Supply Chain Network Optimization
Global Industrial Company: This company had a very extensive global supply network where suppliers dealt with divisions and where supplier performance levels and lead-times differed from division to division around the world. The company was looking for a way to understand and explore all of the different decision alternatives and make those available to the decision makers __ enabling them to make decisions that were more globally optimized versus restricted to a division.
It was possible to gain a global view of the supply network in terms of supplier performance. SMART AI was used on internal data records to consider scenarios to reduce costs globally. Network modeling was used to develop and understand more decision alternatives and enable decision-makers to optimize their supplier performance from a global perspective rather than a more narrow, local perspective.
Result: 20% reductions in inventory levels and logistics costs
End-to-End Inventory Optimization
Global Beverage Company: Responsible for sourcing and managing beverage ingredients globally, this company was challenged to optimize its inventory size and composition per location to reduce waste and storage costs. It asked for assistance in optimizing its stocking strategy for every SKU per storage location. Using analytics, we shifted its organizational focus on local storage optimization to a global, end-to-end storage optimization model by consolidating disparate datasets and automating the company's routine analysis.
Result: 25% reduction in inventory levels without affecting revenue
Supply Chain and Freight Logistics
Global Agrochemical Company (70 Countries): This company was operating with a low level of strategic planning, resulting in an inflexible logistics network, poor compliance with routing, and large disparate shipment data sources. It lacked a repeatable freight transportation process and logistics supply-demand matching, which was increasing costs and causing points of inventory starvation and bloating. The results were excessive premium freight costs, costly logistics inefficiencies, and inaccurate supply matching.
A2Go provided a redesign and optimization of the segmented logistics network, along with a Logistics Visual Workbench which supported a single data hub. In addition, a "self-directing" routing-optimization application was built along with a predictive and prescriptive early-warning application to prioritize alerts and notification.
Overall, freight costs were reduced by 38% and premium freight costs by 67%. The early warning system contributed to a reduction of $90M in operating costs. End-to-end logistical operational efficiency improved by 68%.