Supply Chain Design and Planning with Excel & Python.

Explore production planning strategies, including one-time run, level, and periodic planning, and optimize inventory and production costs to minimize stockouts using Excel and Python.

Explore production scheduling with Excel and Python to minimize costs by formulating inventory and capacity constraints via linear programming.

Identify three unknowns in an Excel production scheduling model: production quantity, inventory, and a binary line-opening decision. Minimize total costs—production (40 dollars), storage (1 dollar), and fixed costs—while meeting demand.

Learn how opening constraints force a production line decision by weighing fixed costs against capacity, and how inventory carryover links current production to prior year initial inventory.

Solve production scheduling by balancing inventory and production constraints, linking production to demand with inventory effects, minimizing cost under capacity and binary decision variables.

Solve a 12-month production planning problem using demand per period, fixed cost of production, production cost, storage cost, capacity, and an initial inventory of 1200, with linking and inventory constraints.

Apply demand, capacity, and inventory constraints to a supply chain design problem, linking production units to capacity and inventory flows while ensuring binary opening line constraints and fixed costs.

Develop a cost-minimizing six-month production schedule for tablets, balancing fixed and variable costs, overtime options, and inventory holding. Meet demand while managing monthly capacity and the opening constraint on production.

Solve a six-month production planning problem using Excel and Python, optimizing production units, inventory, and opening decisions under capacity and overtime constraints with fixed costs.

Explore supply chain optimization using linear programming, model formulation, and transportation and network design, then learn to install Python, Anaconda, and tools like Jupiter Notebook to implement these models.

Explore Python's origins, its broad usage from web development to data science and scripting, and learn to install and switch between notebook environments like Jupyter and Spyder within Anaconda.

Learn how to download and install Anaconda across Windows, Mac, and Linux, following a guided page and waiting for the installer to complete.

Follow a guided installation of Anaconda, taking about two to three minutes. Then explore inside Anaconda to see which applications will work with it.

Explore Spyder overview, switching between Spyder and notebook for Python coding, set up a supply chain data science folder, run simple scripts, and manage projects with shortcuts.

Explore the Jupiter notebook overview, using Spider and Python in an Anakonda environment to write and run code with instant outputs, and switch between tools for your supply chain project.

Explore Python libraries like pandas, matplotlib, and numpy to enable data science tasks in an Anaconda environment, importing and using them in Jupyter notebooks and Spyder for supply chain projects.

Identify Python's core data types: numbers, strings, dates, categorical data, and booleans, and explore lists, dictionaries, and tuples, plus data cleaning for missing values and formatting.

Explore how dataframes organize observations and attributes like Excel tables, and learn Python basics: functions use parentheses, assign variables with equal, and create objects with attributes.

Learn Python fundamentals for supply chain design by performing arithmetic calculations in a Jupyter Notebook, creating variables and lists, and understanding zero-based indexing to retrieve items.

Learn how to create dictionaries with key and value pairs using curly braces, access keys and values, and retrieve specific elements from a dictionary.

Explore arrays as the third core data structure after lists and dictionaries, using numpy for multi-dimensional storage, and perform element-wise arithmetic like adding a constant and dividing arrays.

Import data in Python using pandas in a Jupyter notebook, loading the UK online retail dataset, and explore with head, tail, shape, and describe to examine continuous variables.

Learn to subset dataframes using iloc and loc, selecting rows and columns by index or name, then create a new euro price column from the existing USD price.

Explore how to use conditions in data frames, test single and multiple criteria with and/or operators, and apply them in loops to filter data using age comparisons.

Define a Python function with def to determine a person’s status as child, teenager, or adult using indentation and if-elif logic, and print the results.

Learn how to map a function over every element in a list using Python's built-in map, creating map objects and assigning results to a list.

Explore for loops as an essential iteration tool, showing how to iterate over class lists and print ages, and how to relate for loops to the map function.

Apply a function to each item in a list using for loops and map, store results in a new list, and prepare data frames for future data tasks in Python.

Learn to create a function that flags United Kingdom entries and map it onto a dataframe to add a UK column of true or false, with UK vs non-UK counts.

Learn how to loop over a data frame in Python by applying a function to each row and creating a new column, comparing map and apply for row-wise operations.

Master python fundamentals for supply chain analytics by handling lists and dictionaries, importing data with pandas, and using functions and loops to subset, categorize, and iterate across datasets.

Practice a Python assignment on a 400-car dataset with horsepower, speed, and price; compute basic stats, rename a field to Cardini, and create a price category with colors.

Analyze the cars dataset to identify its shape, columns, and features like horsepower, price, and miles per gallon; count unique cylinders and filter for the most expensive car.

Explore maximizing profit for two products using linear programming in Python with PuLP, enforcing non-negativity and integer quantities, and learn to install PuLP to formulate the problem.

Install PuLP, a Python library for linear programming, to handle bulk problems in supply chain design. Learn setup via pip or conda and address installation issues when tools aren’t available.

