Business Intelligence for Cost Reduction in Mining Industry
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This report discusses the use of business intelligence in the mining industry, specifically focusing on cost reduction strategies. It explores the role of data warehouse, analytics, and technologies in improving business processes and decision-making. The report provides insights and recommendations for BHP Billiton, a leading mining company.
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Running Head: DATA ANALYSIS0 Data Analysis Report Student name
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DATA ANALYSIS1 Executive summary Business intelligence is requiring for betterment in the business processes and operations of an organization. It provides help to improve business processes as well as growth of organization. This report will provide introduction and background of the organization. Mining industry is having daily data of production and other things for managing different processes of organization. It is also requires different things for decision making for mining, such as weather conditions. Manythingsthatmakeminingindustrybetter,suchasbusinessintelligence, technologies, and many others. Business intelligence is providing a way to reduce cost and increase profit of an organization. Data warehouse are backbone of the organization in present era. BHP Billiton is a brand in the field of mining in Australia. It was world Largent mining company in 2017. In mining industry, data is gathered from different equipment and sensors. That data is a key to better decision making for mining. This data is process through different processes and turn it into intelligence. This report will provide different strategies for mining through business intelligence and it will provide help in cost reduction. This report concludes with different recommendations, which are based on the business intelligence capabilities. Those recommendations are focus on cost reduction strategies.
DATA ANALYSIS2 Table of Contents Executive summary.........................................................................................................................1 Introduction and background...........................................................................................................3 Evaluation........................................................................................................................................3 Organizational Memory:..............................................................................................................4 Information Integration and transformation:................................................................................6 Reporting and analysis:................................................................................................................6 Visualization of information:.......................................................................................................7 Discussion and analysis...................................................................................................................7 Conclusions and recommendations...............................................................................................12 Recommendations for BHP Billiton:.........................................................................................13 References......................................................................................................................................15
DATA ANALYSIS3 Introduction and background BHP Billiton is a well-known company in the world, as it is largest company in the field of mining, petroleum and metals.Business Intelligence (referred as BI) is useful for different processes of organization for their betterment. Data warehouse is a source of data analytics that provide benefits for company(Chen, Chiang, & Storey, 2012). This report is based on BI and it will provide different methods of cost reduction. BHP Billiton is a huge company and there are many processes for managing different works. It is a mining company, which is providing business to other industries as well. Business intelligence is requires for most of companies that are having data from different sources. Mining companies are based on the data analytics. Therefore, it requires different softwares and applications for data collection as well as processing(Isik, , Jones, & Sidorova, 2013). BHP Billiton was founded in the 1985. It is situated at broken hill in New South Wales. It is third-largest company in Melbourne by revenue.The Broken Hill Proprietary Company Limited (BHP) operating the lead and silver mine(bhp.com, 2019). BHP Billiton has mining operations in Australia, and petroleum operations in Tobago, Algeria, US, UK and Trinidad. It has coal, copper, petroleum and iron ore operational units with a development potash project. BHP Billiton is having more than 62,000 employees and contractors for different business processes. Headquarter of BHP is situated in Melbourne, Australia. All the huge operations are controlling from headquarter. It is a dual listed company structure with two parent companies, which are BHP Group Plc. and BHP Group Limited. It is running by a management and unified board(BHP, 2019). This report will provide details of data warehouse uses in different processes of BHP Billiton, such as management, processing, and other things.
