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Generative AI and Quantum Computing for Supply chain 

 

 

 

COURSE OBJECTIVE


Advanced course in generative artificial intelligence (AI), classical artificial intelligence and quantum computing applied to supply chain management, warehouse and inventory management, logistics, demand and customer management.

 

AI can be used in supply chain management to manage huge volumes of data, understand demand-supply relationships, optimize earnings before interest, taxes, depreciation and amortization, EBITDA, to improve decision making in an organization as an integrated end-to-end supply chain. AI-based tools can provide valuable information for inventory, logistics, warehouse efficiency, on-time delivery, and supply and demand forecasting. AI solution-based agnostic assessment and strategies help companies improve inventory alignment and control, as well as create an intelligent strategic roadmap for supply chain and logistics.

 

Generative AI has become a valuable tool in various industries and has led to the success of numerous projects.   


One of the main advantages of generative AI is its ability to classify and categorize information from visual or textual data. This feature allows large data sets to be organized and managed efficiently, saving time and resources that would otherwise be spent on manual analysis.  


In addition, generative AI can quickly analyze and modify strategies, plans and resource allocations based on real-time data. This real-time analysis enables fast and effective decision making and adaptation to market or industry changes.  


Another advantage of generative AI is its ability to generate content in various forms automatically. This feature enables faster response times and reduces the time and resources required for content creation. The content generated can be text, images, videos or audio, depending on the needs of the project.

 

About ML, a module on advanced data processing is presented, explaining among other topics: sampling, exploratory analysis, outlier detection, advanced segmentation techniques, feature engineering and classification algorithms.

 

During the course, ML and Deep Learning predictive models such as: decision trees, neural networks, Bayesian networks, Support Vector Machine, ensemble model, etc. are shown. As for neural networks, feed forward, recurrent RNN, convolutional CNN and adversarial generative architectures are presented. In addition, probabilistic machine learning models such as Gaussian processes and Bayesian neural networks are included.

 

Computer vision is a form of artificial intelligence (AI) and machine learning that allows computers to extract meaningful information from images and automate actions based on that information, quickly and on a large scale.

 

Computer vision has the ability to recognize patterns and make diagnoses in medical images with much greater accuracy and speed and fewer errors. It has the potential to extract information from medical images that are not visible to the human eye. Therefore, computer vision models for image classification using powerful ML models are presented in the course.

 

During the course real cases are addressed, among others, the early detection of obesity using classical ML models and Quantum Machine Learning (QLM), the identification and categorization of diabetic retinopathy using convolutional neural networks, drug discovery using generative neural networks and adversarial GAN. 

 

Supply chain and logistics managers must constantly balance many, sometimes conflicting, variables to achieve business goals (e.g., abundant inventories are good for fulfilling orders and satisfying customers, but the cost can hurt the bottom line). To be efficient, it is necessary to optimize supply chain and outbound logistics parameters and balance them with changing customer demand. Everything must run like a fine-tuned machine. To minimize overstocking costs, you need to accurately forecast demand, ensure the right supply levels, and move supply in and product out in a streamlined and agile way.

 

Constrained optimization can provide valuable insights from complex data that logistics decision makers use every day. Classical computers face difficult problems with this method of generating estimates and approximations. But as data volumes increase geometrically, they hit a wall.

 

QUANTUM COMPUTING

 

Quantum Machine Learning is the integration of quantum algorithms within Machine Learning programs. Machine learning algorithms are used to compute large amounts of data, quantum machine learning uses qubits and quantum operations or specialized quantum systems to improve the speed of computation and data storage performed by a program's algorithms. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning. A quantum neural network has computational capabilities to decrease the number of steps, qubits used and computation time.

 

The important goal of the course is to show the use of quantum computation and tensor networks to improve the computation of machine learning algorithms.

 

In addition, the course explains quantum computation, quantum circuits, important quantum algorithms, quantum mechanics, quantum error and correction, and quantum machine learning.

 

But quantum computers can offer more than just very precise solutions. They can also offer a diversity of solutions, any one of which meets your optimization goals. You can get more solutions that are more accurate, using all the data you have worked so hard to collect and store. Classical computers, on the other hand, have difficulty providing accurate and quality responses to optimization requests. If they do not fail completely, they often give only a single likely answer, which may or may not be accurate.

 

Although quantum computers are still years away from full capability, emerging software solutions aim to bridge the worlds of classical and quantum computing by using quantum-ready techniques that produce better results for constrained optimization using larger data sets on classical computers and, eventually, for quantum systems. Understanding and applying quantum computing techniques today can help supply chain and logistics-dependent companies (e.g., e-commerce, manufacturing, transportation, distribution, etc.) stay ahead of the competition.

 

IMPORTANT

 

The great need to correctly apply traditional and quantum artificial intelligence in supply chains has forced us to include a very advanced validation module and powerful model risk techniques as well as probabilistic machine learning methodologies in order to know the uncertainty in the results. We have also included a module called XAI to prevent models from being black boxes and being interpretable.

 

WHO SHOULD ATTEND?

 

The course is aimed at Supply Chain Management professionals interested in developing powerful models of generative artificial intelligence and quantum computing applied to the Supply Chain.

 

For a better understanding of the topics it is necessary that the participant has knowledge of statistics and mathematics.

 

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Schedules:

  • Europe: Mon-Fri, CEST 16-20 h

 

  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h

 

 

 

 

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Price: 7.900 €

 

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Level: Advanced

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Duration: 39 h

 

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     Material: 

  • Presentations PDF

  • Exercises in Excel, R, Python, Jupyterlab y Tensorflow

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AGENDA
 Generative AI and Quantum Computing for

Supply Chain

 

Anchor 10

Machine Learning

Module 1: Machine Learning in Supply Chain

Machine learning in supply chain involves using algorithms and statistical models to analyze and interpret data, optimize processes, and make predictions. It can be applied in various ways, such as demand forecasting, inventory management, route optimization, and anomaly detection. By leveraging machine learning, organizations aim to enhance efficiency, reduce costs, and make more informed decisions throughout the supply chain.

 

  • Definition of Machine Learning

  • Machine Learning Methodology

    • Data Storage

    • Abstraction

    • Generalization

    • Assessment

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • deep learning

  • Typology of Machine Learning algorithms

  • Steps to Implement an Algorithm

    • information collection

    • Exploratory Analysis

    • Model Training

    • Model Evaluation

    • Model improvements

    • Machine Learning in consumer credit risk

  • Machine Learning in credit scoring models

  • Quantum Machine Learning

Module 2: EDA Exploratory Analysis

  • Data typology

  • transactional data

  • Unstructured data embedded in text documents

  • Social Media Data

  • data sources

  • Data review

  • Target definition

  • Time horizon of the target variable

  • Sampling

    • Random Sampling

    • Stratified Sampling

    • Rebalanced Sampling

  • Exploratory Analysis:

    • histograms

    • Q Q Plot

    • Moment analysis

    • boxplot

  • Treatment of Missing values

    • Multivariate Imputation Model

  • Advanced Outlier detection and treatment techniques

    • Univariate technique: winsorized and trimming

    • Multivariate Technique: Mahalanobis Distance

  • ​Exercise 1: EDA Exploratory Analysis

Module 3: Feature Engineering

  • Data Standardization

  • Variable categorization

    • Equal Interval Binning

    • Equal Frequency Binning

    • Chi-Square Test

  • binary coding

  • Binning

    • Kind of transformation

    • Univariate Analysis with Target variable

    • Variable Selection

    • Treatment of Continuous Variables

    • Treatment of Categorical Variables

    • Gini

    • Information Value

    • Optimization of continuous variables

    • Optimization of categorical variables

  • Exercise 2: Detection and treatment of Advanced Outliers

  • Exercise 3: Stratified and Random Sampling in R

  • Exercise 4: Multivariate imputation model

  • Exercise 5: Univariate analysis in percentiles in R

  • Exercise 6: Continuous variable optimal univariate analysis in Excel

  • Exercise 7: Estimation of the KS, Gini and IV of each variable in Excel

  • Exercise 8: Feature Engineering of variables

Unsupervised Learning

Module 4: Unsupervised models

  • Hierarchical Clusters

  • K Means

  • standard algorithm

  • Euclidean distance

  • Principal Component Analysis (PCA)

  • Advanced PCA Visualization

  • Eigenvectors and Eigenvalues

  • Exercise 9: Segmentation of the data with K-Means R

 

Supervised Learning

Module 5: Logistic Regression and LASSO Regression

 

  • Econometric Models

    • Logit regression

    • probit regression

    • Piecewise Regression

    • survival models

  • Machine Learning Models

    • Lasso Regression

    • Ridge Regression

  • Model Risk in Logistic Regression

  • Exercise 10: Lasso Logistic Regression in R

  • Exercise 11: Ridge Regression in R

Module 6: Trees, KNN and Naive Bayes

 

  • Decision Trees

    • modeling

    • Advantages and disadvantages

    • Recursion and Partitioning Processes

    • Recursive partitioning tree

    • Pruning Decision tree

    • Conditional inference tree

    • tree display

    • Measurement of decision tree prediction

    • CHAID model

    • Model C5.0

  • K-Nearest Neighbors KNN

    • modeling

    • Advantages and disadvantages

    • Euclidean distance

    • Distance Manhattan

    • K value selection

  • Probabilistic Model: Naive Bayes

    • Naive bayes

    • Bayes' theorem

    • Laplace estimator

    • Classification with Naive Bayes

    • Advantages and disadvantages

  • Exercise 12: KNN and PCA

Module 7: Support Vector Machine SVM

  • Support Vector Classification

  • Support Vector Regression

  • optimal hyperplane

  • Support Vectors

  • add costs

  • Advantages and disadvantages

  • SVM visualization

  • Tuning SVM

  • kernel trick

  • Exercise 14: Support Vector Machine in R

Module 8: Ensemble Learning

  • Classification and regression ensemble models

  • bagging

  • bagging trees

  • Random Forest

  • Boosting

  • adaboost

  • Gradient Boosting Trees

  • xgboost

  • Advantages and disadvantages

  • Exercise 15:  Boosting in R

  • Exercise 16: Bagging in R

  • Exercise 17: Random Forest, R and Python

  • Exercise 18:  Gradient Boosting Trees

Deep Learning

Module 9: Introduction to Deep Learning

  • Definition and concept of deep learning

  • Why now the use of deep learning?

  • Neural network architectures

  • feedforward network

  • R deep learning

  • Python deep learning

  • Convolutional Neural Networks

  • Use of deep learning in image classification

  • cost function

  • Gradient descending optimization

  • Use of deep learning

    • How many hidden layers?

    • How many neurons, 100, 1000?

    • How many times and size of the batch size?

    • What is the best activation function?

  • Hardware, CPU, GPU and cloud environments

  • Advantages and disadvantages of deep learning

 

Module 10: Deep Learning Feed Forward Neural Networks

  • Single Layer Perceptron

  • Multiple Layer Perceptron

  • Neural network architectures

  • activation function

    • sigmoidal

    • Rectified linear unit (Relu)

    • The U

    • Selu

    • hyperbolic hypertangent

    • Softmax

    • other

  • Back propagation

    • Directional derivatives

    • gradients

    • Jacobians

    • Chain rule

    • Optimization and local and global minima

  • Exercise 19: Deep Learning Feed Forward

Module 11: Deep Learning Convolutional Neural Networks CNN

  • CNN for pictures

  • Design and architectures

  • convolution operation

  • descending gradient

  • filters

  • strider

  • padding

  • Subsampling

  • pooling

  • fully connected

  • Exercise 20: deep learning CNN

Module 12: Deep Learning Recurrent Neural Networks RNN

  • Natural Language Processing

  • Natural Language Processing (NLP) text classification

  • Long Term Short Term Memory (LSTM)

  • Hopfield

  • Bidirectional associative memory

  • descending gradient

  • Global optimization methods

  • One-way and two-way models

  • Deep Bidirectional Transformers for Language Understanding ​

  • Exercise 21: Deep Learning LSTM

Module 14: Generative Adversarial Networks (GANs)

 

  • Generative Adversarial Networks (GANs)

  • Fundamental components of the GANs

  • GAN architectures

  • Bidirectional GAN

  • Training generative models

  • Exercise 22: Deep Learning GANs

Module 15: Tuning Hyperparameters

  • Hyperparameterization

  • Grid search

  • Random search

  • Bayesian Optimization

  • Train test split ratio

  • Learning rate in optimization algorithms (e.g. gradient descent)

  • Selection of optimization algorithm (e.g., gradient descent, stochastic gradient descent, or Adam optimizer)

  • Activation function selection in a (nn) layer neural network (e.g. Sigmoid, ReLU, Tanh)

  • Selection of loss, cost and custom function

  • Number of hidden layers in an NN

  • Number of activation units in each layer

  • The drop-out rate in nn (dropout probability)

  • Number of iterations (epochs) in training a nn

  • Number of clusters in a clustering task

  • Kernel or filter size in convolutional layers

  • Pooling size

  • Batch size

  • Exercise 23: Optimization Xboosting, Random forest and SVM

  • Exercise 24: Optimized Deep Learning

Probabilistic Machine Learning 

 

Module 16: Probabilistic Machine Learning

​​

  • Introduction to probabilistic machine learning

  • Gaussian models

  • Bayesian Statistics

  • Bayesian logistic regression

  • Kernel family

  • Gaussian processes

    • Gaussian processes for regression

  • Hidden Markov Model

  • Markov chain Monte Carlo (MCMC)

    • Metropolis Hastings algorithm

  • Machine Learning Probabilistic Model

  • Bayesian Boosting

  • Bayesian Neural Networks

  • Exercise 25: Gaussian process for regression

  • Exercise 26: Bayesian Neural Networks

Model Validation

Module 17: Validation of traditional and Machine Learning models

  • Model validation

  • Validation of machine learning models

  • Regulatory validation of machine learning models in Europe

  • Out of Sample and Out of time validation

  • Checking p-values in regressions

  • R squared, MSE, MAD

  • Waste diagnosis

  • Goodness of Fit Test

  • multicollinearity

  • Binary case confusion matrix

  • K-Fold Cross Validation

  • Diagnostico del modelo

  • Exercise 27: Validación avanzada de la regression

  • Exercise 28: Diagnostico de la regresión

  • Exercise 29: K-Fold Cross Validation in R

Module 18: Validación Avanzada de modelos de AI

 

  • Integración de métodos de última generación en aprendizaje automático interpretable y diagnóstico de modelos.

  • Data Pipeline

  • Feature Selection

  • Black-box Models

  • Post-hoc Explainability

  •  Global Explainability

  • Local Explainability

  • Interpretabilidad de Modelos

  • Diagnóstico: Accuracy, WeakSpotOverfitReliability, Robustness, Resilience, Fairness

  • Comparativo de modelos

    • Comparativo para la Regresión y Clasificación

    • Fairness Comparison

  • Ejercicio 30: Validación y diagnóstico de modelos avanzados de credit scoring

Auto Machine Learning and XAI

Module 19: Automation of ML

 

  • What is modeling automation?

  • that is automated

  • Automation of machine learning processes

  • Optimizers and Evaluators

  • Modeling Automation Workflow Components

  • Hyperparameter optimization

  • Global evaluation of modeling automation

  • Implementation of modeling automation in banking

  • Technological requirements

  • available tools

  • Benefits and possible ROI estimation

  • Main Issues

  • Genetic algorithms

  • Exercise 31: Automation of the modeling, optimization and validation of pricing models

Explainable Artificial Intelligence

Module 20: Explainable Artificial Intelligence XAI

 

  • interpretability problem

  • Machine learning models

    • 1. The challenge of interpreting the results,

    • 2. The challenge of ensuring that management functions adequately understand the models, and

    • 3. The challenge of justifying the results to supervisors

  • ​Black Box Models vs. Transparent and Interpretable Algorithms

  • interpretability tools

  • Shap, Shapley Additive explanations

    • Global Explanations

    • Dependency Plot

    • Decision Plot

    • Local Explanations Waterfall Plot

  • Lime, agnostic explanations of the local interpretable model

  • Explainer Dashboard

  • Other advanced tools

  • Exercise 32: XAI interpretability of pricing

 

Module 21: Generative AI

Generative artificial intelligence  is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and generate new data that has similar characteristics. Generative AI differs from other types of AI as it is about creating something new that is not modified or copied from its training data. Generative AI is a general-purpose technology used for multiple purposes across many industries. There are many types of multimodal generative AI tasks such as text summarization that produce a shorter version of a piece of text while retaining the main ideas, creating source code from natural language code comments, reasoning through a problem to discover potential new solutions or latent details and assigning a category to a given piece of content such as a document, image, video, or audio clip among other applications.

  • Introducing generative AI

  • What is Generative AI?

  • Generative AI Models

    • Generative Pre- trained Transformer (GPT)

    • Llama 2

    • PaLM2

    • DALL-E

  • Text generation

  • Image generation

  • Music generation

  • Video generation

  • Generating text

  • Generating Code

  • Ability to solve logic problems

  • Generating Music

  • Enterprise Use Cases for Generative AI

  • Overview of Large Language Models (LLMs)

    • Transformer Architecture

    • Types of LLMs

    • Open-Source vs. Commercial LLMs

    • Key Concepts of LLMs

  • Prompts

  • Tokens

  • Embeddings

  • Model configuration

  • Prompt Engineering

  • Model adaptation

  • Emergent Behavior

  • Specifying multiple Dataframes to ChatGPT

  • Debugging ChatGPT’s code
    Human errors

  •  Exercise 33: Embeddings for words, sentences, question answers

  • Exercise 34: Embedding Visualization

  • Exercise 35: First let's prepare the data for visualization

  • Exercise 36: PCA (Principal Component Analysis)

  • Exercise 37: Embeddings on Large Dataset

  • Exercise 38: Prompt engineering

  • Exercise 39: Advanced Prompting Techniques

  • Exercise 40: Large Language Models (LLMs)

  • Exercise 41: Retrieval Augmented Generation

  • Exercise 42: Traditional KMeans to LLM powered KMeans

  • Exercise 43: Cluster Visualization

  • Exercise 44: Semantic Search

  • Exercise 45: Tokens and Words

  • Exercise 46: Tokenization in Programming Languages

Quantum Computing

Module 22: Quantum computing and algorithms

  ​

Objective: Quantum computing applies quantum mechanical phenomena. On a small scale, physical matter exhibits properties of both particles and waves, and quantum computing takes advantage of this behavior using specialized hardware. The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basic" states, meaning that it is in both states simultaneously.

  • Future of quantum computing in insurance

  • Is it necessary to know quantum mechanics?

  • QIS Hardware and Apps

  • quantum operations

  • Qubit representation

  • Measurement

  • Overlap

  • matrix multiplication

  • Qubit operations

  • Multiple Quantum Circuits

  • Entanglement

  • Deutsch Algorithm

  • Quantum Fourier transform and search algorithms

  • Hybrid quantum-classical algorithms

  • Quantum annealing, simulation and optimization of algorithms

  • Quantum machine learning algorithms

  • Exercise 47: Quantum operations multi-exercises

Module 23: Introduction to quantum mechanics

  • Quantum mechanical theory

  • wave function

  • Schrodinger's equation

  • statistical interpretation

  • Probability

  • Standardization

  • Impulse

  • The uncertainty principle

  • Mathematical Tools of Quantum Mechanics

  • Hilbert space and wave functions

  • The linear vector space

  • Hilbert's space

  • Dimension and bases of a Vector Space

  • Integrable square functions: wave functions

  • Dirac notation

  • operators

  • General definitions

  • hermitian adjunct

  • projection operators

  • commutator algebra

  • Uncertainty relationship between two operators

  • Operator Functions

  • Inverse and Unitary Operators

  • Eigenvalues and Eigenvectors of an operator

  • Infinitesimal and finite unit transformations

  • Matrices and Wave Mechanics

  • matrix mechanics

  • Wave Mechanics

  • Exercise 48: Quantum mechanics multi-exercises

Module 24: Introduction to quantum error correction

  • Error correction

  • From reversible classical error correction to simple quantum error correction

  • The quantum error correction criterion

  • The distance of a quantum error correction code

  • Content of the quantum error correction criterion and the quantum Hamming bound criterion

  • Digitization of quantum noise

  • Classic linear codes

  • Calderbank, Shor and Steane codes

  • Stabilizer Quantum Error Correction Codes

  • Exercise 49: Noise Model, Repetition Code and quantum circuit

​Module 25: Quantum Computing II

 

  • Quantum programming

  • Solution Providers

    • IBM Quantum Qiskit

    • Amazon Braket

    • PennyLane

    • cirq

    • Quantum Development Kit (QDK)

    • Quantum clouds

    • Microsoft Quantum

    • Qiskit

  • Main Algorithms

    • Grover's algorithm

    • Deutsch–Jozsa algorithm

    • Fourier transform algorithm

    • Shor's algorithm

  • Quantum annealers

  • D-Wave implementation

  • Qiskit Implementation

  • Exercise 50: Quantum Circuits, Grover Algorithm Simulation, Fourier Transform and Shor

Module 26: Quantum Machine Learning

  • Quantum Machine Learning

  • hybrid models

  • Quantum Principal Component Analysis

  • Q means vs. K means

  • Variational Quantum Classifiers

  • Variational quantum classifiers

  • Quantum Neural Network

    • Quantum Convolutional Neural Network

    • Quantum Long Short Memory LSTM

  • Quantum Support Vector Machine (QSVC)

  • Exercise 51: Quantum Support Vector Machine

 

Module 27: Tensor Networks for Machine Learning

 

  • What are tensor networks?

  • Quantum Entanglement

  • Tensor networks in machine learning

  • Tensor networks in unsupervised models

  • Tensor networks in SVM

  • Tensor networks in NN

  • NN tensioning

  • Application of tensor networks in credit scoring models

  • Exercise 52: Neural Network using tensor networks

 

Supply Chain Analytics using AI

Module 28: Machine Learning for Supply Chain

The term supply chain has become widespread today to represent the complex networks and linkages of business entities to manufacture and deliver products and/or services to customers. Supply chain analytics is simply defined as the application of machine learning and data analysis techniques at various stages of a supply chain to improve overall supply chain management performance and meet or exceed customer expectations. The effective use of supply chain analytics is considered a core supply chain capability, with which a company can achieve superior performance and sustained supply chain-related competitive advantages.

 

  • Gestión de clientes                                       

    • Customers in supply chains                                

    • Understanding customers                                  

    • Customer-centric supply chain                          

    • Defining customers                          

    • Real customer needs            

    • Translating needs into product features         

    • Design supply chain processes             

    • Build efficient logistics systems             

    • Cohort analysis                       

    • Steps for cohort analysis         

    • RFM Analysis                       

    • What is RFM?                 

    • Steps for RFM analysis

  • Supply management                                          

    • Procurement in supply chains                              

    • Vertical integration                                 

    • Subcontracting                                       

    • Supplier selection                                        

    • Supplier evaluation                                       

    • Supplier capability assessment                       

    • Supplier relationship management                          

    • Tiered supply network management                     

    • Risk identification                         

    • Risk assessment                           

    • Development of risk response strategies            

    • Ongoing monitoring and periodic review

  • Warehouse and inventory management                       

    • Warehouse Management System                       

    • Measuring Warehouse Management Performance       

    • Inventory Management

    • Inventory Management Methods                       

    • Warehouse Optimization                                   

    • Introduction to PuLP

  • Demand Management 

    • Demand Forecasting                                      

    • Time Series Forecasting                                   

    • Time Series Components                             

    • Traditional time series forecasting methods          

    • Machine learning methods                                 

    • Univariate vs. multivariate time series              

    • Random Forest Regression

    • XGBoost

  • Logistics Management         

    • Main logistics management activities                  

    • Modes of transport in logistics

    • Taxable Weight

    • Product Value Density  

    • Logistics service providers    

    • Freight companies    

    • Freight forwarders       

    • Freight forwarders    

    • Third party logistics providers                   

    • Fourth party logistics companies                

    • Logistics network design                                  

    • Location decisions                                 

    • Centralization and decentralization

  • Exercise 53: Analysis of customer management cohorts

  • Exercise 54: Supplier Selection Analysis 

  • Exercise 55: Supplier selection using Regression Models

  • Exercise 56: Vendor selection using Support Vector Machine, Decision Trees and Random forest   

  • Exercise 57: RFM analysis 

  • Exercise 58: Customer segmentation with K-Means      

  • Exercise 59: Gaussian Mixture Model for Customer Segmentation

  • Exercise 60: Warehouse optimization with PuLP                    

  • Exercise 61: Designing logistics networks with PuLP

Module 29: Supply chains using AI and Quantum AI 

The term supply chain has become widespread today to represent the complex networks and linkages of business entities to manufacture and deliver products and/or services to customers.  The module shows the prediction of shipment duration of e-commerce products and estimation of late delivery risk. 
Identifying the risk of late delivery of e-commerce goods allows predicting the fastest and normal duration of shipment of goods for customers who could be domestic and foreign buyers. Regression machine learning models are used to determine the maximum shipping time interval by predicting the fastest and normal duration of goods shipment for domestic and international customers. And machine learning classification models to classify orders with high probability of late delivery (late delivery risk analyzer).

  • Introduction                             

  • What is a supply chain?                 

  • Why do we need a supply chain?  

  • Structure of a supply chain        

  • Supply chain processes            

  • Supply Chain Flows              

  • Supply Chain Management               

  • Business Analysis                      

  •  Supply Chain Analysis

  • Supply management                                       

  • Procurement in supply chains                             

  •  Vertical integration                                

  • Subcontracting                                      

  • Supplier selection                                       

  • Supplier evaluation                                      

  • Supplier capability assessment                      

  • Supplier relationship management                         

  • Tiered supply network management

  • Supply risk management                                

  • First step: Risk identification                        

  • Step 2: Risk assessment                          

  • Third step: Development of risk response strategies

  • Fourth step: Ongoing monitoring and periodic review

  • Modelos de Machine Learning de Regresión

    • Support Vector Machine Regression

    • Random Forests Regression

  • Modelos de Clasificación de Quantum Machine Algorithms

    • Qubit and Quantum States

    • Quantum circuits

    • Support Vector Quantum Machine

    • Quantum Neural Network

    • Variational quantum classifier

  • Exercise 62: Random Forest regression and OLS for prediction of goods shipment duration

  • Exercise 63: Quantum Support Vector Machine and classical SVM for predicting goods shipment duration

  • Exercise 64: Quantum Support Vector Machine and SVM for late delivery probability 

  • Exercise 65: Quantum Neural Networks and NN for late delivery probability

Módulo 30: Machine Learning and Quantum Machine Learning for Retail Sales Forecasting

In the context of the supply chain, demand refers to actual orders placed by customers. Demand is essential information for effective supply chain planning and management. Without accurate demand information, companies may have difficulty planning and controlling production. For example, if inaccurate demand information is transmitted down the supply chain, from bottom to top, significant distortions in production planning and order preparation can occur, leading to adverse carry-over effects.
Advanced artificial intelligence and quantum computing forecasting models are applied with the goal of demand management in supply chains is to improve the visibility, predictability and reliability of demand so that companies can design and deliver products and services that meet customer needs in the most effective and efficient manner.

  • ​Machine Learning for Retail Sales Forecasting

  • Multivariate Models

    • Autoregressive Vector Autoregressive VAR Models

    • ARCH Models

    • GARCH Models

    • Multivariate GARCH Models Copulas

    • Vector Error Correction VEC models

    • Johansen Method

  • Machine Learning

    • Supported Vector Machine 

    • Red Neuronal

    • ​Multivariate Adaptive Regression Splines

    • Base de desarrollo y validación 

  • Deep Learning

    • Redes Neuronales Recurrentes RNN

    • Red Neuroal de Elman

    • Red Neuronal de Jordan

    • Estructura básica de RNN

    • Long short term memory LSTM

    • Ventanas temporales

    • Muestra de desarrollo y validación

    • Modelización de la secuencia

  • Bayesian Deep Learning

    • Bayesian Long short term memory LSTM

  • Quantum Machine Learning 

    • Quantum Long short term memory LSTM

  • Exercise 66: Econometric Forecasting ARIMA and SARIMA

  • Exercise 67: Forecasting using Recurrent Neural Networks LSTM 

  • Exercise 68: Forecasting using Quantum LSTM 

  • Exercise 69: Forecasting using Bayesian Neural Networks

  • Exercise 70: Multivariate Forecasting Model with VAR

  • Exercise 71: Multivariate forecasting model with LSTM

 

Quantum Computing and Machine Learning for Supply Chain Optimization

Module 31: Supply Chain Optimization

Supply chain management includes among other functions: demand management, purchasing and procurement, production, inventory management, warehousing and transportation. Supply chain optimization decisions at the strategic level are those that have a long-term impact, typically more than three years, e.g., supply chain network design or capacity planning. At the tactical level are medium-term decisions, typically one to two years in scope, such as supplier and vendor selection, safety stock placement, production and inventory planning, among others. Operational level decisions can be as frequent as weekly or daily, such as machinery scheduling, transportation routing, etc.

