Abstract
We developed a framework for the risk assessment of delaying the delivery of shipments to customers in the presence of incomplete information pertaining to a significant, e.g., weather-related, event that could cause substantial disruption. The approach was anchored in existing manual practices, but equipped with a mechanism for collecting critical data and incorporating it into decision-making, paving the path to gradual automation. Two key variables that affect the risk were: the likelihood of an event and the importance of the specific shipment. User-specified event likelihood, with elliptical spatial component, allowed the model to attach different probabilistic interpretations; uniform and Gaussian probability distributions were discussed, including possible paths for extensions. The framework development included a practical implementation in the Python scientific ecosystem. Although the framework was demonstrated in a prototype environment, the results clearly showed that the framework was quickly able to show scheduled and in-process shipments that were at risk of delay, while also providing a prioritized ranking of these shipments in order for personnel within the manufacturing organization to quickly implement mitigation actions and proactive communications with customers to ensure critical shipments were delivered when needed. Since the framework pulled in data from various business information systems, the framework proved to assist personnel to quickly identify potentially impacted shipments much faster than existing methods, which resulted in improved efficiency and customer satisfaction.
Highlights
In the modern commerce era, many companies have a global presence and rely upon complex supply chains in order to meet customer demand and improve the customer experience (CX).Unexpected events can have severe ramifications that extend throughout the entire supply chain
As our society has advanced in digitization and the availability of data, businesses are working on ways to utilize newly available supply-chain-related data to improve their operations, and to provide awareness of unexpected disruptive events and to know whether, why, how, and with whom they should interact to take proactive mitigation steps to minimize the impact of an expected event onto its business and its customers
The risk assessment framework described in this article is part of a larger system that is intended to improve the speed and accuracy of information flowing through business processes such that mitigation decisions can be made in hours or seconds and proactively communicated with customers, yielding improvements in customer experience
Summary
In the modern commerce era, many companies have a global presence and rely upon complex supply chains in order to meet customer demand and improve the customer experience (CX). Identify SCEM systems based on a level of automation that could support a company’s overall SCEM goals They are: Monitoring system—which would allow a user to monitor planned (known) events and be able to detect disruptive events. The risk assessment (RA) module described in this article is a contribution to the SCEM literature, and would generally be categorized as a “decision support system” based upon the levels of automation for SCEM systems as identified by [2]; since the development of the RA module was the outcome of an attempt to solve a practical problem and a specific use case applied to outbound shipments, a suitable framework that satisfied the constraints was not found in the SCRM and SCEM literature. Since the RA module provides a risk score for each shipment, under the case when an event is known, the RA module can be used to prioritize intervention actions based on each shipment’s risk score
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