AI for Supply Chain: Practical Problem Solving with ChatGPT

Explore the concept of an efficient supply chain as a well-designed, cost-minimizing system coordinating procurement, production, inventory, logistics, and distribution, with Ford's end-to-end control example illustrating economies of scale.

Master the responsive supply chain that prioritizes flexibility and agility to meet customer demand. Learn Dell's direct model, build-to-order, and online customer engagement that enable rapid, customized fulfillment.

Leverage analytics to blend efficiency and responsiveness in the supply chain by using real-time data and advanced models to cut costs, anticipate demand, and optimize inventory.

Apply analytics in the supply chain to drive data-driven decisions in demand forecasting and inventory optimization, using external factors, statistical modeling, and machine learning to improve planning and sourcing.

Explore how descriptive, predictive, and prescriptive models guide supply chain decisions, from transportation mode selection and inventory levels to distribution center design and optimization.

Explore descriptive, predictive, and prescriptive analytics in supply chain, showing how descriptive analyzes past data to cut costs, predictive forecasts demand and capacity, and prescriptive optimizes asset utilization.

Python offers scalable analytics beyond Excel's limits, with visualization, reproducibility, and a large open-source community. In supply chain analytics, Python outpaces Excel and R for data analysis.

Learn to install Python and Jupyter Notebook for course-wide Python operations, following the document's steps and the provided YouTube guide to get started.

Explore Python libraries for numerical computing, data analysis, machine learning, and visualization, including NumPy, pandas, scikit-learn, TensorFlow, PyTorch, Keras, Matplotlib, Seaborn, Plotly, Pillow, and OpenCV.

Get started with Jupyter Notebook by installing, launching in a browser, and creating folders and Python notebooks; run cells to perform basic operations like adding, subtracting, and printing outputs.

Sign up for OpenAI and create a ChatGPT account, then practice prompts, stop generation, and regeneration to explore how ChatGPT can support supply chain analytics.

Learn to code with ChatGPT without coding knowledge by mastering two things: commanding it to generate Python code and troubleshooting errors, including importing NumPy and pandas.

Define the supply chain as a global network delivering products and raw materials through information, distribution, and cash flow from supplier to end customer, with upstream and downstream echelons.

Explore external factors shaping any supply chain—customer expectations, regulations, economic conditions, political and social environments, and competitive pressures—and learn how to adapt your supply chain accordingly.

Explore the five forces framework to analyze substitutes, buyer power, new entrants, supplier power, and industry rivalry with real-market examples.

Analyze industry attractiveness by profitability potential and competitive pressure. Identify high, medium, and low categories to guide strategic planning, considering barriers to entry, supplier leverage, and substitutes.

Explore the product market growth matrix, aka the Ansoff matrix, to choose growth strategies like market penetration, market development, product development, and diversification for existing and new markets.

Explore the four strategies of the product market growth matrix—market penetration, market development, product development, and diversification—through Tech Wave and real-world examples like Coca-Cola, Netflix, and Tesla.

Explain push and pull strategies in supply chains, where push uses forecasted demand and inventory to push products, while pull triggers production by actual customer orders.

Explore Porter generic competitive strategies based on cost, differentiation, and focus. Learn how broad and narrow focus, low-cost provider, differentiator, focus differentiator, and best-cost provider shape competitive advantage.

Explore lead, lag, and tracking capacity strategies to align manufacturing with demand and ensure on-time delivery; lead adds capacity before demand, lag delays capacity, and tracking balances real-time decisions.

Analyze the cost structure of a product by distinguishing fixed costs and variable costs, summing to total cost, and assess profitability and potential savings in manufacturing.

Analyze fixed and variable costs to calculate total cost and determine the breakeven point where total revenue equals total cost, signaling profit above and loss below.

Explore linear functions: y changes with x, plot straight-line graphs with one independent and one dependent variable, and use y = a x + b for supply chain forecasting.

Explore polynomial functions with multiple variables and constants, and see how their graphs model complex relationships, including supply chain transportation costs depending on distance, weight, and mode.

Explore the power function and its equation C = A S^b, showing how warehouse size S affects operational capacity C and throughput, with linear, quadratic, and higher-order behaviors.

Explore how inverse functions reveal demand and price sensitivity in supply chain strategies to identify an optimum price that balances revenue, margins, and customer value.

Explore the exponential function and its use in forecasting demand and inventory optimization in the supply chain, including growth or decay over time.

Explore the logarithmic function and its graph, including bases, and its use in supply chain network optimization for distribution centers, showing how sales grow then plateau due to diminishing returns.

Form supply chain cost functions using linear and quadratic equations with fixed and variable costs, total cost, and shipping cost dependent on distance and weight.

Identify how central tendency summarizes data by selecting a representative value, and compare mean, median, and mode in supply chain demand analyses.

Explore dispersion in data by examining range, variance, standard deviation, coefficient of variation, and interquartile range, and compare spread to central tendency.

Learn Python basics in Jupyter by creating variables, performing arithmetic, and saving outputs, then import NumPy and pandas with ChatGPT help to streamline coding.

Learn Python lists as mutable data structures, declare them with square brackets, and practice indexing and slicing in a Jupyter notebook. Understand type issues and how ChatGPT can assist debugging.

Learn to subset lists of strings in python, using inverted commas, index slicing, and practical examples, while troubleshooting errors with chatgpt to ensure valid variable names.

Explore Python dictionaries as a built-in data structure using key-value pairs with curly braces. See how names map to ages, access values by keys, and treat dictionaries as hash maps.

Define arrays in Python and use NumPy to create 1D and 2D arrays, highlighting memory efficiency over lists, importing NumPy as np, and zero-based indexing for subsetting.

Learn how to read a CSV file in Python with pandas, locate the file path, load using pd.read_csv, and inspect with head, tail, describe, and shape for analysis.

Lighten up learning with memes while exploring supply chain topics, and see how route optimization software can boost delivery performance across procurement, logistics, and marketing teams.

Apply descriptive analytics to identify past patterns and trends in supply chain data using Python, visualizations, and KPI analysis to monitor performance and inform corrective actions.

Plot a line chart of apple sales over time using Python with ChatGPT, troubleshoot date format and column names, and customize plot title and axes.

Plot a scatter chart from a line chart by modifying code to use the scatter function, add a legend, and display the scatter graph alongside the line chart.

Plot a histogram by switching from scatter to histogram, adjust bins and edge color, and try different sizes to customize the histogram output.

Prescriptive analytics provides recommendations to optimize supply chain outcomes by balancing lead time, inventory cost, and production utilization, applying optimization, simulation, and decision analysis.

Examine constrained and unconstrained optimization in supply chain, illustrating capacity, resources, budget constraint, lead times, and regulatory requirements with production planning and demand forecasting examples.

Learn the basic power rule for differentiation, compute first and second derivatives of polynomials, and apply constants and zero results to understand how function rates of change reveal maxima.

Explore local and global maxima and minima in supply chain contexts, using prescriptive analytics and differential calculus to identify critical extreme points for prices and inventory.

Learn how differentiation finds maximum and minimum points for a function, using f(x)=x^2 as an example, and verify with nearby values.

Explore how causal models extend time series by incorporating multiple factors such as promotions, price, and market share to forecast sales using multiple linear regression.

Learn to build a causal model for sales forecasting using multiple factors—price changes and promotion spend—via multiple linear regression, interpreting coefficients and R-squared.

Explore how to generate a regression equation from Excel outputs using ChatGPT, explain coefficients and assumptions, and evaluate model strength with R-squared and regression results.