Live broadcast: https://www.youtube.com/watch?v=iw9uS8yLax8
Machine learning is not only an interesting technology to use today, but it’s also appreciated by management that will hear that the organisation is using “machine learning” to solve time series challenges, such as demand planning with supply chain management. However, this can result in time spent on complex modelling that in general can be accomplished quicker with much simpler models that are easier to deploy and sustain long-term.
Therefore, in this talk we'll show how simple can not only give better results while reducing the complexity in terms of data pre-processing, model development and final deployment. We will look at an example within supply chain management and demand planning for a product and discuss different scenarios based on multiple types of historical demand data.
The presentation will show the actual code, but a big focus will be on the strategic decision-making of selection of models and how to deploy these models.
Description
We break down the talk into four components:
1. “The problem” - The first 5 minutes are about understanding the problem before diving into the code (2 min context of the time series challenges within demand planning in large organisations today + 3 min on time series forecasting and machine learning vs classical statistical models including the importance of good benchmark models)
2. “The setup” - The next 5 minutes are about getting set up correctly on how to analyse this before we test our models (2 min walkthrough of the structure & plan for the code (python & jupyter notebooks + 3 min in terms of data pre-processing and success metrics (comparison to benchmark)
3. “The Models” - Then the next 10 minutes are to select and test models (3 min model selection and explanation + 2 min running models + 5 min explaining & discussing results)
4. “The Deployment” - The final 5 minutes are about deployment and what the pros and cons are with these, depending on the organisation.