Streak To Leverage Machine Learning in Last Mile Logistics
Logistics is the large industry that has a complex network around the world.Last mile logistics refers to the final step of the delivery process from a distribution center or factory to the end user. As the name says, it is the last mile delivery.last mile logistics involves the product carriers to deliver products to consumers.last mile logistics is considered as the cornerstone to driving growth and profitability.Last mile logistics ensure shippers to deliver more products to consumers faster and cost-efficient, critical concerns in the supply chain. In fact, consumers are also ready to pay a lot for better last mile delivery services. While industries like petroleum and gas, Mining and Machinery, FMCG’s, and education can still take advantage in this growing market by meeting the challenges in last mile delivery.
There is a growing agony in last mile logistics. Last mile delivery becomes difficult with out of route problem, and delivery cost may dramatically increase with inefficient fuel usage. Another challenge in last mile delivery goes back to Vehicle Maintenance and safety guidelines. Poor vehicle maintenance and and non-adherence of safety guidelines by drivers may end up with accidents which result in huge loss and failure of delivery.Some products need high level of security during the transit which cannot be monitored by shipper. Unscheduled stops by drivers cause late delivery to the customer.
Shippers must find ways to overcome these challenges and meet the new challenges in last mile delivery to remain competitive.
Logistics Planning has many applications that has relied on operations research or statistics that build the math power to develop these applications. Thus, shippers are supposed to rely on third party machine learning technologies to embed in their solutions. The planning vendors have built, and are building, machine learning solutions into their SCP applications. This may help to resolve the issues in last mile delivery.
Leverage Machine Learning:
In logistics, the branch of AI known as machine learning is where most of the activity is, particularly software developers is to providesupply chain planning (SCP) and logistics planning applications. Adoption of machine learning is the key driver for suppliers to achieve differentiation.Machine learning can eliminate the need for a lot of programming.
Machine Learning is the process when a machine takes the output, observes the accuracy of the output, and updates its own model so that better outputs will occur. Any machine that does this is using machine learning. It doesn’t matter if data science methods are used or not. It does not matter if neural networks or some other supervised or unsupervised learning technique is being used. It’s important not to get stuck down on the specific technique. What matter is if the machine is itself enable of learning and improving with experience.
Faking Of Machine Learning:
Some of the logistics planning companies have been self-deprecating in taking credit for their activities in this area. If they are not able to solve the solution based upon a computational technique generally associated with machine learning, they generally say that their solution has “characteristics” of machine learning, but is not truly a machine learning application.
Adoption of Machine learning in last mile Delivery:
Some companies, like amazon have been applying machine learning to Last mile delivery long before AI became hot. These companies understood how to let the machine automate this process. Today, Enmovil bi solutions are doing the same thing. Over time, many data inputs have been introduced into logistics and SCM planning process, and many companies are doing far more forecasts. For an instance, instead of just doing a monthly forecast, some companies are doing forecasts daily, weekly, monthly and longer time frames.
For last mile logistics forecast at plant level, it may be that algorithms applied to the first mile to last mile data stream have the most predictive power. Forecasting for deliveries at the plant level on a monthly basis, an algorithm applied to plant shipment history and plant patterns has more predictive power. A forecasting engine with machine learning, just keeps looking to see which combinations of algorithms and data streams have the most predictive power for the different forecasting hierarchies. When if finds it can improve the forecast, it either changes the model or suggests to the planner that the model should be changed in a specific way. Enmovil provides analytics which generates data of the logistics that have predictive power of forecasting operational efficiency.
Enmovil is looking for customers with novel problems during last mile delivery and logistics they can work on. It is focusing on real-time analysis of vehicle performance to ensure safety measures with an array of connectivity with enKonnect sensor that drives safety solutions by multitude of sensors. It introduces remote control from anywhere with entrac for proprietary gpsprotocols. It provides seamless Bluetooth integration that eyes everywhere. Enmovil introduced Machine learning engine enalytics with smart generic mobile application on both ios and android platforms where shippers can directly monitor their commuters.