AI at Work in the Supply Chain & Logistics with Wize Analytics
Supply chain and logistics organizations as well as companies with a supply chain or procurement department can use Wize Analytics — as a data science, machine learning, and AI platform — for more granular and accurate management. The bottom line is a more efficient supply chain that delivers major cost savings and increased profits.
AI Applications and Use Cases in Supply Chain & Logistics
Route Planning & Optimization
A few factors contribute to the high costs of last-mile delivery. One primary reason is that last-mile logistics is a very complex process. Machine learning -based route planning and optimization with Wize Analytics allows organizations to consider many variables such as traffic congestion, route restrictions, and customer preferences.
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Demand Forecasting
Machine learning-based demand forecasting with Wize Analytics allows organizations to integrate external data (weather, seasonal stores, holidays, etc.) and assemble models that have different time horizons and granularity levels for better accuracy.
Fleet Management (Predictive Maintenance)
Organizations can use Wize Analytics to implement robust, machine learning-based predictive maintenance to predict component failure and address any issues before they impact other transportation devices in the fleet. Instead of solely taking historical data and performing static analysis, Wize Analytics allows for up-to-the-minute analysis to predict future asset performance and ensure real-time feedback is acted upon for proactive maintenance and intervention.
Procurement
The sourcing and production processes involve a lot of physical goods, and Wize Analytics can help with smarter procurement processes, such as the ability to analyze disparate data sources to better analyze costs and suppliers to avoid supply chain disruption, product fraud, etc. Additionally, natural language processing (NLP) in Wize Analytics can help with contract analysis and invoice matching to help teams get a global view on spending in order to optimize negotiations and the bottom line.
Safer Shipping
The supply route that might offer the fastest possible delivery may also include a higher risk of disruption due to political instability, crime, or labor unrest — AI systems powered by Wize Analytics provide companies a clearer vision of the risks involved with supply chain decisions, allowing them to minimize the chance of disruptions.
Dynamic Pricing
A key challenge in last-mile delivery is pricing. It’s essential to price orders correctly so that you’re making a profit while still providing excellent value to the customer. Brokers are being phased out in favor of automatic machine learning algorithms that assist businesses in choosing the finest possible carrier for operational efficiency and reduced delivery costs.
Chatbots for Improved Customer Experience
Chatbots are commonly used to provide customer support or sell products and services. Recently, there has been a growing interest in using chatbots for last-mile delivery.
Chatbots can be used to provide customers with information about their orders. For example, a chatbot could give the customer a tracking number for their order. This would allow the customer to track the progress of their order online.
Chatbots can also be used to answer questions from customers about their orders. For example, a chatbot could help the customer resolve a problem with their order. This would help improve customer satisfaction with the company’s delivery service.
Automated Visual Inspection Upon Truck Loading
As the last step before a truck leaves for its final destination, a visual inspection is often carried out to check that the goods have been loaded correctly. This process can be time-consuming and error-prone, especially if there are a lot of items to inspect.
Delivery Scheduling
Another vital factor in last-mile delivery is scheduling logistics operations. Scheduling affects everything from how many delivery drivers are needed to how many vehicles are required. It’s essential to schedule deliveries to use resources efficiently, and customers get their orders on time.
Machine learning-based solutions with Wize Analytics can be applied to optimize delivery schedules.