International Business Machines Corporation

Zhengliang Xue

Markus Ettl

Pawan Raghunath Chowdhary

Daniel Patrick Connors

Roger Douglas Lederman

David D Yao

Tim Nonner

Ulrich B Schimpel

Hongxia Yang

Donald Keefer

Gabriel Toma

Yingjie Li

Bhavna Agrawal

Karthik Sourirajan

Anuradha Bhamidipaty

Shuxin Lin

Somnath Mukherjee

Zhenyu Hu

Temporal and spatially integrated forecast modeling includes generating a plurality of forecast models for a plurality of short-term to long-term time periods for a plurality of locations. Temporally integrating the plurality of forecast models sequentially over the plurality of time periods for the plurality of locations and spatially integrating the temporally integrated plurality of forecast models for each location hierarchically over the geographic areas. The forecast models are autoregressive distributed lag models with different explanatory variables for the short-term and long-term forecast models. The temporally integrating includes recursively integrating the plurality of forecast models over the time periods from the short-term to the long-term time periods and the spatially integrating includes recursively integrating the temporally integrated plurality of forecast models hierarchically from larger size geographic areas to smaller size geographic areas. The method includes optimizing the resultant spatially and temporally integrated forecast model based on a plurality of constraints.

Multi-locational forecast modeling in both temporal and spatial dimensions

Systems, methods, and computer-readable media are disclosed for identifying customers having associated opportunities for improved growth and/or profitability with respect to product or service offerings and determining investment solutions that enhance the probability that the customers transition to the higher growth/profitability opportunities. Prior customer transactions are segmented based on segmentation criteria and used to generate a transaction graph. The nodes of the transaction graph represent the segmented transactions and client transaction paths between the nodes represent potential customer life-cycle trajectories. The transaction graph can be used to identify high-value penetration opportunities.

Dynamic management of a customer life-cycle value

A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.

Training a machine to automate spot pricing of logistics services in a large-scale network

Systems, methods, and computer-readable media are disclosed for identifying product configurations that are alternatives to a requested product configuration, ranking the alternative product configurations based on one or more pricing metrics, and presenting the alternative product configurations to a prospective customer, thereby providing the customer with the option of selecting an alternative product configuration in lieu of the initially requested product configuration.

Product configuration recommendation and optimization

System and method that improves cargo logistics may be presented. For instance, shipping capacity in cargo logistics may be best utilized based on providing pricing and scheduling solutions that are jointly optimized and prices differentiated based on flexibility of service request. Scheduled service and pricing may be transmitted as a signal to control execution of the cargo logistics.

Cargo logistics dispatch service with integrated pricing and scheduling

Joint pricing and replenishment of freshness inventory or products having different qualities determines the joint pricing and inventory strategies to optimize the retailer's benefits from product freshness, employing a dynamic programming formulation that incorporates a demand model that captures consumer choice based on both the freshness and price.

Joint pricing and replenishment of freshness inventory

Freshness inventory control problem may be formulated as a stochastic dynamic program. In one aspect, a stochastic dynamic programming formulation takes as input inventory status, stochastic demand forecast and cost information associated with on-hand inventory. The stochastic dynamic programming formulation is maximized to determine order quantity and depletion quantity of the product per period.

Managing fresh-product inventory

Win probability estimation model that statistically computes the probability of winning a bid at a given price, and profit optimization models that compute the optimal price for a bid balancing the probability of winning a bid at a price with the profitability of the bid at the given price. In one stage, an expected profit margin of a product may be formulated as a function of its profit margin and win probability to compute its optimal profit margin. In another stage, an expected profit for one or more product accessories may be formulated as a function of the profit margin and their conditional win probability given the server win to compute their optimal profit margins. The conditional win probabilities for the product accessories may be modeled as a function of the utilities of the various purchase options that contain the product and that accessory.

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Zhengliang Xue