How Can Big Data Affect BMW - Coursework Example

How Big Data can affect BMW By: How Big Data Can Affect BMW Introduction The greatest concern that social scientists have had in the 21st century is the digital divide, which is the state where a segment of the global population can easily make sense out of economy skills whereas the other subdivision is incapacitated in this sector. In as much as the introduction of mobile internet and low-priced handsets have come in handy in bridging the digital divide, a new rift that involves firms that can use data to create value and those that are left behind in this area has emerged (Satell, 2013). Big data management is not easy task, as it requires skills in basic programming, visual communication, collaboration and algorithmic design. According to a survey carried out by researchers drawn from Massachusetts Institute of Technology, firms that embrace data-driven approaches often register close to 6% better results, with their returns increasing by a substantial 60% (Satell, 2013). In fact, it said that data revolution has played a huge role in transforming the fortunes of corporations such as Amazon, Google and Facebook into the much-celebrated establishments they are today. The automotive business, which is dominated by firms such as BMW, Mercedes and Audi, is one of the industries that have in the past been fast to embrace big data. A section of the industry players such as Toyota have often embraced the lean management approach. That notwithstanding, the current big data insurgency has prompted most of the players to come up with mechanisms on how best to manage the massive data that they routinely deal with. This essay aims to provide more insight on how big data can affect BMW. Overview of Data Management within BMW Bavarian Motor Work, commonly known as BMW is a German automobile manufacturer that is globally known for its luxury vehicles and motorcycles. Since its establishment in 1916, the firm has been ranked as one of the best car manufacturers alongside Audi and Mercedes (Beckmann 2012, pg. 14). The essential functional base of the organization is in Munich though it has put up other operational bases across various parts of the world including the United States, China, Australia, South Africa, Japan and Canada among other nations. BMW, just like other entities within various sectors of the economy, has resorted to managing big data because the modern economic environment heavily relies on it. Over the years, BMW has used most of the information within its databases to produce high quality and efficient products. Given that big data has transformed most of the manufacturing industry players including BMW into service providers, both spreadsheets share a substantial percentage of the data within BMW databases on suppliers and customer information (Manyika et al. 2011, pg. 76). As demonstrated in the graph below, online portals, and corporate portals similarly occupy a sizable segment, followed by various platforms of communication such as presentations, emails and social media networks. Impacts of Big Data on BMW Present Effects For BMW to manage big data, the organization will need to adapt early to the immense records mindset and craft a goal oriented plan, which it will employ to collect, manage and store the data. The creation of platforms that will enable BMW to amass, monitor and store data would be costly for the organization. This is because the firm will be forced to invest in substantial information technology devices, and in some instances come up with platforms of sharing the collected data with other relevant entities (Manyika et al. 2011, pg. 79). Moreover, such a process could prove rather expensive for BMW, as it will have to hire the right personalities to oversee the process of data management. Nevertheless, the venture would be a worthy course, as the benefits that come with significant data management, outweigh the cost of the resources used in creating the set-up. Judging by the players from other industries who have resorted to big data management, it is apparent that such an approach rarely fails unless the leadership does not oblige to the new mindset. Long-term Effects Collecting, storing and managing big data will come in handy in reinforcing the position of BMW as a global brand. BMW can accomplish such an objective by using the data they collect to monitor their products and notify the customers on significant aspects such as when to undertake service checks (Manyika et al. 2011, pg. 81). In implementing such an approach, the organization should take great concern of the need to protect the privacy of their customers. Implying that information of such calibre should be availed to employees of a given security level clearance. Having the correct mechanisms for managing big data can come in handy in reducing BMW’s product development time by about 20% to 50%. This can be accomplished if the organization carries out simulation and testing of most of their products thereby assessing and eliminating some of the potential defects. Simulation can come in handy for BMW; for the creation of a digital factory can assist the organization optimize production within new plants, particularly in areas where there are space and utility distribution constraints (Manyika et al. 2011, pg. 86). Additionally, such a strategy will make sure that BMW manufactures products that meet precise consumer needs, as the organization would frequently consider concrete customer input submitted to its Virtual Innovation Agency. Surveys have similarly revealed that adopting such a mechanism does not only enhance innovation, but plays a role in determining the feasibility of specific products thereby aiding in faster decision-making. Given that most of BMW’s operations are based on the