Rajesh Kumar Ravi

Sense-and-respond supply chain using model-driven techniques

The results of an effort to build a sense-and-respond solution for a supply chain by Using a model-driven development framework are described in this paper. One of the components of the framework is the IBM Research-developed model-driven business-transformation (MDBT) toolkit, a set of formal models, methods, and tools. The inventory optimization analytics used to improve supply chain performance are also described. This approach is illustrated through a case study involving the IBM System x™ supply chain.

Social media and customer behavior analytics for personalized customer engagements

Companies in various industries, including travel, hospitality, and retail, increasingly focus on improving customer relationships and customer loyalty. In this paper, we propose a new systems architecture that combines the textual content in social media messages with product information, such as the descriptions summarized in catalogs, in order to provide marketing campaign recommendations. Companies commonly build user profiles based on purchase histories and other customer-specific information; however, when dealing with social media, we often cannot match the social media users with the customers. In this regard, we address the problem of targeting individual social media messages for which no personalized profile information can be retrieved. Our solution combines two disparate computational toolboxes for text analytics—natural language processing and machine learning—in order to select social media users for whom to target with topic-specific advertisements. Natural language processing is used to analyze the context of social media messages, and machine learning is used to analyze product information, with the goal being to match social media messages to products and ranking potential advertisements. To demonstrate the framework, we detail a real-world application in the travel and tourism industry using Twitter® as the social media platform.

Big Data System for Analyzing Risky Procurement Entities

An accredited biennial 2014 study by the Association of Certified Fraud Examiners claims that on average 5% of a companys revenue is lost because of unchecked fraud every year. The reason for such heavy losses are that it takes around 18 months for a fraud to be caught and audits catch only 3% of the actual fraud. This begs the need for better tools and processes to be able to quickly and cheaply identify potential malefactors. In this paper, we describe a robust tool to identify procurement related fraud/risk, though the general design and the analytical components could be adapted to detecting fraud in other domains. Besides analyzing standard transactional data, our solution analyzes multiple public and private data sources leading to wider coverage of fraud types than what generally exists in the marketplace. Moreover, our approach is more principled in the sense that the learning component, which is based on investigation feedback has formal guarantees. Though such a tool is ever evolving, a deployment of this tool over the past 12 months has found many interesting cases from compliance risk and fraud point of view across more than 150 countries and 65000+ vendors, increasing the number of true positives found by over 80\% compared with other state-of-the-art tools that the domain experts were previously using.