International Business Machines Corporation

Ali Koc

Ajay Ashok Deshpande

Brian Leo Quanz

Xuan Liu

Yingjie Li

Arun Hampapur

Dahai Xing

Saurabh Gupta

Christopher Scott Milite

Chek Keong Tan

Xiaobo Zheng

Pavithra Harsha

Ashish Jagmohan

Alan Jonathan King

Yada Zhu

Krishna Chaitanya Ratakonda

Ramesh Ambat Gopinath

Lei Cao

Elisabeth Claire Paulson

Jayant R Kalagnanam

Dingding Lin

Shyh-Kwei Chen

Ding Ding Lin

Pradyumnha Gopalkrishna Kowlani

Chandrasekhar Narayanaswami

Rakesh Mohan

Jae-Eun Park

Soumyadip Ghosh

Xiao Mei Zheng

Yue Tong

Vadiraja S Ramamurthy

Steve Igrejas

Yun Zhang

Michael Yesudas

Yam Huo Ow

Dhruv Shah

Mahesh Ramakrishna

Sanjay E Cheeran

Sachin Sethiya

A computer implemented method and system of evaluating a fulfillment strategy in an omnichannel distribution system is provided. Input parameters are received from a computing device of a user. Historical data related to a network of nodes is received from a data repository. A synthetic demand status is determined based on the historical data and the input parameters. A synthetic network status based on the historical data and the input parameters are determined. A fulfillment strategy is identified based on the synthetic demand status and the synthetic network status. Key performance indicators (KPIs) for the fulfillment strategy are determined based on the synthetic demand status and the synthetic network status.

System for modeling the performance of fulfilment machines

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

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

Embodiments herein describe a return network simulation system that can simulate changes in a retailer's return network to determine the impact of those changes. Advantageously, being able to accurately simulate the retailer's return network means changes can be evaluated without first making those adjustments in the physical return network. Doing so avoids the cost of implementing the changes on the return network without first being able to predict whether the changes will have a net positive result (e.g., a positive result that offsets any negative results). A retailer can first simulate the change on the return network, review how the change affects one or more KPIs, and then decide whether to implement the change in the actual return network. As a result, the retailer has a reliable indicator whether the changes will result in a desired effect.

Simulating a return network

A computer implemented method and system of setting values of parameters of nodes in an omnichannel distribution system, the method comprising is provided. Input parameters are received from a computing device. Historical data related to the network of nodes is received from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. Each node is clustered into a corresponding category. For each category of nodes, key parameters are identified. A range of each key parameter is determined based on the synthetic scenario. A number of simulations N to perform with data sampled from the synthetic scenario within the determined range of each key parameter is determined. For each of the N simulations, a multi-objective optimization is performed to determine a cost factor of the parameter settings. The parameter settings with a lowest cost factor are selected.

Fulfilment machine for optimizing shipping

A computer implemented method and system of calculating labor resources for a network of nodes in an omnichannel distribution system. Input parameters are received from a computing device of a user. Historical data related to a network of nodes is received, from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. For each node, key parameters are identified and set based on a multi-objective optimization, wherein the multi-objective optimization includes a synthetic inventory allocation to the node based on the synthetic scenario. A synthetic labor efficiency is determined for the node from the synthetic scenario. Labor resources are calculated based on the synthetic inventory allocation for the synthetic scenario. The labor resources of at least one node are displayed on a user interface of a user device.

Machine for labor optimization for efficient shipping

Techniques for facilitating estimation of node processing capacity values for order fulfillment are provided. In one example, a computer-implemented method can comprise: generating, by a system operatively coupled to a processor, a current processing capacity value for an entity; and determining, by the system, a future processing capacity value for the entity based on the current processing capacity value and by using a future capacity model that has been explicitly trained to infer respective processing capacity values for the entity. The computer-implemented method can also comprise fulfilling an order of an item, by the system, based on the future processing capacity value.

Estimation of node processing capacity for order fulfillment

Evaluating node fulfillment capacity in node order assignment by receiving a current order for node order assignment, retrieving data of each node, the retrieved data of each node including current capacity utilization, determining a probability of backlog on an expected ship date of each node, the probability of backlog being based on the retrieved current capacity utilization, determining a capacity utilization cost of each node based on the probability of backlog on the expected ship date, automatically calculating a fulfillment cost of each node of the current order based on the capacity utilization cost, identifying one or more nodes for the current order with the lowest fulfillment cost and automatically generating a node order assignment assigning the current order to one of the one or more nodes with the lowest fulfillment cost.

System and method to incorporate node fulfillment capacity and capacity utilization in balancing fulfillment load across retail supply networks

A method for continuously tracking business performance impact of order sourcing systems and algorithms that decide how ecommerce orders should be fulfilled by assigning the items of the order to nodes in a fulfillment network such as stores, distribution centers, and third party logistics&#x2014;to provide automatic root cause analysis and solution recommendations to pre-defined business problems arising from KPI monitoring. A Business Intelligence (BI) dashboard architecture operates with: 1) a monitoring module that continuously monitors business KPIs and creates abnormality alerts; and 2) a root cause analysis module that is designed specifically for each business problem to give real time diagnosis and solution recommendation. The root cause analysis module receives the created alert, and triggers conducting a root cause analysis at an analytics engine. The BI dashboard and user interface enables visualization of the KPI performance and root cause analysis results.

Tracking business performance impact of optimized sourcing algorithms

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White Plains, NY, United States of America

Ali Koc