Summary

Demand & Supply Planning with R

Do you work in Supply Chain and are you looking for a way to perform easily some data processing, run demand and supply planning calculations and share your results into an interactive web app?

If yes, then this book is for you!

It provides a comprehensive, hands-on guide to leverage the robust capabilities of the R statistical programming language to address the core demand and supply planning challenges.

From data acquisition, cleaning and transformation to advanced statistical modeling, data visualization, and automation; you will learn how to plan your demand and supply, balance your charge and capacity, model an End-to-End supply chain network, and run different scenarios.

This book guides you through practical applications of key R packages for demand forecasting, inventory management, Distribution Requirements Planning (DRP), scenarios optimization and the creation of dynamic (shiny) web apps.

R is a popular open-source programming language and is specifically designed for statistical computing, data analysis, and high-quality graphics. It is widely used by researchers, data scientists, and statisticians for its extensive libraries and robust data visualization tools. R consistently ranks among the top 10 programming languages worldwide.

Key Topics Covered in this book

• Data Processing and Visualization : learn the techniques to import, transform and visualize diverse supply chain data sets with R, through famous packages such as tidyverse, highcharter or reactable.

• Automation : break free from Excel constraints and reduce hours of manual work into a single script execution.

• Ready-to-Use scripts : allowing you to plug-in your own data to perform classic Supply Chain calculations.

• Supply Planning and Inventory Management : master methods to calculate projected inventories, stocks coverage, replenishment plans, constrained demand, and explore “what-if” scenarios using various functions from the R package planr.

• Demand Forecasting : utilize R’s statistical and machine learning libraries (e.g., forecast, xgboost) for predicting future demand.

• Optimization & Scenario Analysis : use R to optimize resources allocation or capacity planning through the R package lpSolve.

• Analytics & Reporting : create interactive reports and visualizations, web apps, using the R package shiny for better analysis and decision-making.

Designed for supply chain professionals, students, data analysts, and R users looking for practical applications, this book creates the link between theoretical data science knowledge and real-world supply chain implementation.

You will get the skills to develop efficient, custom, and data-driven Demand & Supply planning solutions.

Ready? Let’s start!