1 Proceedings of the 2015 Industrial and Systems Engineering Research Conference S. Cetinkaya and J. K. Ryan, eds. ERP adoption in enterprises with emerging Big Data Mehdi Khazaeli 1, Leili Javadpour 1 and Gerald M. Knapp 2 1 School of Engineering and Computer Science, University of the Pacific 2 Department of Mechanical and Industrial Engineering, Louisiana State University Abstract Communication and information technologies are reshaping enterprises worldwide. Tremendous amounts of data are being harvested about our browsing and shopping habits and social networks. Data is being aggregated not just within organizations but also across industrial and governmental sectors, such as in healthcare, education, and supply chains. And more and more devices in consumer use, manufacturing, and logistics are equipped with data acquisition capabilities, increasing the pace of data accumulation. Many organizations now have data stores in the petabyte range, with world-wide data now in the Exabyte range. There's no way to analyze this data using traditional statistical techniques, because even at very low sampling rates, data set sizes are in the billions or more observations. To solve this problem, we need to understand the underlying business relationships and bring intuition and judgment into identifying patterns in the data. After identifying the problem, the computer studies the different data and builds a model from a generalization of the samples. These patterns can be used to forecast the system s behavior and this prediction increases the productivity and opens new business opportunities. In this paper we ll show the potential of big data and how analytics and big data can transform industrial engineering and illustrate an implementation of ERP cloud and big data solution in an organization. Keywords Big Data, ERP, Supply chain 1. Introduction In recent years, the rapid development in the field of Information Technology has triggered a change in the way organizations conduct business. The increasing demand of business drivers and technological advances in industry has bridged the gap between science and new E-commerce. Businesses seek to derive insights and information from available data in order to make better decisions and increase productivity. This demand has fueled the growth of different tools and platforms to handle big data problems. Big data has and will continue to change the way businesses operate. The increased volume and velocity of data means that organizations will need to develop new process and systems for gathering, analyzing and interpreting data . Discussing Big Data, the first things that come to mind are click stream analysis on websites and sentiment analysis and keyword search on social media. But Big Data goes well beyond this and serves the unique requirements of industry. Government, aviation, mining, oil and gas, power generation and transportation represent more than 30% of global economy . A Big Data platform that brings new value to the wealth of data coming from these assets, their processes and the enterprises will need new information based services . In this paper the potential impact of Big Data in Industrial Small and Medium Enterprises (SME) companies and cloud Enterprise Resource Planning (ERP) in generating a new wave of productivity gains and information based services will be studied. The industrial Big Data discussed here is used in financial services, retail and generally in SMEs which needs to be deployed on machines as well as run on massive cloud based computing environments.
2 The use of technology will continue to transform the Industrial Engineering field in the years ahead. Those sections that invest on deriving valuable information from their data will have a distinct advantage over their competitors. Data acquisition is growing at exponential rates from nearly every source. The emerging of technologies offers better acquisition; this along with the increasing capacity of hardware enables faster and easier data analysis tools. More and more devices, manufacturing tools, plants and vehicles are being equipped with sensors. These sensors not only collect massive amounts of data from themselves but also from their surroundings. By identifying patterns from these data and making more accurate forecasts, industries can work more effectively. 2. Industrial Big Data Industrial big data increases productivity and opens new business opportunities. Big data is generating an extreme amount of attention among industrial engineering businesses by emerging its applications in cloud based technologies, digital channels and data visualization. The results of Ernst & Young s (EY) 2013 Global Information Security Survey  indicate that while adoption and use of big data is not yet widespread, there is growing confidence and familiarity with the technology. Figure 1 illustrates the respondents familiarities with big data and its technologies. The average rank of big data technologies was around the corner, meaning that big data has been on organizations radar for a period of time but may have not yet been implemented or widely adopted. The survey also indicated that companies typically view these technologies as an opportunity to improve their performance and create competitive advantage. Figure 1: Emerging technologies and trends  Based on EY s Global Forensic Data Analytics Survey 2014 , 72% of respondents believe that big data technologies can play a key role in fraud prevention and detection. But only 7% of them were aware of any specific big data technologies, and only 2% were actually using these technologies. The growth of technologies has enabled companies to collect massive amounts of data and therefore techniques are required to make sense of this data. To solve this problem big data analytics has become a critical business
3 capability. To help companies control the increasing amount of data as well as the complexities of business and regulations, forensic data analytics technologies have emerged. These technologies include real-time processing systems that analyze data in real time and make business decisions such as fraud detection and improper transaction. Although there are many tools available that improve big data processing, many companies don t take advantage of these tools and don t use data mining as a way of monitoring business activities . In the following sections, the emerging use of big data technologies in different sectors relating to the industrial engineering field are surveyed and studied. 2.1 Automobile Industry The automotive industry is also no exception and has also been affected by the trend of big data. Studies show that the auto industry has estimated to be the second-largest generator of data by 2015 . This is not surprising, considering that some plug-in hybrid vehicles generate 25 GB of data in just one hour . Big data is affecting nearly every industry, but there are specific market forces that are changing the automotive industry (see Figure 2). For example, due to the collaborations between customers and environmental groups, the demand for sustainable ecosystems is increasing. In response to this demand, new technologies and capabilities are making vehicles more intelligent. To answer the market s desire, automobile companies are quickly launching sustainable vehicles, capitalizing on services opportunities for intelligent connected vehicles, and transforming the retail environment. Consumer, regulatory and environmental requirements drive the need for collaboration partner ecosystems Market forces Increased globalization drives more integration whiten automotive companies New technologies and capabilities make vehicles more intelligent Rapidly integrating enterprises drive increasingly dynamic operations Sophisticated consumers demand innovative, sustainable vehicles Imperatives Rapidly launch increasingly sustainable, connected vehicles Optimize the goal value chain Capitalize on services opportunities for intelligence connected vehicles Transform retail Big data uses cases Data warehouse optimization Predictive asset optimization Connected vehicles Actionable customer intelligence Figure 2: Market forces affecting the automotive industry  The business requirements of automotive companies can be understood by big data analytics. A research group at IBM incorporated industry use-cases including the connected vehicle, customer insights and predictive asset optimization to build data analytics solutions. Their work show how big data and analytic solutions can be used to increase competitive advantage for automotive manufacturers, suppliers and dealers . 2.2 Supply Chain Another area of industrial engineering that has been affected by big data and is also expected to get improved is supply chain collaboration and supply chain processes . This area includes inventory management, transportation management and relationship management.
