<div align="center>
<p>
<a target="_blank">
<img width="100%" src="https://github.com/jakorostami/supplyseer/blob/feature/development-over-time/assets/supplyseerfront.png" alt="SupplySeer Vision banner"></a>
</p>
# <div align="center"> SupplySeer </div>
Welcome to version 0.2.0 (pre-release alpha)!
⚠️ **Pre-release Software Notice**: This library is currently in pre-release alpha (v0.2). The repo may undergo significant changes before the 1.0.0 release. While the statistical implementations are sound, we recommend testing thoroughly before using in production environments.
`supplyseer` is a Python library focused on providing the tools and methods for real-world Supply Chain & Logistics challenges. <br>
<br>
You'll find Bayesian Economic Order Quantity (dynamical stochastic EOQ), Probabilistic Bayesian Networks, Neural Networks, <br>
Principal Component Analaysis, time series models like ARIMA, and evaluation metrics for models and for information content. <br>
<br>
Supplyseer provides Permutation Complexity as a metric for time series analysis but also Manipulability Index and Hurst Exponent and many more.
<br>
<br>
## <div align="center"> Features </div>
🚀 Features
* Advanced Forecasting Models: ARIMA, Neural Networks, and Mixture Density Networks <br>
* Uncertainty Modeling: Bayesian Networks and Probabilistic Models <br>
* Inventory Optimization: Dynamic Bayesian EOQ and Traditional EOQ <br>
* Time Series Analysis: Complex metrics and tools for deep analysis <br>
* Supply Chain Optimization: Scheduling and routing solutions <br>
## <div align="center"> Models </div>
Below are some models listed
| Model | Use case |
| --- | --- |
| [ARIMA](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average) | Time Series |
| [Bayesian Network](https://en.wikipedia.org/wiki/Bayesian_network) | Uncertainty Modeling, Prediction, and Causal Inference |
| [Bayesian EOQ](https://en.wikipedia.org/wiki/Economic_order_quantity) | Economic Order Quantity with distributions instead of fixed values |
| [Neural Network](https://en.wikipedia.org/wiki/Neural_network_(machine_learning)) | Machine Learning Modeling |
| [Mixture Density Network](https://deep-and-shallow.com/2021/03/20/mixture-density-networks-probabilistic-regression-for-uncertainty-estimation/) | Probabilistic Machine Learning Modeling with multi-modal data |
| [Principal Component Analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) | Embeddings in Machine Learning (dimensionality reduction) |
| [Hawkes Process](https://en.m.wikipedia.org/wiki/Hawkes_process) | Multivariate Hawkes process in supply chains models how disruptions in one area can trigger related issues across the network, predicting ripple effects from initial events. |
| [Supply Chain Digital Twin Network](https://towardsdatascience.com/what-is-a-supply-chain-digital-twin-e7a8cd9aeb75?gi=120a86059486) | A Supply Chain Digital Twin of the real Supply Chain is a computer model that represents the processes and components of the real one |
<br>
## <div align="center"> Tools & Metrics </div>
| Name | Use case |
| --- | --- |
| [Time Upsampling](https://en.wikipedia.org/wiki/Upsampling) | Good when you have missing dates between samples |
| [Taken's Embeddings](https://en.wikipedia.org/wiki/Takens%27s_theorem) | Extract the dynamics of a time series/signal |
| Economic Order Quantity | This is the basic function of EOQ that returns a value while the Bayesian EOQ is a dynamic model |
| [Manipulability Index](https://iaeme.com/MasterAdmin/Journal_uploads/IJMET/VOLUME_6_ISSUE_6/IJMET_06_06_002.pdf) | Another way of measuring volatility and stability of a time series |
| [Hurst Exponent R/S](https://en.wikipedia.org/wiki/Hurst_exponent) | Measure long-term memory or autocorrelation in a time series |
| [Shannon Entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)) | Measures the unpredictability or randomnesss |
| [Permutation Entropy](https://materias.df.uba.ar/dnla2019c1/files/2019/03/permutation_entropy.pdf) | Quantifies the diversity of patterns in the ordinal structure of a time series. It is the first output of `permutation_complexity()`|
| [Statistical Complexity](https://arxiv.org/pdf/1009.1498) | Measures the structural complexity of a system. It combines entropy with disequilibrium (a measure of structure). It is the second output of `permutation_complexity()`|
<br>
## <div align="center"> Optimization </div>
This library also supports basic optimization with Google's `ortools`. See below example for a Truck Driver scheduling problem.
