Rashmi Sharma

Color portrait of Rashmi Sharma

Assistant Clinical Professor, Managing Director of Laboratory for Economics Management and Auctions

Department Supply Chain & Information Systems
Office Address 426A Business Building
Phone Number 814-867-2265
Email Address rashmi.sharma@psu.edu

Download Photo

Education

Ph D, Supply Chain and Information Systems, The Pennsylvania State University, 2018

MBA, Supply Chain Management, The Pennsylvania State University, 2012

Master of Computer Applications, Computer Applications, Indira Gandhi National Open University, 2005

BS, Mathematics, Statistics, Computer Applications, University of Rajasthan, 2001

Courses Taught

BAN 832 – Prog Skills for Bus Analytics (3)
Designed specifically for recent graduates with 0-5 years of practical experience, BAN 832 gives business students the foundational programming skills they need to leverage the power of leading edge general purpose programming languages to acquire, clean, manipulate, query, visualize, and analyze large data sets typical of a variety of business environments. With a focus on developing solutions to business data problems, students will become conversant with a variety of software applications in the context of financial, marketing, supply chain management, and other data-rich business scenarios. Coursework includes individual assignments intended to develop dexterity with foundational programming skills, followed by case-based problems that challenge students' creativity and programming mastery in search of solutions to complex business problems. This course aims to put recent graduates on the same level as more experienced analysts with regard to applying programming skills and implementing widely used algorithms to solve business analytics challenges. Previous programming experience is helpful but not required, and students will have the opportunity to augment their learning with additional online tutorials. Software packages, concepts, and business applications will vary and evolve to keep pace with technology, theory, and instructor interest.

MIS 497 – Special Topics (3)
Formal courses given infrequently to explore, in depth, a comparatively narrow subject which may be topical or of special interest.

BAN 840 – Predictive Analytics for Bus (3)
BAN 840 explores the use of predictive analytics tools and techniques throughout a wide range of business scenarios and problems. Initially focusing on the application of traditional predictive analytics techniques to answer the question, "What will happen in the future?", the course provides opportunities for students to apply regression and forecasting techniques to data from various business contexts to inform business leaders¿ decision. Later, students explore various software applications and techniques for acquiring, preparing, and analyzing "big data", recognizing and taking advantage of the exponential growth in the amount of structured and unstructured data generated by and available to businesses. The course next examines cutting-edge techniques gaining increased attention among analytics experts, including data mining, text analytics, and social media analytics. Finally, students will be given an overview of the future of predictive analytics, developing an awareness of artificial intelligence and machine learning concepts, such as neural networks, to help them advance their organizations¿ business analytics capabilities. Software packages, concepts, and business applications will vary and evolve to keep pace with technology, theory, and instructor interests.

SCM 404 – Dem Fulfil (3)
Analysis of demand fulfillment and the role of distribution operations management in the supply chain. SCM 404 Demand Fulfillment (3)This course introduces the student to how customer demand is managed and how subsequent orders are filled in both business-to-business and business-to-consumer markets. Topics focus on the demand fulfillment process, which encompasses flows of goods, information, and funds from the moment a business receives an order from a customer until all requirements for the order are satisfied in full. These topics include: *role of demand management and distribution operations in the supply chain*transportation management*distribution center processes *inventory control and order management elements*facility costing and productivity analysis*strategic demand management and distribution operations issues in the supply chain.Both theoretical and quantitative perspectives will be offered on these topics. Additionally, each topic will be addressed from strategic and financial perspectives. After completing this course, students will have the knowledge, skills, and abilities to: *Explain the role of demand management in the supply chain*Explain the role of distribution operations in demand management*Determine the strategic and financial impacts of demand management and distribution operations management*Articulate the role of information systems in demand management and distribution operations management*Use quantitative techniques to analyze supply chain processes*Describe related system software. This is one of three prescribed foundation courses for the Supply Chain and Information Systems major for which SCM 301 Supply Chain Management is a prerequisite. This course also satisfies the prerequisite for SCM 421 Supply Chain Modeling and Analysis. Student evaluations are based on individual and group homework assignments and computer-lab exercises, as well as on at least three written examinations.

SCM 421 – Sc Analytics (3)
Models and Methodologies for supply chain analysis. SCM 421 Supply Chain Analytics (3) This course provides a spreadsheet-based, example-driven approach to learn about important supply chain models, problems, and solution methodologies. The objectives of this course are: (1) to develop valuable modeling skills that students can appreciate and use effectively in their careers (2) reinforce and enrich your understanding of supply chain theories, principles, and concepts studied previously in foundation courses. Student evaluation is based on: (1) individual and team group performance on problem-based exercises (2) individual performance on examinations (3) class participation.