Note: Today’s post is part of our “Editor’s Choice” series where we highlight recent posts published by our sponsors that provide supply chain insights and advice. Today’s article is from Lucas Systems and highlights the benefits of machine learning in the warehouse.
Real-world uses of AI in business have exploded in the past decade, but few of those applications are focused on warehousing and distribution. That is changing as companies like Lucas introduce machine learning tools to improve planning and decision-making in the DC. These new tools will free time for managers and engineers, making them more productive and their DCs more efficient and effective. This article provides an introduction to machine learning for warehouse managers.
New AI/machine learning applications will provide DC managers and industrial engineers with insights to:
- Dramatically improve workforce planning and management
- Proactively re-slot products to improve efficiency
- Predict and eliminate stock outs and other exceptions
- Optimize automation/robotics alongside human workers
- Rapidly identify and implement other process improvements
Engineered Standards vs. Machine Learning
As a DC planning tool, machine learning represents an alternative to traditional engineering and process modeling.
For example, the traditional approach to workforce planning is to use an engineered labor standards system. ELS-based systems are programmed to calculate expected work completion times for a given task or group of tasks. They use pre-defined models of the process, a limited number of variables, and pre-determined or recorded average time values. ELS usually requires a significant upfront investment of time and money in engineering and measurement (and maintenance
In contrast, a machine learning solution uses algorithms to analyze warehouse data and develop a predictive model for workforce planning. The data can come from a number of sources, including warehouse optimization systems, mobile devices, and automation systems (or WCS).
One drawback of an engineered approach is that the formulas and measures need to be updated when the operating environment changes. Likewise, the models don’t account for all of the indirect variables that may affect results – for example, how congestion due to volume increases may impact efficiency. Finally, the more complex the engineered model, the longer it takes to process the data and provide an output. Machine learning addresses each of these issues.
To read the full article, click HERE.