2.3 System Dynamics
2.3.2 Relevance of System dynamics
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35 System dynamics can be a useful methodology that can be used to cross fertilise these approaches to develop more robust outputs (Akkermans & Dellaert, 2005). System dynamics enables an organisation or individual to move away from trying to understand the impact of the individual actor on the system but to understand and test theories and policies to the system. It helps us understand and explain the endogenous generation of macro behaviour from the microstructure of human systems (Sterman, 1989).
Akkermans & Dellaert (2005) states that a better understanding of the complex dynamics that determine performance of supply chains has become crucial for superior performance in supply chain management. Insights from system dynamics are now more needed then they have been during the past four decades. This is especially relevant since the supply chain of today has been cut into pieces and diversified in all areas and regions due to increased complexity. System dynamics is well suited to introducing a dynamic approach to developing problem solving and developing organizational strategy. Richardson (1999) stated that as we solve the more visible problems of the physical model with the sciences and world we need to increase focus on the less physical aspects, which is potentially more critical to success. With organisations and supply chains being split and becoming more complex, it is imperative that information sharing is at the highest level to ensure success, which requires a high level of transparency and trust (Akkermans
& Dellaert, 2005).
Whilst other model building methodologies focus on the ideal end state, System dynamics reveals the way in which the model was reached to describe the current state and then moves to the future state (Forrester, 1994). System dynamics hence displays how the problem under consideration is generated in the real world giving the role players an in-depth understanding of the problem and the environment in which it is found.
36 The linking of strategic decision making and feedback thinking is especially relevant given that the strategy and feedback worlds are complex and interdependent and makes mental simulation by individual’s difficult (Richardson, 1999). The efficacy and robustness of decision strategies lies not only in the availability of outcome feedback loops but depends crucially on the nature of the feedback action between decisions and the changes in the environment which condition future decisions. This structure consists of stock & flow diagrams, information networks, time delays and non-linearity, which characterize the organisation, problem and system (Sterman, 1989).
Qualitative maps can show causal relationships, feedback loops and can be used to gain buy in and hence change behaviour (Rouwette & Vennix, 2006).
The literature further highlights that typical behaviours and reward systems make “fire fighting”
an ingrained cultural norm. Changing this type of behaviour and thinking will require policy changes to ensure strict control and milestone gates are maintained (Repenning, Goncalves &
Black, 2001). It is found that individuals too often do not look at cause and effect. When they do, the assumptions are that the cause is closely linked in terms of time and space to the effect. This could lead to incorrect conclusions on root cause and hence on what to fix (Repenning & Sterman, 2001). Bianchi & Bivona, (2002) further highlighted that should decisions be made to drive one success factor without consideration of the others the result will be a longer term failure or loss.
The key here is that the interaction of a number of small events could have a high overall impact on the organisation (Repenning & Rudolph, 2002).
Morecroft, Lane, & Viita (1991) showed how a system dynamics model was used to aid in strategic decision making. Oliva & Sterman (2001) applied system dynamics modeling to service quality within the service industry and identified both qualitative and quantitative factors that impacts service. System dynamics was used to model the interactions between all these factors and to understand their impact. It explores how boundedly rational decisions often lead to unintended long term consequences. Organisations often work in a conflicting and suboptimal manner, in the sense of overall performance (Bullinger, Kuhner & Van Hoof, 2002).
The literature has also highlighted that qualitative analysis when done properly brings a high level of rigour and robustness to the model building process and hence the final model built (Lune- Reyes & Andersen, 2003). System dynamics adds causal factors such as human bounded rationality, information delays, managerial perceptions, etc to the more traditional supply chain management rules (Cagliano, DeMarco, Rafele, & Volpe, 2010).
37 Change and change management is a key component of many of today’s industry leading organisations who look for better methods to compete. System dynamics can be used as a change management tool to get buy in for decisions (Wyland, Buxton & Fuqua, 2000). Senge & Sterman, (1990) stated that for new policies to come into effect, individuals must go through their own learning process, as this is essential to the change management process.
Forrester as repeatedly stated that managers must be involved in the modeling process and the mental models of managers must be accessed. The involvement also helps when implementing changes as there is now buy in. This approach is called group or participative modeling (Rouwette
& Vennix, 2006). The more involved the individual the higher the propensity for buy in and behavioural change. It is therefore important to involve stakeholders at various levels within the organization. However, it is important to note that involving stakeholders in the process and utilizing their mental database as inputs does not guarantee success. The mental models of individuals are not powerful on their own but rather needs to be harnessed into a more holistic view. The use of system dynamics and a computer model is able to provide this (Ledet & Paich, 1994).
It was also noted that the new generation of employee’s job hop frequently. This means that labour turnover will result in churn within the business and supply chains. A system dynamics learning laboratory will hence be useful as a teaching tool to new decision makers who join the organization (Martinez-Olvera, 2008). Some of the benefits of learning labs as stated by Senge & Sterman (1990) include:
• Shortening the learning curve for new managers
• Improving communication skills
• Creating an atmosphere for organisational learning
• Clarifying and testing assumptions
• Making mental models explicit
Cross-functional integration among different departments represents an important aspect of organisational structure in terms of the types of lateral relationships and the degree of collaboration that exists between the different functions. It is stated that those organisations that are able to integrate specific functions in line with their strategy generally have a better performance (O' Leary-Kelly & Flores, 2002). Studies show that increased integration between sales & marketing
38 and operations helps to reduce overall operational costs and hence organisational performance.
Often demand uncertainty and business strategy variables are seen as exogenous variables (O' Leary-Kelly & Flores, 2002). This, however, implies that these leverage points are seen as out of the control of the organisations and hence a mind-set of helplessness could set it. System dynamics states that it can be modelled as an endogenous variable.
Guo, et al, (2001) emphasized the applicability of system dynamics as an appropriate approach to analysing the interactions and impact of various policies on the problem or case study selected.
System dynamics is further able to model a problem, evaluate alternatives as well as what needs to be done to prevent the negative future states from occurring. Whilst there has been numerous studies highlighting the applicability of system dynamics, it is by no means the perfect methodology and there is room for further applications to consider other research areas such as inventory control or queuing theory (Akkermans & Dellaert, 2005).