Big Data and Supply Chain Management Essay

Big Data and Supply Chain Management Essay Introduction Big data has become one of the most important aspects of supply chain management. The concept of big data refers to the massive data sets that are generated when millions of individual activities are tracked. These data sets are processed to yield insights that help inform managerial decision-making. Supply chains in particular have leveraged big data because companies have been able to develop technology to not only capture hundreds of millions of data points, but to process them in meaningful ways to eliminate waste and promote efficiency in the supply chain systems. This paper will examine the concept of big data, how it has arisen and come to dominate supply chain management, and look at the different ways big data is transforming the supply chain function. Lastly, the paper will take a closer look at the future for big data with respect to supply chain management. As it becomes easier to gather data, and as there are diminishing returns to statistical robustness as the number of data points increases, are the competitive advantages of big data going to diminish? The Evolution of Supply Chain Management The field of logistics management was focused on controlling the flow of materials, in-process inventory and finished goods through a companys system from the time that it enters the system until the time that it leaves the system (Cooper, Lambert Pagh, 1997). As the field became more strategic in nature, it came to encompass other issues, such as sourcing materials and building in redundancy (Cooper Ellram,1993). More than simply moving things from point A to point B, the field became holistic in nature, where the quality and price of goods were factored into purchasing decisions as well as the logistics of getting those goods to the right place at the right time. Driving this change was the move towards a globalized marketplace. Globalization increased the complexity of the supply chain, adding longer transportation routes, border wait times, currency exchange, duties and tariffs, and a host of other variables that now had to be taken into consideration – logistics has rem ained important but it always viewed in context with the rest of the supply chain. Big Data The concept of big data really began to arise in the 1990s but has become increasingly important since that point. Big Data refers to the use of very large data sets to enhance managerial decision-making. The concept of big data arose as technology has developed to allow businesses to capture enormous data sets, and process them relatively easily (Boyd Crawford, 2012). Companies have long collected data at a rudimentary level. Loyalty programs and credit cards represented an evolution in the ability of companies to collect data and distill that data into consumer spending habits. This information is then made actionable by letting companies understand more about buying patterns. Big data is similar, but with a lot more data. One of the major advantages of big data is that it allows for complex problems to be solved. A modern supply chain can be exceptionally complex, and one of the important things about this complexity is that no one person can effectively make all the decisions â €“ decision-making tools are needed that can ensure not only consistent decision-making across the company but coordinated decision-making as well (Hult, Ketchen Slater, 2004). It is these coordinating mechanisms where the true power of big data lies – being able to identify things and make decisions that an entire team of humans working without big data would probably never be able to identify (Fugate, Sahin Mentzer,2005). Once big data gets to that point, a company can generate true competitive advantage. And when a company is large enough that is has a data advantage, it will be able to sustain that advantage, which is why there has been such a rush in recent years with respect to big data. As the concept was being fleshed out in academia, businesses were just starting to learn what they could do with all of the information that they were collecting – and one of the applications was to move away from marketing and use data to make decisions about the supply chain (McAfee Bryjolfsson, 2012). One of the first steps that companies needed to make was to hire data scientists – the sort of people who could process these data sets and derive useful information about them. Data scientists suddenly became popular, for their ability to take vast quantities of data, and derive actionable findings from that data (Provost Fawcett, 2013). At the heart of the drive to adopt big data is competitive advantage. Companies have invested in their data programs because they can derive significant advantage from big data under two conditions. The first is that larger companies have access to more data than smaller companies. The incremental cost of data acquisition is lower, and the companys ability to use that data in decision-making is theoretically better. The second is that even among larger companies, there are first-mover advantages to be had. This is evident in the supply chain, especially among companies that are competing on price. Using the classic example of Wal-Mart, one o f the leaders of data-driven supply chains, the company competes on offering the lowest prices, as do most of its competitors. Thus, if it can lower the cost of getting goods to its stores, it can pass those savings along to customers. There is opportunity for competitive advantage under that scenario, if cost leadership is the chosen strategy. Even when cost leadership is not the strategy, making the groundbreaking decision early puts a company in a better competitive position than its competitors (LaValle, et al, 2010). Big Data in the Supply Chain As the largest non-oil company in the world, Wal-Mart is looked to as a leader, so the fact that they were first movers on the use of big data in supply chain management has