Solve a manufacturing optimization model in Python using PuLP to maximize profit with X1 and X2, coefficients 25 and 35, under resource constraints, linking Excel and Python workflows.

Solve a three-variable linear program to maximize profit with x1, x2, x3 and prices 33, 40, 34 under 40 kg constraints, achieving 3350 with x1=50, x2=0, and x3=50.

Learn to solve a production scheduling problem by deciding when to produce or store based on demand, incorporating fixed production costs and period-based decisions in SIPLE.

Apply Python and PuLP to formulate a production scheduling model, defining time periods, inventory, demand, fixed storage cost, capacity, and production variables, then build the optimization with an objective function.

Define the objective function for a multi-period production and inventory model, incorporating storage cost, demand parameters, and binary variables, then enforce inventory balance and capacity constraints across periods.

Compare the optimization results in Python and Excel to show identical outcomes under capacity 4000 and explore sensitivity by relaxing capacity to impact inventory and production costs.

Solve a production scheduling problem with variable demand, variable costs, and storage costs using binary decisions, capacity limits, and linking constraints, and compare results in the next lecture.

Solve a 12-period production planning problem with an optimization model that minimizes inventory, variable production costs, and fixed setup costs under capacity constraints.

Define how supply chain design sets a company’s strategy, weighing speed and proximity to customers against costs, with examples from consumer goods and chocolate industries.

Develop strategic decisions for supply chain network design, including facility locations, numbers, and omni channel strategies. Assess disruptions, costs, service levels, and resilience through scenario analysis.

Explore supply chain design components through optimization, deciding warehouse counts and locations, make-or-buy decisions, and how demand seasonality, surges, and sustainability shape cost, inventory, and logistics.

Analyze warehouse allocation by selecting two warehouse locations among seven cities, assign demand-driven service to each city, and plan shipping flows to minimize distance, transport costs, and carbon footprint.

Minimize total distance by selecting two open warehouses and assigning each city to one, using binary variables and paying attention to distance versus capital costs in Excel.

Develop a Python-based facility allocation model by building distance matrices, indexing warehouses and customers, and using an lp variable dict for binary open-warehouse decisions.

Create a demand dictionary by zipping customer lists, then build a warehouse allocation model in Excel to minimize distance, using objective function that multiplies demand by distance and warehouse flows.

Ensure every customer is served by one open warehouse by requiring the total flow to each customer equals one, and open three warehouses with no flow from unopened warehouses.

Build a minimal cost supply chain model by defining warehouses, distances, and demand, linking flows to cities, and minimize total cost while opening exactly three warehouses.

Explore model sensitivity in supply chain design with Excel and Python by evaluating warehouses selections, flows, and the objective function, then compute the final value and average distance per demand.

Explore how average distance changes with the number of warehouses using a Python function and a warehouse simulation, illustrating delivery time and service level trade-offs.

Extend the dc model to three plants, nine warehouses, and twenty customers, deciding which locations to open while coordinating inbound and outbound transport, capacities, costs, and service levels.

Linking constraints ensure flow balance across factory, warehouse, dc, and retailer. The lecture introduces opening costs with binary variables and demonstrates modeling in Excel and Python.

Introduce a three-plant supply chain model with inbound/outbound transport, production and opening costs, capacities, and shop demand, aiming to minimize total costs.

Formulate demand, supply, flow, and linking constraints to model a supply chain in Excel and Python, open warehouses, and define fixed costs, the objective, and decision variables for planning.

Minimize the objective in this supply chain design and planning model by using a linear model with binary plant and warehouse openings, flow constraints, and demand and capacity limits.

Explore supply chain design decisions by comparing two plants with capacities of 5,000 and 4,000 to minimize transportation and production costs, and plan a distribution center based on distances.

Apply an 80-kilometer threshold to DC-to-shop distances, count shipments within range, and compute the level of service, which is 64 percent in this example.

Relax the number of warehouses and set service-level thresholds to reveal cost, fixed and variable costs, and level of service trade-offs in a warehouse design model.

Explore direct delivery from plant to shops or via a distribution center, balancing service level and 80 km threshold with capacity and demand constraints.

Explore a multi-supply network with manufacturing storage and direct delivery. Learn how capacity, demand, and service level constraints shape costs and allocations.

Import model data from excel, separate costs and parameters into sheets, and read demand to set indices for plants and destinations; calculate transportation costs.

Construct variables to model inbound flows, plant indexes, and binary open-plant indicators, with lower bound zero and no upper bound, using integers to capture demand from dcs to shops.

Construct costs in the model by defining inbound and outbound transportation costs, production costs, and opening and operating costs, comparing Excel and Python implementations step by step.

Define the objective function for a supply chain design problem, including opening, fixed, variable, land, and operating costs, and prepare the optimization model with constraints.

