Blue Yonder Group, Inc.

A system and method are disclosed for planning a product assortment based on a sales forecast without using a cross elasticity by receiving a percentage pricing change for at least two substitutable products of an inventory in a supply chain network having one or more supply chain entities, and at least two substitutable products are grouped in the same product category and at least one of at least two substitutable products is grouped in a product assortment, calculating an average percent pricing change for the product category including at least two substitutable products and a direct effect factor and cross-effect factor for each of at least two substitutable products, and identifying an item of at least two substitutable items to be removed from the product assortment based, at least in part, on a substitutable demand calculated by modeling a price increase of a substitutable item to infinity.

A system and method for a risk management visualization system comprises a computer having a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process, and display a visualization of a risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

A system and method for a risk management visualization system comprises a computer having a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process, and display a visualization of a risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

A system and method for a risk management visualization system having a computer comprising a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process comprising an exploration phase and a learning phase, and display a visualization of the risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

A system and method for a risk management visualization system having a computer comprising a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process comprising an exploration phase and a learning phase, and display a visualization of the risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

A system and method are disclosed for predicting recommendations for a user interface. The method includes generating a graphical user interface that receives a conversation-based input from at least one user of a client portal, receiving a user profile and a navigation history for the user, providing the conversation-based input from the user to a natural language processing engine, decoding using the natural language processing engine an intent and slots from the conversation-based input; and generating one or more recommendations based, at least in part, on the intent, the slots, the user profile, and the navigation history.

A system and method are disclosed for evaluating a forecast. The method includes aggregating a set of predictions and a set of actuals into prediction buckets, rating prediction data of the set of predictions using forecast metrics, computing an ideal benchmark for the forecast metrics, determining a scaling adjustment to calculate one or more non-ideal benchmarks to create a forecast rating scale, determining a score for each of the prediction buckets, the score corresponding to the forecast rating scale; and aggregating the determined scores of the prediction buckets to generate an overall score. The method further includes generating a calibration diagram comprising observed frequency and predicted probability of the set of predictions and the set of actuals.

A system and method are disclosed for predicting recommendations for a user interface. The method includes generating a graphical user interface that receives an input from a user of a client portal, crawling tasks associated with the user in the client portal, ranking the tasks according to an intent associated with the input from the user, fetching at least one task from the ranked tasks, calculating a quantity of steps to complete the task from the ranked tasks; identifying one or more slots used by the task in at least one step from the quantity of steps, and generating one or more recommendations comprising a subsequent action for the user to complete the task.

A system and method are disclosed for predicting recommendations for a user interface. The method includes generating a graphical user interface that receives an input from a user of a client portal, crawling tasks associated with the user in the client portal, ranking the tasks according to an intent associated with the input from the user, fetching at least one task from the ranked tasks, calculating a quantity of steps to complete the task from the ranked tasks; identifying one or more slots used by the task in at least one step from the quantity of steps, and generating one or more recommendations comprising a subsequent action for the user to complete the task.

A system and method are disclosed for consolidating orders for fulfillment. The method includes detecting that a shopper has arrived at a shopping mall, determining and predicting items or orders that the shopper is expected to pick up or likely to purchase during a current visit to the shopping mall, determining whether the shopper is expected to participate in one or more other non-shopping activities in the shopping mall, and determining a predicted exit time of the shopper from the shopping mall, in response to determining that the shopper is expected to carry at least one bag, determining a consolidated pickup or delivery service for the items or the orders, and offer the consolidated pickup or delivery service to the shopper. The method further includes where detecting the shopper arrival is based on one or more of: smartphone location, calendar data, a manual input and the shopper's first order.