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The concept of Supply Chain Management (SCM) is gaining importance among academicians and practitioners due to its impact on firm's competition in today's global economy. Competition today implies Quality, Lead time, Cost, services must be improved in order to stay profitable. Next generation supply chain management is expected to be more of reverse supply chain management with increasing attention during last decade due to its economic impact and stricter sustainable legislations. Present global environment is influenced by increased outsourcing, mergers, new technologies, e-business, Shorter time-to-market, reduced product lifecycle, make-to-order strategies, pull systems, uncertainty, forcing organizations adopt to new ways of doing business (Stefanovic et al.,2009). But, consequently these new approaches to handle supply chains have developed complexity, and uncertainty leading to disruptions. Supply chain disruptions affect not only the supply chain stakeholders cost, but also their risk profile (risk-return trade-off). As a consequence, to handle uncertainty about supply chain disruptions, the stakeholders can not only design risk information-sharing contracts but can also avail various operational risk-management tools and techniques. This paper considers a systems perspective to manage supply chain risks by identifying next generation supply chain issues and it's associated risks by utilizing system dynamics concepts and tools.

Supply chain consists of numerous links interconnecting vast networks and these links are exposed to various operational risk as well as disruption risks (Craighead et al., 2007) Operational risks are referred to inherent uncertainties such as uncertain customer demand, uncertain supply and uncertain cost whereas disruption risks are referred to major disruptions caused by natural and man-made disasters such as earthquakes, floods, hurricanes, volcanoes, terrorist attacks (Tang, 2006). Supply chain risk management consists of four management processes:

(1) Identifying the risk sources and drivers

(2) Evaluating and assessing the risks

(3) Mitigating risks within the supply chain

(4) Controlling risks by continuous process

Implementing the above mentioned risk management processes; we identify risks for next generation supply chains by systemic thinking. Applying system of systems concept to a specific supply chains, the supply chain network is represented in terms of nodes rand connectors with their interrelations. In next section, brief literature review on System Dynamics and supply chain risks is provided to get insights into research background. Later Systems of Systems approach is used to understand the typical supply chain and interrelationships. By identifying next generation supply chain issues and risks, model for system dynamics modelling process is developed for aerospace supply chain. With help of case studies and feedback loop diagrams attempt is made to identify future supply chain risks. Conclusion is provided in last section explaining the future scope of work.


The application of System Dynamics modelling to supply chain management has its roots in Industrial Dynamics (Forrester, 1961). The supply chain flows often create important feedbacks among the partners of the chain, thus making System dynamics (SD) a well-suited modelling and analysis tool for next generation supply chain management. System dynamics consists of causal loop diagrams with stock and flow equations. Causal loop diagrams play two important roles in SD. First, during model development, they serve as preliminary sketches of causal hypotheses and secondly, they can simplify the representation of a model (Georgiadis et al., 2005). Stock equations define the accumulations within system and flow equations define the flows among the stocks as function of time. The typical purpose of a SD study is to understand how and why the dynamics of concern are generated and then search for policies to further improve the system performance. Here policies refer to the long-term, macro-level decision rules used by strategic level management (Vlachos et al, 2007).

Earlier, Wikner et al. (1991) and Towill et al. (1992) simulated different supply chain improvement strategies on demand amplification. Sterman (2000) presents two case studies where SD is used to model reverse logistics problems. Minegishi and Thiel (2000) use SD to understand the complex logistics behaviour of an integrated food industry. They present a generic model and then provide simulation results applied to the field of poultry production and processing. Pierreval (2007) provides a continuous simulation approach to study a French automotive company. Similarly, SD model for capturing dynamic capacity planning for remanufacturing process in reverse supply chain is also developed (Vlachos, 2007). Oehmen (2009) has attempted system oriented modelling approach to develop two supply chain models, to determine causes and effects of supply chain risks. From literature it is observed that, in supply chain risk management only few attempt are made to understand the risks using system dynamics approach.

Supply chain risks are potential disruptions associated with inter-organisational logistics, caused by process inherent or external sources that negatively impact the objectives of the logistics network (Juttner et al. 2003). The literature suggests four categories of risks: supply, demand, operational, and security risks (Christopher and Peck, 2004; Manuj and Mentzer, 2008) similarly, Ghoshal (1987) has classifies risks as:

Macroeconomic risks associated with significant economic shifts in wage rates,

interest rates, exchange rates, and prices

Policy risks associated with unexpected actions of national governments

Competitive risks associated with uncertainty about competitor activities in

foreign markets

Resource risks associated with unanticipated differences in resource requirements in foreign markets.

Chopra and Sodhi (2004) classify supply risks as disruptions, delays, systems, forecast, intellectual property, procurement, receivables, inventory and capacity. There are several other classifications of supply chain risks in literature (Sinha et al, 2004; Finch, 2004; Kleindorfer and Saad, 2004; Tang, 2006; Tang and Tomlin, 2008).

Next generation supply chain managers need to consider the degree of complexity in their various global supply chains, and then classify risks to define their mitigating strategies.


The SD methodology, which is adopted in this research, is a modelling and simulation technique specifically designed for long-term, chronic, dynamic management problems. It focuses on understanding how the physical processes, information flows and managerial policies interact so as to create the dynamics of the variables of interest. The totality of the relationships between these components defines the "Systems" of the system. Hence, it is said that the 'Systems of Systems', operating over time, generates its dynamic behaviour patterns. The Systems of systems (SoS) concept originally evolved in the defence sector, but now it being widely applied in various fields of space exploration, health care, logistics, software integration etc.

