Monitoring and Warning Mechanisms of Supply Coordination in Assembly System under Delivery Uncertainty

Introduction

In a common JIT assembly system, if one of the suppliers does not deliver its components on time or in incorrect quantity, the manufacturer cannot assemble the product on time or in less quantity than original target, which will not only damage the interests of other suppliers but also jeopardize the interests of enterprises and reduce the competitiveness of the supply chain. Obviously the delivery of a certain components is disrupted or postponed, the manufacturer's production will be greatly affected, followed by the penalty cost increased dramatically (Tomlin, 2005, 2009).According to Frank et al. (2008) statistics, dissatisfaction that upstream enterprises can’t provide components for the downstream enterprises on time or in insufficient quantity in China had reached 18.35%, and 2.33% of them was very disappointed. While dissatisfaction that the downstream enterprises cannot provide accurate information for the upstream enterprises had accounted for 10.47%, and 1.60% of them was very dissatisfied. In order to achieve the predetermined goal of the assemble system and ensure each supplier coordination in delivery time and delivery quantity, supply process in assembly system should be monitored and warned, which can contribute to taking effective measures timely when there are deviations during the supply process.

In recent years, the uncertainty of assembly system in upstream supply chain increased significantly due to influence of natural disasters, strikes, terrorist attacks and political instability and other factors. Especially after U.S. 9/11 incident, the supply uncertainty and its associated potential losses are becoming much larger. Some studies suggest that the frequency of disaster events is increasing year by year, and its harms are gradually rising up. Natural disasters and unexpected social events often cause the supply process of assembly system unstable and even interrupted, which eventually leads to enterprise’s production shutdown, loss of market share and other serious consequences. In 2000, Philips Semiconductor Factory’s fire leads to Ericsson’s supply disruptions of the chip, which caused the loss of 1.8 billion Ericsson and 4% of market share loss (Norman and Jansson, 2004). In July 2010, Hitachi’s unexpected shortage of car engine control part resulted in Nissan’s plant shutdown for 3 days in Japan, and the production of 1.5 million cars affected. In March 2011, Japan 9 earthquakes in northeast region devastated the area of ​​industrial enterprises. Car production of three major Japanese automakers, Toyota, Honda, and Nissan, are affected by the disruption of supply chain and some joint ventures in China also had different levels of supply disruptions. Generally, after investigation of 800 companies’ disruption cases, Hendricks and Singhal (2003, 2005a, 2005b) find that firms that experienced supply glitches suffer from declining operational performance and eroding shareholder value (e.g. the abnormal return on stock of such firms is negative 40% over three years).

Supply chain risk management has emerged as an important source of competitive advantage and an effective method of reducing vulnerability in a supply chain (Lin and Zhou, 2011).Therefore, the field of supply chain risk has attracted more and more attention from both practitioners and researchers over the past decade (Sawik, 2011). Research addresses the two risk levels: operational risks or disruption risks. Operational risks with high likelihood and low impact refer to inherent uncertainties arising from the problems of coordinating supply and demand such as uncertain customer demand, uncertain supply, and uncertain cost. Disruption risks with high impact and low probability refer to the major disruptions to normal activities caused by natural and man-made disasters such as earthquakes, floods, fires or equipment breakdowns, labor strikes, terrorist attacks, etc (Sawik, 2011b). The issue of linking risk assessment with risk mitigation for low-probability high-consequence events such as disruptions of supplies is discussed by Kleindorfer and Saad (2005), Cohen and Kunreuther (2007), and Hallikas (2004). In Kleindorfer and Saad (2005) a set of 10 principles is formulated for specifying sources of risk, assessment and mitigation of risk. Following Paul Kleindorfer’s framework for risk analysis, Knemeyer et al. (2009) consider a proactive planning for catastrophic events in supply chains. The proposed proactive planning process involves four critical steps: identification of key supply chain locations and threats, estimation of probabilities and loss for each location, evaluation of alternative countermeasures for each location, and selection of countermeasures for each location.