Supply Chain Big Data Analytics (Essay Sample)

Supply Chain Big Data Analytics
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Big data is regarded as a complex or gigantic data set that normally encompasses a range over Exabyte. It surpasses conventional systems with limited ability to handle, store, decipher, oversee, and visualize. In today's competitive marketplace, information technology development, economic globalization, rising customer expectations, and the other current competitive priorities have forced firms to change. Thus, competition among firms is replaced by competition among firms and their supply chains. In today's competitive environment, supply chain experts struggle to handle the massive data to reach an efficient, integrated, agile, and effective supply chain. Therefore, explosive growth in different kinds and volume of data throughout the supply chain has developed the need to create technologies that could rapidly and intelligently analyze huge data volumes. Big data analytics ability is one of the precise technologies that could aid firms overcome that issue. Big data analytics offers an instrument for extracting valuable information and patterns in large data volumes.
To entirely comprehend the application and impact of big data analytics, one must understand what it really is. Big data entails large data quality. Big data typically entails large data sets with their size so large that their quantity could no longer fit into the memory. That data could be stored, analyzed, communicated, and aggregated. As the data volumes have grown, the requirement to revamp the instruments has grown (Ittmann, 2015). That data never needs to be set in neat rows and columns as conventional data sets to be evaluated by today's technology. Big data appear totally in different types of data. They integrate all kinds of data from each probable source. They could be fully-structured, semi-structured, or structured. Big data comprises image data, numerical data, discourse, text, and voice as another classification. They could come in the radio-frequency identification form, point-of-sale, global positioning system, or they could be in the frame of Facebook, Twitter feeds, and call centers, Instagram, or customer blogs (Nguyen et al., 2018). Today's advanced analytical technologies enable individuals to extract knowledge from all types of data. Analytics is a mix of statistics and math to large data quantities. Big data analytics utilizing math and statistics to evaluate big data. However, combining analytics and big data makes the different instruments that help decision-makers obtain valuable, meaningful information and turn that information into business intelligence.
In the supply chain, big data analytics is significant. Supply chain analytics implies utilizing big data techniques to extract hidden vital knowledge from the supply chain. That analytics could be classified as prescriptive, descriptive, and predictive analytics. Well-planned and executed decisions lead to the bottom line by declining storage, sourcing, disposal costs, transportation, and stockout costs (Kache & Seuring, 2017). Thus, utilizing big data analytics to solve supply chain management issues has a critical and positive effect on the supply chain's performance. For a long time, researchers and managers have utilized operational and statistical research methods to solve demand and supply balancing issues. The various tools to be utilized in the supply chain process will be the online analytical processing method supported by GPS, RFID, and transaction barcode and real-time data to realize new issues and opportunities. Descriptive statistics are utilized to analyze, collect, and describe the raw past events data. It describes and analyzes past events making them something understandable and interpretable to humans. Descriptive analytics enable firms to learn from their past and comprehend the relationship between the variable and how it could influence future results (Tiwari, Wee, & Daryanto, 2018). Predictive analytics methods are utilized to answer the queries of what will happen or are likely to happen in the future by scrutinizing past data trends utilizing programming, simulation, and statistical techniques. These techniques aim to discover events, causes, and phenomena and predict the future precisely or fill in the information or data that already never exists. Statistical methods could not be utilized for future prediction with 100 percent accuracy. Predictive analytics is utilized to project customer behavior, purchase patterns, and purchasing patterns to project and identify the sales activities' future trends.
Several potential advantages could be attained by using data-supported choice-making strategies in supply chain management. Indeed, big data analytics methods normally utilize prescriptive and predictive approaches instead of descriptive approaches. Big data analytics is significant in the improvement of supply chain management. It solves some pain points at operational, tactical, and strategic levels. Big data advantages range from enhancing delivery times to realizing reducing the communication gap between suppliers and manufacturers. Big data analytics will aid supply chain management in analyzing the usage habits and patterns of their customers (Wang et al., 2016). The information obtained from the analytics reports helps firms retain their subscribers and augment revenue significantly. Another benefit of big data analytics in supply chain management is augmented inventory management. Top online stores and big-box retailers with a big inventory have to stun several challenges. Big data analytic enhances operation managers to obtain the minute-to-minute summary of operations and realize bottlenecks slowing down supply chain procedures—additionally, customer trends aid firms in promoting best-selling goods and optimizing inventory.
There are various legal issues associated with big data analytics in su