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  3. Supply Chain Design and Planning (Using Excel and Python)

Supply Chain Design and Planning (Using Excel and Python)

The course will take you to step by step on understanding optimization problems in supply-chain that can help you make strategic and tactical supply-chain decisions effectively. As always, our courses follows a learn by doing approach

Haytham Omar
Haytham Omar
Data Science | core | 11 hours |   Published: Aug 2021
In partnership with:  Coursera

    Discussions

Overview

1.3KSTUDENTS*
94%RECOMMEND*

This course includes:

  • 8+ hours of on-demand video
  • 8 modules
  • Core level
  • Direct access/chat with the instructor
  • 100% self-paced online
  • Many downloadable resources
  • Shareable certificate of completion
As a supply chain and logistics professional in this modern age, having analytical skills, know-how, and a data science coding language is imperative for the success and progress of one's supply chain career.  The course will take you to step by step on understanding optimization problems in supply-chain that can help you make strategic and tactical supply-chain decisions effectively. As always, our courses are highly practical and follows a learn by doing approach.  After this course we will be able to: 
  • Understand the basics of python. 
  • Understand linear programming. 
  • Work with python library Pulp for linear programming. 
  • Maximize the profit of our business given the resources available. 
  • Implement production scheduling to minimize cost in excel and python. 
  • Decide on the optimal location of facilities. 
  • Develop Optimization problems Masterfully in excel and python. 
  • Optimize the flow of the supply chain network. 
  • Optimize the design of the supply chain network. 
  • Develop your supply chain network with the service level in mind. 
  • Understand the sensitivity and the binding constraints of your supply chain decisions. 
  • Route optimization for logistical Flows with OR tools. 
     

Skills You Will Gain

Excel
Logistics
Product Planning
Python
Supply chain

Learning Outcomes (At the end of this program you will be able to)

  • Master supply chain analytics 
  • Learn how to increase service level while minimizing cost analytically for transportation and production. 
  • Learn how to design supply chain networks. 
  • Add optimization to your Supply chain and Data science skills 
  • Learn how Uber, DHL, Fedex and Amazon schedule their shipments. 
  • Leverage the power of Pulp and OR tools 
  • Introductory section on Python if you are not familiar with Python 
  • Tackle supply chain problems with optimization 
  • Production scheduling 
  • Routes scheduling for shipments 
  • Resource utilization. 
  • Integration of service level. 

Prerequisites

  • Basic knowledge of Microsoft excel 
  • Basic understanding of supply chain 

Who Should Attend

  • Supply chain professionals 
  • Procurement professionals 
  • Forecasting and inventory practitioners 
  • Logistics professionals 

 

Curriculum

Instructors

Frequently Asked Questions

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*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
1Welcome to the course!
2Module 1: Strategic Supply Chain design
3Module 2: Production Planning
4Module 3: Installing Anaconda
5Module 4: Python Crash Section
6Module 5: Introducing Pulp
7Module 6: Supply Chain Network Design
8Module 7: Advanced Network Modelling in Excel
9Module 8: Advanced Network Models with Pulp
10Module 9: Route optimization
Haytham Omar

Haytham Omar

He brings a wealth of expertise in supply chain management and data science to the online education platform. With a deep-rooted background in strategic consulting and academic instruction, he has held pivotal roles in optimizing global supply chains and pioneering data-driven solutions. Holding a doctoral degree focused on basket data-driven forecasting and inventory management, his research has significantly advanced understanding in omni-channel logistics. He has cultivated this knowledge through extensive hands-on experience and academic pursuits, including a Master’s in Global Supply Chain Management from KEDGE Business School and a MicroMasters in Supply Chain Management from MITx on edX.

Throughout his career, he has championed numerous innovative projects in collaboration with international institutions and corporations. His contributions extend beyond theoretical frameworks to practical implementations that enhance operational efficiency and profitability. Notably, he has developed specialized courses and workshops covering diverse aspects of supply chain management, from purchasing and inventory control to logistics optimization and lean management. His teaching approach blends rigorous academic insights with real-world applications, empowering learners to navigate complex supply chain challenges with confidence.

As an instructor on the platform, he is dedicated to equipping students with essential skills in supply chain analytics, data-driven forecasting, and strategic management. His courses are designed to foster critical thinking and problem-solving abilities, essential for navigating the dynamic landscapes of global business operations. Whether aspiring supply chain professionals, business owners seeking to optimize logistics, or data enthusiasts looking to delve into practical applications, learners gain invaluable insights and actionable knowledge from his expertise.

