Big Data's Role in Transforming Supply Chain Management

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 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 not only to capture hundreds of millions of data points, but to process them in meaningful ways — eliminating waste and promoting efficiency throughout their supply chain systems.

This paper examines the concept of big data, how it has arisen and come to dominate supply chain management, and the different ways big data is transforming the supply chain function. Lastly, the paper considers the future of 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, will the competitive advantages of big data begin to diminish?

The Evolution of Supply Chain Management

The field of logistics management was originally focused on controlling the flow of materials, in-process inventory, and finished goods through a company's system — from the time that goods enter the system until the time they leave it (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, with the quality and price of goods factored into purchasing decisions alongside the logistics of getting those goods to the right place at the right time.

Driving this change was the shift toward 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 had to be taken into consideration. Logistics remained important, but it came to be viewed always in context with the rest of the supply chain.

Big Data: Origins and Competitive Advantage

The concept of big data began to arise in the 1990s but has become increasingly important since that time. Big data refers to the use of very large data sets to enhance managerial decision-making. It arose as technology developed to allow businesses to capture enormous data sets and process them relatively easily (Boyd & Crawford, 2012). Companies had 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 it into consumer spending habits, making that information actionable by revealing buying patterns. Big data is similar — but with far more data.

One of the major advantages of big data is that it allows complex problems to be solved. A modern supply chain can be exceptionally complex, and an important consequence of this complexity is that no single person can effectively make all the decisions. 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 in these coordinating mechanisms that the true power of big data lies — the ability to identify patterns 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 reaches that point, a company can generate true competitive advantage. When a company has a data advantage, it can sustain that advantage — which is why there has been such a rush in recent years to develop big data capabilities.

As the concept was being elaborated in academia, businesses were just beginning to learn what they could do with all of the information they were collecting, and one of the early applications was to move beyond marketing and use data to make decisions about the supply chain (McAfee & Brynjolfsson, 2012).

One of the first steps companies needed to take was to hire data scientists — people who could process large data sets and derive useful information from them. Data scientists suddenly became highly sought after for their ability to take vast quantities of data and produce actionable findings (Provost & Fawcett, 2013). At the heart of the drive to adopt big data is competitive advantage. Companies have invested in their data programs 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 company's 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 especially evident in the supply chain among companies competing on price. Using the classic example of Walmart — one of the leaders of data-driven supply chains — the company competes on offering the lowest prices, as do most of its competitors. 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 when cost leadership is the chosen strategy. Even when cost leadership is not the strategy, making groundbreaking decisions early puts a company in a stronger competitive position than its rivals (LaValle et al., 2010).

Big Data in the Supply Chain

As one of the largest non-oil companies in the world, Walmart is regarded as a leader, so the fact that it was an early mover in the use of big data in supply chain management has ensured that the rest of retail — and other industries as well — have followed. Technologies that Walmart has adopted allow the company to track its inventory from the moment it leaves the supplier, if not before, all the way through the logistics channel. Once Walmart takes possession of a good, that good is scanned regularly throughout the process. The company's 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 must carry. Goods are turned over more quickly because Walmart receives them only days before it expects to sell them. Big data plays a significant role in making this process possible.

Demirkan and Delen (2013) note that data, and how a company uses its data, is one of the ways it can truly differentiate itself from competitors. It can be difficult to consistently attract superior talent, and it can take time to shift brand image; but data has become a popular means of finding competitive advantage largely because it is relatively new, and firms in many industries are essentially in a data arms race to find innovative ways to extract competitive advantage from their information.

The first major application is predictive analytics. Data science often focuses on using past events to predict future ones, and that is one of the primary uses for big data in supply chain management. For example, if a Walmart 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 large quantity of shovels and ensure supply; if spring arrives on schedule, those shovels will sit in a warehouse until the following November. Alternatively, the store could run out of shovels just as a late-season snowstorm creates demand. Modeling both weather patterns and local buying patterns can help the company settle on optimal order quantities. Even when weather is not a factor, examining past purchasing patterns allows the company to set order quantities effectively. The earlier it can set these quantities, the better response it can get from suppliers. Walmart already knows the typical volume of hot dogs it sells on the Fourth of July, for example, and can feed that information to suppliers to ensure that exactly the right quantity is at the warehouse at the right time.

