Today will be a one-article-long-excursion in the world of production planning models.
Supply chain management of course should take a high level view of the supply and demand networks, nonetheless there is probably no supply chain which will work without physical products and most even have one or more at their core.
The quantification of supply chain planning is the next step in the field of supply chain optimization. After operational and logistical aspects have been modeled and optimized, margins for further improvement remain slim.
Based on this premise the paper I review today suggests and tests several alternative multilevel planning approaches to gain further supply chain improvements by optimizing the mid-term supply chain design.
This week is dedicated to the works on supply chain management from Greek supply chain researchers. Today’s article has been published in the Journal of Management Sciences (Omega) by four researchers from northern Greece and the UK.
This review is about a preprint article which already has been accepted for publication by the “European Journal of Operational Research”. But since there is only a limited space for articles in each issue of the journal, final publication of the article is delayed.
Defining a conceptual framework for supply chain risk management can support thinking about risks in supply chains and streamline the decision making process, and therefore improve the current supply chain at hand.
This is similar to a brown-field approach, where gradual changes and risk mitigation strategies are employed onto an existing supply chain. Thus another source for improvement strategies can be a green-field approach, where the supply chain is modeled and optimized to generate new input for real-world optimization.
Today I want to describe yet another supply chain case study where Monte Carlo simulation is used as decision support for strategic / tactical supply chain decisions.
Risk in supply chains can be included in several different ways into the decision making process.
No Risk
A statement in many supply chain models is that some/most/all parameters of the model are fixed (e.g. fixed demand, zero probability of a hurricane).
The result is, if the real value of this parameter diverges from the assumptions, the results of the model will be flawed to a certain degree (up to completely unusable).
Supply chains risks can also be analyzed in a specific industry context and this is exactly what Agrell et al. (2004) did with telecom supply chains. They used a three tier SC (2nd tier supplier, EMS, OEM) to include the selection, coordination and motivation of independently operating suppliers in the model.
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.
