Abstract: A method aggregates an advanced planning and forecasting raw data by multiple database management systems (DBMS) communicatively coupled to an extensible computation engine. Performing an advanced planning simulation modeling a multi-level bill of materials, a periodic model revision, and an obsolesce risk of the computer and electronics industry supply chain by multiple processing nodes of the extensible computation engine. The method caches a result of the advanced planning simulation in an extensible memory cache communicatively coupled to the extensible computation engine and edge caching the result of the advanced planning simulation in an edge cache server near a geographical point of origin of the advanced planning and forecasting raw data. The extensible computing engine may employ a large number of processors (or separate computers) to perform a set of coordinated computations in parallel through a distributed computing infrastructure (e.g.

Abstract: Disclosed are methods and systems of geospatially relevant supply chain optimization through a networked mobile device that leverages a distributed computing environment.

Abstract: A method aggregates an advanced planning and forecasting raw data by multiple database management systems (DBMS) communicatively coupled to an extensible computation engine. Performing an advanced planning simulation a seasonality, a bundling structure, a reverse logistics chain, a logistical complexity, a replenishment demand of the retail goods, a downstream supply chain, an obsolesce risk of the retail goods and downstream supply chain by multiple processing nodes of the extensible computation engine. The method caches a result of the advanced planning simulation in an extensible memory cache communicatively coupled to the extensible computation engine and edge caching the same in an edge cache server near a geographical point of origin of the advanced planning and forecasting raw data. The extensible computing engine may employ a large number of processors to perform a set of coordinated computations in parallel through a distributed computing infrastructure for a specific advanced planning query.

Abstract: A method aggregates an advanced planning and forecasting raw data by one or more database management systems (DBMS) communicatively coupled to an extensible computation engine. Performing an advanced planning simulation modeling a supply risk, a subassembly risk, a regulatory risk, a distribution risk, a hazardous waste risk and an environmental impact in automotive industry supply chain by multiple processing nodes of the extensible computation engine. The method caches result of the advanced planning simulation in an extensible memory cache communicatively coupled to the extensible computation engine and edge caching the result of the advanced planning simulation in an edge cache server near a geographical point of origin of the advanced planning and forecasting raw data. The extensible computing engine may employ a large number of processors to perform a set of coordinated computations in parallel through a distributed computing infrastructure (e.g.

Abstract: A method aggregates an advanced planning and forecasting raw data by multiple database management systems (DBMS) communicatively coupled to an extensible computation engine. Performing an advanced planning simulation modeling a supply risk, a mining risk, a regulatory risk, a distribution risk, a hazardous waste risk, and an environmental impact in chemical industry supply chain multiple processing nodes of the extensible computation engine The method caches a result of the advanced planning simulation in an extensible memory cache communicatively coupled to the extensible computation engine and edge caching the same in an edge cache server near a geographical point of origin of the advanced planning and forecasting raw data. The extensible computing engine may employ a large number of processors (or separate computers) to perform a set of coordinated computations in parallel through a distributed computing infrastructure (e.g., cloud based infrastructure) for a specific advanced planning query.

Abstract: Disclosed are methods, devices, and systems to provide sales and operations planning (S&OP) for a business enterprise. In one embodiment, a machine-implemented method includes aggregating a S&OP raw data by one or more relational database management systems (RDBMS) communicatively coupled to a big data computation engine; performing a S&OP simulation, by one or more processing nodes of the big data computation engine, using the S&OP raw data; caching a result of the S&OP simulation in a big memory cache communicatively coupled to the big data computation engine; and edge caching the result of the S&OP simulation in an edge cache server near a geographical point of origin of the S&OP raw data. The S&OP raw data may be a historical or forward-looking data input from an ERP program, a CRM program, an SRM program, an MRP program, an SKU database, or a user client device.

Abstract: In one embodiment, a method of a server device includes determining a date to send an e-mail alert based on a flexible calendar of a sales and operations plan. The method also includes recording a response of a user to the e-mail alert. The method further includes tracking a participation of the sales and operations plan based on the response of the user to the e-mail alert. The method also includes generating a report based on the participation. In addition, the method includes generating a reminder when the participation is below a threshold of the participation. The method also includes increasing the participation of the sales and operations plan such that a percentage of the response of the user is increased.

Abstract: In one embodiment, a method includes creating a demand plan in a distributed cloud infrastructure based on a demand-forecasting algorithm that considers multi-party input in client-side visualizations of a certain aspect of the demand plan appropriate to a demand-side stakeholder based on a rules-based algorithm that considers a demand-side access privilege and a demand-side role of the demand-side stakeholder. In addition, the method includes creating a supply plan in the distributed cloud infrastructure based on another supply-forecasting algorithm that considers multi-party input in client-side visualizations of a particular aspect of the supply plan appropriate to a supply-side stakeholder based on a rules-based algorithm that considers a supply-side access privilege and a supply-side role of the supply-side stakeholder.