Reference number : GC-2017-Q1-02674, amendment number : , agreement type : G, recipient legal name : Levin, Yuri (Queen’s University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Revenue management (RM) is defined as &ldquo;charging the right price to the right customer at the right time&rdquo; and pertains to the ability of companies to profitably manage their demand. Supply chain management (SCM) puts emphasis on the supply and matching it with demand. The proposal seeks to advance analytic models for RM and SCM and techniques to increase the efficiency of companies facing dynamic decisions in an interconnected (networked) world. The connections come in various forms including the flows of information and physical goods, competition or partnerships between companies, complementary or substitute products, customer-supplier relations and influences of the end-consumers on one another. 
Companies make many decisions that affect customer demand, including decisions regarding prices, inventories, assortments, features and quality of products, and special offers such as low-priced combinations (bundles) of products. Successful use of these instruments requires detailed mathematical models of customer behavior, often down to the individual level, that cover customer choice among products, demand evolution due to customer learning, strategic timing of purchases (called strategic behavior), and changes in demand due to the number of users for similar products. The latter phenomenon, called &ldquo;network externality,&rdquo; arises because the value of a product or service for a given customer is affected by the presence and behavior of other customers. The presence of many rational decision makers in combination with networks requires network-based extensions of game theoretic and other SCM and RM models. 
The fast-paced and competitive marketplace compels companies to use as complete and current a view of the market as possible, and to actively leverage emerging &ldquo;Big Data&rdquo; technologies in operations. These technologies help to collect, store, and effectively process large Volumes of data that may arrive at high Velocity from a Variety of data sources, as well as to address the problems arising from the data Veracity (four V&rsquo;s of Big Data). Given this challenging environment, this research program will 
1. Help companies make operational decisions in the presence of networks of various types and under intense competition. 
2. Provide insight into how operational decisions are affected by strategic and choice customer behavior, in particular, when customers have limited information and learn over time. 
3. Leverage Big Data tools in the operational decisions of businesses affected by the above issues. 
4. Advance game theoretic and large-scale stochastic optimization models for SCM and RM. 
The outcomes of the program are important for industry practitioners, decision makers, and academic researchers in RM and SCM. They provide ample opportunities for training of HQP in advanced business Analytics., program purpose french : Revenue management (RM) is defined as &ldquo;charging the right price to the right customer at the right time&rdquo; and pertains to the ability of companies to profitably manage their demand. Supply chain management (SCM) puts emphasis on the supply and matching it with demand. The proposal seeks to advance analytic models for RM and SCM and techniques to increase the efficiency of companies facing dynamic decisions in an interconnected (networked) world. The connections come in various forms including the flows of information and physical goods, competition or partnerships between companies, complementary or substitute products, customer-supplier relations and influences of the end-consumers on one another. 
Companies make many decisions that affect customer demand, including decisions regarding prices, inventories, assortments, features and quality of products, and special offers such as low-priced combinations (bundles) of products. Successful use of these instruments requires detailed mathematical models of customer behavior, often down to the individual level, that cover customer choice among products, demand evolution due to customer learning, strategic timing of purchases (called strategic behavior), and changes in demand due to the number of users for similar products. The latter phenomenon, called &ldquo;network externality,&rdquo; arises because the value of a product or service for a given customer is affected by the presence and behavior of other customers. The presence of many rational decision makers in combination with networks requires network-based extensions of game theoretic and other SCM and RM models. 
The fast-paced and competitive marketplace compels companies to use as complete and current a view of the market as possible, and to actively leverage emerging &ldquo;Big Data&rdquo; technologies in operations. These technologies help to collect, store, and effectively process large Volumes of data that may arrive at high Velocity from a Variety of data sources, as well as to address the problems arising from the data Veracity (four V&rsquo;s of Big Data). Given this challenging environment, this research program will 
1. Help companies make operational decisions in the presence of networks of various types and under intense competition. 
2. Provide insight into how operational decisions are affected by strategic and choice customer behavior, in particular, when customers have limited information and learn over time. 
3. Leverage Big Data tools in the operational decisions of businesses affected by the above issues. 
4. Advance game theoretic and large-scale stochastic optimization models for SCM and RM. 
The outcomes of the program are important for industry practitioners, decision makers, and academic researchers in RM and SCM. They provide ample opportunities for training of HQP in advanced business Analytics., agreement title english : Game Theoretic Models for Revenue Management in the Presence of Networks, agreement title french : Game Theoretic Models for Revenue Management in the Presence of Networks, agreement number : RGPIN, agreement value : 140000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada