Forecast Inventory demand using historical sales data in R

In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

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What will you learn

  • How to use forecasting methods
  • Selection of best forecasting method
  • Model comaparison and validation
  • Implementation using R
  • Usage in supply chain and logistics department

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Project Description

Planning a celebration is a balancing act of preparing just enough food to go around without being stuck eating the same leftovers for the next week. The key is anticipating how many guests will come. Grupo Bimbo must weigh similar considerations as it strives to meet daily consumer demand for fresh bakery products on the shelves of over 1 million stores along its 45,000 routes across Mexico.

Currently, daily inventory calculations are performed by direct delivery sales employees who must single-handedly predict the forces of supply, demand, and hunger based on their personal experiences with each store. With some breads carrying a one week shelf life, the acceptable margin for error is small.

In this machine learning project, we will develop a model to accurately forecast inventory demand based on historical sales data. Doing so will make sure consumers of its over 100 bakery products aren’t staring at empty shelves, while also reducing the amount spent on refunds to store owners with surplus product unfit for sale.

Curriculum For This Mini Project

 
  Problem Statement
03m
  Data Set Overview
00m
  Read Data Set
05m
  Cleaning and Combining Data
02m
  Descriptive Statistics
11m
  Forecasting Steps
05m
  Types of Forecasting
03m
  Data Preparation
14m
  Apply K-Means clustering
07m
  Rolling Data Set
01m
  Grouping Data
14m
  Outliers
02m
  Add Time Element
13m
  Plot Time Series
00m
  Seasonal Decomposition of Time Series
05m
  Time Series Model - Arima
26m
  Build Arima Model
03m
  Libraries required for Arima
00m
  Auto Arima Model
10m
  Manual Arima Model
05m
  Single Exponential Method - Holt-Winters
03m
  Double Exponential Method
05m
  Triple Exponential Method
03m
  Auto Exponential Method
01m
  Neural Network
06m
  Conclusion
01m