Today we have a look at current research regarding the improvement of resilience within a supply chain.
In their 2012 paper “Supply chain redesign for resilience using simulation” Carvalho et al. analyze supply chain resilience on the basis off a Portuguese automotive parts manufacturer.
Agent-based supply chain models are build using small entities (agents), which might represent a single company.
Each of the agents has its own goals and rules of operation programmed into a computer. The interaction between several agents of this kind leads to a more realistic and complex behavior of the system.
Today’s article is from the late 90s, but sets a great example for research methodology in supply chain risk management. But don’t worry, I will focus on the results, since they’re very interesting as well. The objective of today’s article (Supply Chain Management in Food Chains: Improving Performance by Reducing Uncertainty) is to show strategies (here called principles) to reduce uncertainty, and at the same time show the beneficial effects of reduced uncertainty.
Sometimes I am really amazed by the research topics of others. Even though I already read much about simulation and its potential benefits, up to now I have never seen a analysis of supply chain simulation performance on a larger sample. So I would like to share those insights here.
Today I would like to talk about a non-essential, but helpful part of supply chain management: Simulation.
Simulation can be used in a supply chain setting on many different levels. On a strategic level there are models to analyze scenarios for the optimal locations of one’s factory, on a tactical level inventory management and distribution policies are treated and on the operations side route-optimization is a generally used. Of course there are also non-simulation models for these tasks, but this article is not about the pros and cons of that.
I already reviewed some articles by Denis Towill primarily because he does some interesting research on simulation and supply chains, but also because I like his clear style in his articles.
In one of his early papers (1992) he teamed up with Naim and Wikner and described state of the art strategies to fight the bullwhip effect or as it is called in the paper by its older name: Industrial Dynamics.
Computer simulation has not been used on a professional scale until after World War II, and also then mostly for military uses like war games or simulations of atomic bomb explosions nowadays.
One of the first scientific papers on simulation has been published in the late 70s by Ören and Zeigler. They aggregate some fundamental knowledge about simulation and suggest an conceptual model for simulation, which I want to introduce today from the perspective of a supply chain
After the last more general entries on managers perception of risk and measuring SC performance I wanted to make a detour back to the basics.
Simulation is one of the tools, which can be used for analyzing supply chain dynamics, optimization and to support corporate decision making.
In his 2009 paper Brian Tomlin analyzes strategies to mitigate disruption risks in a three echelon supply chain.
Focus in his research is a single company, with its suppliers and customers. The objective is to maximize expected utility, while demand and supply are uncertain. There are two products available which can be used as substitutes. The time horizon for the decision maker is one season where the products can be sold.