Forecast Inventory demand using historical sales data in R

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

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.

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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