Blog

Implementation of Complex High-Fidelity Terminal Simulation Models


Implementation of Complex High-Fidelity Terminal Simulation Models

The design and development of a new tank terminal, an extension to an existing tank terminal, or the optimization of a current facility is a large undertaking. The typical project has to deal with numerous design challenges and design trade-offs. Simulation is the only effective way to understand how the various terminal components (tanks, manifolds, transfer lines, pipelines, etc.) interact and how they are affected by controllable and non-controllable events. At the AnyLogic Conference 2014, Allan Chegus, CEO, and Dumitru from Stream Systems, Ltd. demonstrated how simulation models can be used to increase throughput, improve operating efficiency, and reduce cost by avoiding unnecessary capital expenditures.

Disruptive Technology Change in Distribution Center Automation


Disruptive Technology Change in Distribution Center Automation

There has been a dramatic increase in investment by both venture capital and strategic investors in new robotics technologies for supply chain automation. These investments have been driven by rapid changes in expectations for consistent, fast, flexible consumer experience across all channels. Traditionally, automation and associate order fulfillment software has been optimized around a limited set of products (or SKU’s) and for the requirements and constraints of a specific channel between manufacturer and consumer. This may result in different, and potentially incompatible technical solutions being implemented at the same distribution center. The new expectation is that the retailer provide a consistent, and hopefully superior, consumer experience, regardless of which channel is most convenient for the consumer to use.

AnyLogic Logistics Network Manager: Live Demonstration


AnyLogic Logistics Network Manager: Live Demonstration

Recently, Timofey Popkov, Director of Business Development at The AnyLogic Company held a “Simulation Modeling for Logistics Optimization” workshop in Berlin, Germany, covering a range of topics from general benefits of simulation modeling and the various business uses, to how simulation modeling can improve a logistics network and a live demonstration of how it works. Beginning with an explanation of what simulation and modeling software solutions mean for various business challenges (streamline business processes, analyze consumer behavior, prevent bottle necks, optimize supply chains, etc.), the workshop videos also display a comprehensive look into simulation modeling specifically for the logistics network.

Utilizing Real-World Data and Multimethod Modeling to Battle Food Insecurity


Utilizing Real-World Data and Multimethod Modeling to Battle Food Insecurity

A Major Consulting Firm Serving an International Civil Agency chose AnyLogic to evaluate solutions to developing countries potential food insecurity issues. Food security is a complex sustainable development issue, linked to health through malnutrition, but also to sustainable economic development, environment, and trade. Issues such as whether households get enough food, how it is distributed within the household and whether that food fulfills the nutrition needs of all members of the household is an ongoing problem in developing countries.

Supply Chain Model Using Agent Based Simulation- Live Demonstration


Supply Chain Model Using Agent Based Simulation- Live Demonstration

Managing a supply chain requires complex analysis and strategic planning. Modeling and simulation with AnyLogic software allows you to stress your system and optimize strategies – all in a virtual environment. An example supply chain model was demonstrated live by Andrei Borchshev, CEO of The AnyLogic Company and is also available for you to run on www.runthemodel.com. This model of a supply network, comes with an Adaptive Supply Chain tool. It is US-wide and consists of part suppliers, producers, distributors and retailers whose properties can be viewed and edited with just one click. For example, the stationary (s,S) inventory policy applies throughout the whole supply chain, but you can change both the “order point” and the “order up to” level of any individual object.