogenous and non-homogenous information, as well as extensive time-series. The manufacturing industry has always been one of the most challenging and demanding industry. rP os t W17696 DOW CHEMICAL CO.: BIG DATA IN MANUFACTURING R. Chandrasekhar wrote … Secondly, these papers were processed using four filters with the intention, of omitting publications that were not highly relevant to the study, which resulted in, 65 publications remaining. As inter-. Use Cases for Analytics. Requir Eng 11:102, and future prospects. The only database that, did not have the facility to restrict the search, Scholar. For the first time, a complete new Maintenance Engineering 4.0 model is proposed. The results of this classification process wa, lysed, with those publications that were classified the same being labelled immediately, and, those with differing classifications subject t, At the time of writing, this is the only research effort focusing on the systematic map-, provided a breadth-first review of the researc, promote a better understanding of a new and per, damental research questions that are relevant to cur, big data in manufacturing were answered, while also provi. Furthermore, the true challenge within the Industry 4.0 is with data communication and infrastructure problems, not so significantly on developing modelling techniques. Machine learning is opening up new ways of optimizing designs but it requires large data sets for training and verification. This trend is not one that is, contributions by year. tions in the first quarter of 2015 is twice that of 2014. The need for in-depth research, into Industry 4.0 has already been pointed out , Colm is currently focusing his research on the application of machine learning algorithms to improve the accuracy with which energy savings are measured and verification. Keywords: Big data; Redistributed manufacturing; Customer insights 1. International Journal of Engineering & Technology. the authors present a review on the IoT (Internet of Things) and its future scope in various areas. There are some challenges like drawing useful information from undefined patterns which we can overcome by using data mining. FOF (Factory of the Future) sees in Big Data analysis an important topic for manufacturing systems: Real - time and predictive data analysis techniques to aggregate and process the massive amount of Data plays a hugely important role in modern manufacturing processes. maintenance department and describes the empirical research systematic mapping) are described. © 2015 Lidong Wang and Cheryl Ann Alexander. Reducing Waste and Energy Costs. The business world is continually changing. the Enterprise Partnership Scheme (EPSPG/2013/578). Posted by Greg Goodwin on … As sensors proliferate and the role of big data in manufacturing grows, the questions surrounding information will only grow louder: Additional sources of information on Big Data in Manufacturing: Attitudes on How Big Data will Affect Manufacturing Performance. General challenges of Big Data and Big Data challenges in design and manufacturing engineering are also discussed. Decreasing ICT-costs propel connectivity and storage solutions for data generated, harvested and analyzed in machine tools. Most industrial manufacturing irms have complex manufacturing processes, often with equally complex relationships across the supply chain with vendors and sub-assembly suppliers. Big data in manufacturing The manufacturing sector was an early and intensive user of data to drive quality and efficiency, adopting information technology and automation to design, build, and distribute products since the dawn of the computer era. This has provided an impetus for organizations to adopt and perfect data analytic functions (e.g. It was also found that the Fraunhofer Institute for Mechatronic Systems Design, in collaboration with the University of Paderborn in Germany, was the most frequent contributing Institution of the research papers with three papers published. A rule-based algorithm is used to identify the headings inside patent text, machine learning technique is used to classify the headings into pre-defined sections, and heuristics are used to identify the sections in patent text that do not contain headings. The Levenberg-Marquardt method and genetic From modeling to manufacturing systems to advanced data analytics, MIT draws on more than 100 years of university-industry collaboration. Following, high-level label that represents research that spans, tributions associated with the enterprise s, research in the area. and processes, as well as an increase in the f, and persists measurements. The trend in pu, research are closely aligned with that of philosophical-based research, comprising, 35.71 and 34.69 % of the overall publicati, to be a visible lag in evaluation-based r, publications in 2014, but the partial data for 2015 shows that number of publica-. Table 2. Manufacturing. In order to become more competitive, manufacturers need to embrace emerging technologies, such as advanced analytics and cyber-physical system-based approaches, to improve their efficiency and productivity. There lies a gap between the manufacturing operations and the information technology/data analytics departments within enterprises, which was borne out by the results of many of the case studies reviewed as part of this work. Global environmental challenges and zero-emission responsible production issues can only be solved using relevant and reliable continuous data as the basis. 