Linnart Felkl M.Sc., Author at Supply Chain Data Analytics

Backlog simulation in FIFO production system

In this brief example I implement a simmer backlog simulation, using discrete-event simulation. I have stated that free tools such as simmer can be a strong support and can replace many of the commercial discrete-event simulation licenses currently in use. […]

Machine learning and discrete-event simulation: Exemplary applications

In this article I discuss machine learning as a supportive technology for making discrete-event simulation more resource efficient and effective. Discrete-event simulation is a technique used in manufacturing and logistics for problems that cannot be investigated with conventional analytical techniques. […]

Simmer R-package applied to simulate simple receival inspection process

In a previous blog post I documented simmer in R for discrete-event simulation. In this post I want to provide a more hands-on practical example. It is a very simple example, to be honest. Regardless of this, and regardless of […]

Simmer in R for discrete-event simulation

There are various simulation techniques of importance to supply chain data analysis and modelling. One of them is discrete-event simulation. Discrete-event simulation is especially useful for higly dynamic and interdependent systems, as is often the case in complex manufacturing systems […]

Python namespaces: Local, nonlocal and global assignments

Together with my co-authors I am proud to see that we have achieved a substantial amount of coding examples and use cases on this blog. Our contribution related to Python are growing. For this reason I want to keep publishing […]

Reading SPSS, SAS and Stata data into R using the haven package

In this short article I introduce the haven package in R, and how it can be used for importing data from SPSS in SAV-file format into R. I already demonstrated this as part of a longer coding example, in which […]

Monte-carlo simulation in R for warehouse location risk assessment

Monte-carlo simulation is a very popular technique when it comes to risk assessment. In previous posts I have introduced implementations of monte-carlo simulations in Python and R. I e.g. used monte-carlo simulation to assess the risk associated with price developments of […]

Commenting code in Python

This post provides a quick tutorial on how to comment Python code. There are three ways of commenting Python code: Single-line comments Multi-line comments Docstring comments Single-line comments In Python, single-line comments can be implemented with the # operator. This […]

Animated monte-carlo simulation with gganimate in R

In one of my previous posts I introduced monte-carlo simulation by demonstrating how one can assess the risk associated to a stock price using monte-carlo simulation. As described in my overview of simulation techniques for supply chain analysts and operations […]

Animating ggplot2 histograms with gganimate

I have already published various posts containing gganimate animations. For R programmers or analysts with profciency in R this is a very interesting package. It converts ggplot2-plots into animations. See e.g. my blog post introducing comtradr for international trade analysis (containing […]

Analyzing global commodity trade with comtradr in R

I have already introduced various interfaces in R and Python for querying data directly from within a script. Examples covered by me inlcuded the OECD database and its interface in R, i.e. the OECD package in R. I also e.g. covered twitteR, […]

Spatial data visualization pipeline in Python and R

This article introduces the concept of spatial data visualization pipeline in R or Python. I have written various posts covering spatial data visualization for supply chain analysis and management, in both R and Python. Relevant libraries comprise e.g. Leaflet in […]

Spatial data animation with ggmap and gganimate: A use case example

Following a series of introductionary posts on spatial data visualization in R, covering packages such as ggmap, leaflet, tidygeocoder, geopy, deckgl and osmdata, I here present a framework utilizing some relevant packages. The framework was developed in R. It was […]

Object identities in Python

In Python there is an inbuilt function for viewing the identity of an object. Id() returns the identity of an object. The identity of an object is a unique integer value. The identity is unique during the lifetime of an […]

bool() function in Python

As part of our Python documentation I introduce the bool() function in Python. The bool() function in Python implements default truth testing for converting an input argument into a Boolean value. The function returns the converted Boolean value. The bool() […]

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Linnart Felkl M.Sc.

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