This is a guest post by Foxconn. The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post.
In their own words, “Established in Taiwan in 1974, Hon Hai Technology Group (Foxconn) is the world’s largest electronics manufacturer. Foxconn is also the leading technological solution provider and it continuously leverages its expertise in software and hardware to integrate its unique manufacturing systems with emerging technologies.”
At Foxconn, we manufacture some of the most widely used electronics worldwide. Our effectiveness comes from our ability to plan our production and staffing levels weeks in advance, while maintaining the ability to respond to short-term changes. For years, Foxconn has relied on predictable demand in order to properly plan and allocate resources within our factories. However, as the COVID-19 pandemic began, the demand for our products became more volatile. This increased uncertainty impacted our ability to forecast demand and estimate our future staffing needs.
This highlighted a crucial need for us to develop an improved forecasting solution that could be implemented right away. With Amazon Forecast and AWS, our team was able to build a custom forecasting application in only two months. With limited data science experience internally, we collaborated with the Machine Learning Solutions Lab at AWS to identify a solution using Forecast. The service makes AI-powered forecasting algorithms available to non-expert practitioners. Now we have a state-of-the-art solution that has improved demand forecasting accuracy by 8%, saving an estimated $553,000 annually. In this post, I show you how easy it was to use AWS services to build an application that fit our needs.
Forecasting challenges at Foxconn
Our factory in Mexico assembles and ships electronics equipment to all regions in North and South America. Each product has their own seasonal variations and requires different levels of complexity and skill to build. Having individual forecasts for each product is important to understand the mix of skills we need in our workforce. Forecasting short-term demand allows us to staff for daily and weekly production requirements. Long-term forecasts are used to inform hiring decisions aimed at meeting demand in the upcoming months.
If demand forecasts are inaccurate, it can impact our business in several ways, but the most critical impact for us is staffing our factories. Underestimating demand can result in understaffing and require overtime to meet production targets. Overestimating can lead to overstaffing, which is very costly because workers are underutilized. Both over and underestimating present different costs, and balancing these costs is crucial
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/how-foxconn-built-an-end-to-end-forecasting-solution-in-two-months-with-amazon-forecast/