Machine Learning for Demand Forecasting
In this video, we will discuss how machine learning can be used to improve the accuracy of demand forecasting. We will start by discussing the challenges of traditional forecasting methods. We will then discuss how machine learning can be used to overcome these challenges. Finally, we will discuss how o9 can help you use machine learning to improve your demand forecasting.
We will show you how machine learning can help you:
- Improve forecast accuracy
- Identify complex relationships between drivers
- Incorporate a wider range of data
- Respond to unexpected events
We will also show you how o9 can help you:
- Build and deploy machine learning models
- Manage your data
- Collaborate with other users
We believe that machine learning is the future of demand forecasting. If you are looking to improve the accuracy of your forecasts, then o9 is the solution for you.
Machine learning is a branch of artificial intelligence that enables computers to collect data and make predictions. Machine learning is used in a variety of applications, such as credit card fraud detection, recommender systems, and even virtual assistants such as Siri and Alexa. Machine Learning is going to play a key role in next generation supply chain planning systems.
Predicting the future demand is one of the more challenging aspects of planning. Classical time series methods use historical data to predict the future demand, but these overlook a lot of critical information such as weather, financial information, promotions, and so on. All this intelligence is added back by planners during the demand planning process. Now, this is where machine learning can help us Machine learning forecasts are good at picking up the drivers and the complex interrelationships between these drivers and generate a forecast which is much more accurate and has less bias. Think about a promotion in category a in the summer or a promotion in category b in the winter.
Think about cannibalization between different promotions and other competitor activity.
Also think about SKUs that are very difficult to forecast because they have very little historic data. This is where classical time series based methods are not very accurate. Machine learning based forecast can help us.
Well, data, the more data, the better. Can start with historical transaction data, demand data, store and product attributes, which will give us a good forecast. However, you can add more costly drivers to this data to generate an even better forecast some examples of these causal drivers are trade promotions, events, weather, financial information, customer sentiment, and so on. We at o9 can help you identify these data elements and integrate with your databases and data lakes to bring these data elements into o9. Once we have built a machine learning model in the online platform, you'll be able to see the importance of each of these drivers in your forecast.
With Olin being an open platform for data science, we support a wide range of model We support classical time series models such as exponential smoothing and Arima. We support machine learning based models such as random forest and gradient boosting, even deep learning models such as LSTMs and multi layer plus up trunks.
Yes. One end natively supports calls to RN Python, the two major analytics platforms.
We have a plug and play architecture where any existing code in our Python can be plugged into the online system, and the results can be seen right away. We also have a very experienced team of data scientists. Who can work with the business to collect data, cleanse and analyze it, and then generate a completely new and custom machine learning based forecast model.
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