About this Presentation
One of the world’s largest mineral resources companies plans to establish a long-term mining business and develop an outbound logistic network to deliver the finished product to customers all over the world. During the identification phase study, the company needed to evaluate the most optimum logistic network configurations, compare and choose stock management policies that will provide highest availability at lower cost and determine required quantity of rail cars and storage volumes at sea ports and distribution hubs to achieved desired world class service levels. An AnyLogic model was developed by Goldratt Research Labs to support the study. The model covers production of 2 grades of finished products at the mine, loading the product to trains, transportation to hubs and ports, loading to sea vessels at ports and land distribution across several countries via Hub to customers by trucks. The simulation was able to demonstrate that using PULL stock management policy with dynamic target stock adjustment algorithm instead of traditional PUSH policy allowed Amalgama to reach 12% higher service level and provide world class performance with 16% less storage capacity in ports and hubs. In the presentation, the team leads from GLR and Amalgama will talk about the challenges we’ve overcome during the project, demonstrate the model and discuss the business value of the results achieved with the help of simulation.
What Will You Learn
To help you get the most value from this session, we’ve highlighted a few key points. These takeaways capture the main ideas and practical insights from the presentation, making it easier for you to review, reflect, and apply what you’ve learned.
The simulation model was designed to answer four types of questions related to the optimum design of the supply chain, including the number of ports and hubs, storage capacities, number of rail cars and trucks, and the most optimum stock management rules.
The model was developed in Anylogic and was designed to be generic enough to be used for other supply chains within the same company in the future.
The model could run in three modes: single experiment, scenario comparisons, and sensitivity analysis, allowing for a comprehensive analysis of various factors and scenarios.
Instructor(s)
Dr Alan Barnard
Dr. Alan Barnard is a leading decision scientist and Theory of Constraints (TOC) expert with almost 20 years of experience working directly with Dr. Eli Goldratt, the creator of TOC. As the CEO of Goldratt Research Labs (USA), Alan focuses on using advanced technologies and decision sciences to develop Apps and Decision Support methods, enabling organizations to achieve more with less time. Under his leadership, Goldratt Research Labs has collaborated on innovation and research projects with renowned Fortune 500 companies like Microsoft, Cargill, BHP, Tata Steel, Amazon, and others, along with public sector organizations like Utah Governor's Office and UN WFP. Alan has held prominent roles in various organizations, including past-President of the South African Supply Chain Society SAPICS and past-President of TOCICO. His significant contributions have been recognized through prestigious awards, including inclusion in the 2020 Marquis Who's Who Global Listing, membership in the Forbes Technology Council, and the TOCICO Lifetime Achievement Award. He is an accomplished author and frequent presenter on Decision Making in a Complex World and Theory of Constraints at international conferences. Alan's latest book, "The Bottleneck - How to resolve our attention crisis," is currently in progress. As the chief architect of the award-winning HARMONY Decision Maker, Change Simulator, and Change Maker series of Decision Support Apps, Alan continues to shape the landscape of decision science and TOC. Please let me know if you need any further information or details.