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DATA ANALYSIS4 Evaluation BHP Billiton is increasing their day-by-day in various fields especially in mining field. There few things to focus that are business intelligence through technologies by BHP. Data warehouse and data mining techniques are beneficial for different business processes of BHP Billiton(bhp.com, 2019). BHP Billiton is having many data about the mining as well as weather reports. They are taking helps from other government and private sectors for data and information for their business. Mining field is requiring a database and data warehouse for different processes of their business as well as operations. Mining industry is based on two things, which are data and technology for mining. (Priebe, Torsten, & Pernul, 2003) Above diagram is describing about four capabilities of business intelligence, which are organizational memory, information integration, insight creation and presentation(Sabherwal & Becerra-Fernandez, 2012). Organizational Memory: It is depend on the organization data collection. Every company is having many data about different processes as well as employees.There are two types of data is stored in the system that is structures and unstructured data(Ngai, Xiu, & Chau, 2009). Structured data is gathered from mine planning, processing of plants, and many others. Unstructured data is
DATA ANALYSIS5 gathered from emails, weather monitoring, and others. Both structured and unstructured data are useful for system processes(Berkhin, 2006). In below diagram, train management system is explained, which is used by the BHP Billiton for transportation. Source:(Dickerson, 2018) All the data is stored in the data warehouse and it is used for the Train control system as well as business systems(Berry & Linoff, 2009). Data warehouse system is providing help to all the business processes to manage them properly( Brown, 2012). Source:(Chowdhury, 2014)
DATA ANALYSIS6 Data is collected form different processes such as billing, human resources, payroll, ERP, product, sensors, orders, and many others. All the data is transformed to data warehouse, which is extracted from different sources( Creagh, 2018). Online Analytical Processing (OLTP) is providing different report formatted for marketing, sales and purchases. These reports are beneficial for decision-making(Linoff & Berry, 2011). Information Integration and transformation: Information is processed data through different methods that are useful for decision- making. BHP Billiton is having different systems for many business processes. As an example, weather monitoring system is providing many data on daily basis. Therefore, that data can be used for the different business processes as well as mining processes( Linden, 2015). Internet of Things (IoT) can be used for the integrations of data from different sources, such as sensors, monitors and many other devices. Data warehouse is collecting information from reliable sources. Therefore, IoT provides better automation and decision making using data warehouse. Reporting and analysis: First two sections are providing raw material for decision-making. Insight creation is third capabilities that provide better data for effective decision-making.Insight creation uses functional analytics for identify cause of issues then analysis them. After analysis, make predictions for future outcomes. Analytics provide real data about the production and legging with issues of machine or labor. It provides benefits to different processes as well as cost reduction. Monitoring of weather condition is beneficial for mining processes of different plants. It reduce cost, as bad weather conditions are affecting the working as well( Porritt , 2011). Business intelligence is helpful for reporting and decision-making for new and old plants workings. It is helpful for demand and supply operations. It provides data about the demands of customers(Obeidat, North, Richardson, & Rattanak, 2015).
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DATA ANALYSIS7 Based on the data, managers can promise for delivery of products as per given time. It provides real time data. Therefore, it is beneficial for the company as well as consumers. BHP Billiton is providing different services to their consumers(Laney, 2012). Visualization of information: Data presentation is a good skill of BI and it will provide benefits to different processes. Many processes are required previous data and real time data for next decision. BI system is providing both the data to system as well as managers. It provides maximum benefit and customizable input for different processes(Larose & Larose, 2014). Different software applications are provide better visualization for decision-making. They also provide data in fast way. Those reports are based on the millions of records that are stored in the data warehouse(Liao, Chu, & Hsiao, 2012). Discussion and analysis BHP Billiton is a huge company in the field of mining as well as Australia. It provides many benefits to their vendors as well as country. They produce a huge amount of coal, iron and petroleum in Australia as well as different countries(Mentz, Jooste, & Van Biljon, 2014). Source:(BHP, 2019) Data mining is providing many advantages to the BHP Billiton. Few of them are improving safety, increasing production, and reducing cost(Crozier, 2017).
DATA ANALYSIS8 BHP Billiton is producing the entire mineral from different site at worldwide level. There productions are showing in below diagram. Source:(BHP, 2019) In below diagram, operational performance of BHP Billiton is showing. It shows production for Dec 2018 half year and guidance for the 2019 financial year in summarized details. Source:(BHP, 2019) BHP Billiton is increasing prices according to year and different cost of business processes. Below figure is showing the average realized prices achieved for major commodities of BHP Billiton(VANTAZ, 2014).
