Abstract: Computer-readable media, methods, and systems are disclosed for electronically creating an improved timeline of events for product-location pairs. A machine learning model is trained on historical data to generate a demand forecast. Input requirements including an initial timeline of events for product-location pairs are received. Using combinatorial branch-and-bound along with the trained machine learning model, the improved timeline of events for product-location pairs is generated.

Abstract: A system, a computer program product, and a method for order planning and optimization are disclosed. A first data is received, where the first data represents historical shipment data of an item from a distributor to a location. The received first data is processed and a model for at least one shipping pattern of the item from the distributor to the location is determined based on the processed received first data. A forecast for a future shipping demand of the item by the location is generated based on the determined model. At least one shipping pattern of the item from the distributor to the location is optimized based on the generated forecast.

Abstract: A computing system (100) receives transaction records (130) for loans taken at various interest rates (1904) for a loan segment (902). Performance indicators (1716) indicative of customer behaviors (1702) are computed (1806) using independent demand models (300, 302, 304, 306, and 308). Computing system (100) includes a performance indicator forecaster (112) that determines relationships between the performance indicators (1716) and various prices, or interest rates (1904). These relationships can include profit (1906) and/or volume (1908) relative to the various interest rates (1904). The relationships are utilized to select an interest rate (1912, 2102) for the product segment (902) for implementation by a financial institution.

Abstract: According to some embodiments, a system and method includes receiving historical data of promotional offers associated with a product or service, the promotional offers including at least one tactic effect; receiving a request to forecast a demand for the product or service, the request including an indication of a promotional offer tactic effect; generating a demand forecast including a tactic lift for the requested promotional offer tactic effect, the demand forecast based on the at least one tactic effect contributing to the demand for the product or service; and providing an output of the generated demand forecast

Abstract: According to some embodiments, a method and system provides separating demand models and forecasts into demand components thereof. The methods include receiving a demand model to forecast a demand for a product or service; decomposing the demand model into a plurality of distinct demand components, each of the demand components associated with a causal contribution to a demand for the product or service; and generating a demand forecast including an indication of the plurality of demand components associated with the causal contributions contributing to the demand forecast for the product or service. Some embodiments include a method to determine demand components of a demand model for a product or service based on historical data.

Abstract: A computer-implemented method transforms transactional data into a forecast of demand for controlling a commerce system. Goods move between members of a commerce system. The transactional data is recorded. Daily disaggregating parameters (DDP) are estimated by minimizing an error function of day of week and transactional data grouped according to promotion, price range, or customer. Model parameters are estimated based on the DDP and transactional data using a demand model to generate a weekly forecast of demand for the good. The weekly forecast of demand is disaggregated into daily components using the DDP. The daily components of the forecast of demand are provided to a member of the commerce system to control the movement of goods in the commerce system. Any variation in the demand model due to promotions, price changes, out-of-stock, and low selling product is taken into account when determining the daily components.

Abstract: A computer-implemented method transforms transactional data into a forecast of demand for controlling a commerce system. Goods move between members of a commerce system. The transactional data is recorded. Daily disaggregating parameters (DDP) are estimated by minimizing an error function of day of week and transactional data grouped according to promotion, price range, or customer. Model parameters are estimated based on the DDP and transactional data using a demand model to generate a weekly forecast of demand for the good. The weekly forecast of demand is disaggregated into daily components using the DDP. The daily components of the forecast of demand are provided to a member of the commerce system to control the movement of goods in the commerce system. Any variation in the demand model due to promotions, price changes, out-of-stock, and low selling product is taken into account when determining the daily components.

Abstract: A computing system (100) receives transaction records (130) for loans taken at various interest rates (1904) for a loan segment (902). Performance indicators (1716) indicative of customer behaviors (1702) are computed (1806) using independent demand models (300, 302, 304, 306, and 308). Computing system (100) includes a performance indicator forecaster (112) that determines relationships between the performance indicators (1716) and various prices, or interest rates (1904). These relationships can include profit (1906) and/or volume (1908) relative to the various interest rates (1904). The relationships are utilized to select an interest rate (1912, 2102) for the product segment (902) for implementation by a financial institution.