An international company with an FMCG distribution network in Turkey faced challenges related to high transportation costs, optimizing their supply chain, and meeting the demands of a growing market. They partnered with Phoenix Analytics to build a digital twin using anyLogistix, which would enable data-driven decisions. The project resulted in a 10% reduction in transportation costs and improved supply chain demand forecasting.
COVID-19 negatively impacted the Public Distribution System in India by restricting both human movement and the transportation of goods. The National Institute of Industrial Engineering, now the Indian Institute of Management Mumbai, was committed to overcoming these challenges and so developed a simulation that would help manage supply chain disruptions, mitigating the effects of this pandemic as well as any future ones.
A Canadian agricultural supplier faced significant logistical challenges due to the high running costs of the logistics network. The company wanted to assess its supply chain to improve its efficiency and effectiveness. By using anyLogistix's supply chain modeling and design capabilities, the company was advised to make changes in the logistics network that would deliver substantial improvements in both operational efficiency and environmental sustainability.
As the automotive aftermarket supply chain grows more complex and competitive, leading original equipment manufacturers are adopting smart sourcing strategies and advanced optimization tools. In this case study, discover how one of Europe’s top manufacturers partnered with MEVB Consulting to build a digital twin using anyLogistix, transforming its spare parts network, reducing lead times by 60%, and cutting logistics costs by 20%.
This case study explores how Decision Lab leveraged anyLogistix to cut costs and drive strategic supply chain redesign for a major UK wastewater utility. By modeling transport flows and testing facility scenarios, the team quickly identified logistics cost reduction opportunities and optimized the end-to-end waste-to-energy network without writing a single line of code.
SimWell addressed the cutting stock problem, typically solved at the plant level, by integrating it into a network-wide supply chain optimization model. Using anyLogistix and Python-based data pipelines, they developed a digital twin that incorporated cutting stock optimization into sourcing, production, and distribution decisions. As a result, the project delivered more than $20 million in annual profit improvements and reduced waste by 5%.
