How Machine Learning and Exception Management Shaping Logistics?

Machine Learning

Machine Learning

Deciphering the importance of machine Learning and Exception Management in logistics.

The logistics industry currently is on the cusp of digitalization making significant head turn towards digital technologies such as AI and machine learning. For several years, machine learning has been garnering a lot of interest in logistics management. It makes faster and better decisions that assist shippers in optimizing carrier selection, routing and quality control processes to save costs and enhance efficiencies. Machine learning with its ability to glean and assess a large number of distinct data points can help logistics managers to detect and address a problem they don’t aware of.

Logistic resources, such as tanks, pipelines and ships, can make businesses more productive and maximize profits by making products, equipment, and raw material flow easier throughout processes.

Machine learning delivers its value by utilizing data from multiple systems and data sets. Coalescing all the data with external data sources including GPS systems, historical pricing performance and FMCSA can help shippers to more accurately envisage demand, examine supply chain trends, monitor seasonal calendars, and track daily patterns within lanes.

 

Exception Management in Logistics

Since complexity and uncertainty in business operations are increased, adaptive and collaborative business processes along with exception management are gaining attention. With the growing significance of logistics globally and snowballing complexity of logistics networks, meeting service requirements of customers have become a challenge for companies. This is because the current logistics exceptions rely extensively on the power of the human brain and the traditional technology-enabled supply chain management or other logistics tools. This generally models and manages business processes and anticipated exceptions based on predefined logical events from a centralized perspective, resulting in inadequate decision support for flexibility and adaptability in logistics exception management.

However, to win exception management, companies must deliver sequential planning to ensure the smooth functioning of the operations that can help a company to be stable. Prioritizing data and its capabilities to optimize and industrialize exception management is also crucial. As a complex business process, exception management may involve multiple organizations and a mixture of human activities and automated tasks.

Whilst the occurrence of exceptions is a fundamental part of business activities, competently handling the volatility of exceptions is a major component of sound risk management in logistics.

The machine learning aspect of exception management tempts accountability and efficiency within a company’s and logistics network’s culture. According to an article published on readwrite, there are 6-stages of machine learning that enabled the exception management system. Those are discovery – identifying and reporting issues or anomalies within the processes. through temperature sensors, real-time movement tracking, and order journey tracking, among others. Analysis – analyzing and processing issues or exceptions as per protocols or learnings. Assignment – matching the exception with the right person or department. Resolution – tracking the speed and effectiveness of the resolution of an accountable person. And in the end, recording and assessing each exception right from discovery to resolution.