International Business Machines Corporation

Pavithra Harsha

Ali Koc

Shivaram Subramanian

Ashish Jagmohan

Ajay Ashok Deshpande

Krishna Chaitanya Ratakonda

Ramesh Ambat Gopinath

Elisabeth Claire Paulson

Brian Leo Quanz

Wei Sun

Markus Ettl

Max Biggs

Rakesh Mohan

Rajesh Kumar Ravi

Yun Zhang

Yam Huo Ow

Dhruv Shah

Mahesh Ramakrishna

A machine learning system that incorporates arbitrary constraints is provided. The machine learning system selects a set of domain-specific constraints from a plurality of sets of domain-specific constraints. The machine learning system selects a set of general functional relationships from a plurality of sets of general functional relationships. The machine learning system maps the selected set of general functional relationships and the selected set of domain-specific constraints to a set of learning transforms. The machine learning system modifies a machine learning specification according to the set of learning transforms, wherein the machine learning specification specifies a model construction, a model setup, and a training objective function. The machine learning system optimizes a machine learning model according to the modified machine learning specification.

Domain-specific constraints for predictive modeling

A processor may estimate uncensored demand from historical supply chain data. The processor may ingest historical data. The processor may convert the historical data to a dataset of multiple time series corresponding to sales for different products and locations and channels across multiple time points that is usable by an uncensored demand estimation machine learning model. The processor may train the uncensored demand estimation machine learning model by applying optimization solver techniques for deep learning.

Supply chain demand uncensoring

A machine learning system that incorporates arbitrary constraints into deep learning model is provided. The machine learning system provides a set of penalty data points en a set of arbitrary constraints in addition to a set of original training data points. The machine learning system assigns a penalty to each penalty data point in the set of penalty data points. The machine learning system optimizes a machine learning model by solving an objective function based on an original loss function and a penalty loss function. The original loss function is evaluated over a set of original training data points and the penalty loss function is evaluated over the set of penalty data points. The machine learning system provides the optimized machine learning model based on a solution of the objective function.

Loss augmentation for predictive modeling

A machine learning system that uses a split net configuration to incorporate arbitrary constraints receives a set of input data and a set of functional constraints. The machine learning system jointly optimizes a deep learning model by using the set of input data and a wide learning model by using the set of constraints. The deep learning model includes an input layer, an output layer, and an intermediate layer between the input layer and the output layer. The wide learning model includes an input layer and an output layer but no intermediate layer. The machine learning system provides a machine learning model comprising the optimized deep learning model and the optimized wide learning model.

Split-net configuration for predictive modeling

An example operation may include one or more of receiving, by a retailer node, an encrypted inventory of goods data from a plurality of supplier nodes over a blockchain network, computing, by the retailer node, an ordering proportion based on the encrypted inventory of goods data, generating, by the retailer node, an ordering policy based on the ordering proportion, and executing a smart contract to order goods from the plurality of the supplier nodes based on the ordering policy.

Upstream visibility in supply-chain

An example operation may include one or more of acquiring, by a retailer node, an inventory data from a supplier node over a blockchain network, receiving, by the retailer node, outstanding orders data of the supplier node, generating, by the retailer node, an order distribution policy based on the inventory data and the outstanding orders data, and executing a smart contract to order goods from the supplier node based on the ordering policy.

An example operation may include one or more of receiving, by a first node, a freshness of goods data from a second node over a blockchain, and executing, by the first node, a smart contract to: calculate an initial order quantity based on a pre-set critical order number and a non-expiring goods order quantity and alter a final order quantity based on the initial order quantity and the freshness of the goods data.

Freshness visibility in supply-chain

An example operation may include one or more of collecting, by a first node, a plurality of permissioned data inputs from a plurality of second nodes of a supply-chain, performing, by the first node, a granular simulation based on the permissioned data inputs to generate a plurality of key performance indicators (KPIs), and executing a smart contract to adjust order processes of the supply-chain based on the KPIs.

Supply-chain simulation

A computer generates an optimized decision distribution vector for a plurality of related, demand-correlated products. The computer receives data indexed by product, with each entry including several entry attributes. The computer receives decision context data for the products. The computer determines a set of primary attributes and trains a first machine learning model based upon those attributes. The computer receives a decision optimization request that includes an associated set of attributes corresponding to the primary attributes. The computer scores the associated set of attributes, using the first machine learning model, to generate a baseline purchase propensity. The computer trains a second machine learning model, based upon the baseline purchase propensity and the decision context data, to generate own-product and cross-product elasticity data. The computer, using the own-product and cross-product elasticity data, generates a decision distribution vector for the group of related, demand-correlated products.

Automated hybrid propensity decision vector generation using artificial intelligence

A system that compresses data during neural network training. A memory stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An anticipatory value of inventory (VOI) optimization component calculates optimal VOI and prices for immediate-future inventory levels in parallel and writes latest price updates for respective states to a price stack; and a recommendation component provides customized pricing recommendation for a product relative to a unique customer as a function of the latest price updates for respective states to the price stack.

Real time personalized pricing for limited inventory assortments in a high-volume business environment

System and method for omni-channel retailer operations that integrate a network of brick-and-mortar stores with their online channel. The system and method includes calibrating a demand model for both brick-and-mortar sales and on-line channels over which a product is sold, the calibrating based upon a cross-channel fulfillment-aware inventory effect. An omni-channel sales prediction and fulfillment model is then constructed based on the calibrated demand model. Using constructed linear demand and revenue models, a plan is generated to optimize one or more: allocating of the product across physical stores, partitioning of the product for sales, and pricing of the product. Customer choices are jointly modeled across channels to allow switching, and a ship-from-store (SFS) inventory effect feature only for brick choice is applied to capture asymmetry. Inventory decision variables are introduced into an omni-channel pricing formulation to manage SFS-effect induced non-convexity and specific reformulations applied to recover a linear MIP.

Managing cross-channel fulfillment impact within shared inventory demand systems

A computer implemented method and system of allocating a target commodity product onto an omnichannel distribution system is provided. Historical data related to the target commodity product is retrieved. Data mining is performed on the retrieved historical data to identify patterns therefrom. An omnichannel nominal demand prediction model is developed based on the identified patterns of the data mining for the omnichannel distribution system. A life-cycle demand is forecast. An allocation for the target commodity product based on the omnichannel nominal demand prediction model is created. A worst case scenario of allocation of the target commodity product for the omnichannel distribution system is identified. The allocation is adjusted to prevent the worst case scenario.

Growing community of inventors

White Plains, NY, United States of America

Pavithra Harsha