Predictive analytics is used in supply chain management to remove as much variability from the system as possible. Inventory usage is reduced, as is the potential for waste, especially with perishable goods. The likelihood of disappointing customers is also reduced. It is nearly impossible — certainly for a company the size of Walmart — to have exactly the right goods delivered at exactly the moment the customer needs them. That means there is always room for improvement. The pathway to improvement lies with larger data sets, better analytics, and scale, where even small incremental gains in data robustness or analytical capability can yield meaningful financial results (Waller & Fawcett, 2013).

Using data for predictive analytics requires good data, large quantities of it, and the means to process it effectively. This is where larger companies enjoy scale advantages. The technology to track events is not cheap — it can involve scanners and certainly requires large amounts of servers, routers, and cloud storage. Larger companies have an advantage in purchasing this hardware, and they also benefit from having many more data points. Walmart can estimate sales because it has several years of sales history, broken down by product, store, day, or even time of day. Rather than relying on intuition, managers can look at the data and make the decision that, on average, delivers the greatest outcome. Data replaces decision-making heuristics when it is sufficiently robust. Because the transfer of big data relies on internet and communications technology (ICT) infrastructure, that infrastructure becomes both a risk point and a critical area of investment — how quickly data collected on-site reaches decision-making tools matters enormously in businesses where time is of the essence (Lu et al., 2013).

Predictive analytics has value beyond ordering. It can help businesses identify trends more quickly, which can be critical in some industries. Consider a "fast fashion" retailer: it needs to identify trends as soon as possible to get its clothing onto the market while fashions are still current. Instead of anticipating trends — a process fraught with error — it can react to trends verified with data. By understanding buying patterns and market cycles, companies can make better choices about what they produce and when. This in turn is important to the supply chain, because companies must also know what they need to produce their goods and when. If there are fluctuations in material availability, or variability among suppliers, big data has the ability to surface these factors and give the company an opportunity to address them proactively (Wang et al., 2016).

Conclusion

Supply chain management had already emerged as a force in business — a holistic view of the supply chain that began with logistics but incorporated purchasing, product design, and marketing as well, so that supply chain decisions were not based on a simple 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 precisely the time that managers discovered 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 acquisition costs declined.

Data is typically used to aid managerial decision-making. Some companies have focused on low-level decisions, seeking incremental gains on repeatable processes, while others have pursued insights that allow them to completely transform their supply chains. Big data has become so important because the companies using it tend to be market leaders. It is apparent that there is a scale value to data, which means that the largest companies — those with more data and lower data acquisition costs — are positioned to enjoy sustainable competitive advantage. This has driven demand for data experts, creating a shortage of such professionals.

Big data will 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 an increasing ability to gather data, another emerging reality is that many companies will be in the business of selling data. A company like Google already sells data by proxy through its advertising model, but as data becomes commoditized, the market for data will become more developed. An interesting aspect of this is that competitive benchmarking will become more common with respect to data practices. Firms will need to ensure that their proprietary data is secure so that they can maintain the competitive advantages their data provides. If they succeed, they can gain first-mover advantages for tactics that deliver incremental gains — or achieve the complete overhaul of a system based on insights gleaned from the data.

References

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). Tesla's 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. Retrieved from https://www.researchgate.net/profile/Brian_Fugate/publication/228349679_Supply_Chain_Management_Coordination_Mechanisms/links/0c96051e3eaaa0280f000000/Supply-Chain-Management-Coordination-Mechanisms.pdf

Ghazal, A., Rabl, T., Hu, M., Raab, F., Poess, M., Crolotte, A. & Jacobsen, H. (2013). BigBench: Towards an industry standard benchmark for big data analytics. Middleware Systems Research Group. Retrieved April 1, 2017 from

Hazen, B., Boone, C., Ezell, J. & Jones-Farmer, L. (2014). Data quality for data science, predictive analytics, and big data in supply chain management. International Journal of Production Economics, 154(2014), 72–80.

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. Retrieved from 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 Production 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. (n.d.). Sensing as a service and big data. Retrieved from