ensured that the rest of retail – and other industries as well – have followed. Some of the technologies that Wal-Mart has adopted allow the company to track its inventory from when it leaves the supplier –if not before – all the way through the logistics channel. Once Wal-Mart takes possession of the good, that good is scanned regularly through the process. The companys trucks are tracked via satellite. Stores use automatic re-ordering triggers to ensure that goods can be received as soon as they are needed. The goals of all this are to lower inventory holding costs by reducing the amount of inventory that stores have. Goods are turned over more quickly, because Wal-Mart receives them only days before it expects to sell them. Big data plays a significant role in ensuring that this pro cess can be achieved. There are a couple of key areas highlighted for big data in supply chain management. Demirkan Delen (2013) note that data, and how a company uses its data, is one of the ways it can truly differentiate from its competitors. It can be difficult to truly and consistently attract superior talent, and it can take time to move the needle on brand image, but data has become a popular means of finding competitive advantage largely because it is new, and firms in many industries are basically in a data arms race to find innovative ways to use their data to extract competitive advantage. The first is predictive analytics. Data science often focuses on using past events to predict future ones, and that is one of the main uses for big data in supply chain management. For example, if Wal-Mart in Smalltown, OH is running out of shovels at the end of February, and it takes twenty days to order new ones from China, including manufacturing and shipping times, three things can happen. The company can order a lot of shovels and ensure that they have supply. If spring comes, those shovels will sit in a warehouse until next November. They could also run out of shovels, but a late-season snow could leave demand on the table if the store lacks inventory. Modelling both weather patterns and local buying patterns can help the company to settle on demand. Even when weather is not a factor, the company can examine past purchasing patterns to set order quantities. The earlier it can set these quantities, the better response it can get from suppliers. Wal-Mart knows already what the no rmal amount of hot dogs it sells on the 4th of July, for example, so it can feed that information to its suppliers to ensure that they have those dogs at the Wal-Mart warehouse, exactly in the quantity Wal-Mart needs. Predictive analytics is used in supply chain management to take the variability out of the system as much as possible. Inventory usage is reduced, as is the potential for waste, especially with perishable goods. The chances of disappointed customers is also reduced. It is almost impossible – and certainly it is impossible for a company like Wal-Mart – to have exactly everything delivered exactly when the customer needs it. That means that there is always room for improvement. The pathway to improvement lies with bigger data sets, better analytics, and at scale even small incremental gains in the robustness of data or the ability of the company to analyze the data can yield meaningful financial gains (Waller Fawcett, 2013). But using data for something like predictive analytics – managerial decision-making, essentially – requires having good data, lots of it, and the means by which to process it. This is where larger companies enjoy scale advantages in big data. First, the technology to track events is not necessarily cheap. It can involve scanners, and certain involves large amounts of servers, routers, cloud storage – a lot of hardware. Larger companies are at an advantage in buying this hardware but they also have advantage in that they have many more data points. Wal-Mart can estimate sales because it has several years worth of sales, and can break these down by product, store, day, or even time of day. And instead of guessing for decision-making, the companys managers can look at the data and make the decision that on average delivers the greatest outcome. Data replaces decision-making heuristics when the data is sufficiently robust. Because the transference of big data relies on the Internet and communications technology infrastructure, that ICT infrastructure becomes a risk point for many companies but it also becomes a critical point of investment for companies that work with big data – how fast can the data collected on-site make its way to the decision-making tools matters in many businesses where time is of the essence in decision-making (Lu, et al, 2013). Predictive analytics has more than just value in ordering; it can help businesses to identify trends more quickly. This can be critical to advantage in some industries. Think of a fast fashion retailer – it needs to identify trends as soon as possible to get its knock-off clothes onto the market while the fashions are still fresh. Instead of anticipating, which is fraught with error, it can react to trends that have been verified with data. By understanding buying patterns and market cycles, companies can make better choices about what they make and when. This, in turn, is important to the supply chain, because companies also need to know what they need to produce their goods, and when. If there are fluctuations in availability, of if there is any variability among suppliers, then big data has the ability to point these factors out, and give the company an opportunity to deal with them proactively (Wang et al, 2016). Impact of Big Data When the concept of big data was first being elaborated, it promised major impact on business. Instead of guessing, firms would be able to make data-driven decisions that would reduce error, reduce waste and improve speed. As firms understand how to gather the data that they need, and to process it, they become more adept at this, big data has a bigger