DATA ANALYSIS9 Source:(BHP, 2019) Below chart is showing BHP Billiton part in mining field and other companies in percentage. Source:(BHP, 2019) Below figure is showing the changes in the production of BHP Billiton between 2016 and 2017.
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DATA ANALYSIS10 Source:(TheValuePortfolio, 2018) Below figure is showing the data about the BHP Billiton ltd. Shares market status from 2015 to 2018. It goes down in the 2016 because of natural disasters. Source:(BHP, 2019) Below figure is showing BHP Billiton estimated earning of years 2018, 2019 and 2020.
DATA ANALYSIS11 Source:(Gilroy, 2018) BHP Billiton is makes changes in their working and it adopts different processes that are better for sustainable development. BHP is fully supported of sustainable development. They are using renewable energy sources for different processes. Below diagram is showing a flow infrastructure for fulfill the sustainable development goals(Shmueli, Bruce, Yahav, Patel, & Lichtendahl Jr., 2017). Sources:(BHP, 2019)
DATA ANALYSIS12 Sustainable development is also based on the business intelligence. However, technologies are providing different things to modified processes that are reduce cost of company. In addition, BHP Billiton is using best technologies for sustainable development (Vercellis, 2011). Conclusions and recommendations It is concluded that form the basis of above sections of this report that, business intelligence is play a key role for growth of an organization. BHP Billiton is uses technologies and business intelligence for their business process and they got success in their field. They are always working in the field of mining with technologies and business intelligence. Technological changes are must for any business, such as BHP Billiton is applied in their mining processes. They also support sustainability goals and they created different activities for sustainable development. Data warehouse are providing better data analytics for decision- making. Innovations are beneficial for the growth of organizations. BHP Billiton is having their own research and development team at different places for disruptive innovations. This report evaluates different things with the help of business intelligence. BI is beneficial for decision- making that will reduce cost of different operations. New IT-services and operations technologies provide new advantages to the company in terms of revenue and growth. It is a way to merge both services and use them for better results. It provides better growth and it reduces the cost. Data mining provides data for mining as well as for business. Therefore, one technology provides different advantages to the organization. It will also make changes in business processes that are based on the statistics. Data warehousing is providing growth in different terms. Innovations are useful for different processes of the BHP Billiton. IoT can make it better through sensors and different application. Finally, it is included that business intelligence is beneficial the organizations and it provides an exponential growth in their business.
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DATA ANALYSIS13 Recommendations for BHP Billiton: Business intelligence is playing an important role in mining companies for sustainable cost management. Business intelligence is having different capabilities, such as data warehouse. It uses internal data sources for cost reduction. BHP Billiton must follow these recommendations for growth of company in future. 1.Develop business intelligence through technologies, such as data warehouse and mining 2.Establish central databases for providing real time information to business systems. 3.Create analytic tools with the help of business intelligence and use them for cost reduction strategy. 4.Many vendors are providing tools for such types of facilities, such as oracle, IBM and many others. 5.BI is an agile approach to sustainable development and it provides better decision-making skills to managers for different processes that are based on the data and information of internal and external sources. 6.Develop different domains for collection of data that is used for decision-making of other sites. 7.Introduced business intelligence system in organization for managing different operations 8.Develop cost reducing strategies for increasing benefits through business intelligence 9.Data mining techniques can provide benefits for decision-making to BHP Billiton business processes. 10.Merge operational technologies with new IT technologies for more growth.
