Stock Control And Inventory Dynamics With Excel & Python.
Published 8/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Apply the Best forecasting model and inventory policy for all the Products in your Assortment and much more!!
What you’ll learn
Apply EOQ policies with discount , promotions , multiple suppliers and multiple products.
Simulate inventory policies for unlimited number of products.
Know which forecasting is better for your products.
Know which inventory policy is better for your products.
understand the impact of aggregation and dis-aggregation on inventory.
Understand How inventory interact with demand ?
Work with the specialized inventory library in python Inventorize.
apply markdowns on seasonal produce to move inventory
Requirements
Anaconda (How to install inside)
Microsoft Excel
Description
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Types of inventory
Lecture 3 Cycle inventory
Lecture 4 Holding cost and ordering cost
Lecture 5 Zara and seven eleven
Lecture 6 EOQ
Lecture 7 EOQ-One product
Lecture 8 EOQ-same truck
Lecture 9 EOQ-many suppliers
Lecture 10 Algorithm for flexible order subsetting
Lecture 13 Algorithm for product subsetting step 4
Lecture 14 Volume discounts
Lecture 15 Volume Discounts in Excel
Lecture 16 marginal discounts
Lecture 17 EOQ with limited time discounts
Lecture 18 Limited time discounts
Lecture 19 Demand Curve change with EOQ
Lecture 20 Summary
Lecture 21 Game plan
Section 2: Inventory aggregation, metrics and distribution.
Lecture 22 Introduction
Lecture 23 A brief on inventory policies
Lecture 24 Fill rate and service level for uncertain demand.
Lecture 25 Bike Co
Lecture 26 Bike Co, flow time and average inventory.
Lecture 27 Bike Co, Desired service level
Lecture 28 Bike co, cycle service level.
Lecture 29 Desired fill rate
Lecture 30 Inventory aggregation
Lecture 31 Shopico Example
Lecture 32 Scenario 1: Disaggregation
Lecture 33 Scenario 2: Aggregation
Lecture 34 HighMed
Lecture 37 Inventory Cost
Lecture 38 Which option is better?
Lecture 39 Temporal aggregation
Lecture 40 Conclusion
Lecture 41 Summary
Section 3: Welcome to Python
Lecture 42 Python!
Lecture 43 downloading Anaconda
Lecture 44 Installing Anaconda
Lecture 45 Spyder overview
Lecture 46 Jupiter Notebook overview
Lecture 47 Python Libraries
Lecture 48 inventorize
Lecture 49 Summary
Section 4: Python Programming Fundmentals
Lecture 50 Intro
Lecture 51 Dataframes
Lecture 52 Arithmetic calculations with python
Lecture 53 Lists
Lecture 54 Dictionaries
Lecture 55 Arrays
Lecture 56 Importing data in Python
Lecture 57 Subsetting Dataframes
Lecture 58 Conditions
Lecture 59 Writing functions
Lecture 60 Mapping
Lecture 61 For loops
Lecture 62 For looping a function
Lecture 63 Mapping on a DataFrame
Lecture 64 For looping on a Dataframe
Lecture 65 Summary
Lecture 66 Assignment
Lecture 67 Assignment answer 1
Lecture 68 Assignment answer 2
Section 5: Inventory with Python.
Lecture 69 Program for EOQ aalgorithm
Lecture 70 Revisiting BikeCo
Lecture 71 Cycle service level in python
Lecture 72 Expected item fill rate in python
Lecture 73 Goal seek function
Lecture 74 Desired fill rate
Lecture 75 Revisiting EOQ with many suppliers.
Lecture 76 Introducing the case in python
Lecture 77 EOQ with inventorize
Lecture 78 Step 2 and step 3
Lecture 79 global order frequency for product subsetting
Lecture 80 Total cost of the system
Section 6: Segmentation, classification and inventory simulations
Lecture 81 Inventory Dynamics intro
Lecture 82 Why we need segmentation.
Lecture 83 An example in Excel.
Lecture 84 Product classfication
Lecture 85 ABC dynamic with inventorize
Lecture 86 The long tail
Lecture 87 Category mix on multiple products
Lecture 88 Min max policy
Lecture 89 Min Q and Periodic Policy
Lecture 90 R_s_S and base_stock_policy
Lecture 91 Connecting demand with service level
Lecture 93 Understanding the results
Lecture 94 Assignment
Lecture 95 Assignment solution
Lecture 96 Summary
Section 7: Which forecasting is better for a specific inventory policy ?
Lecture 97 Introduction
Lecture 98 Forecasting demonstraion
Lecture 99 Max Policy
Lecture 100 Preparing the notebook
Lecture 101 Preparing for forecasting
Lecture 102 Forecasting our first sku
Lecture 103 Forecasting all SKUs
Lecture 104 Max_policy_Dynamic
Lecture 105 Inventory Comparison between KNN and ARIMA
Lecture 106 Assigning cycle service level and preparing for simulation.
Lecture 107 Running the simulation
Lecture 108 Which forecast is better in terms of inventory?
Section 8: Seasonal Inventory
Lecture 109 Intro
Lecture 110 Seasonal Products
Lecture 111 Point of Maximum profit
Lecture 112 How much I will sell?
Lecture 113 Dataable
Lecture 114 Critical ratio
Lecture 115 Critical ratio in excel
Lecture 116 What’s actually happening ?
Lecture 117 Critical ratio in python
Lecture 118 Preparing the data for MPN.
Lecture 119 Creating margin of error.
Lecture 120 Applying MPN on all data.
Lecture 121 Conclusion
Lecture 122 Seasonal inventory summary
Lecture 123 Assignment solution
Lecture 124 Seasonal inventory answer
Lecture 125 Markdown model by J.Walker
Lecture 126 Markdown model in python
Lecture 127 Final note
Inventory Managers,Supply chain managers,Buyers,Demand planners,Supply planners,Retail planners
