Learn Data Science for Business

Supply Chain Analysis with R Using the planr Package

Written by Matt Dancho

Hey guys, welcome back to my R-tips newsletter. Supply chain management is essential in making sure that your company’s business runs smoothly. One of the key elements is managing inventory efficiently. Today, I’m going to show you how to estimate inventory and forecast inventory levels using the planr package in R. Let’s dive in!

Table of Contents

Here’s what you’ll learn in this article:

Get the Code (In the R-Tip 084 Folder)

How to Project Inventories with the planr Package

Why Inventory Projections Are Crucial to Supply Chain Management

Supply chain management is all about balancing supply and demand to ensure that inventory levels are optimized. Overestimating demand leads to excess stock, while underestimating it causes shortages. Accurate inventory projections allow businesses to plan ahead, make data-driven decisions, and avoid costly errors like over-buying inventory or getting into a stock-outage and having no inventory to meet demand.

Enter the planr Package

The planr package simplifies inventory management by projecting future inventory levels based on supply, demand, and current stock levels.

Supply Chain Analysis with planr

Let’s take a look at how to use planr to optimize your supply chain. We’ll go through a quick tutorial to get you started using planr to project and manage inventories.

Step 1: Load Libraries and Data

First, you need to install the required packages and load the libraries. Run this code:

This data contains supply and demand information for various demand fulfillment units (DFUs) over a period of time.

  • Demand Fullfillment Unit (DFU): A product identifier, here labeled as “Item 000001” (there are 10 items total).
  • Period: Monthly periods corresponding to supply and demand.
  • Demand: Customers purchase and reduce on-hand inventory.
  • Opening: An initial inventory of 6570 units in the first period for Item 000001.
  • Supply: New supplies arriving in subsequent months.

Step 2: Visualizing Demand Over Time

The first step in understanding supply chain performance is visualizing demand trends. We can use timetk::plot_time_series() to get a clear view of the demand fluctuations. Run this code:

This code will produce a set of time series plots that show how demand changes over time for each DFU. By visualizing these trends, you can identify patterns and outliers that may impact your projections.

Step 3: Projecting Inventory Levels

Once you have a good understanding of demand, the next step is to project your future inventory levels. The planr::light_proj_inv() function helps you do this. Run this code:

This function takes in the DFU, Period, Demand, Opening stock, and Supply as inputs to project inventory levels over time by item. The output is a data frame that contains the projected inventories for each period and DFU.

Step 4: Creating an Interactive Table for Projected Inventories

To make your projections more interactive and accessible, you can create an interactive table using reactable and reactablefmtr. I’ve made a function to automate the process for you based on the planr’s awesome documentation. Run this code:

This generates a beautiful interactive table where you can filter and sort the projected inventories. Interactive tables make it easier to analyze your data and share insights with your team.

Conclusion

By using the planr package, you can project inventory levels with ease, helping you manage your supply chain more effectively. This leads to better decision-making, reduced stockouts, and lower carrying costs.

But there’s more to mastering supply chain analysis in R.

If you would like to grow your Business Data Science skills with R, then please read on…

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