Complex systems like supply chains are characterized by having large number of dimensions, nonlinear models, strong interactions, volatile parameters, time delays in dynamic structure (Jamshidi, 1983). Supply chain is similar such complex system consisting of complex network of stakeholders and their dynamic interrelationships. For SoS, there is no universally accepted definition (Sage and Cuppon, 2001). Various definitions for SOS are provided in literature. (Sage and Cuppon,2001; Pei, 2000; Luckasik,1998; Manthorpe, 1996).

Figure 1. Typical supply chain network links represented as 'System of Systems'

For understanding Supply chain, we follow one defined by Kotov (1997) as large scale concurrent and distributed systems that are comprised of complex systems. Figure 1 represents the system of systems approach to typical supply chain network showing the hub and spoke structure of supply chain system and interrelating network links within supply chain entities.


Next generation supply chain would be highly dominated by Information and Communication technology (ICT) and sustainability concerns. Based on literature survey, Identified issues for next generation supply chain are represented below:

1. Environmental regulations

2. Information and communication technology

3. 3PL/4PL Logistics service

4. Global market

5. Customer expectations

Theses identified next generation supply chain issues will feed as input to System Dynamics modelling process to identify impact of these issues on risk assessment parameters within aerospace supply chain. For assessment of risk the identified parameters are quality, delivery performance, cost, environmental initiatives, customer service and technical expertise.


System dynamics is effective for the study of the important flows of products or components through the main production areas of the network, rather than a detailed study of the flow of each product through the set of resources available in the network. System Dynamics (SD) is commonly used for analysing complex, dynamic, uncertain behaviour of supply chain network and to capture transient effects of flows (material, Information, Financial) in supply chain. SD provides valid description of real processes and integrates human with process and tools.

Based on our understanding, following are key characteristics of SD modelling:

Captures dynamic / stochastic behaviour

Ability- Holistic view of system

Integrates people, process & tools

Feedback/Inter-relationships of system

Compatibility-Mental model to computer model

Early warning for potential risks

Tool for structured development process

Pugh and Richardson (1981) suggest that a system dynamics modelling effort begins and ends with systemic understanding, adopting the System Dynamics Modelling Process proposed in this section we explain the proposed System dynamics modelling process for aerospace supply chain. Next generation supply chain issues and disruptions are identified and analysed for aerospace supply chain following the proposed model as shown in figure 2 leading to development of SCRM Toolkit.

Figure 2. Proposed System Dynamics modelling process for aerospace supply chain

Building the SD model for aerospace supply chain follows the steps by step approach from defining the problem, followed by developing generic supply chain later developing

the causal loop diagrams and developing the stocks and flows diagrams, followed by simulation model. It is most crucial in SD that the model structure provides a valid description of the real processes.

Defining the problem- from case studies

A case study is an empirical investigation that probes and examines responses of convenient influences within the real operational environment of the task, user, and system. The case study approach generally refers to group methods, which emphasise Qualitative analysis (Yin, 1984; Gable 1988), although some case studies are quantitative in nature. In the SD literature, Quantitative case studies have been used to validate SD simulation models (Senge and Forester, 1980; Graham, Morecroft, Senge and Sterman, 1982). In this paper for defining the problem, case studies in aerospace and automotive industries is carried to identify next generation supply chain risks. Table 2 shows the case studies identifying the supply chain disruptions and their risk impacts seen from quality, cost, performance, reputation perspective.

Table 2: Supply chain risks-case study

Risk variables: causal relationship

Causal loop diagrams are the basis on which the SD model is built. They depict, graphically, the interactions and cause-and-effect relationships among the different system parameters (Lertpattarapong, 2002). During model development, Causal loop diagrams serve as preliminary sketches of causal hypotheses and they can simplify the representation of a model. The structure of a dynamic system model contains stock (state) and flow (rate) variables. Stock variables are the accumulations (i.e. inventories), within the system, while flow variables represent the flows in the system (i.e. order rate). The model structure and the interrelationships among the variables are represented by causal loop diagrams. Figure 3 shows the causal relationship of identified next generation supply chain issues with risk assessment parameters.

Figure 3: Causal loop diagram for next generation supply chain risks

However, using simulation to study problems involving supply chain disruptions has its problems and challenges. Theses challenges are most evident in four areas like, Describing and modelling the events triggering the supply chain disruption for example how to describe SCD and its associated critical traits and location of disruption and identifying and setting approximate policies and parameters. Hence in this paper we have restricted the scope of research upto identifying risk factors that influence next generation supply chain and further investigation would be carried into possibilities of developing simulation model for risk assessment for further developing mitigation strategies as a part of SCRM toolkit.

Next Generation supply chain: Future risks

Due to strict green regulations:

Due to ICT failure:

Due to Global outsourcing:

Due to customer expectations:

Impact of logistics providers:


The research approach for the paper is based upon application of systems engineering techniques to understand supply chains and its use for managing various risks associated within the network. Next generation supply chain issues and risk variables are identified. A Causal loop diagram is depicted which considers the variables affecting next-generation supply chains. The causal linkages between the variables are then highlighted with regards to the supply chain process and the nodes and causes of future risks are identified. A proposed model for System dynamics modelling process for aerospace supply chain is developed to finally achieve SCRM toolkit. In this paper, The SD modelling approach identifies influential risk parameters for next generation supply chains which further would be mitigated through different risk management strategies as a future work towards developing SCRM toolkit. The paper thus presents a new perspective towards using systems thinking to manage future supply chain risks.