manufacturing process, the organization heavily relies on suppliers to routinely provide most of the materials they need in their production process. Moreover, as an automobile BMW interacts with well over four thousand vendors who deal in different items (Manyika et al. 2011, pg. 89). Basing their decisions on big data will ensure that the organization gets high quality and specific materials at a considerable cost. Such an approach would come in handy in reinforcing efficiency within the establishment, as their products will be of high quality and respectable design. Over and above, such a strategy will supplement BMW’s efforts of expanding its customer base, thereby enjoying a substantial share of the market. Apart from monitoring supplier performance and complementing innovation, running of big data will help BMW in making its manufacturing process more efficient. The sensors contained in their automobile products play a role of notifying the user on when to carry out routine maintenance as highlighted above. However, it can come in handy in alerting the organization in case of defects (US Chamber of Commerce, n.d). Such a strategy would be of great significance to the organization, as it will be able to rectify any probable shortcomings within the products going into manufacturing. According to Forbes, executives of most automobile firms have overtime reaffirmed the fact that decision-making is not an easy process, as each policy being implemented should always be in line with the corporate strategies of the organization (Kerschberg, 2013). Adapting early to the big data revolution will help BMW executives in arriving at faster verdicts that are in line with the objectives of the organization. As a corporation, BMW will have an upper hand in understanding the dynamics of the business environment within which they operate, enabling the executives of the organization enact policies that are in line with the market trends. The graph below illustrates some of the areas that BMW stands to gain once it embraces the big data insurgency. Conclusion Several information technology experts hold the opinion that the effect of big data management on manufacturing organizations can be compared to the influence of social media on the population (Khatro, 2013). Big data management is far-reaching for several project administrators because if properly embraced, then the organization stands to add value by a substantial margin (Maguire 2014, pg. 143). BMW has in the past used data as the basis of its invention, thereby producing vehicles of high quality. Big data management seeks to take the innovation a step further by reducing the time used to develop new products (Weinmann 2012, pg. 303). Similarly, it will come in handy in monitoring the performance of suppliers and making the manufacturing process more efficient (Tata Consultancy, n.d). The only factor that can avert BMW from easily adapting to the big data insurgency is the event that it has to establish a reliable IT system, including a platform for sharing information with other entities. Though creating such a platform would be costly, it is better than the usual process of research where analysts take a lot of time interpreting the data collected from surveys. The short-term consequence of big data management on BMW may be costly, but its long-term impacts are worthy, as the organization would eliminate defects in their cars and produce vehicles that meet the clients demands (Mayer et al. 2013, pg. 327). The process of adapting early to the insurgency will give BMW an added advantage, notably when it comes to competing with Mercedes and Audi. The dominance of Walmart can be traced back to the IT investments that the organization made in the seventies. For this reason, it is imperative for the BMW executives to adapt to the big data mindset, for it will be a huge step in ensuring that the firm establishes its position in the global market. References Beckmann, M. (2012). The BMW book. Kempen, teNeues. Kerschberg, B. (2011, January 11). Manufacturing Moneyball: Using Big Data and Business Intelligence To Spur Operational Excellence. Forbes Magazine. Retrieved from: http://www.forbes.com/sites/benkerschberg/2011/11/01/manufacturing-moneyball-using-big-data-and-business-intelligence-to-spur-operational-excellence/ Khatri, H. (2013, January). Trends in Manufacturing Operations: Leveraging Big Data across the Value Chain. Profit Magazine. Retrieved from: http://www.oracle.com/us/corporate/profit/archives/opinion/011813-hkhatri-1899121.html Maguire, M. (2004). BMW brand audit: scientific study. Munich, Germany, Grin. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. (2011, May). Big Data: The Next Frontier for Innovation, Competition and Productivity. McKinsey Global Institute. McKinsey & Company. Mayer, V., & Cukier, K. (2013). Big data: a revolution that will transform how we live, work, and think. Boston, Houghton Mifflin Harcourt. Satell, G. (2013, August 25). Companies That Cant Figure Out Data Are Getting Left Behind. Business Insider. Retrieved from: http://www.businessinsider.com/how-big-data-affects-strategy-2013-8. Tata Consultancy Services. (n.d). Manufacturing: Big Data Benefits and Challenges. Retrieved from: http://sites.tcs.com/big-data-study/manufacturing-big-data-benefits-challenges/ US Chamber of Commerce (n.d). Big Data and the Change It Brings. U.S Chamber of Commerce Foundation. Retrieved from: http://www.uschamberfoundation.org/big-data-and-change-it-brings Weinman, J. (2012). Cloudonomics: the business value of cloud computing. Hoboken, N.J., Wiley. Appendix 1 Graph on the areas BMW will benefit most from big data management (Tata Consultancy) Appendix 2 Graph on challenges BMW will face in an attempt to embrace the big data insurgency (Tata Consultancy) Read More