4 Accenture research indicates that even though many companies have high expectations for supply chain big data analytics, not all of these companies are ready to adopt it . The survey reveals that 97 percent of executives have an understanding of how big data analytics can benefit their supply chain, but only 17 percent have implemented data analytics solutions in their supply chain functions . Based on Sadovskyi et al. there is a significant shortage of new theoretical and practical developments in this direction . Existing big data concepts in the field of supply chain management attempts to target relevant tasks such as faster tracking and classification of goods, collecting data for transportation, logistics planning and scheduling, and data analytics for health checking of suppliers and creditors or keeping track of partners compliance conditions using web mining [11,12]. 2.3 Healthcare The amount of health care data that is being digitally collected and stored is massive and expanding rapidly. Using data analytics on patient and practitioner data could be an important way to improve quality and efficiency of health care delivery. A survey by the American Hospital Association showed that the use of electronic health records has doubled from 2009 to Most electronic health records now contain quantitative data (e.g., laboratory values), qualitative data (e.g., text-based documents and demographics), and transactional data (e.g., a record of medication delivery) . However, much of this rich data is currently perceived as a by-product of health care delivery, rather than a central asset to improve its efficiency. Big data can be a vital step for expanding the capacity of generating new knowledge. The required time and cost of answering many clinical questions by collecting structured data is very high. Being able to analyze the unstructured data contained within these electric records using computational techniques allows finer data acquisition in an automated fashion. These techniques include using natural language processing to extract medical concepts from free-text documents. Currently patients are being treated averagely, everyone in the same manner. By applying predictive analysis and big data analytics those patients that a treatment is proven not to be effective on can be identified and helped with a different medicine. This will result in the benefit of the patient. Big data can also be employed to transform health care by delivering information directly to patients, empowering them to play a more active role. The current model stores patients' records with health care professionals. In the future, medical records may reside with patients. One problem with this model will be the considerable privacy concerns. This will require solutions similar to, if not more extensive than those required to protect confidential financial data in other sectors. 2.4 Construction For the construction industry, volume and variety becomes particularly relevant. Construction project participants are confronted with the need to make high quality and timely decisions based on the information content that can be deduced from the very large data sets required to represent the various facets of a project through its development life cycle. From project planning to the project close out, a lot of structured as well as unstructured data is being generated and recorded for each construction projects. Examples of those data include daily work report, data generated from various sensors and equipment, images and videos of the construction site, etc. . Currently, more and more attention is drawn to high performance buildings (HPBs), aka green, sustainable, and low energy/ carbon buildings, discussed in many studies . Many researchers are working to facilitate rapid deployment of real time, mobile sensor networks in construction companies. ESRI with cooperation of 1800 partners provided geoenabled solutions for urban planning emergency services and GIS and big data solutions. Other areas of work are building energy management, building information modeling for visualization/facility management, construction visualization and data and information integration models for decision making.
