Demand forecasting
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Demand Forecasting and Planning in Retail: Datrics use cases

Do you have access to historical data and wonder how it can be easily used to predict future results? Your business may benefit from demand forecasting models and so can you. Make various business decisions such as inventory and replenishment management, product pricing, planning the manufacturing processes and even more. Demand forecasting models can help you in making well-considered, accurate data-driven decisions, while taking complex criteria and patterns into account.

Add custom code bricks to your pipeline, set up the arguments, and run pipeline as usual

How to leverage from demand forecasting with is a web-based end-to-end data science platform that enables people with limited data science and machine learning background to work with data, extract meaningful insights and patterns and build advanced machine learning models without any need for code writing or installation. Moreover, you don't need to think about the model or infrastructure maintenance, we provide a fully managed environment to automate MLOps activities needed. If you do have extensive knowledge in data science and machine learning - you still can leverage our platform for this as well as to automate some routine tasks. Spend less time preprocessing and cleaning the data and more time working with the models.

We have created template solutions for particular issues to provide our users with more benefits. This means that you can take the "pipeline" which was created by our Data Science team, upload your data, do minor tweaks and have a working forecasting model. We decided to start with a demand forecasting use case to create our first template. The issue seems quite straightforward: you have the historical sales data and you would like to infer how the same product sales might behave in the future. In other words, knowing how the sales over the last year(s) you will be able to make predictions for the next week, month or quarter. This might have quite a few implications: you can manage your inventory better and avoid overstocking or running out of goods, you might optimize the prices if you know there are going to be major fluctuations in the amounts sold, or you just might be able to forecast your financial and sales KPIs and have data-driven assumptions for decision making on SKU or category level. Most of the applications fall under the planning activities umbrella category.

Which model is the best for demand forecasting?

There is a plethora of different models that can be used when trying to tackle demand forecasting issues. Some are more intuitively understandable, like using regression analysis in order to decide future behavior of data at any given point in time. Autoregressive moving average model (ARMA), and other generalizations and variations of it are widespread when doing time series analysis and forecasting, and demand forecasting in particular. Using those methods requires substantial knowledge of statistics and how the underlying models work. In a couple recent years, though, time series forecasting had been made drastically easier thanks to the Prophet algorithm. The algorithm works exceptionally well out of the box, handles missing values and outliers pretty well and requires virtually no knowledge of time-series analysis or algorithm fine-tuning. Thanks to Prophet, a person who has knowledge of his data and business domain can easily create powerful forecasts without actual hands on experience with time-series analysis on demand forecasting in particular.

That is why we recommend using Prophet as a first choice algorithm for demand forecasting. It is an algorithm developed by Facebook and aimed at forecasting behavior of time series. The key thing here is that you don't need to know under-the-hood dynamics, you can apply it in a few clicks, including advanced seasonality effects incorporation. It is robust to outliers and missing data and behaves quite well without extra fine-tuning required if you have enough historical data. For model evaluation, we will be looking mostly at Mean Absolute Percentage Error (MAPE), it basically tells you by how many percentages points your forecasts are off, on average. Do not try to chase the numbers, they are useful when comparing different models and give you a general feeling on how the model might perform. The actual thing you should focus on is benefits to your business because you are making informed, data-driven decisions, rather than trusting gut feeling and making decisions based on the assumptions.

Add custom code bricks to your pipeline, set up the arguments, and run pipeline as usual

Demand forecasting with no advanced skills, just with a tool

For certain products or industries, it can be very easy to attain a highly accurate assessment, while it might be very difficult for others. Thus, you should not chase the metrics numbers, and there are no rules of thumb when it comes to demand forecasting. Focus on the wider issue that you're trying to solve and our tool will help you to get the best results.

Who can test the Alpha version? Will the background of business analysts be enough?
You do not need to have any data science, ML or coding experience to use platform. Knowing your business domain and understanding of which data can influence your demand, you can easily import, preprocess the data and run the demand forecasting model with no coding.

Check out the demo recording of the alpha version with the ready pipelines for the demand forecasting. will prepare for you robust models and pipelines, which are resistant to outliers and able to catch trends, seasonality and different dependencies so that you can make an accurate decision when you have enough data. Users still can experiment with data processing, trying different models and analyzing metrics and outputs to choose the most suitable model.

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