Case studies

Walmart, the world’s largest retailer by revenue, was looking for an automation technology that would help complete orders faster and at a lower cost in the company’s fast-growing online grocery business. They wanted to evaluate Alert Innovation’s Goods-to-Person (GTP) concept, Alphabot (an AGV or robot-based system), which could automate the grocery pickup process by using autonomous mobile carts capable of operating in all three dimensions within a multilevel storage structure.

The global e-commerce sector has seen 320% growth in five years, and demand is increasing following the COVID-19 pandemic. To reduce costs and remain competitive, DHL Supply Chain ran a warehouse operations optimization project. The project involved developing a smart and robust warehouse simulation tool for testing wave picking strategies.

Cardinal Health, a billion-dollar pharmaceutical distribution and logistics firm, manages multiple products from brand name pharmaceuticals and generic drugs to over the counter drugs, health & beauty items and their own private label. They face a multitude of typical distribution warehouse challenges that are further complicated by the nature of pharmaceutical products. Brian Heath, Director of Advanced Analytics at Cardinal Health, and an experienced user of AnyLogic software, employed agent based modeling to solve various business problems, saving Cardinal Health over $3 Million annually.

Kuehne+Nagel, a leading global provider of logistics solutions, was involved in planning a new warehouse for one of their clients. The warehouse would process 13K order lines or 750 picking cartons per day. The project included the development of the best algorithm for multi-order picking. It was planned that the orders in the warehouse would be served by workers with trolleys (or fangos). Workers with trolleys would pick the goods and put them in cartons by order.

How DHL optimized warehouse order picking and improved efficiency. Analysis using simulation-based modeling increased picking speed and reducing order completion times. The tool integrates with warehouse management systems.

A warehouse in Thailand was encountering difficulties because of a number of complicated processes involved in their logistics and storage systems. In order to overcome these and achieve more efficient warehouse operations, EPIC InnoLabs was contracted to develop a forecasting simulation model. This model would be used to predict system behavior based on a number of inputs as well as retrieve the expected values of selected KPIs.

In this warehouse management system project, a footwear retailer in Canada upgraded an automated warehouse system to keep up with rising demand. Working with SimWell, they used AnyLogic to test new technologies within a warehouse. The goal was to streamline operations and maintain their high standards in the retail industry.

Migros Online, a Swiss online supermarket, teamed up with Decision Lab to optimize their warehouse order picking operations and improve overall warehouse efficiency through simulation-based digital twin implementation. Discover how this project not only reduced Migros Online shipping costs by around 5% but also opened doors for future advancements.

Micro-fulfillment centers are small, sometimes highly automated facilities that can fulfill online orders for delivery as well as click-and-collect in-store pickups. They use a combination of automated picking technology and manual picking. Dematic wanted to create a workforce optimization model that they would be able to present to retailers. It would help them understand the staffing required to operate their micro-fulfillment centers and how different scenarios could impact their ability to fulfill orders.

Symbotic is a warehouse automation solutions provider. Its systems are based on mobile robots that can travel freely throughout a dense storage structure, accessing products in all locations and handling them at a very high throughput rate. The company needed a tool to help their customers learn the impact of warehouse reorganization and compare capital investments against expected operational savings before the actual introduction of automation systems.