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Big Data Usage in Supply Chain Management


This paper gives a summary of the research that was conducted to understand the unique issues surrounding the use of big data in the supply chain. The discussion identifies the major opportunities associated with the continued use of big data. The emerging obstacles affecting the use of big data are also outlined. The best recommendations and frameworks that can be implemented by supply chain managers who want to drive performance are presented.

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Big data has become a powerful phenomenon that businesses cannot afford to ignore. The term big data refers “to unstructured and structured data that inundates a company on a day-to-day business” (Schoenherr and Speier-Pero 121). The use of information today presents a wide range of opportunities as well as obstacles. The loss of privacy and jobs are some of the concerns emerging from the concept of big data. Additionally, competent employees capable of completing different activities and managing data are required. Despite such issues, the ability to deliver goods to the consumers promptly remains the main goal of supply chain management (Jaggi and Kadam 1013). This discussion will present numerous insights regarding the use of big data and implications for supply chain managers.

Questions, Issues, and Problems

The targeted study seeks to answer several questions surrounding the current use of big data. The first question or issue revolves around the level of transparency associated with big data. The research will go further to examine the privacy and personalization issues arising from the use of big data for the supply chain. The loss of jobs and a shortage of technical experts are the other issues emerging from the targeted topic (Jaggi and Kadam 1014). The study will go further to describe supplier and customer-centric business operations. The analysis will identify some of the best approaches to maximizing the use of big data in the supply chain and addressing the issues associated with it.

Literature Review

The past two decades have been characterized by increased collection and storage of business data. This has led to numerous challenges because many companies are unable to deal with the stored data. The concept of big data, therefore, emerged due to the nature of this kind of information. The biggest challenge is how to use the data to predict the behaviors of different customers. This development has even become more promising for supply chain managers (Richey et al. 729). Experts have been focusing on the best methods to analyze big data and use it to influence supply chain decisions.

Navickas and Gruzauskas indicated that players in the supply chain industry could collect and interpret information promptly (18). The practice was observed to present new opportunities for setting competitive prices for different products (Schoenherr and Speier-Pero 125). The other challenge emerging from the research is that many customers have become unpredictable. This is due to the changing population dynamics, availability of substitute products, and the emergence of new competitors. Scholars have gone further to indicate that the use of big data can make a difference for many logistical operators. The strategy can manage costs and delivery different products to the final users promptly.

A study by Zhong et al. indicated that big data was presenting numerous challenges to the users (584). For instance, the approach was observed to require competent individuals to analyze and make appropriate inferences from the collected data. Intelligence extraction was also becoming a major obstacle for many supply chain managers. Companies that have implemented new cultural strategies have managed to achieve positive results. The analytical competencies and skill sets required by many companies are still unavailable (Navickas and Gruzauskas 22). This gap explains why it has become impossible for many companies to embrace the power of big data.

Davenport and Dyche indicated that the use of big data was expected to present new opportunities and challenges to future users (17). The author argued that the use of big data could result in personalization. Similarly, Richey et al. observed that big data was a possible cause of reduced privacy for many customers (735). The issue of transparency was also singled out as one of the benefits emerging from the use of big data. These mixed issues explain why analysts have been identifying the best measures to make big data beneficial in supply chain management.

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Analysis and Discussion

The completed research study indicated that many supply chain managers were aware of the benefits of big data. Additionally, the managers argued that their firms were struggling with the implementation of big data due to several barriers. For instance, many firms were unable to purchase the right equipment to collect information from different customers, suppliers, and logistical operators. The first challenge was the issue of cost. The “overwhelming amount of information was another barrier towards the use of big data” (Schoenherr and Speier-Pero 127).

The issue of privacy has surrounded the use of big data in business operations. In supply chain management, companies have been using big data to forecast the behaviors of different consumers and ensure the right products are delivered to them promptly. Skeptics, on the other hand, have been against the idea because it encourages companies to use customers’ confidential information (Sanders 3). A specific customer might decide to purchase the required products from other companies if he finds out that his or her sensitive information is no longer secure. This challenge has made it impossible for many companies to pursue the use of big data to support their logistical activities.

Big data has led to automation in different companies. This is the case because many firms have purchased powerful equipment capable of analyzing data and presenting adequate ideas. The future might be dim for job seekers due to the continued use of big data (Sanders 4). This means that more people might be entrenched to pave the way for the big data experience.

