Revolutionize Warehouse Management with AI and Data Science
Practical Uses of AI in Warehouse Management
Table of Contents
- Introduction
- Understanding Artificial Intelligence and Machine Learning
- Types of Analytics
- Dynamic Slotting
- Workforce Planning and Performance Management
- Other Applications of Machine Learning in Warehouse Management
- Conclusion
- FAQ
Introduction
Warehouse management is a complex process that involves a lot of moving parts. From inventory management to order fulfillment, there are many factors that need to be considered to ensure that everything runs smoothly. With the rise of artificial intelligence (AI) and machine learning, there are new approaches to improving warehouse management that can make the process easier, faster, and more accessible to more distribution centers.
In this article, we will explore the practical uses of AI and machine learning in warehouse management. We will cover common examples of what AI and machine learning look like, as well as practical uses of machine learning on the applications within the warehouse and distribution center. We will also discuss how machine learning applications can make Warehouse Optimization much easier, faster, and more accessible to more DCs as time goes on.
Understanding Artificial Intelligence and Machine Learning
Artificial intelligence (AI) is a broad term that refers to the ability of machines to perform tasks that would normally require human intelligence. This includes capabilities like vision and speech, as well as complex optimization problems and decision-making functions.
Machine learning is a technique where the computer learns from data, which is very different than traditional programming where You program rules to come up with answers. Machine learning enables you to get to a state where you can make decisions rather than doing those back-of-the-napkin calculations.
Types of Analytics
There are different types of analytics that can be used in warehouse management. Descriptive analytics is looking at data that was captured as part of a process and is typically looking backwards. Diagnostic analytics is more important to understand what will happen and how can we make it happen. Predictive and prescriptive analytics are machine learning approaches that enable you to get to a state where you can make decisions rather than doing those back-of-the-napkin calculations.
Dynamic Slotting
Optimized slotting can have many benefits, including reducing picking and replenishment costs. A traditional approach here would be to Create a scaled model of the warehouse and program rules that weigh a ton of different factors together. This is something that would require extensive manual tuning and in the end, wouldn't yield the best results and wouldn't automatically adapt to changing conditions.
Machine learning can be applied to automatically calculate these rules. From an operational modeling perspective, machine learning will take a look at all of this historical data and actually build a model automatically. The rules themselves manifest out of the data, and the optimization happens automatically as part of the machine learning. It learns how to optimize the model Based on what it learned from the data.
Workforce Planning and Performance Management
Workforce planning and performance management are good examples of where the traditional approaches can be labor-intensive and lead to suboptimal results. For these applications, we leverage the rich and granular data that our system generates. This is time-series data about activities that take place on the floor during order picking and other tasks.
This data, along with machine learning techniques, allows us to very accurately predict when work delays will happen, which in turn allows us to provide recommendations about moving users around to ensure work is completed in each zone on time. We have another case here where taking a machine learning approach is a win-win. You get better answers without a labor-intensive process, and you get an approach that automatically adapts to changing conditions.
Other Applications of Machine Learning in Warehouse Management
Machine learning can be applied to the overall throughput in the objectives of a distribution manager and automate those decisions in regards to where a person should go and even considering what they should do next. Along with that, we can also look at waving and batching, which are traditional WMS functions that happen Upstream and typically in a monolith-Type approach where a big chunk of work is released onto the floor, and then people start working through that Wave.
Machine learning can be applied to streaming orders out onto the floor and optimizing the batches for all the objectives that you have. Going even a little bit further, you can imagine how you plan the people within the warehouse and even the aisles that they're working in that can be handled through machine learning to minimize traffic and congestion, which of course, impacts productivity.
Conclusion
Machine learning is a new approach that can be taken for warehouse optimization. We see many potential uses of machine learning within the distribution center. To get started with machine learning, you need the right data, and more importantly, you need to have the right machine learning algorithms applied to that data to give Meaningful and actionable insights to supervisors to make changes. With less effort maintaining the models, it becomes more valuable and pays for itself.
FAQ
Q: What is machine learning?
A: Machine learning is a technique where the computer learns from data, which is very different than traditional programming where you program rules to come up with answers.
Q: What are the benefits of machine learning in warehouse management?
A: Machine learning can make warehouse optimization much easier, faster, and more accessible to more DCs as time goes on.
Q: What are some practical uses of machine learning in warehouse management?
A: Some practical uses of machine learning in warehouse management include dynamic slotting, workforce planning and performance management, and waving and batching.
Q: How can machine learning be applied to workforce planning and performance management?
A: Machine learning can be used to very accurately predict when work delays will happen, which in turn allows us to provide recommendations about moving users around to ensure work is completed in each zone on time.
Q: What is the difference between descriptive analytics and predictive analytics?
A: Descriptive analytics is looking at data that was captured as part of a process and is typically looking backwards. Predictive analytics is a machine learning approach that enables you to get to a state where you can make decisions rather than doing those back-of-the-napkin calculations.