Four AI Use Cases In Logistics That’s Transforming Supply Chain Operations For Good

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Created on August 30, 2022

Owing to its consequential nature and endless utility, data has been often compared to oil (precious and crucial for operations) and water (businesses cannot survive without it). However, when it comes to deriving actionable insights from the large and unstructured data sets that are generated daily in an organization, say a warehouse, the analogy takes a subtle shift. The raw data is just like impure water, not potable, or in business terms, not useful for direct business decision-making.

But filter it using AI and this water not only becomes potable but enriched and fortified for taking specific and strategic business decisions. AI derives its utility and application use cases from its ability to replicate human learning behavior and to process and parse huge data sets at a rapid pace to produce understandable and hidden patterns. 

This is exactly why it has played a critical role in transforming the global logistics and supply chain space of late. Optimizing the resource and asset use, planning every process, delivery orchestration, finding the best routes, dynamic allocation of consignments or shipments, analyzing data for insightful reports that indicate the points of cost leakages, and arresting redundancies – AI finds amazing and disruptive applications in logistics.

AI in Logistics and Supply Chain: Trends

  • 51.3% of businesses voted AI as a disruptive force in logistics making it rank next only to the Blockchain which takes the first place.
  • A global survey done on technology and business stakeholders revealed the most promising AI use cases – inventory management (40%), quality control (34%), customer care (32%), monitoring/diagnostics (31%), cybersecurity (31%) and fraud detection (29%).
  • AI in the supply chain market is expected to be USD 10,110.2 million by 2025 and demonstrate a growth rate of 45.55% CAGR.

The Wave of Transformation: AI Use Cases in Logistics

1. Route Planning 

Route planning is a crucial process in smart logistics management that has a direct impact on revenue and costs. AI rapidly processes multiple data sets in real-time, such as consignment data, rider data, vehicle data, address data for source and destination, real-time road conditions, and any upcoming dynamic changes to create the most optimal routes for every single order.

This processing happens at such a rapid pace that no stakeholder encounters any process or information lag anywhere during the entire order movement. This automated route generation ensures operational excellence by enabling businesses to monitor the ongoing delivery events at multiple touch points. They can check whether a rider is deviating from a system-generated route, automate routing for multiple orders at once and rest assured that the operational costs stay the lowest.

2. Clustering

This is a core process optimization technique that has multiple applications across diverse logistical and supply chain operations. For instance, clustering is done to group the delivery locations together in a planned delivery situation such that the constraints like, vehicle capacity, fuel consumption, rider movement, distance traveled, etc., are obeyed at all times without disrupting system efficiency.

Technically speaking, clustering is an intelligent manner of grouping the fixed variables in the entire order movement journey such that all the resources are used in the most cost-efficient way and no cost leakages happen. AI helps in reducing the overall clustering time to mere seconds and instills scalability and efficiency in logistics movements.

3. Order Allocation

The modern business landscape for logistics is extremely diverse and daunting as there are multiple business use cases, such as 10-minute deliveries, QSRs, cross-border trade, etc. Pitch in the daily parcel volume of billions and the entire scene becomes an enigma. Managing the order or parcel allocation such that no asset (vehicle, rider, etc) is wasted and all the delivery SLAs are met well within the promised limits is something that AI has made possible.

AI automates the allocation of orders to fulfillment agents, drivers, trucks, hubs, and warehouses so that no shipment gets delivered wrongly, no wrong shipment gets dispatched, RTOs can be minimized, asset utilization is optimal, and multiple orders can be allocated to a rider for different locations in a highly cost and time efficient manner.

AI processes and pairs the multiple variables in a logistical scenario, such as drivers, trucks, different vehicles, fuel types, delivery locations, etc. such that expenses stay bare minimum and businesses can run highly efficient, scalable, and sustainable operations.

4. Image Proofs

Another excellent use case of AI in logistics that is finding applications in multiple processes, industries, and business models is image reading and processing. 

Some of the most compelling and impressive use cases of AI-based image proofs in the logistics space are as follows:

  • Driver apps using odometer and speedometer images for driver movements
  • Package and order bag images to ensure quality in last mile deliveries
  • Barcode images and scans for multiple verifications and quality checks
  • AI-based scanning, sorting, and bagging to ensure quality in fresh produce deliveries
  • AI-based image processing for modern farm-to-plate business models

The image proofs are getting such a wide and rapid adoption across multiple industries and a plethora of business use cases, that it can easily be termed as one of the most disruptive catalysts for the transformation of logistics.

As the AI applications in logistics keep on evolving into more complex and granular transformation agents, the world is in for a highly enticing and exciting journey. From inventory management to inventory control, autonomous deliveries to predictive maintenance, alerts, and data analysis, and from highly controlled supply chain planning to arrest inefficiencies and redundancies during change management – AI in association with other disruptive technologies such as ML, Big Data, and cloud computing is certainly painting an awesome picture to marvel at!

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