Managing Risk of Supply Chain Disruptions

Tong Shu, Shou Chen, Kin Keung Lai, Xizheng Zhang, Shouyang Wang

• Publisher: Routledge
• Year: 2014
• Print ISBN: 9780415841955
• eBook ISBN: 9781317691891
• Edition: 1
• Topic: Business
• Subtopic: Business General
• Index: Business

This book discusses important issues related to managing supply chain disruption risks from various perspectives. It explores the essence and principles relating to managing these risks and provides the framework and multi-goal model groups for managing such risks.

The book also discusses research development of managing supply chain disruptive risks, supply chain risk conduction and loss assessment methods of supply chain disruptive events. It also includes the consideration of supply chain coordinating models in the cases of demand and supply disruption risks. It also deals on the subject of managing models of supply chain disruption risks by looking at manufacturers and responding decision methods oriented towards demand in disruption and coordination. It also summarizes the relevant findings and provides future research questions and orientations.

The book will contributes significantly to the growing body of knowledge concerning the theory of managing supply chains.

1 Introduction

In the past decade, a multitude of events related to supply chain disruptions have occurred in succession, causing great losses to enterprises. As a result of the earthquake in Central Japan in 2007, Toyota, Nissan, and other major automobile-manufacturing enterprises had to shut down their production lines either completely or partially for 1–4 days. The main reason was that companies that produced auto parts and components, such as sealing rings for automatic transmission and piston rings, were affected by the earthquake and had to shut down, and thus the supply was interrupted.

Supply chains face disruption risks caused by natural disasters, events, and catastrophes; social unrest and related events; and other factors or events related to the logistics process, market demand fluctuations, information gap, and human errors, or a combination of several such factors (Sheffi, 2001). It follows that diverse factors of significantly varying forms might induce supply chain disruptions. Consequently, supply chain disruption risks refer to events that might emerge in supply chains and influence flows of materials and components (Svensson, 2000).

According to theories such as the lean, just-in-time, and centralized manufacturing, enterprises have entered an era of super-strong connections. The risks induced by supply chain disruptions often get transferred to the entire system of enterprises, and the cascading effects can destroy safe operations of supply chains. Entire supply chains can become interrupted and invalid, and the value chain system related to supply chains can get disturbed and then collapse.

Currently, global economy is maintaining a rapid growth rate, and the living standards of common people are improving significantly. The processes of marketization and liberalization have accelerated in several countries, where various risks will emerge in their development. Many enterprises in each country across the world are gradually getting integrated, and, therefore, risks of supply chain disruptions are inevitably transmitted to the markets and the real economy in each nation.

Recent research has focused on functions and strategies related to intra-enterprise and inter-enterprise transactions in supply chains, laying particular stress on the reduction of enterprise operating cost, optimized resource allocation, cutting manufacturing and logistics expenditure, and coalition strategies, among others, for the sake of improving the long-term performance of supply chains and their associated enterprises. The management and minimization of supply chain risks constitute not only a major component of supply chain management but also management of enterprise risks. In both theory and practice, the importance of managing supply chain risks has been acknowledged. However, the behavior of supply chain disruptions varies dramatically, making its standardization difficult.

The status quo

Origin, categorization, influence, and conduction of supply chain disruptions risk

Supply chain risks have attracted growing attention from enterprises and scholars; the Center for Transportation and Logistics at MIT has studied this phenomenon and defined its origin (Sheffi, 2001). Chapman et al. (2002) analyzed the vulnerability of supply chains to disruptions and proposed the 3P management approach for managing supply chain disruption risks. Zsidisin (2003) defined supply chain risks and noted that buyers encounter supply risks emanating from impact factors, market characteristics, and supply chain risk events related to suppliers. Cavinato (2004) analyzed the sources of supply chain risks from five aspects, namely, logistics, capital flow, information flow, relationship network, and innovation network. Chopra et al. (2004) discussed nine risks, including supply chain risks, and examined potential sources and preventive measures related to risks. They pointed out that different risks might interact, and that it is important to strike a balance between risk and profits when risk management measures are implemented. At the same time, Lei and Xu (2004) introduced the issue of managing events-related supply chain disruptions, defined and categorized events, and analyzed emergency management of disruptive events. Built on causes and forms of supply chain events, Ding (2006) explicated forecasting methods and preventive mechanisms for supply chain events. Zhu and Li (2006) also discussed the methods of studying supply chain events by typology, classification, and staging.

Kleindorfer and Saad (2005) further categorized supply chain disruptions as a whole and evaluated their influence, introduced 10 corresponding risk management methods, and summarized experiences of American chemical enterprises in managing supply chain events and risks. Elkins et al. (2005) also introduced 18 suggestions proposed by the Supply Chain Resource Association to deal with supply chain disruptions. Zhou et al. (2006) analyzed supply chain disruptions and discussed their findings from the perspective of risk management. They state that inventory reduction decreases the possibility of supply chain disruptions for enterprises, and that the variations of supply chain forms reduce the loss. Kull and Closs (2008) investigated supply chain disruptions in the second tier on the basis of theories related to inventory and resource dependency. The results indicated that a rise in inventory increases risks rather than decreasing them in the hierarchical supply chain, requires requests enterprises to treat supply chain disruptions more systematically.

Supply chain risk induction consists of basic elements such as risk initiators, propellants, conduction carriers and recipients, forward and reversal conduction in risk chains related to supply chains, and network-centralized and interactive conduction patterns in the course of conduction (Cheng and Qiu, 2009). Cranfield (2002) suggested that supply chain risks emanate from a variety of factors; as supply chain enterprises are interdependent, problems emerging from any enterprise spread to and affect the whole supply chain, which amplifies the risk. Chen and Xu (2007) introduced the coefficient of resilience and discussed the effects of supply chain risk conduction on enterprises. Their results showed that the price risk of the supply chain gradually declines as products move downstream and manufacturers’ and retailers’ profits transmit risks to enterprises in the next node gradually, through risk propellants, and eventually achieve risk adjustment. Zhai (2008) constructed risk conduction models and provided mathematical analysis related to collaboration and innovation among enterprises on the basis of the definition of risk conduction, knowledge transfer and collaboration, and innovation. Also, he proposed that the management of risk conduction can be enhanced through prevention and process controls.

As discussed in the preceding text, extant literature has shown that research on the origin, classification, and effects of supply chain disruption risks are grounded on public events or macro-level dimensions, while, in reality, it is the micro-level mistakes or errors in the supply chain that lead to supply chain disruptions. Different supply chain enterprises cause different types of disruptions, which are not consistent in the macro or micro dimensions. Even though the supply chain risk conduction has already been expounded, it is necessary to further investigate the specific supply chain risk conduction mechanism.

Early warning and control of supply chain disruption risk

The identification of supply chain disruption risk is an important step in managing the frequency and impact of supply chain disruptions. Thus, Trkman and McCormack (2009) demonstrated new methods to identify and predict supply chain risks and discussed supplier assessment and classification methods based on supplier characteristics, performance, and supply chain properties. As the transformation has been made from storage to progressive perspective in the supply chain among modern enterprises, a number of advantages as well as new risk sources are generated for enterprises, which reduces the vulnerability of supply chains. Neiger et al. (2009) noted that value-centered projects could help identify supply chain disruption risks and improve performance of the supply chain and its members. Ellis et al. (2010) studied the importance, possibility, and risk of supply chain disruptions from the perspective of the behavior risk theory and constructed a product and market factor model. They verified their model by analyzing data related to 223 purchasing managers and buyers, which indicated that the possibility and importance of supply disruptions play a pivotal role in identifying supply chain risk.

In terms of the assessment of supply chain disruption risks, Harland et al. (2003) decomposed the management of supply chain risks into a number of cyclic processes such as risk identification, risk assessment, formulation of risk control plans, and implementation plans. Grounded on case studies in the electronics department, they suggested that a framework of warning, assessment, and management of early supply chain disruptions should be constructed. Kleindoffer and Saad (2005) claimed that natural disasters, strikes, economic crises, and terrorists’ actions might result in disruption risks, and as such a conceptual framework is constructed to assess and reduce supply chain disruption risks. He studied the issue of supply chain risk management system by using data related to American chemical industry events. Wu et al. (2006) proposed a comprehensive approach to classify and assess supply disruption risks, found supply risk factors, and constructed a model for the classification of supply risk factors in the form of stages. Schoenherra et al. (2008) assessed supply chain disruption risks in relation to American manufacturing enterprises purchasing decisions, found 17 risk factors, and classified them by integrating action research and analytic hierarchy process. They came up with an assessment model grounded on supply chain disruption risk factors in an empirical study. With respect to the assessment of supply chain disruption risks, it is common to use chance constrained programming (CCP), data envelopment analysis (DEA), and multiple objective programming (MOP) models. Wu and Olson (2008) simulated supply chain risk at the third stage and its probability distribution features with the risk assessment models. The new model constructed enabled the enterprise decision-makers to strike a balance between the anticipated cost, quality levels, and delivery time.

Smelzer and Siferd (1998) discussed supply chain disruption risks from the perspective of purchasing managers, analyzed supply disruption risks by applying the transaction cost theory and the resource dependency model, and pointed out that active purchasing management meant managing the supply disruption risk. Zsidisin (2003) analyzed suppliers’ operational risks, suppliers’ productivity constrained risk, product quality risk, technology change risk, and various disaster risks, and maintained that a huge number of strategies and techniques could be applied to minimize supply disruption risks and their effects. Hallikas et al. (2004) came up with the general structure of supply network risk control, examined supplier network risk control in complicated network environments, and showed that risk exposure increases as the interdependency among enterprises grows.

For the sake of cost advantages and market shares, many enterprises change their organizations and production. Sheffi (2005) showed that the construction of flexible supply chains could deal with disruption risks. Tang (2006) reviewed a number of quantitative models related to supply chain disruption risks and proposed a framework of supply chain risk control in relation to the issue of vulnerability of supply chain systems induced by outsourcing of manufacturing and product diversification. As supply chains become active, it is vital to respond to real-time emerging events. Goh et al. (2007) applied the Moreau-YosidaLiu method and proposed a random model concerning the issue of hierarchical global supply chain network for the purpose of achieving maximum profits and minimum risks. Liu et al. (2007a) showed that the Timing Petri Network Model could be applied for coping with supply chain disruption risks. They designed models to simulate supply chain disruption risks, and studied enterprise performance by using sensitivity analysis in relation to parameter values. Yoo et al. (2010) came up with the optimized budget allocation method to reduce the simulated value related to supply chain disruptions by applying the nested partition method, aiming to simulate discrete events to improve supply chain optimization efficiency.

For example, Hendricks and Singhal (2003, 2005) examined the effect of supply chain disruption risks and uncertain factors on enterprise performance by drawing on data from listed companies.

As discussed earlier, extant literature has shown that identification of supply chain disruption risk constitutes a significant link in managing supply chain risks. Although there are some findings concerning supply chain progress, enterprise value, and behavior risk theory, research on disruptions risk is still at the preliminary stage. Conceptual and mathematical models have been proposed to assess supply chain disruption risks, but the majority of them are empirical, and, therefore, the models need to be verified by extensive application. In order to manage supply chain disruption risks, many feed-forward control methods have been proposed, such as active purchasing management, supplier network risk control method, enhancement of flexibility in supply chain, and sensitivity analysis through simulation.

Optimization of supply chain disruptions

It is essential for enterprises to assess both internal and external environment and conditions, and initiate the corresponding measures to reduce the potential consequences and optimize the performance of various resource allocations to cope with risks. As mentioned earlier, what follows will be concerned with management strategies of inventory or manufacturing in the supply destabilization caused by supply chain disruptions, coordinating measures to manage supply chain disruption risks, and to recover from supply chain disruptions.

