Powered by big data-based predictive algorithms, anticipatory logistics enables logistics providers to significantly boost process efficiency and service quality, shortening delivery times by predicting demand before a request or order is even placed. In addition, new predictive maintenance and supply chain risk concepts will further optimize logistics operations.
Key developments and implications
Anticipatory logistics continues to be strongly driven by increasing customer demand for shorter lead times from order to delivery. First experiments are being made by retailers to anticipate demand. In an industrial context, predictive maintenance will continue to become a key area of focus thanks to the Internet of Things which is enabling new applications through intelligent machines and vehicles capable of predicting a logistics or maintenance need.
Anticipatory shipping can be used by online retailers who have analyzed their customers’ purchasing behaviors to predict an order before it occurs. This can then be used to move goods to distribution centers that are closer to a customer who is likely to purchase the products. It can enable retailers to offer same-day or even one-hour deliveries. In future, prediction-based shipping will run alongside the traditional order-based delivery – the challenge will be to integrate both methods in the network.
Predictive maintenance using the data gathered from real-time monitoring of smart assets (such as machines and vehicles) can be analyzed to predict maintenance needs; this will reduce downtime both for logistics providers and their customers. These intelligent assets can also anticipate spare parts logistics. For example, the general wear of certain heavy machinery components would be predicted ahead of time so that replacements can be delivered at the right time and to the right place.
Predictive supply chain risk management
Predictive supply chain risk management supports the logistics provider in detecting risks in trade lanes and potential damages to cargo (e.g., monitoring shock movements) to take corrective action and minimize operational delays.
Smart capacity planning
Smart capacity planning using anticipatory algorithms can be used to match the right level of logistics resources to meet demand (e.g., accurately predicting the required logistics capacity for peak shopping seasons).
- Increases customer satisfaction through improved order and delivery experience (e.g., reduces lead times)
- Lean inventory management through accurate prediction of demand
- Efficient resource and capacity utilization based on improved precision in planning.