Explore forming demand and flow constraints in a supply chain design model with Excel and Python. Include binary variables, capacity constraints, and linking constraints to align inbound flows with demand.

Linking constraints connect inbound and outbound flows across distribution centers and plants, enforce the flow constraint, and apply opening and capacity bounds for plants in a supply chain model.

Solve the minimized supply chain design model with demand and capacity constraints. Analyze flow streams and open plant decisions, then print variable volumes to verify results.

Show only variables used by building a dictionary with name and value via a list comprehension, filtered by value greater than zero, revealing inbound and outbound data for each shop.

Define the service level by building a distance matrix in Python (80 km threshold), weighting flows by distance, yielding a 63 percent service level and informing future warehouse relaxations.

Relax the model constraints to study effects on the objective function and service level, compare scenarios, and push the service level toward 95 percent.

Convert the service level into a constraint in the relaxed model, requiring at least 95% of shipments within 80 kilometers, with Excel showing three sites open and 100% service level.

Explore binding constraints and non-binding constraints in a supply chain optimization model, using slack and capacity constraints, demand, and sensitivity analysis to assess final value.

Develop a multi-product assignment for a four-plant, four-product supply chain, including warehouses and shops, with plant capacity, fixed costs, and transportation costs, solved via PuLP and Excel data.

Model a multi-level linear programming supply chain design with shops, plants, warehouses, and products, incorporating inbound, outbound, production, opening, and operating costs.

The lecture explains demand constraints linking warehouse output to shop and product demand, with capacity and opening constraints, and introduces a 170 km service level metric showing 78 percent.

Explore route optimization for door-to-door deliveries, from the traveling salesman problem to multi-vehicle scheduling, incorporating capacity and time-window constraints using Python tools.

Learn to solve the traveling salesman problem to optimize routes for last-mile deliveries with multiple vehicles, capacity, and loading and unloading constraints, using Python libraries such as Melrose and OR-Tools.

Solve the traveling salesman problem using a genetic algorithm in the melrose library, minimizing distance across eight nodes with a fitness function, crossover, and mutation.

Analyze a multi-vehicle routing problem using OR-tools in Python, guiding the model to minimize distance with multiple vehicles and a defined distance matrix of 18 nodes.

Build a distance function from a distance matrix and integrate it into a routing model to minimize distance with four vehicles and a 3000 capacity, using the cheapest insertion algorithm.

The lecture shows configuring a vehicle routing problem with a distance matrix and index manager, minimizing routes for four vehicles and mapping routing indices to nodes, starting from zero.

Automate vehicle routing by implementing a Python function that iterates each vehicle, computes successive destinations with for loops and a while loop, using indexing to reach the next address.

Conclude by building vehicle routes using lists and loops, tracking destinations per vehicle, and preparing for adding constraints that limit load per vehicle.

Evaluate how removing distance constraints shifts a multi-vehicle routing problem toward a minimum-distance single-vehicle solution, using distance APIs and the traveling salesman framework.

Learn to add capacity constraints to the vehicle routing problem by modeling per-node demand and a fixed vehicle capacity of 15 units, and prepare to incorporate distance and demand callbacks.

Explore adding capacity dimensions to routing models by implementing distance call back and demand call back, enforcing vehicle capacity constraints, and integrating demand data with Excel and Python workflows.

Set up a routing problem by defining truck capacity and load, initialize the index manager, and interpret routing indices to understand start points and vehicle assignments in the solution.

Explore how to loop through vehicles in a routing model, set start indices, build per-vehicle load and demand dictionaries, and map routes to vehicle trips using Python.

Explore how to display capacity constraints by routing multiple destinations per vehicle, balancing loads, and minimizing distance across a logistics network using Excel and Python, with Google APIs.

Compute each vehicle’s total distance and load using a dictionary, and verify every vehicle stays under 3000 km and 50.

Add a time dimension to the routing model by incorporating time windows and a time metric, balance minimizing time and distance, and use enumeration to handle time window indexing.

Define time window ranges for every node index, specifying start and end times per window and per vehicle to align routing and day planning.

Instantiate route start and end by cumulatively adding travel durations between nodes, embedding time windows for vehicles, and minimizing total duration through routing optimization and time-dimension variables.

Define and extract routes and their time windows from the optimization solution, comparing solution time windows with model time windows across multiple vehicles to analyze routing behavior.

Analyze the Python-generated vehicle routing solution, mapping nodes and travel times to visualize routes. Compare time windows and capacity constraints across scenarios to optimize supply chain design.

Explore routing for a restaurant delivery service, using a duration matrix and time windows to minimize total delivery duration with two motorcycles, and prepare data in Excel and Python.

Route planning with time windows is demonstrated, converting a dataframe to a list of tuples and setting time and start constraints. This yields on-time deliveries with minimized duration.

Take the operations manager role to plan the restaurant layout and multi-destination routes for six vehicles, minimizing duration while honoring time windows, using the Excel timeline and Python model.