Shylu John

Bio: Shylu John is an academic researcher from Indian Institute of Management Indore. The author has contributed to research in topics: E-commerce & Analytics. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

Refund fraud analytics for an online retail purchases

Abstract: Online shopping is growing fast across the globe and so are its complexities. Fraud is a complicated phenomenon and its mitigation is critical for running a smooth business. The case considered for...

An integrated approach to renew software contract using machine learning.

Abstract: Contract renewal is critical to maintaining a company’s recurring revenue source. Therefore, there is a significant emphasis on setting up an efficient process for renewal. In this study, a machine...

Decision Trees for Business Intelligence and Data Mining - Using SAS Enterprise Miner, by Barryde Ville

Abstract: This book presents a series of intellectual discussions concerning some of the most basic topics in statistics, including interpreting probability and statistical models of induction, including the concepts of inference, p values, and the comparison of evidential theories. The book is a well-researched and thoroughly professional approach to the various dilemmas faced by statisticians in interpreting data. The author’s knowledge of the literature as it pertains to the various issues discussed in the book is excellent and reminds me of much of the literature I read and studied during my lengthy and varied education in statistics and the practice of statistical science. Furthermore, he provides for teachers of statistics a lengthy list of quotes from many great statisticians on subjects of interest to students of statistics, including Deming, Shewhart, and others, all the way to deFinetti and other Bayesians. The wealth of knowledge present in this 152-page book is remarkable; the book is a worthy of addition to any library of statistical literature. Important questions as to why this book was written, what is its purpose and how does a practicing statistician use the knowledge therein (e.g., “what is the purpose of the nature of statistical evidence?”) is left to the reader to answer for himself or herself. The underlying theme of the book is never stated by the author in a simple, clear-cut manner. Does one use the knowledge of the text to make one a better statistician or a better teacher of statistics, or simply to understand the logic of statistical science in a way that is meaningful to all? As I read the book intensively, I asked myself whether this book is good for teaching others or is simply an intellectual exercise for some. What does the author have in mind when he discusses such common and useful concepts as p values or true value? Does he contribute to the understanding of these concepts, or does he make them more difficult to comprehend? Furthermore, he translates at least one German word, iterationen, incorrectly. The German ending-en indicates that the word is plural and is translated as iterations or runs. This simple error makes one think that there are many places in the text, especially some of the mathematics, which also may be in error. Early in the book, the author attempts to determine some definition of statistical evidence (last paragraph on p. 3). But after reading the paragraph and the extensive discussion of the entire text, one cannot be sure what one has read. When teaching statistics at the college level, I try to keep the ideas as simple as possible without relying on an overwhelming exercise in sophistication as the goal. The author seems to define concepts, redefine concepts, and then define the same concepts yet again, with the end result being imperfect and probably misunderstood. Much of the ideas expressed and discussed in the book appear in a manner that is exasperating to comprehend. As an analogy, reading this book is like entering a room to learn something new but in the end leaving the room with no more knowledge and understanding than when one entered. There is simply too much confusion resulting from lengthy discussions of relatively simple concepts. Because the book is part of a series titled “Lecture Notes in Statistics,” I can only deduce that it is nothing more than a series of lecture notes. There is no flow to the book and little thought given to presenting the details in a style that would induce one to want to read it. When I was young, I would hear that many statisticians were very wise and intelligent persons who should be listened to and understood but presented their knowledge in a direct and understandable way. I fear that this book is full of confusing discussions of very important issues. I would like to see this material rewritten in a manner that we as readers would not have to work so hard to understand the aims of the author. Although the author produces and quotes the works of many great thinkers, he does not bring out their points in a style that is easy for the reader to follow. Often, he places difficult mathematical theorems and proofs in the midst of lengthy paragraphs not needing such mathematical sophistication. When I started reading this book, I expected a thoughtful text introducing the reader to the origins of statistics as a science and methodology. The book did not do this, nor did it present the numerous wonderful applications of statistics to the collection, analysis, and interpretation of data that illuminate our knowledge of the world. Where are the examples in quality control, experimentation, and the like that the author eludes to and how does statistics provide the foundation for us to understand our world? When I finished the book, I felt that the author had failed to communicate to the reader why statistics is so important; instead, he clarifies less than he confuses.

