Big Data Analytics in Supply Chain Management
Read this paper for an overview and examples of how big data is used in specific areas, such as supply chain management, risk management, and logistics of business in industry. One of the biggest issues for analysts with big data is knowing how to separate the valuable data from that which does not help answer their requirements.
Sometimes people describe intelligence as "connecting the dots", but it is rarely simple like a "paint-by-numbers" art project. The dots are not just lying around waiting to be connected. More appropriately, it has been described as filtering out the right radio signals from the fray in a huge city. You have to be carefully tuned to your requirements, which will be discussed at length in Unit 2 and again in Unit 8, as these are the guide stars that keep you on track to finding the right data to answer the questions you need to focus on.
1. Introduction
1.1. Big data
"I have just bought a house! I have bought a big house!" When people talk about big objects, generally there is a common sense of the word "BIG". When people use the word "big house", they are usually talking about the house area or the number of bedrooms. But, what does it mean when we use the word "big data", and what differentiates "big data" from the usual usage of the term "data"?
Big data is a developing phenomenon in the field of Information Technology. Big data includes data sets that can't be analyzed by the common traditional data analysis tools. Big data refers to a high volume of data with a high velocity and a high variety; these properties require more efficient methods than the current ones used in conventional database systems for decision making. Big data enables systems to manage their processes using a large volume of real-world data.
Big data entered the field of practical research in the 21st century; there was no noteworthy research applying big data analytics in other fields before 2000. The main characteristic of big data is simply its huge volume of data, but some other characteristics have been added to this definition over the years. The first time that Big Data was defined by the 3V model (Volume, Velocity, and Variety) was in a study by Laney. Volume refers to the amount of available data; Velocity refers to the timeliness of the data; and Variety refers to the diversity of the data types, including unstructured, semi-structured, and structured data sets.
Two other important Vs have been added to the definition of big data in the most recent decade. The economic Value refers to the profit gained by analyzing a huge volume of data, and Veracity refers to the considerable amount of uncertainty and imprecision in the big data. integrated all of the Vs in one place and introduced the 5V big data framework for the first time. Figure 1 represents the evolutionary timeline of the big data concept, as well as the most-cited articles using big data in manufacturing, logistics, and supply chain management.
Figure 1 Big data framework evolution during the time.
Big data analysis is a process that transforms terabytes of low-value data into a small amount of high-value data, which shows an overview of the company using just a small slice of the overall picture. A big data system can be separated into four consecutive phases: data generation, data acquisition, data storage, and data analytics.