Since, it encompasses wide range of activities, which most of time transcend factories or national boundary, complex interdependencies are built into it. As the power base continues to shift from companies towards customers, customer demands have gotten more complex. Companies are looking at Big Data analytics to revamp their supply chain, thereby using Big Data Analytics as a strategic lever. Companies are collecting vast amount of supply chain related data with help of technologies such as sensors, Barcode and GPS, Jacob House (2014). Big Data Analytics offers companies the ability to leverage on the enormous amounts of information driving their global supply chains, Harvard Business review, (2013).
Data Science vs Statistics Data science is one of the rapidly emerging trends in computing and is a vast multi-disciplinary area. Data science combines the application of subjects namely computer science, software engineering, mathematics and statistics, programming, economics, and business management. Data science is based on the collection, preparation, analysis, management, visualization and storage of large volumes of information. Data science in simple terms can be understood as having strong connections with databases including big data and computer science. A data scientist is an individual with adequate domain knowledge relevant to the question addressed.
Stated by (Chopra & Meindl, 2001, p.489), “ISCM includes all the processes involved in planning for fulfilling a customer order”. These processes are as follows: Strategic planning, this process concentrate on the network design of supply chain. The use of IT in this process has significant impact on the network design decision Characteristics of available production technologies have a significant impact on net- work design decisions. If production technology displays significant economies of scale, a few high-capacity locations are most effective. This is the case in the manufacture of computer chips, for which factories require a very large investment.
Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. B.2 Introduction The growing popularity and development of data mining technologies bring serious threat to the security of individual's
Through Big Data, developers can now access and tap into varied sources, which can even come from far-flung areas. Something that developers and statisticians from before the Big Data revolution dare to only dream about. According to Jonathan King and Neil Richards, authors of “What’s up with Big Data Ethics?”, man’s ability to discover new patterns and knowledge from data is moving faster than what the current legal and ethical guidelines can manage. As technicalities in the realm of Big Data continue to expand, debates have already ensued as to how Big Data challenges pre-existing policies, as well as the public understanding. According to a study conducted by the Royal Statistical Society (UK), there are 2 primary reasons of questioning big data: · Trust in the findings · Truth in the methods The leading concern about the uses of
1.1. DATA MINING Data mining refers to extracting or mining knowledge from large amounts of data. Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the forthcoming need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration. Data mining can be viewed as a result of the natural evolution of information technology.
As big data things continue to grow in this modern era, today we can learn how to predict or assume anything that will happen in the future with data from the past. This studies known as Predictive Analytics. Predictive analytics combine methods from machine learning, data mining and statistics to find meaning or pattern from a huge volume of data. Tom H Davenport, a senior advisor at Deloitte Analytics has broken down three primer models on doing predictive analytics: the data, statistics, and assumptions. The first model is by using data.
Knowledge discovery also known as data mining is the processes involve penetration into tremendous amount of data with the support from computer and web technology for examining the data. Data mining is a process of discovering interesting knowledge by extracting or mining the data fromlarge amount of data and the process of finding correlations or patterns among dozens of fields in large relational databases [3, 4]. Privacy Preserving in Data Publishing (PPDP) is very important in data mining when publishing individual information on web . The improvements are toward producing more effective methods that preserve the privacy and also reduces information loss to the researchers. There are also researches related to improvements of the algorithm that avoids some attacks on data.
The main important purpose of the accounting information system is to promote the activity of the enterprise and to form a reliable and real picture of it. In addition, the accounting information system promotes the activity of the enterprise effectively by preparing up-to-date information statements, providing as much information as possible so that the data should be understandable all users not only for the experts(bookkeepers) and tracking liquidity. Nowadays accounting software is a programme which makes accounting work processes easier and faster and which makes it possible to meet the information demand of the management. It also can support the accountants’ work, helping to compile reports by in helping to compile reports by recording and processing the events concerning the