Abstract: Organizations/manufacturers have used scenarios to make business decisions. It has been difficult to apply scenarios in dealing with tactical opportunities due to lack of integration of changing inputs for consistent decision making. Conventionally, tools are cumbersome and depend on pre-structured and individually validated data requiring significant expert involvement. Present disclosure manages decision scenarios and optimizes total sourcing cost by obtaining various inputs and retrieving decision scenarios from a database. An optimization technique and/or a simulation technique is performed on the decision scenarios to obtain the total sourcing cost that is based on a quantity filled for each source-entity-destination combination and a corresponding unit lane cost. A decision scenario is selected from the pre-defined decision scenarios based on the total sourcing cost and an ordering schedule for associated demands is created accordingly.

Abstract: Sourcing is a practice of locating and selecting entities based on set criteria. Determining most appropriate supplier entities at the lowest cost can develop a competitive advantage. However, existing sourcing techniques lack effective ways to target best suppliers for right part bundling and to efficiently optimize sourcing cost for sustained savings. Present disclosure leverages machine learning and optimization methods with technology data to provide a computationally efficient solution. Relationships between parts and corresponding attributes are obtained for parts bundling and best supplier locations are mapped for selected part bundles. A sourcing cost minimizer is used in conjunction with an iterative process to grow or limit supplier capacity by constraining award amount, bundle mix, or number of supplier entities based on multiple alternative scenarios being defined to systemically minimize cost and manage risks driven by changes in customer demand or manufacturing location.

Abstract: The present disclosure relates to systems and methods for risk management in a supply chain. The system is configured to maintain a supply chain repository storing an original buffer level, and a master plan. Further, the system is configured to monitor each stage associated with the supply chain to identify at least one of a short term disruption or a long term disruption associated with a stage of the supply chain. Further, the system is configured to resolve the short term disruption by updating the original buffer level of an inventory or by revising the current plan and at least one of the set of future plans associated with the supply chain. Further, the system is configured to resolve the long term disruption in the supply chain using a set of problems and a set of solutions stored in a historical database.

Abstract: The present disclosure relates to systems and methods for risk management in a supply chain. The system is configured to maintain a supply chain repository storing an original buffer level, and a master plan. Further, the system is configured to monitor each stage associated with the supply chain to identify at least one of a short term disruption or a long term disruption associated with a stage of the supply chain. Further, the system is configured to resolve the short term disruption by updating the original buffer level of an inventory or by revising the current plan and at least one of the set of future plans associated with the supply chain. Further, the system is configured to resolve the long term disruption in the supply chain using a set of problems and a set of solutions stored in a historical database.