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Segment - 34 - Introduction

Segment - 35 - Dataframes

Segment - 36 - Arithmetic Calculations in Python

Segment - 37 - Lists

Segment - 38 - Dictionaries

Segment - 39 - Arrays

Segment - 40 - Importing Data in Python

Segment - 41 - Subsetting Dataframes

Segment - 42 - Conditions

Segment - 43 - Writing Functions

Segment - 44 - Mapping

Segment - 45 - For Loops

Segment - 46 - For-looping a Function

Segment - 47 - Mapping on a Dataframe

Segment - 48 - For Looping on a Data Frame

Segment - 49 - Summary

Segment - 50 - Assignment 

Segment - 51 - Assignment Answer 1

Segment - 52 - Assignment Answer 2

Segment - 18 - Production Planning

Segment - 19 - Production Scheduling Demonstration

Segment - 20 - Production Scheduling in Excel

Segment - 21 - Opening Constraints

Segment - 22 - Solving the Production Scheduling

Segment - 23 - Assignment

Segment - 24 - Answer PS

Segment - 25 - Global Production Scheduling Assignment

Segment - 26 - Answer

Segment - 27 - Summary Section 2

About this course: Overview, Learning Outcomes, Who Should Enroll...

Segment - 01 - Supply Chain Design

Segment - 02 - Supply Chain Planning

Segment - 03 - Curriculum

Segment - 04 - Strategic Supply Chain Design - IKEA

Segment - 05 -Strategic Supply Chain Design - Zara

Segment - 06 - Strategic Supply chain Design - Walmart

Segment - 07 - Strategic Supply Chain Design - Dell

Segment - 08 - Plan of Attack

Segment - 09 - What is supply chain optimization?

Segment - 10 - Types of Optimization Problems in Supply Chain

Segment - 11 - Mathematical Formulation

Segment - 12 - Transportation Problems

Segment - 13 - Resource Utilization Demo

Segment - 14 - Manufacturing Problem

Segment - 15 - Model in Excel

Segment - 16 - Assignment

Segment - 17 - Assignment Answer

Segment - 64 - Introduction

Segment - 65 - Supply Chain Network Design

Segment - 66 - Supply Chain Design Components

Segment - 67 - Warehouse Allocation Part 1

Segment - 68 - Warehouse Allocation Part 2

Segment - 69 - Developing Variables in Python

Segment - 70 - Objective Function Facility Allocation

Segment - 71 - Constraints

Segment - 72 - Summary Models

Segment - 73 - Model Sensitivity

Segment - 74 - Checking Average Distance Vs Number of Warehouses

Segment - 75 - DC Extended Model

Segment - 76 - Linking Constraints

Segment - 77 - Introduction to the Model

Segment - 78 - Developing Costs

Segment - 79 - Formulating Constraints

Segment - 80 - Solving the Model

Segment - 81 - Commentary

Segment - 82 - Level of Service

Segment - 83 - Relaxing DCs Constraints

Segment - 28 - Python History

Segment - 29 - Downloading Anaconda

Segment - 30 - Installing Anaconda

Segment - 31 - Spyder Overview

Segment - 32 - Jupiter Overview

Segment - 33 - Packages

Segment - 84 - Importing Model Data

Segment - 85 - Constructing Variables

Segment - 86 - Constructing Costs in Model

Segment - 87 - Objective Function

Segment - 88 - Demand and Flow Constraints

Segment - 89 - Linking Constraints

Segment - 90 - Solving the Model

Segment - 91 - Showing only Variables with Output

Segment - 92 - Setting the Service Level

Segment - 93 - Relaxing the DC Constraints

Segment - 94 - Service Level Constraints

Segment - 95 - Binding Constraints

Segment - 96 - Assignment

Segment - 97 - Assignment Answer 1

Segment - 98 - Assignment Answer 2

Segment - 53 - Pulp Orientation

Segment - 54 - Installing Pulp

Segment - 55 - Manufacturing Model in Pulp

Segment - 56 - Assignment

Segment - 57 - Assignment Answer

Segment - 58 - Production Scheduling Orientation

Segment - 59 - Production Scheduling in Python

Segment - 60 - Defining Constraints

Segment - 61 - Model Conclusion

Segment - 62 - Assignment

Segment - 63 - Assignment Solution

Segment - 99 - Introduction

Segment - 100 - Traveling Sales-Man Problem

Segment - 101 - Travelling Males Man with ML-rose

Segment - 102 - Routing Optimization with or Tools

Segment - 103 - Adding Constraints

Segment - 104 - Getting the Results of the Solution

Segment - 105 - For Looping the Solution

Segment - 106 - Conclusion

Segment - 107 - What if we remove the distance constraint?

Segment - 108 - Capacity Constraints

Segment - 109 - Adding the Capacity Dimensions

Segment - 110 - Understanding Model index

Segment - 111 - For Looping on Vehicles

Segment - 112 - Capacity Constraints Display

Segment - 113 - Calculating Total Distance and Total Loads

Segment - 114 - VRPTW model

Segment - 115 - Adding time Dimension

Segment - 116 - Setting Time Window Range

Segment - 117 - Instantiate Route Start and End

Segment - 118 - Getting Routes and Windows

Segment - 119 - Mapping the Solution

Segment - 120 - Routing Example: Restaurant

Segment - 121 - Routing Example Part 2

Segment - 122 - Assignment

Segment - 123 - Conclusion