اثربخشی قیمت گذاری واکنش گرا در برابر اختلالات زنجیره تامین
Abstract
1- Introduction
2- Literature review
3- Research methodology
4- Model description
5- Discussion of simulation results
6- Sensitivity analysis of structural parameters
7- Conclusion, limitations, and future research
References
Abstract
Disruptions of the material flow can cause serious financial damage for supply chain partners to the extent of even jeopardizing their survival. To cope with this hazard, several proactive and reactive mitigation strategies have been suggested in the research literature of supply chain risk management. To our knowledge, only back-up supply and structural changes of the information flow have been incorporated by network level simulation models. Moreover, a detailed assessment of the disruption length’s effect on the resulting costs of a disruption is still missing. We have conducted a simulation study with system dynamics, split into three simulation experiments, to quantify the disruption costs subject to various disruption lengths and to evaluate the effectiveness of responsive pricing regarding the costs saved as well as the order fulfillment rate. We have analyzed the height of the price change and the operating time of this reactive strategy to be able to provide insights into the general behavior of the system and the strategy. For this purpose and since no real supply chain data has been available, we have extended the approach of Wilson (2007) to a two-product model with monetary parameters. A uniform space-filling approach combined with Kriging interpolation has been selected to create response surfaces for two of our three research questions. Our study reveals that there is a disproportionate influence of the disruption length on the overall disruption costs. In our model, the effect of the height of the price change is significant. The ideal height of price change initially increases for longer disruptions until an optimum range is reached. The operating time of the price change has a smaller effect on the effectiveness. Despite the fact that the effectiveness can be further optimized by considering the operating time, our study indicates that near-optimum results can be obtained by having responsive pricing in effect for the acute time span of the disruption.
Introduction
Supply chain disruptions are considered to be a combination of an unforeseen triggering event and the resulting consequences which jeopardize the flow of material and normal business activities significantly (Wagner and Bode, 2006). Well documented examples of costly supply chain disruptions have been the fire at a Phillips semiconductor plant in 2000, Hurricane Mitch’s catastrophic damage to banana production in Central America in 1998, and the 1999 earthquake in Taiwan, which caused the spot price of memory chips to increase by a factor of five (Chopra and Sodhi, 2004; Norrman and Janson, 2004; Latour, 2001; Griffy-Brown, 2003; Papadakis, 2006). In 2011, an earthquake off the northeastern coast of Japan caused major disruptions in multiple supply chains due to a combination of earthquake damages, flooding by the resulting tsunami, and the radiation exposure in consequence of core meltdowns of the Fukushima Daiichi nuclear power plant stemming from the damaged water cooling system (Tabuchi, 2011; Srinivasan and Rethinaraj, 2013). Japan’s gross domestic product dropped by 2.1% in the second quarter of 2011 and exports by 8% (Fujita and Hamaguchi, 2012). Direct economic losses due to these events have been estimated around $340 billion (Srinivasan and Rethinaraj, 2013; Chakravarty, 2013). Toyota, as one of the many effected automotive manufacturers, experienced immediate out-of-stock events for over 400 parts and a reduction of production capacity for the following six months (Tabuchi, 2011; Norio et al., 2011). Hendricks and Singhal (2005) have detected an increase in the number of reported disruptions between 1989 and 2000 and empirically identified drastic immediate and long-term effects for the associated company’s stock value and equity risks.