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.
Technologies that underpin supply chain solutions such as AI/ML, analytics, IoT, SaaS, and ERP.
We explore the use of AI and machine learning in optimizing pricing and promotions to reduce markdowns, increase sales and profit margins, and maximize various other objectives.
Uses of AI and Machine Learning in pricing, promotions, demand planning and forecasting, and inventory management.
This is our assessment of One Network, who has one of the most sophisticated/elegant architectures, providing a flexible and scalable multi-enterprise platform. We look at their intelligent control towers for autonomous supply chain management providing multi-tier, multiparty optimization, and automated planning and execution from inbound supply to outbound order fulfillment and logistics.
A ‘self scorecard’ can help shippers improve their own turn-around times, detention metrics, dock door scheduling, load tender timing, and fulfillment of volume commitments. We also examine how trade data can be used for supply chain risk management, supplier discovery, price discovery, total landed cost optimization, and competitive intelligence.
Analytics can be used to improve carrier performance, enabling data-driven negotiations, improving delivery performance, reliability, responsiveness, and information sharing.
AI/ML requires a reimagining of the system development and adoption lifecycle. We discuss the move to a more agile approach, potential use of AI/ML for data cleansing, and the new skillsets and changing roles and responsibilities required.
The mandatory adoption of ELDs provides all kinds of data that can be used to improve driver performance in many ways. Analytics can also be used to improve route and service planning, optimizing the cost of service tradeoffs.
We examine the potential for AI/ML in enabling an ‘always on’ business model via continuous planning and execution. As well, we look at how AI/ML is a foundation for autonomous supply chains.
How shippers and carriers can leverage analytics to improve the performance of their private fleets using near-real-time shipment location data, combined with customer orders, routing plans, electronic proof-of-delivery data, service/work order schedules, vehicle inspection/maintenance data, and other sources of data.
A discussion of how AI/ML can help companies become more resilient and deal with change and uncertainty.