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How Machine Learning Helps inventory management software?

Ripples-FMS machine learning in inventory management software in warehouse

Inventory management software with machine learning

The introduction of modern and new technologies such as Artificial Intelligence, Machine Learning, and blockchain has transformed inventory management software for predicting the arrival and dispatch of cargo in warehouses.

Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Let’s look into how machine learning helps the freight management industry. Some of the key problems facing freight transport are rising freight rates, skilled employee shortages, and driver management. In the logistics industry, everything is time-bound and there are minor unpredictable issues.

Each process in this sector depends on the one preceding it and a minor delay in one stage causes a ripple effect on all the other stages. At the time of delivery, there ends up being a delay of days because of a few hours of downtime.

As in all areas of business, the COVID-19 pandemic has resulted in severely affected thousands of supply chains globally, the economic impact of which will linger for months to come. Even Global demand for transport will continue to grow dramatically over the next three decades, with global freight demand expected to triple by 2050, according to projections by the International Transport Forum (ITF), an intergovernmental think tank.

To make ease the processes of transportation and management, the Logistics sector adapts the benefits of Machine learning and the greatest number of companies are already making active use of it. AI and ML help in automating various time-consuming processes. Optimizing logistics processes so that they can adapt independently and dynamically to changing market requirements.

Logistics and freight movement or transportation management would be the playing ground, eventually, to create positive delivery experiences and cost leadership among competitors. Most of the companies would win or lose based on how they optimize their last-mile deliveries.

Nowadays, companies are not only looking into the costs but better utilization of available vehicles and so prefer and invest in such a reliable and scalable technology. That’s why they choose Machine learning. ML helps in predictive analysis and thus can determine the number of vehicles they require to fulfill the demands. It also helps them to balance their shipment movements.

Optimal capacity utilization of vehicles would help divide the freight movement cost so that the marginal cost for each unit transported is less, and hence profit margins are higher.

Better driver management would help allocate the right trip to the most suited driver well versed with the route and learned in the type of vehicle assigned. Automated allocation of shipments to vehicles and drivers would speed up transportation, bringing down lead time and downtime.

Machine learning has a clear focus on tracking driver behavior and service hours. This will turn into the design of safe and fast routes for trips. This will bring down the turnaround time, in turn, the fuel and maintenance costs.

Companies would be able to track driver behavior such as Harsh Acceleration, harsh braking, speeding,Idle Time, unnecessary detention, deviation from planned routes, etc. with instant alerts and notifications passed on to the supervising manager or stakeholder. This may help in the KPI analysis of drivers and also for management.

Machine learning in inventory management software

Live GPS tracking of moving resources would make transport companies agile and responsive. Real-time traffic pattern analysis would help predict the best route and accurate ETAs for reaching the in-transit hubs and destination locations. Fast scanning with in-app or connected scanners would help with fast loading and unloading at hubs, reducing the total time spent there while increasing transparency.

All this would help companies better manage the hours of service of each driver, to comply with regulatory and service level agreements. Instances, where a driver’s mandatory break time ends up delaying critical orders, would be almost nullified as the driver’s time would be well-tracked and managed right from a single logistics software dashboard giving end-to-end visibility of overall moving and on-ground resources.

A Transport Management software using Machine learning will become a self-optimizing inventory management software. It generates additional value to the customer and employee experiences and gains efficiency with less physical work than ever. Such automation of allocation, routing, tracking, and compliance would be the primary need for most companies to run their operations sustainably and profitably.