Big data is a generic word that describes the volume of data in zettabytes, both structured and unstructured, that floods a business every day.
Big data could be a term that describes the big volume of data, each structured and unstructured, that inundates a business on a daily basis. massive data will be analyzed for insights that cause higher selections and strategic business moves. In Recent years massive information on Data Analytics has attracted a major role within the field of logistics and supply chain management. Analytics uses quantitative data and strategies to increase stakeholder discretion. On a large scale, data analytics technologies and techniques offer organizations a way to investigate data sets and gather new information. Business intelligence (BI) helps us to understand what had happened based on the data on business operations and performance.
Big Data analytics could be a kind of advanced analytics, that involve advanced applications with parts like predictive models, Statistical models, applied math algorithms, and what-if analysis which is battery-powered by analytics systems. Big Data Analytics offers an almost endless supply of business and informational insight which will lead to operational improvement and new opportunities for organizations to supply unrealistic revenue across almost every industry. From use cases like client personalization to risk mitigation, to fraud detection, to internal operations analysis. Big data Analytics takes it a step further because the technology will access a spread of each structured and unstructured dataset (such as user behavior or images). massive data technologies can bring this data alongside the historical info to see what the likelihood of an incident was to happen supported by past experiences
The advent of big data has ushered in a new era of analytics in which data is no longer siloed and compartmentalized but rather leveraged across various business functions to make better-informed business decisions. As a result, big data is having a major impact on the way businesses approach their supply chains. Logistics has long been a bastion of data-driven decision-making, but the new breed of big data in logistics has revealed untapped potential in this area that was previously unseen.
This article will provide an overview of big data and its impact on supply chains, including examples of big data in logistics, the various types of data involved, and the use of big data in logistics that are likely to have the greatest impact on the near term.
How it Impacts Supply Chains
Operational Capacity Planning
Big Data Analytics facilitates operational capacity planning by predicting demand and planning accordingly.
Real-time Fleet Tracking
companies can track their vehicles in real-time using big data technology, using the advent of RFID tags, GPS devices, bar codes, etc
Improved Responsiveness
Big data analytics assists supply chain managers in understanding the market scenario and their companies’ competitive requirements. They have the opportunity to improve customer responsiveness, gain better inventory control, save money, and increase agility.
Excellent Customer Experience
Big data analytics provides logistics companies with the right set of customer data. They can use these to apply historical and predictive analytics models and learn how to build customer loyalty and improve customer experience. As a result, they are successful not only in acquiring new customers but also in retaining existing ones.
Route Optimization
Big data analytics helps to identify the best path for transportation from source to destination. Through big data analytics, we will be able to identify the root cause of bottlenecks. Sensors that are installed in the vehicle can be used to identify the traffic which can be used to study for the route optimization.