Product development, fashion buying and merchandising

Abstract: Abstract This issue of Textile Progress provides a critical literature review and reflection relating to academic research in the field of fashion buying and merchandising, with a specific focus on the fashion product development process. As the topic has not been reviewed before in Textile Progress, the paper follows the process of fashion product development, a key task that forms one of the many responsibilities of fashion buyers and merchandisers and explores the literature from its origins to the present day, capturing the significant elements that have changed and shaped the process over time. Establishing the challenges and changes in contemporary fashion retailing enables the development of an understanding of how and why these fundamental factors impact not only the process of getting products from idea to concept, but also the roles and responsibilities of the buyers and merchandisers; added to this, the review provides a critical overview of the Buying Cycle. The review further explores the external and internal components and participants influencing the fashion product development process, thereby updating what can be found in the existing product development literature to reflect the current state-of-play in the industry. By illustrating and reviewing the process models of new product development and fashion product development from their original and early forms to the present day, the review establishes the links, connections, and differences across both the more-general and specific areas of research. Subsequently, there is a review of the roles and responsibilities of the fashion buyer and merchandiser, alongside a discussion of how the developments, advancements and transformation of the industry have changed the nature of the involvement of these crucial personnel in the fashion product development process over time. This aspect of the review provides a base upon which to analyse the contemporary fashion-retail buying cycle, establishing its early connection to organisational decision-making process models and the implications and challenges that product assortment planning, development, and retailing pose on the cycle. The last two chapters of this review are dedicated to two crucial areas of the contemporary fashion industry, namely sustainability and technology and address how they have become key drivers in determining the roles of the fashion buyer and merchandiser and how the fashion product development process is now addressed.

Recognising and detecting patterns of village corruption in Indonesia

Abstract: The increasing trend of village-level corruption cases in Indonesia needs to be addressed by the government. The National Strategy of Corruption Prevention (NSCP) policy couldn't improve Indonesia's corruption perception score. Corruption prevention policies have so far not targeted villages with certain characteristics. The Village Development Index (IDM), measured by the Ministry of Villages, Disadvantaged Regions, and Transmigration, should identify villages affected by corruption cases. This research uses the qualitative descriptive method. The results show that those most affected by corruption cases in Indonesia are developing villages on Java Island. Other results find the need for an analysis of fraud or fraud analytics in villages using information technology or data processing. The practical implication of the research is a basis for revising corruption prevention and detection policies and determining the target groups.

Machine Learning Techniques for Predicting Risks of Late Delivery

Abstract: Supply chain is a cornerstone of the eCommerce industry and is a key component in its growth. Supply chain data analytics and risk management in the eCommerce space have picked up steam in recent times. With the availability of suitable & capable resources for big data and artificial intelligence, predictive analytics has become a significant area of interest to achieve organizational excellence by exploiting data available and developing data-driven support systems. The existing literature in supply chain risk management explain various methods assisting to identify & mitigate risks using big data and machine learning (ML) techniques across industries. Although ML techniques are used in various industries, not many aspects of eCommerce had utilized predictive analytics to their benefit. In the eCommerce industry, delivery is paramount for the business. During COVID-19 pandemic, needs changed. Reliable delivery services are preferred to speedy delivery. Multiple parameters involve delivering the product to a customer as per promised due date. This research will try to predict the risks of late deliveries to online shopping customers by analyzing the historical data using machine learning techniques and comparing them by multiple performance metrics. As a part of this comparative study, a new hybrid technique which is a combination of Logistic Regression, XGBoost, Light GBM, and Random Forest is built which has outperformed all the other ensemble and individual algorithms with respect to accuracy, specificity, precision, and F1-score. This study will benefit the eCommerce companies to improve their customer satisfaction by predicting late deliveries accurately and early.