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. Alphabot robots, or “bots”, are self-driving vehicles that can gather items in ambient, chilled, and frozen temperature zones in a high-density storage system and bring them to associates that pick individual items to build a customer’s order. This technology was supposed to make the online order fulfillment process more efficient.
The retailer wanted to evaluate the feasibility of the Alphabot concept, and its suitability for Walmart, prior to making a large financial investment towards product development. Alert Innovation had already made some static spreadsheet calculations for the project, however, both Alert Innovation and Walmart agreed that the spreadsheets could not be relied upon due to the system complexity and variability in demand and execution. Before making any investments and deploying the system in Walmart stores, it was decided to task MOSIMTEC, a simulation consulting firm, with designing a material handling simulation model for an independent technology feasibility assessment. The goals of this initial modeling assessment were to:
- Understand the throughput and turnaround time capabilities of the Alphabot system during peak customer demand periods and assess service levels.
- Understand any potential performance weaknesses of the Alphabot system with realistic demand data and variability.
- Identify the best design of the material handling system, including racking structure, automated bots, and each-picking workstations required for a variety of store profiles.
- Compare the capital expenditure requirements predicted by the model vs. the business case spreadsheets.
- Use model throughput and turn-around-time results to compare the proposed performance of Alert’s Alphabot with other industry leading Automated Storage and Retrieval Systems (ASRS) and Goods-to-Person (GTP) technologies.
Material handling simulation would not only help understand the true cost of rolling out the system in the real world, but also identify store-specific requirements for deploying Alphabot across Walmart’s numerous stores in future.
To model Alphabot’s behavior and operations in a computer simulated environment, with real world complexity and variability, MOSIMTEC chose AnyLogic material handling design simulation capabilities for the project. MOSIMTEC’s and AnyLogic’s abilities to dynamically build facility layouts from data inputs, without accessing the development environment for each layout change, would help cut model development time significantly and enable faster evaluation of multiple Alphabot configurations. AnyLogic also offered unparalleled ease of deployment so that multiple Walmart engineers could run the material handling design model without the need to install additional software or purchase expensive developer’s licenses. AnyLogic was also selected because the AlphaBot system would require extensive artificial intelligence and control algorithms. AnyLogic’s ability to integrate with Java eliminated excessive time spent translating algorithm ideas back and forth between a propriety scripting language and a format programmers would be comfortable with.
Walmart’s initial goal was to make a go/no-go decision about the Alphabot project launch. In seven weeks, MOSITMEC was able to learn the system, design initial control algorithms for bot decision making, build the material handling simulation model, analyze results, and present its findings to Walmart leadership.
In the final delivered model, Walmart managers could specify different inputs, like number of bots, their length, width, acceleration, and speed in different areas. Physical racking configurations, like number of aisles, levels, space between levels, and number of workstation tiers, along with other physical components of this system, were all configurable via model input parameters. Control logic parameters, including selecting from various work assignment approaches or setting various thresholds, were also exposed and available for Walmart to run their own analysis.
Model input and output statistics were integrated in an Excel front-end so the users could easily configure and run the model. At this concept evaluation stage, MOSIMTEC incorporated basic 3D model animation that scaled based on the layout defined by the user within Excel. The output results in Excel included a summary report with key metrics, log files, scenario comparisons, charts, and graphs.
After completing the independent analysis of system capabilities, MOSIMTEC transitioned the AnyLogic material handling design simulation model to Alert Innovation for long-term use in fine-tuning software control algorithms for eventual production deployment. Based on the outcome of Stage 1, Walmart proceeded with investing in the Alphabot product development. Alert engineers used the Stage 1 model as the foundation for Stage 2 by increasing the level of detail and testing various control algorithms to simulate different system design alternatives. Model enhancements incorporated included:
- More bot-specific movement logic including bot paths, reservations, collision avoidance logic, and movement profiles, using AnyLogic agent-based modeling and Java capabilities.
- Logic for separate temperature zones for shelf-stable, refrigerated, and frozen items in the storage system.
- Inventory tracking functionality within the racks to track every item in the system.
- Bot sequencing logic to help simulate robots sorting items on the desk.
With the updated material handling system model, engineers were able to validate the original system design assumptions and provide feedback to Alphabot product development teams.
By estimating equipment requirements needed to meet various turn-around-time thresholds, the outputs from the initial material handling design model informed the business case for deploying Alphabot across various stores in Walmart’s retail network. The simulation model quantified system performance capability under unconstrained demand conditions to benchmark its limits. The model showed that Alphabot would be able to pick 95% of the orders in less than eight minutes, with an average pick time of under five minutes.
The initial model was later updated and expanded to understand the impact of various detailed design alternatives. The model helped Alert determine which design alternatives would result in the largest ROI, along with better sizing out the system for future stores.
Walmart and Alert Innovation started a pilot implementation of Alphabot at a Walmart supercenter in Salem, New Hampshire, along with a flagship store in Bentonville, Arkansas.
Watch the video of Amy Brown Greer, Dr. Christian Hammel and John Lert presenting this case study at The AnyLogic Conference, or download presentation.