Excel is a great tool. I love it. But let’s say you want to play with lots of data from lots of different files, and you want to do a lot of operations on that data. Or maybe you need to maintain data that is being entered and updated from many different sources and in […]
Inventory optimization refers to reducing excess inventory, avoiding lost sales due to unavailability of items in stock, well-defined safety stocks and reorder points, and much more. In this article I demonstrate how simulation can be used to master the challenges of inventory optimization. A commercial project example from a pharmaceutical supply chain (case study) underlines […]
Following the previous articles on interfaces (+) and (exact) solvers (+) for optimization in Python, in this article, I introduce some packages that provide an easy-to-use “interface” for artificially intelligent algorithms (AIAs) (e.g., heuristics, meta-heuristics, math-heuristics, learn-heuristics, hyper-heuristics, or sim-heuristics). AIAs may not reach globally optimal solutions, which means that the user should always be […]
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 but also operational processes. In this post I will provide a conceptual introduction to simmer. […]
In this article I implement a monte-carlo animation using gganimate in R. In one of my previous posts I introduced monte-carlo simulation. Monte-carlo simulation is one of the widely applied simulation techniques in supply chain management and operations management. I explained how one can assess the risk associated with stock price volatility using monte-carlo simulation. […]