Inventory Planning - How to Optimize Inventory Policies

Inventory Planning - How to Optimize Inventory Policies free download

Apply, Simulate, and Optimize Inventory Policies

What will you learn?

In this course, using Excel templates, you will learn how to

1. Apply inventory policies

2. Simulate them using historical demand and forecast data

3. Optimize policies based on cost or service/inventory trade-offs

As a bonus, we will also cover all simulations and optimizations of these policies using Python.

These Excel templates and Python scripts can then be easily tweaked for your own products and data.

How is this course different?

I have been teaching inventory optimization to master students at the university (in Brussels, Belgium, and then Paris, France) and to professionals since 2015.

Most inventory optimization courses focus on solving equations, such as the Economic Order Quantity (EOQ), safety stocks, and newsvendor models. Not this one.

Over the years, I have drastically changed how I taught inventory optimization and utilize inventory policies because my experience delivering models to my clients taught me that,

1. Being able to solve a formula doesn't mean that you know how to apply it in practice.

2. Even if you can properly apply a formula, the underlying theory doesn't apply in practice.

3. Other models - that don't rely on specific theoretical foundations - usually deliver more value.

So I changed

• The content of my course: from theory-driven to simulation-driven,

• How I taught it: from a focus on equations to a focus on 'how do you apply this in practice using real-life data'

My objective is that by the end of this course, you will be able to,

1. Simulate different policies using your own data

2. Optimize them

3. Select the one that best fits your objective (cost, service level) based on your own data (historical demand and forecast)

What deliverables do you get?

• Excel templates that you can use with your own data

• Python scripts to simulate and optimize your inventory policies

• Corrected templates for all exercises and simulations

• All the slides

What's not covered in this course?

• EOQ model

• Newsvendor model

• Variable lead times

Pre-requisites

• Excel intermediate level - The course includes a brief introduction to the Excel Solver

• How to compute the RMSE (see my other course) - The course includes a brief reminder on how to compute RMSE.

• Not Mandatory - Python beginner level - Python scripts are an add-on to this course, so if you don't know Python, you won't lose any insight/content.

How much content is in this course?

• 2h15 of videos (including theory, discussions, and corrections)

• Depending on your Excel proficiency, approximately 4 to 8 hours of personal work (including mostly simulations in Excel and a bit of theory)

• 30 minutes of videos related to Python scripts

• Depending on your Python proficiency, it'll take approximately 1 to 2 hours of personal work to go through the scripts