10 Crucial Use Cases for Big Data Now that you are aware of the advantages of investing in analytics, let’s look at some real-world examples based on various business scenarios that demonstrate the growing significance of data in logistics.Logistics n
The last mile of a supply chain is notoriously inefficient and can add up to 28% of the cost of a package’s complete delivery. There are numerous barriers in the way of this, including:
- Large delivery trucks may find it difficult to find a parking space close to their destination in cities. Drivers frequently have to park far from the destination and then walk the package there. They could then need to climb numerous flights of stairs or wait for a lift in a tall building.
- When a customer is not at home, some things cannot be delivered since they need to be signed for.
- Delivery staff must take special precautions to avoid damaging the cargo during this final foot, and they must show themselves professionally to the recipient.
In addition to these difficulties, it might be difficult to tell exactly what is going on during the final leg of delivery. Packages are frequently traced up to this point, prompting some to refer to the last mile as the “black box” of delivery data.
Big data attempts at solving several of these issues. In an interview with the Wall Street Journal, Matthias Winkenbach, head of MIT’s Megacity Logistics Lab, discusses how last-mile analytics are generating useful data. Because of the affordable and widespread availability of fast mobile internet and GPS-enabled smartphones, as well as the proliferation of the Internet of Things via sensors and scanners, shippers can track the delivery process from start to finish – even during the last mile.
Consider this: a UPS delivery vehicle equipped with a GPS sensor makes a delivery in downtown Chicago. After parking nearby, the delivery man’s phone GPS continues to send data to the UPS center, providing an accurate estimate of how long the delivery will take. This is beneficial not only to the customer, but it also allows logistics companies to identify patterns that can be used to optimize their delivery tactics. According to Dr. Winkenbach’s data, “deliveries in big cities are almost always improved by creating multiple levels of infrastructure with smaller distribution centers spread out in several communities, or simply pre-designated parking spots in garages or lots where smaller vehicles can take packages the rest of the way.”
The transparency of reliability has improved.
As sensors grow more common in transportation vehicles, shipping, and throughout the supply chain, they will be able to offer data that will enable greater transparency than has previously been possible.
Shippers, carriers, and customers like this transparency. If a shipment will be late, carriers want to know as soon as possible so that bottlenecks can be avoided further down the supply chain. Furthermore, carrier businesses can utilise aggregate data to bargain with shippers by demonstrating how frequently they deliver on schedule.
Consider the following scenario: logistics businesses have installed sensors in all of their delivery vans, with GPS-enabled smartphones filling in the gaps. A third party evaluates the accuracy of these sensors, and the dependability and timeliness data from these sensors is used when logistics are being planned.
Visual inspection and Damage Detection
Visual inspection and Damage Detection
Unhappy customers and churn are two consequences of damaged items. Businesses can employ computer vision technology to detect defects and maintain quality control in warehouse operations. Logistics managers can assess the extent and nature of damage and take appropriate action to stop further harm.
preventing future problems
Predictive maintenance uses real-time data gathered from IoT sensors in machines to foretell possible machine faults in the production. Tools for analytics powered by machine learning improve predictive analytics and find trends in sensor data so that technicians can intervene before a failure happens.
Companies can stay competitive by using time-based pricing based on consumer demand and rival strategies. As a result, supply chain case studies using insights from AI analyse the effect and propose dynamic pricing based on customer psychology, perceived value, and other aspects.
Analytics for text
The tracking of data from internal (shipment, supply, partner) and external (news, SM platforms, compliance) sources can be aided by web scraping, social media (SM) listening, and translation. Therefore, users of supply chains and AI use Natural Language Processing (NLP) methods.
Businesses require accurate customer data as automation, virtual support, and facial recognition technology improve the customer experience. Consequently, it is becoming essential to integrate AI in the supply chain to boost consumer engagement.
In order to better manage their supplies, cut down on delays, and provide better customer service, business owners can use artificial intelligence to gather real-time data points and increase supply chain visibility.