Pallet-level monitoring enables a more intelligent approach to distribution—Intelligent distribution and FEFO inventory management (First Expired, First Out)—as well as providing the data needed to optimize end-to-end processes for maximum shelf life. Implementing these approaches can cut losses in half for retailers and growers.
Technologies that underpin supply chain solutions such as AI/ML, analytics, IoT, SaaS, and ERP.
Perfecting the customer’s last mile delivery experience can be achieved by reducing logistics complexity and increasing efficiency, while controlling costs.
There’s so much buzz about Chatbots right now. Is this a dangerous road or a high-value tool? Do Chatbots have a place in supply chain applications? We explore the controversies, the technology, and potential applications and abuses of chatbots.
Supply Chain Networks are evolving to become increasingly autonomous, letting intelligent software agents make simple decisions.
Maintaining freshness of produce and other fresh products is challenging for grocers and their suppliers. Here we discuss why, and how some of these challenges can be solved.
We examine integration, interoperability, and extending supply chain networks. Then we compare enterprise applications vs. integrator networks vs. Real-time SVoT networks.
Predictions for rollout of driverless cars have shifted from “before 2020” to “after 2030”. Here we explore the many reasons we will likely see driverless long-haul trucking in a hub-to-hub model well before driverless cars are plying our city streets.
Customers’ expectations for delivery excellence continue to climb for faster, error-free delivery, more granular visibility, more convenience, and increased flexibility. Here we discuss specific strategies and capabilities that leading companies are using to try and perfect the customer’s delivery experience, such as via hyperlocal delivery, dynamic dispatch and routing, adaptable workflows, dwell time reduction, real-time visibility, expanding the range of delivery windows, locations, and options, sustainable delivery, last minute rescheduling, and more
AI/ML has created radically new and different capabilities in supply chain solutions. This requires a rethink of the process of achieving results and value from those solutions.
AI/ML has had a big impact on demand planning, improving forecast granularity and accuracy. It also has revolutionized inventory optimization, which has become ever more critical as companies increasingly push more inventory out to the edges of their distribution network and implemented hyper-local distribution strategies to meet ever more rapid delivery-time expectations.