Lecture 2

Generative AI models, such as Generative Adversarial Networks (GANs) and autoregressive models, work by learning the statistical patterns present in a dataset. GANs consist of a generator and a discriminator that compete against each other to create authentic-looking content. Autoregressive models generate content step by step, conditioning each step on the previous ones. These models have found applications in creating realistic images, generating text, and even composing music, illustrating their potential to identify potential trends and produce innovative outputs.
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Predictive AI forecasts future events by analyzing historical data trends to assign probability weights to the models. Generative AI creates new data, which might be in the form of text and images. "Think of the first [predictive AI] as a powerful analyst doing magic with numbers, while the second [generative AI] is a creative kind -- a writer, an artist or an assistant in research," said Inna Kuznetsova, CEO at ToolsGroup, a supply chain planning and optimization firm. Read more on this link

Enhanced Supply Chain Optimization:
Generative AI can be utilized to optimize supply chain operations by analyzing vast amounts of data, identifying patterns, and generating optimal solutions. This includes everything from determining the most efficient transportation routes to dynamically adjusting inventory levels in response to shifting demand.
Improved Demand Forecasting:
By leveraging the power of Generative AI, logistics providers can more accurately predict future demand, enabling them to make informed decisions about resource allocation, inventory management, and transportation planning. This helps minimize waste, reduce excess stock, and ensure that products are available when and where they are needed.Streamlined Warehouse Management:Generative AI can be employed to design and manage warehouse operations more effectively, optimizing space utilization, labor allocation, and material handling processes. By automating these tasks, logistics companies can significantly reduce their operational costs and improve overall efficiency.Reduced Environmental Impact:As a Green Logistics provider, Waredock is particularly interested in how Generative AI can help minimize the environmental impact of logistics operations. By optimizing routes, reducing waste, and promoting more efficient resource utilization, Generative AI can play a crucial role in promoting a greener, more sustainable future.
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Generative algorithms do the complete opposite — instead of predicting a label given to some features, they try to predict features given a certain label. Discriminative algorithms care about the relations between x and y; generative models care about how you get x.

A generative algorithm aims for a holistic process modeling without discarding any information. You may wonder, “Why do we need discriminative algorithms at all?” The fact is that often a more specific discriminative algorithm solves the problem better than a more general generative one.

But still, there is a wide class of problems where generative modeling allows you to get impressive results. For example, such breakthrough technologies as GANs and transformer-based algorithms.
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The k-nearest neighbors algorithm, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. While it can be used for either regression or classification problems, it is typically used as a classification algorithm, working off the assumption that similar points can be found near one another.
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Garvis lets you leverage the richness of AI to better understand your customers and improve your forecast accuracy while staying in control and
gives you full transparency.

The implementation of traditional solutions in the market today is complex, requires heavy investments, and takes over four months to do. Moreover, these solutions rely heavily on external support to be implemented resulting in inflexible and expensive processes.
Garvis’ unique features use the insights of you, the planner, in combination with AI to give you full control on the onboarding, resulting in a same-day process that allows you to reduce your forecast error up to 30% within 24 hours. And as you are in control, everything is in your hands to address changes.
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