Ideally, your material requirements planning will help you minimize understocked and overstocked products while maximizing the number of products that have just about the right amount of stock compared to their demand.
If we were to create a histogram of the the ideal forecasted demand vs. actual demand of the all the products, their distribution would look something like the following:
- Majority of products have just about the right amount of supply vs. demand
- Very low number of products are understocked or overstocked
But in reality, most forecasting methods lead to a large number of understocked and overstocked products, leading the distribution graph to look more like the following:
- Majority of the products don't have the right amount of supply
- Very low number of products have just about the right amount of supply
This means, to make up the low amount of stock, you have to bear the extra costs in shipping and production to meet the demand. On the other end, when you have excess stock you have to keep the extra inventory in your warehouses for unknown lengths of time which is very expensive and when the product line changes you are left with dead stock.
Terrene Can Help You Improve Your Inventory Levels Using Machine Learning
Terrene uses Machine Learning to learn from historical data and analyze what leads to overstocked and understocked products in your warehouses. Terrene learns automatically learns from the mistakes of traditional forecasting methods and itself and continues to iterate on itself until it achieves the ideal distribution.
Terrene will generate a live report for every single product on your supply chain with machine learning forecasts and will update them as more data becomes available.
Terrene will use your historical demand data and whether or not overstock or understock events happened to train a series of Deep Learning Neural Networks on the data. These models are automatically designed by Terrene from scratch for each customer with custom architectures to make best use of the available data.
Calculate Initial Demand Forecast Probability Distribution
The trained model will then construct probability distribution profile for each of your products to calculate their demand. You will then be able to use your own confidence intervals (i.e. 95% confidence) to generate a forecast for each product. The confidence interval can be modified to be more or less averse for each individual product.
Update Demand Forecast in Real-Time
Terrene will automatically update its forecasts to certain events such as changes in weather. For example, in event of a heavy snowstorm being forecasted, then Terrene will automatically change its demand forecast to alert you about an increase in demand for boots in accordance to how similar snowstorms in the past increased the demand for winter gear.
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What is Machine Learning?
Machine learning is a technique of getting computers to learn and act as humans do. Instead of specifically implementing algorithms to perform certain tasks, machine learning takes in a set of inputs and the desired output then learns how to produce the desired output without explicitly being given instructions.
Machine learning algorithms are used every day to in medical diagnosis, stock trading, energy load forecasting, and more. For example, media sites such as Netflix use it to give you movie recommendations and e-commerce websites such as Amazon use it to display products to you that you are more likely to purchase.
When Should You Use Machine Learning?
Machine learning works best when you have a complex task or problem involving a large amount of data and lots of variables, but no existing formula or equation. For example, machine learning is a good option if you need to calculate the demand for your products based on variables such as market preferences, weather, etc.
How Machine Learning Works?
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
In the case of demand forecasting, supervised machine learning would work the best because we would know the outcome (i.e. the historical demand of your products). A supervised learning algorithm takes a known set of input data and known outcomes to the data (output) and trains a model to generate reasonable predictions for the response to new data.
How Do You Decide Which Machine Learning Algorithm to Use?
There are many variables that go into selecting the best machine learning algorithm for the task on hand. Some of these variables are the size and type of data you're working with, the insights you want to get from the data, and how those insights will be used.
There is no best method or one size fits all and choosing the right algorithm can be overwhelming. There are dozens of supervised and unsupervised options to choose from, and each takes a different approach to learning.
This is where Terrene comes in. Terrene can quickly and automatically train multiple machine learning models without any data science knowledge to fit your data and the outcome you are looking for. Terrene will then automatically pick the model that performs the best after running some tests on them.
How Does Machine Learning Work With Terrene?
Terrene makes machine learning easy. Terrene offer tools for reading and combining data sets from nearly any source, automatically training machine learning models, and visualizations tools to display the results. Terrene allows your organizations to better forecast demand with minimal change to your current process.
Terrene lets you:
- Read and combine data from multiple sources
- Build optimal machine learning models with the click of a button
- Integrate machine learning models into your workflows and generate predictions in real-time