DATA ANALYSIS14 References Brown, M. (2012, December 11).Data mining techniques. Retrieved from IBM: https://www.ibm.com/developerworks/library/ba-data-mining-techniques/index.html Creagh, B. (2018, February 1).The top mining trends of 2018. Retrieved from australianmining: https://www.australianmining.com.au/news/top-mining-trends-2018/ Linden, A. (2015, July 9).Advancing Business With Advanced Analytics. Retrieved from www.gartner.com: https://www.gartner.com/doc/3090420/advancing-business-advanced- analytics Porritt , K. (2011, January 11).Copper. Retrieved from www.ga.gov.au: http://www.ga.gov.au/data-pubs/data-and-publications-search/publications/aimr/copper Berkhin, P. (2006).A survey of clustering data mining techniques. In Grouping multidimensional data.Berlin: Springer. Berry, M., & Linoff, G. (2009).Data mining techniques.New Jersy: John Wiley & Sons. BHP. (2019, January 28).About us. Retrieved from bhp: https://www.bhp.com/our-approach/our-company/about-us bhp.com. (2019, February 13).Our approach. Retrieved from www.bhp.com: https://www.bhp.com/community/our-approach Chen, H., Chiang, R., & Storey, V. (2012).Business intelligence and analytics: from big data to big impact.London: MIS quarterly. Chowdhury, S. (2014, May 27).Big data and data warehouse augmentation. Retrieved from IBM: https://www.ibm.com/developerworks/library/ba-augment-data-warehouse1/ index.html Crozier, R. (2017, January 17).BHP Billiton to double permanent IT workforce. Retrieved from www.itnews.com.au: https://www.itnews.com.au/news/bhp-billiton-to-double- permanent-it-workforce-448438
DATA ANALYSIS15 Dickerson, G. (2018, april 5).AusRAIL PLUS 2005 Presented by:. Retrieved from /slideplayer.com: https://slideplayer.com/slide/13070373/ Gilroy, A. (2018, December 27).Why Are Analysts Projecting Falling Earnings for BHP Billiton?Retrieved from marketrealist.com: https://marketrealist.com/2018/12/why-are- analysts-projecting-falling-earnings-for-bhp-billiton Isik, , O., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments.Information & Management, 50(1), 13-23. Laney, D. (2012, February 1).Ten Reasons to Reach Beyond Basic Business Intelligence. Retrieved from www.gartner.com: https://www.gartner.com/doc/1911314/reasons-reach- basic-business-intelligence Larose, D. T., & Larose, C. (2014).Discovering knowledge in data: an introduction to data mining.New Jersy: John Wiley & Sons. Liao, S.-H., Chu, P. H., & Hsiao, P. Y. (2012). Data mining techniques and applications–A decade review from 2000 to 2011.Expert systems with applications, 39(12), 11303- 11311. Linoff, G., & Berry, M. (2011).Data mining techniques: for marketing, sales, and customer relationship management.New Jersy: John Wiley & Sons. Mentz, J., Jooste, C., & Van Biljon, J. (2014). Usability evaluation for Business Intelligence applications: a user support perspective .South African Computer Journal, 32-44. Ngai, E., Xiu, L., & Chau, D. (2009). Application of data mining techniques in customer relationship management: A literature review and classification.Expert systems with applications, 36(2), 2592-2602. Obeidat, M., North, M., Richardson, R., & Rattanak, I. (2015). Business intelligence technology, applications, and trends.International Management Review, 11(2), 47-56. Priebe, Torsten, & Pernul, G. (2003). Towards integrative enterprise knowledge portals. In Proceedings of the twelfth international conference on Information and knowledge
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DATA ANALYSIS16 management . (pp. 216-223). ACM. Sabherwal, R., & Becerra-Fernandez, I. (2012).Business Intelligence: Practices, Technologies and Management.John Wiley & Sons, Inc. Shmueli, G., Bruce, P., Yahav, I., Patel, N., & Lichtendahl Jr., K. (2017).Data mining for business analytics: concepts, techniques, and applications in R.New Jersy: John Wiley & Sons. TheValuePortfolio. (2018, January 8).BHP Billiton - Enormous Mining Company With Strong Assets. Retrieved from seekingalpha.com: https://seekingalpha.com/article/4135881-bhp- billiton-enormous-mining-company-strong-assets VANTAZ. (2014, Auguest 14).Big Data Analytics : the Hottest Disruptive Technology in Mining Right Now?Retrieved from vantaz.com: https://vantaz.com/big-data-analytics- hottest-disruptive-technology-mining-right-now/ Vercellis, C. (2011).Business intelligence: data mining and optimization for decision making. New Jersy: John Wiley & Sons.