Technologists are required by companies that plan to use big data in their logistical operations. Unfortunately, these professionals can be expensive for the company. This gap explains why big data has not been embraced by many companies. Supplier and customer-centric approaches are new ideas emerging from the use of big data. When used adequately, big data can result in powerful business strategies that focus on the emerging needs of the customers. The business can make timely decisions and deliver different consumer goods in a convenient manner (Zhong et al. 589).

Findings and Discussions

The completed study has revealed numerous issues regarding the implication of big data for the supply chain management. It is agreeable that the practice presents numerous challenges that can affect business performance. For example, supply chain managers will have to breach their customers’ privacy in an attempt to develop personalized marketing channels (Wamba and Akter 7). The issue of transparency has been embraced by supply chain managers because it promotes service delivery. These issues cannot be ignored because they dictate the success of the supply chain process. Businesses that fail to consider these gaps and emerging issues might find it hard to address the changing needs of the targeted consumer.

On the other hand, the use of big data is a revolutionary idea that is capable of transforming the performance of every business organization. The approach is even more beneficial if it is applied to a company’s supply chain process. The completed study has, therefore, come up with a powerful framework that can be used to apply big data in an organization (Wamba and Akter 9). The framework can be used by companies that want to achieve these two objectives: Realize the benefits of big data in the supply chain and overcome the hurdles affecting the process.

The first step towards using big data sustainability is known as segmentation. The goal of segmentation is to come up with a powerful supply chain that is competitive in terms of time, cost, and flexibility (Jaggi and Kadam 1016). Alignment is the second step whereby the supply functions of the targeted organization are matched properly. This strategy should be implemented to ensure the major stages of the supply chain benefit from the use of big data. Measurement is the other critical attribute of the framework. This step is used by supply chain managers to develop key metrics (also known as key performance indicators) to analyze the performance of every segment.

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Big data has been observed to result in a loss of jobs. This is the case because many companies decide to employ competent individuals who can support the use of big data. It would, therefore, be appropriate for business organizations to outsource the services of different providers (Richey et al. 725). This approach can support the goals of companies that do not have the required technologies or equipment. Issues of privacy must be addressed from a professional perspective to meet the needs of the customers.

A focus on the needs of the customers (customer-centric approach) should be the driving force behind every supply chain process (Wamba and Akter 9). The collected data should be used in such a way that it delivers value to the customer. The supplier network should be matched with the other functions of the organization.

Conclusions and Recommendations

The modern age has presented numerous challenges and opportunities for business organizations. The emergence of modern practices and technologies continues to revolutionize how business operations are done. Big data is, therefore, one of the recent phenomena that should be adopted by firms that were to compete in the modern business world (Davenport and Dyche 27). The outstanding fact from the completed research is that big data presents numerous opportunities for supply chain managers while at the same time remaining difficult to execute. This is the case because big data can guide companies to offer personalized supply chain networks for their customers. Additionally, big data can present new opportunities for making evidence-based decisions. It can also be impossible for supply chain leaders to maintain a strategic focus for their operations (Wamba and Akter 9). With proper planning and the ability to address every emerging challenge, more companies will use big data to improve customer satisfaction.

Works Cited

Davenport, Thomas, and Jill Dyche. “Big Data in Big Companies.” International Institute for Analytics, vol. 1, no. 1, 2013, pp. 1-31.

Jaggi, Harjeet, and Sunny Kadam. “Integration of Spark Framework in Supply Chain Management.” Procedia Computer Science, vol. 79, no. 1, 2016, pp. 1013-1020.

Navickas, Valentinas, and Valentas Gruzauskas. “Big Data Concept in the Food Supply Chain: Small Markets Case.” Scientific Annals of Economics and Business, vol. 63, no. 1, pp. 15-28.

Richey, Robert, et al. “A Global Exploration of Big Data in the Supply Chain.” International Journal of Physical Distribution & Logistics Management, vol. 46, no. 8, 2016, pp. 710-739.

Sanders, Nada. “How to Use Big Data to Drive Your Supply Chain.” CMR, vol. 58, no. 3, 2014, pp. 1-12.

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Schoenherr, Tobias, and Cheri Speier-Pero. “Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential.” Journal of Business Logistics, vol. 36, no. 1, 2015, pp. 120-132.

Wamba, Samuel, and Shahriar Akter. “Big Data Analytics for Supply Chain Management: A Literature Review and Research Agenda.” NEOMA Business School, vol. 1, no. 1, 2015, pp. 1-11.

Zhong, Ray, et al. “Big Data for Supply Chain Management in the Service and Manufacturing Sectors: Challenges, Opportunities, and Future Perspectives.” Computers & Industrial Engineering, vol. 101, no. 1, 2016, pp. 572-591.

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