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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.
4.84.8

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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.

Prerequisites

  • Language used: R

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.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Problem Statement
00:03:45
  Data Set Overview
00:00:49
  Read Data Set
00:05:40
  Cleaning and Combining Data
00:02:19
  Descriptive Statistics
00:11:02
  Forecasting Steps
00:05:07
  Types of Forecasting
00:03:53
  Data Preparation
00:14:29
  Apply K-Means clustering
00:07:23
  Rolling Data Set
00:01:34
  Grouping Data
00:14:42
  Outliers
00:02:46
  Add Time Element
00:13:50
  Plot Time Series
00:00:48
  Seasonal Decomposition of Time Series
00:05:53
  Time Series Model - Arima
00:26:58
  Build Arima Model
00:03:04
  Libraries required for Arima
00:00:44
  Auto Arima Model
00:10:59
  Manual Arima Model
00:05:16
  Single Exponential Method - Holt-Winters
00:03:57
  Double Exponential Method
00:05:25
  Triple Exponential Method
00:03:09
  Auto Exponential Method
00:01:48
  Neural Network
00:06:05
  Conclusion
00:01:18