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README.md
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@@ -4,6 +4,12 @@ This repository contains a conceptual outline (in the form of this README.md and
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It is *not* a fully implemented software package, but a detailed blueprint, incorporating feedback and addressing real-world concerns.
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This model focuses on shared predictive forecasting using advanced machine learning (specifically Transformer-based models), enforced by contracts, and incorporating *dynamic flexibility* to address practical business needs.
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**Key Idea:** All participants in a supply chain (manufacturers, wholesalers, retailers, and potentially key suppliers) contribute data to a centralized platform. This platform utilizes Transformer-based machine learning models to generate highly accurate demand forecasts. Participants contractually agree to use these forecasts as the primary basis for ordering and production decisions, but within a framework of *structured flexibility* and *shared risk/reward*. This promotes efficiency, reduces waste, optimizes inventory, and mitigates the impact of human unpredictability.
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The blackbox "inputs" and "outputs" of the model are listed in the tables in the accompanying Inputs-Outputs.md
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It is *not* a fully implemented software package, but a detailed blueprint, incorporating feedback and addressing real-world concerns.
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This model focuses on shared predictive forecasting using advanced machine learning (specifically Transformer-based models), enforced by contracts, and incorporating *dynamic flexibility* to address practical business needs.
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The necessity providing the inspiration for this invention was revealed to the inventor Martial Terran during the first webinar in the March 13, 2025 ODSC Webinar titled "Time Series Mastery: Hands-on Workshops"
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Subsequently, sent me an email stating "Hi Martial, Thank you for joining us at the Time Series Mastery: Hands-on Workshops event! ...
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We appreciate you taking the time to participate in the event. Your engagement and questions were fantastic!"
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"Access the Recordings & Continue Your AI Learning! You can access the recordings of the Time Series Mastery event here."" https://ccslg04.na1.hubspotlinks.com/Ctc/49+113/ccSLg04/VWLnM53QrwNGW8zSb533fcJkjW4CCSrm5t7HykN6YWdsT3m2ndW7lCdLW6lZ3mYW7vdJ5S1nHmKcW1Hn_7n2J9W-mW2k38q65Mq408W6tYJKM4ft8gSN3ZQd9Zkk7T5W7pBlk84mzMt7W4gtLXv5TlVyRW44Cjq85TjWgqW6HBcBL2b-HKKW30HdJk8Dm18DN5M2fjrQ7Rh_N2lqZTtTLRnvN4pB-gSMWVZqW4rG3Bq393Q8sW7fhF4t4WP34ZW2ykcKY4SSSh0W9g4KfD3gM3WPW5WqztL8ZknsyW5fzfWt1DM8J8W1yl7_x7yWYnkW3P6tjf8NhPBrN3jc6cldsvD0W2mdYJN31brXcW7tK3mB1mhvBGf27868804
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"(Available for free with an Ai+ Premium subscription!) Get $50 off Yearly Premium Subscription use coupon: time_series_50"
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**Key Idea:** All participants in a supply chain (manufacturers, wholesalers, retailers, and potentially key suppliers) contribute data to a centralized platform. This platform utilizes Transformer-based machine learning models to generate highly accurate demand forecasts. Participants contractually agree to use these forecasts as the primary basis for ordering and production decisions, but within a framework of *structured flexibility* and *shared risk/reward*. This promotes efficiency, reduces waste, optimizes inventory, and mitigates the impact of human unpredictability.
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The blackbox "inputs" and "outputs" of the model are listed in the tables in the accompanying Inputs-Outputs.md
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