Classical models such as linear programming, integer programming and nonlinear programming have been used to solve optimization problems.

 However, as the number of transactions expands due to globalization, the requirement will be to solve thousands of variables in a reasonable time. The increase in the number of variables tends to exponentially increase the time required to solve these problems on classical computers. Whereas quantum computers can outperform, encoding large problems in a reasonable time and solving them much faster with quantum algorithms.

 In the coming years quantum computers will reduce the costs associated with warehousing and transportation by using Quantum Machine Learning for order forecasting and quantum algorithms in route optimization.

  • Linear Programming

  • Constraint Programming

  • Integer Programming

  • Network Optimization

  • Nonlinear problems

  • Scalar functions optimization

    • Local optimization

    • Global optimization

  • Genetic Algorithms for optimization

  • Quantum Computing

  • Quadratic Unconstrained Binary Optimization (QUBO) Modeling

  • The Max-Cut problem and the Ising model

  • Adiabatic Quantum Computing and Quantum Annealing

  • The Leap annealers

  • Solving optimization problems on quantum annealers with Leap

  • QAOA: Quantum Approximate Optimization Algorithm

  • VQE: Variational Quantum Eigensolver

  • Hamiltonians

  • Supply chain network design

  • Problem of locating facilities with capacity

  • Production planning

  • Supply chain configuration

  • Machine Scheduling

  • Traveling Salesman Problem

  • Vehicle routing problem

  • Supply Chain Sustainability Optimization 

  • Environmental, social and economic sustainability

  • Ejercicio 73: Transportation Network Analysis with Graph Theory

  • Ejercicio 74: Vehicle routing problem Solution in QUBO

  • Ejercicio 75: Containers Loading Optimization

  • Ejercicio 76: The Wagner-Whitin algorithm

  • Ejercicio 77: Simulation Model to Test the Robustness of Supply Chains

  • Ejercicio 78: Non-linear programming for Procurement management

  • Ejercicio 79 : Algorithms to calculate the optimal sales volume

  • Ejercicio 80: CBC Linear Programming

  • Ejercicio 81: SLSQP Non-linear Programming

  • Ejercicio 82: Trust region constraint Non-linear Programming

  • Ejercicio 83: BFGS Non-linear Programming

  • Ejercicio 84: Genetic algorithm

Generative AI for Supply Chain

Module 32: Generative Artificial Intelligence applied to Supply Chain Management

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Supply chain management employs disparate data sets and multiple ERPs, making it increasingly complex and time-consuming for managers and analysts alike to sort through large volumes of data in enterprise information systems and obtain relevant information. Generative AI could improve supply chain visibility and team productivity by obtaining more expedient information. Users could ask questions in natural language and receive answers
accurate answers about supplier performance, sourcing activity, compliance risks, manufacturing schedules, demand plans and transportation costs. 

  • Inventory analysis 

  • Benchmark supplier delivery performance

  • Sales order analysis, manufacturing status and customer request management

  •  Manufacturing schedules, production capacity and resource utilization

  • Demand forecasting and variations in forecasts and orders

  • Analyze supply costs 

  • Selection of optimal delivery locations

  •  Critical network information

  • Exercise 85: Generative AI in supply chain data analysis

  • Exercise 86: Generative AI for data quality assurance 

  • Exercise 87: Statistical analysis using Generative AI in Supply Chain Management

  • Exercise 88: Using Generative AI for interpretation of results and formulation of recommendations in supply chains

  • Exercise 89: Basic text mining using Generative AI for supply chain management

  • Exercise 90: Advanced text mining with Generative AI for supply chain management

  • Exercise 91: Performance optimization in logistics management 

  • Exercise 92: Risk management and mitigation in supply chain management

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