Warehouse managers must ensure that shipments from the warehouse, whether individual parcel shipments or bulk packages, are sent cost-effectively to the delivery location on time and in full. Workflows within the warehouse start with processing orders, which involves picking the right products, packaging them appropriately, creating shipping labels, and sending the parcel or package to the right truck for delivery.

By augmenting workflows with artificial intelligence (AI), machine learning (ML), and digital twins, distribution operations can become more agile and responsive to changing market conditions and better adapt to customers' needs. These technologies can help distributors make more informed decisions, reduce costs, and improve operational efficiency, improving customer satisfaction and profitability.

AI Predicts Delivery Issues

Customers want to receive their shipments on time and in good condition and know their shipment status from the dock to the door. This involves communicating with customers about shipment status, addressing issues, and proactively seeking feedback to improve the overall customer experience.

Artificial Intelligence (AI) can improve parcel shipping by using the data collected from past deliveries to predict delivery times and identify potential issues, such as delays due to traffic or other disruptions. Combining AI with real-time tracking sensors helps shippers update customers about their packages' location and estimated delivery time.

ML Analyzes Shipping Patterns

Machine Learning (ML) algorithms can analyze delivery patterns and identify suspicious activity, helping shippers reduce losses and improve security. For example, ML algorithms can analyze data from past deliveries, such as package weight, delivery location, and delivery time, to identify patterns consistent with standard deliveries. Then, when a new delivery comes in, the algorithm can compare the data for that delivery to the established patterns to determine whether it is consistent with regular deliveries or an outlier.

ML algorithms can be trained to identify different types of suspicious activity, such as package theft, false claims of lost or damaged packages, or attempted delivery to an incorrect address. By analyzing data from past deliveries, the algorithm can learn to recognize patterns consistent with these types of suspicious activity and flag them for further investigation.

ML algorithms can also be used to forecast demand for shipping services, helping companies to optimize their logistics processes and manage inventory. ML will predict changes in demand so that companies can adjust their shipping capacity and resources accordingly to improve efficiency and reduce costs. The more data collected, and analysis performed with ML, the more your parcel distribution operations will learn – and improve.

Digital Twins Understand the Future State of Operations

Digital twins are virtual replicas of physical objects, systems, or processes created using real-time data from sensors or other sources. To improve distribution operations, it is important to use the data from the digital twins to understand the future state of your distribution operations.

In warehousing, a digital twin is a mathematical model of a warehouse that analyzes all future-facing activities to predict what is likely to happen in the future. An excellent digital twin will account for labor, shipments, inventory availability, tasking, and space/resources. Digital twin technology can improve warehouse activity orchestration by forwarding inbound and outbound inventory over time. The digital twin will be able to relate that a particular order going out in 18 hours is missing specific inventory to complete the shipment.

Digital twins can simulate and optimize distribution operations, helping identify inefficiencies and improve resource utilization. Companies can make informed decisions about improving operations by testing different scenarios and configurations in a virtual environment.

When the digital twin is paired with advanced mathematics known as constraint-based optimization, the technology can prescribe a sequence of events, such as filling the order mentioned above with the right amount of inventory at the right time.

Artificial intelligence (AI), machine learning (ML), and digital twins can augment workflows to improve parcel distribution operations so that orders arrive on time, in full, and at the correct location in the most cost-effective and efficient way.

Keith Moore is CEO of AutoScheduler.AI, a warehouse resource planning and optimization platform that dynamically orchestrates all activities on top of your existing WMS in real-time. He focuses on bringing the future of technology into warehousing. He works with the top 10 Consumer Goods, Beverage, and Distribution companies to drive efficiency in distribution centers.