In this course, we'll make predictions on product usage and calculate optimal safety stock storage. We'll start with a time series of shoe sales across multiple stores on three different continents. To begin, we'll look for unique insights and other interesting things we can find in the data by performing groupings and comparing products within each store. Then, we'll use a seasonal autoregressive integrated moving average (SARIMA) model to make predictions on future sales. In addition to making predictions, we'll analyze the provided statistics (such as p-score) to judge the viability of using the SARIMA model to make predictions. Then, we'll tune the hyper-parameters of the model to garner better results and higher statistical significance. Finally, we'll make predictions on safety stock by looking to the data for monthly usage predictions and calculating safety stock from the formula involving lead times.
This course is part of the Machine Learning for Supply Chains Specialization
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About this Course
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Shareable Certificate
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 4 of 4 in the
Intermediate Level
We reccomend you take the first two courses in the specialization (or are familiar with the content) before attemptign this capstone project.
Approx. 10 hours to complete
English
What you will learn
Calcualte safety stock using SARIMA predictions combined with manipulaitng lead times.
Skills you will gain
- Machine Learning
- SARIMA modeling
- timeseries
- demand prediction
- Safety Stock
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 4 of 4 in the
Intermediate Level
We reccomend you take the first two courses in the specialization (or are familiar with the content) before attemptign this capstone project.
Approx. 10 hours to complete
English
Offered by
Syllabus - What you will learn from this course
4 hours to complete
Exploratory Data Analysis Using Pandas and Groupby
4 hours to complete
4 videos (Total 6 min)
3 hours to complete
Demand Predictions Using SARIMA
3 hours to complete
2 videos (Total 2 min), 1 reading, 1 quiz
3 hours to complete
Calculating Safety Stock
3 hours to complete
2 videos (Total 2 min)
About the Machine Learning for Supply Chains Specialization

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