impact. Some leading firms have used the predictive powers of big data to help with their marketing. Amazon, for example, will recommend products to its customers based on what they have viewed and what they have purchased. Netflix does the same thing – and thereby encourages binge-watching of its shows. Both of these companies have become leaders in their respective businesses, and Netflix has done this specifically in the era of big data, by using that data to foster brand loyalty (Chen, Chiang Storey, 2012). If a company ends up as a first mover in big data, it will be able to gain advantage, and in many cases will make market share gains. Amazon faced a challenge from Wal-Mart a few years, ago, but has made use of big data to driver a high level of brand loyalty, while Wal-Mart fell short on its ability to use big data on the marketing side of its business. Netflix faced threat when major studios wanted to charge more for their content – so it created its own content and even more importantly used big data to improve the information architecture of its platform, allowing people to find content they want to consume. This increased the value of Netflix for many customers, thereby driving business value. Google uses data to target ads better, and charge its customers a premium. Customers are willing to pay more for a Google ad because they know that they will get more traction. So it is important that companies understand data on a conceptual level. One of the reasons that this is so important is that data today comes from a variety of different sources. This ties back to the concept of supply chain management, where the supply chain is a highly-integrated system with many parts from one end to the other. Understanding how the different variables within this system interact so that supply chain systems can be redesign in a more optimal way. Consider the way FedEx used the hub-and-spoke model before passenger airlines thought to do so. Consider how Wal-Mart designed its entire logistics network around lowering the amount of time that it takes for stores to restock. There are different approaches, but the innovations should derive from analysis of the data that identifies areas where the company might potentially perform better. Maybe sourcing goods from a certain country is no longer the lowest cost method, given how long it takes to get those goods to marke t. There are different ways of conceptualizing a supply chain, and now that companies are able to use data analytics to make those decisions, it is likely that many firms will start to restructure their supply chain (Tan et al, 2015). Total cost will become more important, but so too will overall responsiveness. Sourcing locally might provide a company with the responsiveness it needs for certain products that have higher variability in demand, for example. Future Directions While there is presently a shortage of people who have strong data analysis skills, these skills are becoming increasingly in demand, and schools are starting to train more students in the use of big data. One of the important factors here is that data has become much cheaper – big data arises because the cost of acquiring any given data point is very small, and continuing to shrink. Retailers in particular have been able to reduce their cost of data acquisition dramatically (Chen, Chiang Storey, 2012). Key to learning about the use of data is how to identify the problems that can be solved with data, how to match the data you have with the problems that you want to solve, and then developing systems to acquire the data that you do not have. At this high level of understanding, a company that thinks a good data game is in a much better position because having the right data matters just as much as knowing what to do with that data (Hazen, et al, 2014). The cloud and the Internet of Things (IoT) are driving a lot of changes in the way companies do business, and big data is playing a significant role in this restructuring of business. Zaslavsky, Perera and Georgakopoulos (n.d.) note that data is becoming a service function, with companies preparing to offer the means by which data can be acquired as a service, and the same for data analytics. We know that data is cheap to acquire, but combine that with lowering costs of processing data and there is a business model here, as well as one that focuses on using data to enhance business. The IoT will be more engaged in the data gathering process. For example, while convention supply chain data gathering might involve devices at the store level, the IoT might drill down further, to the individual level. Ovens could know how many people are cooking a frozen pizza and this information could be sold to frozen pizza makers, so that they can get a better sense of not only the performance of the ir products but of their competitors as well. This is the example a hungry person thinks up, but with more devices having some internet capability, it seems likely that type of application will emerge. Tesla is already a leader in gathering data about driving from its cars (Edelstein, 2016 Hull, 2016). Another progressive idea is that of big data benchmarking. If it is possible to buy and sell data to the point where a company can learn about the best practices at all levels for multiple companies in an industry, that would be incredibly valuable information to any firm in that industry. With the data explosion has come a rapid pace of innovation in the gathering and use of data. With this will come firms that buy and sell data, without actually gathering their own. Until now, data has largely been proprietary in nature, as a key source of sustainable competitive advantage, but as the cost of data acquisition declines, this might not be the case much longer. Secondary markets for data are already emerging and ultimately data will become commoditized – this process might take many years but it will happen and that will make for interesting analysis about the future of data , in particular the extent to which data can continue to be a driver of competitive advantage going forw ard (Ghazal et al, 2013). Finally, big data is also becoming a competitive weapon, which makes security of big data a major issue. Companies that gather and own data sets, and in particular the usable intelligence that has been gathered from those data sets, are increasingly going to be targeted with hacks. Security of big data is going to be an issue going forward. This is especially true of supply chain data, because that is powerful business intelligence. So it will be necessary, especially when using remote or cloud solutions, that data security is paid attention to, as the more that data becomes a source of competitive advantage the more at risk it will likely be. Conclusion Supply chain management had already emerged as a force in business, a holistic view of the supply chain that started with logistics but incorporated purchasing, product design and marketing as well, in order that supply chain decisions were not just based on a simply understanding of cost, but a complex one that took into account a number of different variables. Ultimately, supply chain management required significant amounts of data to be effective, and this realization occurred at just the time that managers realized they had the ability to gather, store and process data much more cheaply and easily than before. The transactional value of data grew at precisely the time that the acquisition cost declined. Data is typically used to aid in managerial decision making. Some companies have focused on the low-level decision where they seek out incremental gains on repeatable processes, knowing that those processes and other companies have sought insight that will allow them to completely transform their supply chains. Big data has become so important because the companies that are using it tend to be the market leaders. It is apparent that there is a scale value to data, which means that the largest companies, ones that have more data and lower data acquisition costs, are going to have sustainable competitive advantage. This has driven demand for data experts, such that there is a shortage of such individuals. Big data is going to continue to influence supply chain decision-making. There will be more points at which data is gathered, and the cost of processing data will continue to drop. There will still be a strong need, however, for talent that can conceptualize how that data should be used – after all, companies need to ask the right questions to get the most out of their data. If they can do that, they can sustain competitive advantage. In addition to there being an increasing ability to gather data, another reality is that many companies are going to be in the business of selling data. A company like Google sells data by proxy with its advertising, but as data becomes commoditized, the market for data will become more developed. An interesting aspect of this is that competitive benchmarking will be more common with respect to data practices. Firms will need to be careful to ensure that their proprietary data is secure so that they can maintain the competitive advantages that their data is giving them. If they can, then they can gain first mover advantage for tactics that deliver incremental gains, or the complete overhaul of a system to take advantage of something gleaned from the data. References / Works Cited Boyd, D. Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication and Society. 15 (5) 662-679. Chen, H., Chiang, R. Storey, V. (2012) Business intelligence and analytics: From big data to big impact. MIS Quarterly 36 (4) 1165-1188. Cooper, M. Ellram, L. (1993). Characteristics of supply chain management and the implications for purchasing and logistics strategy. International Journal of Logistics Management 4 (2) 13-24. Cooper, M., Lambert, D., Pagh, J. (1997). Supply chain management: More than a new name for logistics. The International Journal of Logistics Management. 8 (1) 1-14. Demirkan, H. Delen, D. (2013) Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems. 55 (2013) 412-421. Edelstein, S. (2016) Teslas autonomous-car efforts use big data no other carmaker has. Green Car Reports. Retrieved April 1, 2017 from http://www.greencarreports.com/news/1108065_teslas-autonomous-car-efforts-use-big-data-no-other-carmaker-has Fugate, B, Sahin, F. Mentzer, J. (2005) Supply chain management coordination mechanisms. University of Tennessee. https://www.researchgate.net/profile/Brian_Fugate/publication/228349679_Supply_Chain_Management_Coordination_Mechanisms/links/0c96051e3eaaa0280f000000/Supply-Chain-Management-Coordination-Mechanisms.pdf Hull, D. (2016) The Tesla advantage: 1.3 billion miles of data. Bloomberg. Retrieved April 1, 2017 from https://www.bloomberg.com/news/articles/2016-12-20/the-tesla-advantage-1-3-billion-miles-of-data Hult, G., Ketchen, D. Slater, S. (2004). Information processing, knowledge development and strategic supply chain performance. Academy of Management Journal. 47 (2) 241-253. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. Kruschwitz, N. (2010). Big data, analytics and the path from insights to value. MIT Sloan Management Review. http://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/ Lu, T., Guo, X., Xu, B., Zhao, L., Peng, Y., Yang, H. (2013). Next big thing in big data: The security of the ICT supply chain. IEEE Computer Society. Retrieved April 1, 2017 from http://diyhpl.us/~nmz787/pdf/Next_Big_Think_in_Big_Data__the_Security_of_the_ICT_Supply_Chain.pdf McAfee, A. Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review. Retrieved April 1, 2017 from http://www.rosebt.com/uploads/8/1/8/1/8181762/big_data_the_management_revolution.pdf Provost, F. Fawcett, T. (2013) Data science and its relationship to big data and data-driven decision-making. Big Data. 1 (1) 51-59. Tan, K., Zhan, Y., Ji, G., Ye, F. Chang, C. (2015) Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Economics. 165 (2015) 223-233. Waller, M. Fawcett, S. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics. 34 (2) 77-84. Wang, G., Gunasekaran, A., Ngai, E. Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics. 176 (June 2016) 98-110. Zaslavsky, A., Perera, C. Georgakopoulos, D. (no date). Sensing as a service and big data. https://arxiv.org/ftp/arxiv/papers/1301/1301.0159.pdf

