Log hub Forecasting AI Tutorial
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Introduction
In this tutorial, we will explore how U.S. manufacturers can optimize their production and supply chain management using Clock Hub's three innovative apps for demand forecasting. The primary aim is to enhance cost-effectiveness and product availability throughout the supply chain.
Understanding Forecasting AI
Clock Hub offers a suite of predictive analytics applications designed to make demand forecasting easier and more efficient. The first step is to access these apps through the Clock Hub platform, focusing on the "Truth Forecasting AI" feature, which generates templates that outline the necessary data structure.
Data Requirements
To successfully import your data, it is crucial that the column headers match exactly with the provided template. The required data includes several periods, typically days, and the corresponding historical demand values.
Linking Tables
Once you have prepared your data, the next step involves linking it to the model. Be sure to establish connections with the header to ensure accurate data mapping.
Setting Up Parameters
Following the data import, you can customize the parameters for your forecasting model. It allows you to aggregate historical time series data based on various intervals, such as daily, weekly, monthly, or yearly. The aggregation method also plays an essential role in how the data will be processed, using options like sample mean or median to handle aggregate calculations.
You can define the approach for managing any missing data—whether to treat these instances as zero or apply other methods. Furthermore, the system enables you to generate future factors based on desired criteria such as year, month, week, or day. This feature greatly assists during the learning process for the forecast period, which you can set (for example, to 35 days) before executing the calculation.
Exporting Results
After calculating the forecasts, results are exported into a table that clearly presents the total data alongside the predicted demands. You'll observe several key metrics, including historical trends and projections for the next four to five days.
Conclusion
By following these steps, manufacturers can attain a clearer understanding of the demanded product quantities and the necessary stock levels to maintain. This process not only leads to substantial cost savings through effective resource utilization but also helps avoid overcapacity issues.
Now, it's time for you to try using this tool yourself and witness the benefits of improved demand forecasting.
Keywords
- Clock Hub
- Forecasting AI
- Demand Forecasting
- Predictive Analytics
- Historical Demand
- Data Aggregation
- Missing Data Handling
- Future Factors
- Cost Savings
- Resource Utilization
FAQ
1. What is Clock Hub Forecasting AI?
Clock Hub Forecasting AI is a suite of predictive analytics applications designed to help manufacturers optimize their production decisions based on demand forecasting.
2. How do I prepare my data for the forecasting model?
You need to ensure that your data columns match the headers in the provided template. Required data includes historical demand values and corresponding periods, typically in days.
3. What parameters can I set up for data aggregation?
You can aggregate your historical time series data by day, week, month, or year. Additionally, you can choose how to handle missing data and generate future factors based on various criteria.
4. What benefits does the forecasting model provide?
The model helps manufacturers determine the appropriate quantities of products to produce daily and the necessary stock levels, leading to cost savings and better resource utilization.
5. How can I see the results of the forecast?
After executing the calculations, you can export the results into a table that displays both total data and predicted demands for the upcoming days.
