Supply Chain Analytics transforms supply chain activities from guessing, to ones that makes decision using data. An essential tool in Supply Chain Analytics is using optimization analysis to assist in decision making. According to Deloitte, 79% of organizations with high performing supply chains achieve revenue growth that is significantly above average. This course will introduce you to PuLP, a Linear Program optimization modeler written in Python. Using PuLP, the course will show you how to formulate and answer Supply Chain optimization questions such as where a production facility should be located, how to allocate production demand across different facilities, and more. We will explore the results of the models and their implications through sensitivity and simulation testing. This course will help you position yourself to improve the decision making of a supply chain by leveraging the power of Python and PuLP.
Basics of supply chain optimization and PuLPFree
Linear Programming (LP) is a key technique for Supply Chain Optimization. The PuLP framework is an easy to use tool for working with LP problems and allows the programmer to focus on modeling. In this chapter we learn the basics of LP problems and start to learn how to use the PuLP framework to solve them.
Modeling in PuLP
In this chapter we continue to learn how to model LP and IP problems in PuLP. We touch on how to use PuLP for large scale problems. Additionally, we begin our case study example on how to solve the Capacitated Plant location model.LpVariable dictionary function50 xpLogistics planning problem 2100 xpTraveling salesman problem (TSP)100 xpExample of a scheduling problem50 xpScheduling workers problem100 xpPreventative maintenance scheduling100 xpCapacitated plant location - case study P150 xpReview data for case study50 xpDecision variables of case study100 xpObjective function of case study100 xpLogical constraints50 xpLogical constraint exercise100 xpLogical constraints exercise 2100 xp
Solve and evaluate model
This chapter reviews some common mistakes made when creating constraints, and step through the process of solving the model. Once we have a solution to our LP model, how do we know if it is correct? In this chapter we also review a process for reasonableness checking or sanity checking the results. Furthermore, we continue working through our case study example on the Capacitated Plant location model by completing all the needed constraints.Common constraint mistakes50 xpDependent demand constraint exercise100 xpConstraint combination exercise100 xpCapacitated plant location - case study P250 xpConstraints of case study exercise100 xpAdding logical constraint in case study exercise100 xpSolve the PuLP model50 xpChoose the model status exercise50 xpSolving production plan exercise100 xpSanity checking the solution50 xpReviewing model specification exercise50 xpSanity checking exercise100 xp
Sensitivity and simulation testing of model
In our final chapter we review sensitivity analysis of constraints through shadow prices and slack. Additionally, we look at simulation testing our LP models. These different techniques allow us to answer different business-related questions about our models, such as available capacity and incremental costs. Finally, we complete our case study exercise and focus on using sensitivity analysis and simulation testing to answer questions about our model.Shadow price sensitivity analysis50 xpSensitivity analysis exercise100 xpShadow price and slack exercise pt1100 xpShadow price and slack exercise pt2100 xpCapacitated plant location - case study P350 xpSolving the model case study exercise100 xpSensitivity case study exercise50 xpSimulation testing solution50 xpSimulation testing solution exercise100 xpWhat is the risk exercise50 xpCapacitated plant location - case study P450 xpSimulation testing capacitated model100 xpInterpreting simulation results exercise100 xpFinal summary50 xp
PrerequisitesData Manipulation with pandas
Aaren StubberfieldSee More
Senior Data Scientist @ Microsoft
I am a Senior Data Scientist with expertise in Machine Learning, AI, and data governance. Currently, I work for Microsoft's Digital Advertising, which has revenues of more than $10 billion in the fiscal year 2023. However, my experience is not limited to just the advertising industry. I have worked in the Supply Chain and Data Governance industries. With my vast experience, I have led numerous teams of data scientists and have been instrumental in the successful completion of many projects. My technical skills include the use of AI, like LLMs, Python, and other various tools necessary for the execution of data science projects. My passion lies in using data to gain insights and making data-driven decisions. I constantly strive to improve my skills and knowledge and am always open to learning new techniques and tools.