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A few days after he was interviewed he sent in the documentation that proves he did indeed ask specific needs from FEMA (Marcela 2009). FEMA Director Brown lied on television. So often politicians lie about issues, why can’t they ever own up to their mistakes and just tell the truth. I believe the American public deserves to know the truth on issues, especially when dealing with something like Hurricane Katrina. In a natural disaster it is at first the state that has control. If the state becomes overwhelmed and can no longer control the situation, the state then needs to clearly articulate what services they are in need of, and that is when FEMA will then intervene. Several days after the storm, Director Brown apparently believed the state of Louisiana had the situation under control (Marcela, 2005). While the Mayor and Governor thought FEMA was going to step in and help. There was a period of time where nobody knew who was in command. Consequently there was a period of time where the many of the people of New Orleans had no food, water or shelter, and that is when many lives were lost. It bewilders one to think that this sort of miscommunication and misunderstanding could happen to a country like the United States. One would assume that our country would have one of the best disaster protocols in the world, considering our global supremacy. I will now touch on another part of U. S. government that I consider to have flaws, and that is bureaucracy. A bureaucracy as defined by Greenberg Page (2009) is â€Å" A large, complex organization characterized by a hierarchical set of offices, each with a specific task, controlled through a clear chain of command, and where appointment and advancement of personnel is based on merit† (Ch. 13). Individuals are appointed not elected and therein lies a problem. Anyone that the president chooses can be in a position of power. Many believe Michael Brown was absolutely not qualified for the position of FEMA Director (Greenberg Page, 2009, Ch. 13). After all he had no formal training in disaster relief or anything of that sort in his resume. Positions like FEMA director should have to have some sort of qualifications. One cannot just be put into that situation like Director Brown was. I believe that positions such as director should be an elected one. When someone who is not qualified is put in charge of one of biggest natural disasters in U. S. history, nothing good can come from it. And indeed that is exactly what happened, total chaos and confusion took place due to Director Michael Brown’s incompetence. After the attacks of 9/11 President Bush reorganized FEMA into a Bureaucracy. FEMA was downgraded and became a sub department of homeland security. Many believe this reorganization really hurt FEMA. Personnel, IT systems and other resources were dramatically reduced due to President Bush’s budget cutting agenda. The President also cut project impact, a program started by FEMA to help communities become disaster resistant, due to tax cuts (Marcella 2009). In addition 80 million dollars was taken from FEMA and distributed to other sectors in homeland security. This is just another problem of FEMA becoming a part of the U. S. bureaucracy. How would anyone expect an organization to do their job when they have had significant funds and resources cut? Clearly part of FEMA’s inadequacies can be linked to President Bush’s choice to make tax cuts and cut parts of their program. Again, the reorganization of FEMA was really detrimental. Hopefully in the future leaders will make more conscientious choices about which agencies should be cut back. Most would believe a disaster relief program such as FEMA should have some of the most funding and personnel available. Apparently former President Bush thought otherwise. Although President Bush does deserves a little bit of a break considering â€Å"the sheer size and complexity of the executive branch. There is so much going on, in so many agencies, involving the activity of tens of thousands of people, that simply keeping abreast of it all is no easy task† (Greenberg Page, 2009, Ch. 13). He is in charge of so much in the U. S. there is no way he could keep an eye on everything. So while the President may have indirectly affected FEMA by inducing budget cuts, he cannot be held directly responsible for the government’s lack of involvement in Katrina at the national level. The president relies on his appointees to do their jobs, which Director Brown did not fulfill. However, President Bush choose Michael Brown, so maybe Bush is in fact responsible for FEMA’s inadequacies. Obviously having a large federal bureaucracy is an inconvenience to say the least. I believe Hurricane Katrina has taught our country a few lessons. First of all that while having a federal bureaucracy has its pluses, it also brings with it it’s many flaws, and the main one being communication. Our government should be striving for interoperability. The ability for several groups at all levels of the government to communicate what resources are needed during a natural disaster will prove to be the single most important tool during a relief operation. I hope that in the future, Presidents will choose more wisely when filling high-level positions such as FEMA Director, and not just appoint those who helped with their campaign. I also hope more cities around the country have better evacuation plans than those of New Orleans. I hope National Guard buildings are better equipped with resources and manpower in the future, especially along costal areas that are known to have hurricanes.