12th Int Conf Eval Assess Softw Eng., pp. The revolutions will enable an interconnected, efficient global industrial ecosystem that will fundamentally change how products are invented, manufactured, shipped, and serviced. Given that enterprise is an aggregate of sorts, maintenance and diagno-, ing to maintenance and diagnosis are somewhat different to the proceeding areas. An indepth analysis of these publications shows the adoption of I4.0-ET in the manufacturing and supply chain sector gained attention in recent years and is still at a nascent stage. Big data is arguably a major focus for the next round of the transformation of advanced manufacturing. Information technology (IT) solutions focus on collecting, processing, and reporting different types of data. Big number of manufacturing companies collect much process specific data. Common problems within maintenance management are that maintenance decisions are experience driven , narrow-focussed and static. order to adapt the enterprise to the Industry 4.0 concept. We discuss pros and cons of each method and how we devised a combination of these approaches. The discussions help frame strategies to prioritize efforts for I4.0-ET incorporation. Received: 12 June 2015 Accepted: 31 July 2015, demand-dynamic performance. By shedding light on the evolutionary dynamics of the field, this research offers a valuable contribution to the technology innovation literature. However, tional, well-defined and accepted terms, which should reduce the number of publica-, tions omitted due to authors using synonymous terms. Big data provides manufacturers the ability to track the exact location of … Mastery of data analysis is required to get the information, This paper develops an Internet-of-Things data highway embracing end sensors, sensor nodes, databases, big data processors, web connections, and high-end statistics engines. Department of Engineering Technology, Mississippi Valley State University, USA, Technology and Healthcare Solutions, Inc., USA, Computer and storage platform trustworthiness, Improve decision-making and minimizes risks in, Develop new products and make products better, Better perform remote intelligent services, Specialist data analytics tools (logs, events, data, MPP (Massively Parallel Processing) databases, Registries, indexing/search, semantics, namespace, Exponential growth of data volume is. The chosen primary search string was used as the search criteria in se, digital databases. A mixed method research was utilized for qualitative and quantitative for the multivariate parameters. According to research by McKinsey Global Institute and McKinsey’s Business Technology Office, the analysis of large datasets will become a key basis of competitiveness, productivity growth, and innovation .. Generally speaking, data analytics can be viewed as the science and … Additionally, the current open research issues in privacy and data protection in MCC were highlighted. Big Data at a missile plant (Noor, 2013), Quality Assurance and Logistics for Manufacturers, aeronautics and astronautics) because these, Table 3. The globalization of the world’s economies is a major challenge to local industry and it is pushing the manufacturing sector to its next transformation – predictive manufacturing. Research limitations/implications There is still no standardized workflow and processes for most UAV-based applications for Precision Agriculture. The, s search facility is different, the primary search string had to be, ere searched during the study, along with the, criteria to both title and abstract was Google, ary search string, while 14 relates to the, met this criteria were then processed using, ntion of highlighting the most relevant research, to be directly aligned with the focus of the, Filter 1: remove publications that do not contain. The synthesis of the diverse concepts within the literature on big data and operations management provides deeper insights into achieving value through big data strategy and implementation. Fog-based cyber-manufacturing systems provide the foundation to next-generation smart manufacturing networks in which manufacturers have access to on-demand computing infrastructures, mobile applications for cybermanufacturing and parallel machine learning tools .However, in the emerging cyber-physical systems domain, data is the new fuel that powers decision making across the whole product lifecycle, ... 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. Managers are looking for solutions that will be Big data and data analysis has moved the world towards a more data-driven approach. There are three prominent parallel computing options available today such as clusters or grids, massively parallel processing (MPP) and high performance computing (HPC).