5 3. ERP in SMEs Small to medium-sized enterprises need to put the power of technology solutions to work. For their businesses to grow and prosper, SMEs need to implement a powerful IT solution such as Enterprise Resource Planning systems. ERP systems can lead SMEs to long term growth, competitiveness and success. ERP system links various departments of enterprise and gives employees a holistic view of all the information that has a financial impact on the business . In this section the effect of big data on ERP has been discussed and the evolution of ERP throughout time has been illustrated. Traditional ERP systems were designed to support in-house processes and supply chains that rarely changed. Modern logistics are far more fluid, and stakeholders demand personalized services. Moving ERP to the cloud can bring flexibility and real-time decision support to businesses which allow converting flexibility into efficiency and responding to customer demand before the customer even asks. Downside of moving to cloud is that due to sensitivity of ERP data, companies might feel hesitant to go cloud unless they see a huge benefit in doing so. Traditionally, organizations were divided into different units (for example sale, production, logistics, billing, etc.) based on the functions performed by them (Figure 3). Each of the departments had their own goals and objectives, which from their point of view were in line with the organizations goals. Each department functioned in isolation and had data collection and analysis performed separately. In this type of organizations no-one knew what the other was doing and this led to chaos in the organization. Figure 3: Business structure before ERP To solve the isolation issue, the enterprise system was developed by integrating the information systems and enabling smooth flow of information across different section and department. Information about all the aspects of the organization is stored centrally and is made available to all the departments (Figure 4). ERP system integrates the information system and maps all the processes and data of an enterprise into a comprehensive integrative structure. The emergence of cloud computing, changes in business dynamics, and increase of competition, have led enterprises to enhance information communication and technology. Cloud simply means that your software, data or related infrastructure are hosted remotely via the Internet. Industries are leaning toward cloud solutions for hosting their ERP software and system. Originally everything was hosted onsite and each business was responsible for managing their business management software. By using cloud ERP, organizations are not only reducing their straight up costs but are experiencing a significantly simple implementation procedure. New areas of innovation lie at the intersection of multiple trends of SaaS (Software as a Service), PaaS (Platform as a service), IaaS (Infrastructure as a Service), Big Data, and mobile which is making current solutions better, faster and cheaper (Figure 5). Cloud ERP is one of the vital areas in organizations especially in SMEs. By implementing a cloud ERP, SMEs have the versatility to choose the deployment model that most closely fits their needs, with a reduced price and complexity. They will have the ability to make use of the same amounts of configuration, functional and ease-of-use which they did using the traditional ERP solutions.
6 Figure 4: Traditional ERP The advantages of ERP cloud solutions compared to the traditional ERP are [17, 18]: Lower upfront costs Lower operating costs Rapid implementation Scalability Focus on core competencies Access to advanced technology Rapid updates & upgrades Improved accessibility, mobility and usability Easier integration with cloud services Improved system availability and disaster recovery 4. Conclusion The field of big data is evolving rapidly and organizations cannot ignore the benefits. To be able to have access to the relevant information at the right time and have the technology to analyze data are of the key success factors for companies. The demand for insights that helps improve the productivity and performance of companies in real time fuels the growth of big data. This will lead to data driven decisions that will change the way industry handles their operations and compete in the marketplace. The convergence of data availability and processing power is helping to unlock the potential of big data for most sectors and industries. The results of big data can benefit a wide range of stakeholders across the organization from executive management and business professionals to customer-facing departments such as sales and marketing. Data can be collected from various sources; the key challenge is being able to interpret the huge amount of data and gain the required insight that will help companies survive in a competitive market.
7 Figure 5: Cloud ERP  References 1. Davenport, T. H., Barth, P., & Bean, R., 2013, How big data is different, MIT Sloan Management Review, 54(1). 2. GE Intelligent Platforms: The Rise of Industrial Big Data. Whitepaper (2012) 3. Under cyber-attack: EY s Global Information Security Survey Mining big data to mitigate corruption risk: Global Forensic Data Analytics Survey McKinsey Global Institute. Big data: The next frontier for innovation, competition, and productivity. June, Gardner, D., 2013, Ford scours for more big data to bolster quality, improve manufacturing, streamline processes, January 9, BriefingsDirect on ZDNet.com. 7. IBM big data for the automotive industry. Performance-Content/IBM-Big-Data-for-Auto-Industry.pdf 8. Waller, M.A., and Fawcett, S.E., 2013, "Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management", Journal of Business Logistics, 34(2),
8 9. Big Data Analytics in Supply Chain: Hype or Here to Stay?, 2014, Study-Big-Data-Analytics.pdf 10. Sadovskyi, O., Engel, T., Heininger, R., Böhm, M., Krcmar, H., 2014, "Analysis of Big Data Enabled Business Models Using a Value Chain Perspective", Multikonferenz Wirtschaftsinformatik (MKWI 2014), Swaminathan, S., 2012, "The Effects of Big Data on the Logistics Industry, Profit Oracle. 12. Young, M., and Pollard, D., 2012, "What Businesses Can Learn from Big Data and High Performance Analytics in the Manufacturing Industry", Big Data Insight Group. 13. Murdoch, T.B., Detsky, A.S., 2013, The Inevitable Application of Big Data to Health Care, JAMA, 309(13): Russell, A. D., Chiu, C. Y., and Korde, T., 2009, Visual representation of construction management data, Automation in Construction, 18(8), Hong, T., Yang, L., Hill, D., and Feng, W.,2014, Data and analytics to inform energy retrofit of high performance buildings, Applied Energy, 126, Sistla, S. H, and M. Babu, S.P., 2013, ERP implementation for Manufacturing Enterprises. International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) 3(4), Marston.S., Li Z., Bandyopadhyay S., Zhang J., Ghalsasi A., 2010, Cloud computing: The business perspective, Decision Support Systems, 51, Duan, J., Faker, P., Fesak, A., Stuart, T., 2012, Benefits and Drawbacks of Cloud-Based versus Traditional ERP Systems, Advanced Resource Planning
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