Digital Strategies to Improve the Performance of Pharmaceutical Supply Chains

Some supply chain managers at pharmaceutical companies lack strategies to digitalize integrated supply chain systems impacting their profitability. Digitalized supply chain management in a pharmaceutical company can help reduce operation costs, improve assets, enhance shareholders’ value, positively respond to customer demand, and generate profits. Guided by the theory of constraints, the purpose of this qualitative multiple case study was to explore strategies some pharmaceutical managers use to digitalize integrated supply chain systems to increase their profitability. The participants were five managers from four pharmaceutical companies in New Jersey with strategies to digitalize their integrated supply chain systems. Data collection included semistructured video conferencing interviews and publicly available company documents analysis. Data were analyzed using the six-step thematic process, and three themes emerged: (a) constraints or barriers in current supply chain system, (b) digital technology enablers, and (c) sustainable, resilient, and agile supply chain systems. The primary recommendation for pharmaceutical supply chain managers is to identify constraints and then follow a digital road map using digital enablers. Implications for positive social change include the potential to improve the delivery and quality of pharmaceutical products needed for patient care

The Managerial Impact on Small Business Global Supply Chain

In a global economy, companies that are able to engage in international trade may have a competitive advantage over others. Increased globalization is also increasing the demand for effective global supply management practices. Building on Lorenz\u27s chaos theory, this instrumental case study explored the strategies that 4 senior managers from small and medium-sized enterprises in Indianapolis, Indiana used to reduce disruptive vulnerabilities in the supply chain continuum. Review of company documents served as the second data collection method. Rowley\u27s 3-step analysis guided the coding process of the interview data, and the trustworthiness of interpretations was enhanced through methodological triangulation of company records. Findings revealed 3 strategies that these senior managers relied on for remaining strategically competitive in a global environment: understanding the communication challenges and addressing the issues, risk mitigation, and human capital management. Findings from this study may contribute to business practice and social change by providing business leaders with information about effective strategies to remain competitive or to explore international ventures while focusing on environmental causes. Sustainable practices lead to cost reduction for the organizations and a cleaner environment for the surrounding community

ANSWERING TOPICAL INFORMATION NEEDS USING NEURAL ENTITY-ORIENTED INFORMATION RETRIEVAL AND EXTRACTION

In the modern world, search engines are an integral part of human lives. The field of Information Retrieval (IR) is concerned with finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need (query) from within large collections (usually stored on computers). The search engine then displays a ranked list of results relevant to our query. Traditional document retrieval algorithms match a query to a document using the overlap of words in both. However, the last decade has seen the focus shifting to leveraging the rich semantic information available in the form of entities. Entities are uniquely identifiable objects or things such as places, events, diseases, etc. that exist in the real or fictional world. Entity-oriented search systems leverage the semantic information associated with entities (e.g., names, types, etc.) to better match documents to queries. Web search engines would provide better search results if they understand the meaning of a query. This dissertation advances the state-of-the-art in IR by developing novel algorithmsthat understand text (query, document, question, sentence, etc.) at the semantic level. To this end, this dissertation aims to understand the fine-grained meaning of entities from the context in which the entities have been mentioned, for example, “oysters” in the context of food versus ecosystems. Further, we aim to automatically learn (vector) representations of entities that incorporate this fine-grained knowledge and knowledge about the query. This work refines the automatic understanding of text passages using deep learning, a modern artificial intelligence paradigm. This dissertation utilized the semantic information extracted from entities to retrieve materials (text and entities) relevant to a query. The interplay between text and entities in the text is studied by addressing three related prediction problems: (1) Identify entities that are relevant for the query, (2) Understand an entity’s meaning in the context of the query, and (3) Identify text passages that elaborate the connection between the query and an entity. The research presented in this dissertation may be integrated into a larger system de-signed for answering complex topical queries such as dark chocolate health benefits which require the search engine to automatically understand the connections between the query and the relevant material, thus transforming the search engine into an answering engine