This article was published as a part of the Data Science Blogathon.
What is AI?
Artificial intelligence (AI) in this Digital World has slightly deviated from earlier definition and is refers to the mimics of the intelligence of humans in machines using data, that are well programmed using new cutting-edge technology to think like a human and execute the actions trained perfectly. It would be playing around with available structured, semi-structured, and unstructured data or even sometimes without historical data.
Why we need AI?
Artificial intelligence (AI) makes targeted tasks by completing pre-planned approaches, process methodology, faster, and more accurate ways. Artificial Intelligence certainly improves the existing working process model in all kinds of industries and supports the development of enhanced solutions to solve the problems that are challenging to execute manually.
Where AI can be? ANYWHERE
You should understand that Automation is different from AI, Automation is basically following business rules and steps that we defined. But AI has more freedom to learn and make decisions based on their ability to continue the learning process. AI is certainly dynamic. Artificial intelligence is transforming all kind of industries and logistics industries is one of them, you can understand from the above picture.
Since we are targeting Transportation, let us focus on this from the above list of AI domains.
What are Transportation businesses/Logistics Industry: Simply say “Logistics is the management of the flow of different products between different locations.”
A business that generates the revenue running commercial vehicles for intra-city, state, and across the countries, which would be the goods transport or travels perspective. For current demand, this business growing considerably on the large scale and there is expertise who are looking to improvise all aspects. Definitely, there are challenges and opportunities for AI-based solutions in this space.
In the logistics sector, AI is actively contributing to safer driving environments for the drivers and optimization of vehicle maintenance and performance, to enhance the overall transportation businesses/Logistics Industry.
Besides, AI doing greater millstone and actively involved in the development of the introduction of self-driving or driverless cars.
The research says that 60-65% of the industry leaders believe that logistics, transportation, and supply chain would require profound transformation. The report by Accenture reveals that 36% of large, mid-, and small-size organizations have successfully adopted AI capabilities for their logistics and supply chain processes.
The survey and research say that AI is absolutely claimed to increase productivity by more than 40-45% by the year 2035. And revenue growth in the logistics domain would increase and renovate the industry in different aspects and reaching their customer deeply and increase the market competition in this domain with AI.
Let’s throw this question, what are the applications of AI in the logistics industry that is looking or mandatory to improve the business?
The above areas are very interesting space in logistics and the scope for AI is exponential if you think out of the box.
Planning & Scheduling
Planning is required for any industry to supply the demand based on the market and potential for the same. It should synchronize the overall supply chain as a continuous process and effective supply chain platform.
As per Gartner’s statement, by 2020 95% of Supply Chain Planning vendors will relying on machine learning models in their solutions. Gartner also predicts that by 2023, intelligent algorithms and AI techniques will be embedded across 25% of all supply chain technology solutions.
AI/ML determines patterns in supply chain data, with appropriate model selection based on the nature of the day by exploring the history and It, also enhances the experiences of the customer and improves the logistics processes.
The imbalance between demand and resource availability and inadequate area mapping/ vehicle breakdown are the major challenges here. AI/ML has the capabilities to enable logistics industries to use real-time data in their demand and forecasting formulation. Following models we can do in our supply chain domain “, Moving Average, ARIMA and other time-series models.”
Predictive Analytics – Planning & Scheduling ( Supply Chain Planning)
Predictive analytics is the process of estimating customer demand by extracting and organizing historical data by applying the analytical test on them. With various demand forecasting methodology, the organization can improve their decision-making processes in the following area Capacity planning, Resource planning, Risk assessment, sales, and marketing operations Strategy, etc.,
Sales and Marketing
In the Logistics industry sales and marketing activities is one of major pillar and leader should concentrate on this and take care of all aspects to improvising the sales and marketing, Even S&M also enhanced by artificial intelligence, here few of them are “Sales and Marketing Analytics, E-mail Marketing, Predictive Sales Analytics, Sales content customization, and analytics, Decision guidance for a Sales representative, Sales representative chatbot, In-store sales robots and many more.”
Warehouse Management (WHM)
In recent days, the warehouse (WH) management becomes a more agile model, with accurate, faster to fulfill the market needs in new solutions AI implementation to satisfy with high volume and streamlined product flow in and out WH. We can build a warehouse using various to form a complete system whereby an entire business operation end-to-end using AI technologies. Basically, goods are received at the warehouse (Inbound), identified, and sorted, processed, packaged, and pulled for shipment(outbound), all steps are automatically carried out and with minimal negligible flaws and even track the same and eliminate in subsequent stages.
If we are implementing Machine Learning in WH management, absolutely we can help to automate many tasks and develop impactful business activities and strategic plans for upcoming years.
In What ways AI & Machine Learning helps WH Management
IIoT and ML in Warehouse
Combining Machine Learning with IIoT sensors, real-time monitoring data are providing excellent visibility across WH. This holistic architectural platform will predict the real-time data and developing insights and patterns this gives more opportunities to automate and understand the logistics business and enhance the supply chain management in different heights. And this smart warehouse can also be linked to the centralized data processing unit, so that volume of order processing increases overall productivity.
Since warehouses are IoT-enabled data processing will vastly improve both speed and accuracy. This wireless cloud data communications connects all elements of our system and engage, with a dialogue that incorporates system monitoring and control. With this, we could improve the productivity in pick-and-pack processes, slotting routes for packages, parking, and delivering the right package based on size, number, unit, weight.
Shipping and Delivery
AI helps to keep track of road traffic, reduce fuel consumption, and improve air quality and urban planning deliveries. On top ease traffic congestion, Identifies the driver’s free time, facilitates parking much easier, and many more. It helps businesses to analyze existing routing, route optimization techniques using shortest path algorithms to identify the most efficient route for logistics trucks to deliver the product, this will help to speed up the shipping process and considerably reducing shipping costs and ultimately makes customer happy and maintaining the good relationship and enhancing the same.
Hope you got some background on how AIML helps the logistics industry in various aspects. Here I am pausing, and we can talk more about this space in detail with the right algorithm which supports the logistics industry. Thanks for reading…. Will connect shortly with another interesting topic. Until then Bye and see you soon – Shanthababu!
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