5 research outputs found
Planning for avian flu disruptions – A DMAIC case study
No full textThe author aims to assess the spread of avian flu, its impact on businesses operating in the USA and overseas, and the measures required for corporate preparedness. Six Sigma DMAIC process is used to analyze avian flu\u27s impact and how an epidemic could affect large US business operations worldwide. Wal-Mart and Dell Computers were chosen as one specializes in retail and the other manufacturing. The study identifies avian flu pandemic risks including failure modes on Wal-Mart and Dell Computers global operations. It reveals the factors that reinforce avian-flu pandemic\u27s negative impact on company global supply chains. It also uncovers factors that balance avian-flu pandemic\u27s impact on their global supply chains. Avian flu and its irregularity affect the research outcomes because its spread could fluctuate based on so many factors that could come into play. Further, the potential cost to manufacturers and other supply chain partners is relatively unknown. As a relatively new phenomenon, quantitative data were not available to determine immediate costs. SOCIAL IMPLICATIONS: In this decade, the avian influenza H5N1 virus has killed millions of poultry in Asia, Europe and Africa. This flu strain can infect and kill humans who come into contact with this virus. An avian influenza H5N1 outbreak could lead to a devastating effect on global food supply, business services and business operations. The study provides guidance on what global business operation managers can do to prepare for such events, as well as how avian flu progression to a pandemic can disrupt such operations. This study raises awareness about avian flu\u27s impact on businesses and humans and also highlights the need to create contingency plans for corporate preparedness to avoid incurring losses
Social Responsibility of Alternative Media and Indian Democracy
No full textMedia always considered as fourth pillar of democracy. India has the largest democracy in the world, which usually put all its efforts in restoring the faith of masses in the system. Media being its driving force always supported or criticised time to time. Media constantly showed their presence in Indian democracy. Media not only performed their surveillance function but they also cater their duty of social responsibility perfectly. With the advent of new technologies the traditional media also shifted towards a relatively new alternative media that is (social media). Democracy also faces various crisis time to time and media always tried to find a path to overcome those crisis. Alternative media being the media of masses and by the masses have the factor of immediacy and proximity more than any traditional form of media, due to which it can circulate any piece of information rapidly as compare to radio and television as well. This paper actually examines that how this alternative media is performing their duty of social responsibility and how and what they are adding, towards Indian democracy. This research is based on qualitative research approach. Researcher would find out the role of alternative media towards democracy.
Scalable Text Mining with Sparse Generative Models
No full textThe information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods.
This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines.
The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places
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An investigation into Indian apparel and textile supply chain networks
No full textThe activities of the Indian clothing industry supplying Western markets have been investigated, with particular reference to identifying where improvements could be made to supply chain management. Focus group discussions, case studies and questionnaire analysis established that long lead-times in pre-production areas were of great concern. However Indian apparel manufacturers were found to be more cost conscious and rather less conscious about the value of time in pre-production areas. It was found that pre-production activities constituted 73% of total manufacturing lead time and have high positive correlation (0.96) with total manufacturing lead time. Preproduction activities in India mainly consist of prototype making and pre-production sample development; of which approval processes were found to have a high correlation (0.63) with pre-production. A significant (more than 50%) time of all activities consist of waiting time, which has positive influence on total lead time (0.86)
