Decision Making in Supply Chain: Python with Simulation

Align the project objective and kpis with stakeholders, collect data, build and validate a simulation model, and explore scenarios to support multi-criteria decisions on cost and capacity.

Lead a meeting with the customer or manager to reaffirm project scope, define the main process, and design the conceptual model. Explore scenarios, forecasts, and KPIs for validation.

Explore the main use cases of discrete event simulation for supply chain decisions. Apply simulation to fleet sizing and capacity estimation of facilities and warehouses to save money.

Explore the general concepts of assimilation, focusing on entities with attributes, their relations and processes, and how a conceptual model translates to a mathematical model for supply chain decisions.

Explore discrete event simulation in Python with the Salomon library, compare it to commercial software, and learn benefits like Excel integration, database connectivity, object-oriented capabilities, animation, and zero cost.

Practice concepts of components, animations, and distributions by modeling three trucks traveling between Zurich and Milan, with a factory and a map, using a uniform departure interval up to 150.

This exercise shows building a simple Python simulation by importing a library, setting up an environment, and creating components like a factory and truck, with troubleshooting for library recognition.

Position the factory and truck components on the map, configure image elements, and start the animation in Zurich within the simulation.

Define and simulate truck movement between coordinates using travel time and loading time, updating positions on a factory map with a truck and components.

Explore a two-city transport simulation using uniform loading times and adjustable travel times. Visualize truck movements on a map from factory to customer, updating the model to reflect process changes.

Build a simulation model of a Zurich warehouse where trucks arrive every 10–35 minutes and load two pallets in 18 minutes to reach 4200 pallets per month.

Create a new exercise in the project, set up the template and environment, and start the animation to configure loading times and assimilation metrics for the supply chain simulation.

Explore building and validating a truck logistics simulation in Python, including modeling expeditions, loading counts, and queue constraints to analyze max queue size and length of stay.

Demonstrates loading simulation inputs from an Excel file, setting parameters in column B (loading time, number of looks, minutes of assimilation), and saving before running.

Fix library recognition and styling glitches in exercise 3 part 2 to improve the user experience in Python with simulation for supply chain decisions.

Compile your code, leverage a library, and run a loop to generate results, then zip the output into a Word file for packaging and analysis.

Locate the built executable in the project folder, copy it to the map folder, open the input file, and execute the program to observe results via a friendly user interface.

Drive improvement by updating the code and rerunning the compilation to ensure meaningful results in supply chain decision making with Python simulation.