A discussion of how AI/ML can help companies become more resilient and deal with change and uncertainty.
Supply Chain Networks
Technologies and strategies for networks for trading partners
The first installment of our ADSA solution assessments focuses on Alloy, a solution for CPG companies to improve their downstream visibility and build a supply chain driven by end-consumer demand.
With Transparent Cost-to-serve Customer Relationships (TCR), the salesperson has full visibility into the cost-to-serve implications of the customers’ requests, such as requests for short-supply items, special services, custom pack sizes, specific delivery dates, and frequent small quantity orders. Here we examine a potential path to achieving TCR.
This article describes a ‘rethinking of AI’, looking at data first, establishing a hierarchy of data stores, ‘best fit’ capabilities, and reducing nervousness in supply chain decision making.
We discuss an approach to evaluating potential Agile Demand-Supply Alignment solutions, including discovering, shortlisting, and selection processes.
A major automotive OEM buys steel for its entire supply base. In the process, it has been able to rationalize the materials specifications for all of that steel, resulting in consolidated spend, more efficient use of inventory, and more flexibility.
Profit is reduced when salespeople make deals that don’t take into account supply chain constraints and inventory imbalances. This article explores this phenomenon and a new approach to bringing supply chain awareness into the sales process.
We look at the use of AI for demand planning and forecasting, as well as inventory management and making MEIO (multi-echelon inventory optimization) work better.
This series explores the many supply chain-related use cases of AI, starting with pricing and promotion
A discussion of requirements for Agile Demand-Supply Alignment (ADSA) solutions for demand management (demand-side visibility, time-phased views, POS and channel data visibility, order pegging, demand sensing, retail planning capabilities), and analytic capabilities (e.g. supplier performance, carrier performance, lead-time analytics, total landed cost optimization.)
We explore the kinds of data being produced by transportation and logistics systems, how that data can be used to create a data-driven enterprise, and the substantial obstacles to achieving an analytic advantage.