<p>
<a target="_blank">
<img width="100%" src="https://github.com/jakorostami/supplyseer/blob/feature/development-over-time/assets/truckdriver.png" alt="SupplySeer Vision banner"></a>
</p>
[Truck Driver Scheduling problem](https://github.com/supplyseer-ai/supplyseer/blob/develop/examples/truck-driver-routing.ipynb) - You have some truck drivers that you need to schedule for over a time window of 3 days with 3 shifts. Morning, afternoon, and evening. If they had the evening shift they cannot have the morning shift the day after because they need to rest. Also, they have to deliver at least 2 shifts during the 3 day window. <br>
<br>
Problem: Schedule truck drivers over a 3-day window with multiple constraints: <br>
* Three shifts per day (Morning, Afternoon, Evening)
* Rest period required between evening and next morning shift
* Minimum 2 shifts per driver over 3 days
<br>
[Demand & Inventory Control](https://github.com/supplyseer-ai/supplyseer/blob/develop/examples/demand-inventory-control.ipynb) - A Supply Chain department for a retail company needs to balance their inventory and demand such that there is also enough inventory to match the demand but the inventory is not allowed to go below a certain level nor above a certain level. <br>
<br>
Problem: Optimize inventory levels while,
* Meeting demand requirements
* Maintaining minimum safety stock
* Respecting maximum storage capacity
## <div align="center"> Contributing 🤝 </div>
We love contributions! Whether you're fixing bugs, adding features, or improving documentation, your help makes `supplyseer` better for everyone.
Check out our [Contributing Guide](CONTRIBUTING.md) to get started, and join our friendly community. No contribution is too small, and all contributors are valued!
Want to help but not sure how? See our [Issues](https://github.com/supplyseer-ai/supplyseer/issues) or start a [Discussion](https://github.com/supplyseer-ai/supplyseer/discussions). We're happy to guide you! 🎲✨
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While the statistical implementations are sound, we recommend testing thoroughly before using in production environments.\n\n`supplyseer` is a Python library focused on providing the tools and methods for real-world Supply Chain & Logistics challenges. <br>\n<br>\nYou'll find Bayesian Economic Order Quantity (dynamical stochastic EOQ), Probabilistic Bayesian Networks, Neural Networks, <br>\nPrincipal Component Analaysis, time series models like ARIMA, and evaluation metrics for models and for information content. <br>\n<br>\nSupplyseer provides Permutation Complexity as a metric for time series analysis but also Manipulability Index and Hurst Exponent and many more.\n<br>\n<br>\n\n## <div align=\"center\"> Features </div>\n\ud83d\ude80 Features\n\n* Advanced Forecasting Models: ARIMA, Neural Networks, and Mixture Density Networks <br>\n* Uncertainty Modeling: Bayesian Networks and Probabilistic Models <br>\n* Inventory Optimization: Dynamic Bayesian EOQ and Traditional EOQ <br>\n* Time Series Analysis: Complex metrics and tools for deep analysis <br>\n* Supply Chain Optimization: Scheduling and routing solutions <br>\n\n## <div align=\"center\"> Models </div>\nBelow are some models listed\n\n| Model | Use case |\n| --- | --- |\n| [ARIMA](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average) | Time Series |\n| [Bayesian Network](https://en.wikipedia.org/wiki/Bayesian_network) | Uncertainty Modeling, Prediction, and Causal Inference |\n| [Bayesian EOQ](https://en.wikipedia.org/wiki/Economic_order_quantity) | Economic Order Quantity with distributions instead of fixed values |\n| [Neural Network](https://en.wikipedia.org/wiki/Neural_network_(machine_learning)) | Machine Learning Modeling | \n| [Mixture Density Network](https://deep-and-shallow.com/2021/03/20/mixture-density-networks-probabilistic-regression-for-uncertainty-estimation/) | Probabilistic Machine Learning Modeling with multi-modal data |\n| [Principal Component Analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) | Embeddings in Machine Learning (dimensionality reduction) |\n| [Hawkes Process](https://en.m.wikipedia.org/wiki/Hawkes_process) | Multivariate Hawkes process in supply chains models how disruptions in one area can trigger related issues across the network, predicting ripple effects from initial events. |\n| [Supply Chain Digital Twin Network](https://towardsdatascience.com/what-is-a-supply-chain-digital-twin-e7a8cd9aeb75?gi=120a86059486) | A Supply Chain Digital Twin of the real Supply Chain is a computer model that represents the processes and components of the real one |\n<br>\n\n## <div align=\"center\"> Tools & Metrics </div>\n\n| Name | Use case | \n| --- | --- |\n| [Time Upsampling](https://en.wikipedia.org/wiki/Upsampling) | Good when you have missing dates between samples |\n| [Taken's Embeddings](https://en.wikipedia.org/wiki/Takens%27s_theorem) | Extract the dynamics of a time series/signal |\n| Economic Order Quantity | This is the basic function of EOQ that returns a value while the Bayesian EOQ is a dynamic model |\n| [Manipulability Index](https://iaeme.com/MasterAdmin/Journal_uploads/IJMET/VOLUME_6_ISSUE_6/IJMET_06_06_002.pdf) | Another way of measuring volatility and stability of a time series |\n| [Hurst Exponent R/S](https://en.wikipedia.org/wiki/Hurst_exponent) | Measure long-term memory or autocorrelation in a time series |\n| [Shannon Entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)) | Measures the unpredictability or randomnesss |\n| [Permutation Entropy](https://materias.df.uba.ar/dnla2019c1/files/2019/03/permutation_entropy.pdf) | Quantifies the diversity of patterns in the ordinal structure of a time series. 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