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FIELD

The field relates generally to information processing systems, and more particularly to the development of predictive models for the automatic generation of revised delivery dates.

BACKGROUND

When customer orders are put on a hold, due to, for example, a failure to receive or process payment, part shortages, fraud, and/or compliance problems, a revised delivery date (RDD) of the order is generated. Conventional information processing systems use outdated linear models to calculate RDD using static and delayed data, and manually input lead times, leading to inaccurate delivery commitments. As a result, compliance with RDDs calculated using conventional methods has been very low (e.g., <10%). This adversely impacts order experience when delivery commitments are missed, especially when orders are on extended lead times or holds, leading to support calls, early or late delivery, and/or incorrect notifications regarding order status.

Accordingly, there is a need for improved information processing systems, which can generate more accurate revised delivery commitments.

SUMMARY

Illustrative embodiments provide a predictive services platform which utilizes machine learning develop predictive models (e.g., algorithms) to automatically generate RDDs. In accordance with an embodiment of the present invention, the development of the predictive models and the predictive calculations are based on boosted decision trees and neural networks to increase accuracy of generated RDDs. For example, some embodiments are configured to improve accuracy of RDD calculation and reduce multiple RDDs by using machine learning incorporating expanded variables and real time data into the delivery date calculation. Embodiments of the present invention further provide a data science platform (e.g., a cloud-based data science platform) that enables data scientists to collaborate, experiment, re-use data sources, measure performance and manage versioning of data science models. Embodiments of the present invention also provide a scalable, high velocity, and highly available database of order information structured specifically for data science. Such embodiments can advantageously provide significantly improved accuracy and efficiency in RDD generation, enabling order communication that is consistent, clear, accurate and timely, and early identification of and proactive actions on orders at risk of missing initial delivery commitments. According to embodiments of the present invention, the predictive services and data science platforms are cloud-based platforms.

In one embodiment, an apparatus comprises at least one processing platform including a plurality of processing devices. The processing platform is configured to receive a request to execute one or more predictive models for generating a delivery date, initiate execution of the one or more predictive models responsive to the request, and invoke a plurality of machine learning algorithms using data from a plurality of data sources when executing the one or more predictive models. The processing platform is also configured to capture the data from the plurality of data sources and organize the data into a sparse matrix, automatically generate the delivery date, and automatically transmit the delivery date to one or more user devices.

These and other illustrative embodiments include, without limitation, apparatus, systems, methods and computer program products comprising processor-readable storage media.