Inventory or production management strategies in supply disturbance caused by supply chain disruption

To improve supply reliability, enterprises can adopt either duplex or multiplex patterns. That is to say, enterprises should order from more than one supplier. For this, researchers have considered enterprises that divide their orders between two suppliers and the effects of this strategy on inventory management, and have constructed and analyzed supply models accordingly (Anupindi and Akella, 1993). Moinzadeh and Aggarwal (1997) applied the production strategy (s, S) to examine the optimal decision related to unreliable production storage system in response to supply chain disruptions. Meanwhile, Parlar (1997) investigated the issue of inventory management with assumed random demand and lead time, when such events as machine faults and strikes by workers occur. The supply disturbance is considered a Markov process. When events lead to supply chain disruptions, the optimal inventory is achieved by target function of average cost in the constructed time frame.

Abboud (2001) constructed a production storage system model based on Markov chain and analyzed suppliers’ cost function, and treated machine faults and repair time as random parameters. He also compared the results when time was treated as the continuous parameter. Li et al. (2004) showed that unpredictable events such as machine fault, order cancellation, maintenance errors, and strikes can lead to supply disruptions and affect the production storage system; the construction of inventory checking system within one period can help cope with random demand and uncertain supply, and hence supply certainty can be described as an alternative renewal process. Xiao and Yu (2006) discussed the gaming conditions related to maximal strategies of income and profits when supply chain environment was normal and analyzed the optimal selection with the strategy of stable plans. As events result in demand changes and materials supply discontinuity, the optimal strategy of retailers in supply chains is provided. Taskin and Lodree Jr. (2010) discussed the purchase and production decision in hurricane seasons and constructed a random model of inventory control at different stages on the basis of hurricane forecast.

In conclusion, the literature has shown that enterprises can adopt duplex and multiplex patterns to deal with supply chain disruption risks. Although the ordering quantity and inventory strategies are determined, the previous studies were based on homogeneous time spans. A random model of inventory control at different stages has been constructed with hurricane forecast as the premise in some research works but it failed to extend to normal or accidental supply chain disruption risks.

Coordinating methods in supply chain disruption risk management

When logistics, information flow, and capital flow lag behind relatively in supply chains, it is essential that the supply chain be established on the basis of disruption risk management to improve their capacity and performance levels (Li and Zhang, 2003). Disruption risk is a significant factor that affects supply chain efficiency, and the supply chain risk management system and flow can be constructed to discover disruption risk rapidly (Zhang et al., 2004). Enterprises, suppliers, and retailers can collaborate (Wang and Chen, 2004), such that the supply chain is able to respond swiftly, and competitiveness is enhanced. Liu et al. (2007b) established a model of fuzzy search through the fuzzy mathematical method, and the relative managerial information can be found by integrating database technology to facilitate enterprise managers to make correct decisions concerning supply chain disruption risks.

As the demand function is fixed, sudden changes in demand can affect a supply chain consisting of suppliers and retailers in the second stage. For this, Xu et al. (2003) considered the optimal response in the context of power centralization and separation under a quantity discount contract. Wang and Hu (2006) studied the optimal strategy to deal with emergency events in supply chain related to power centralization and decentralization in the third stage of supply chains, when the market demand alters suddenly and the extra cost is non-linear. Xiao et al. (2005) showed that manufacturers can change unit wholesale price and discount rate to modify supply chain when demand changes. They also showed that manufacturers alter their production if investment sensitivity coefficient changes beyond a fixed range. Zeng and Wang (2007) discussed the issue of demand distribution d...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• The authors
• List of figures
• List of tables
• Acknowledgments
• 1. Introduction
• 2. Risk conduction of supply chain disruptions
• 3. The loss assessment methods of supply chain disruptive events
• 4. Supply chain coordinated models under the risk of demand disruptions
• 5. Supply chain coordinated models under the risk of supply disruptions
• 6. Supply chain disruption risk management models with manufacturers as the core
• 7. Responding decision-making methods oriented to collaboration demands in disruption
• 8. Conclusions and further research
• References
• Author index
• Subject index