Strategic management context

ABM has been increasingly used to model business interactions issue. Many of them simulate or adopting a well-proven theory to the agent-based model, such as the Prisoner’s dilemma or game theory (Axelrod 1997a) in business and politics, NK model (Robertson and Caldart 2009) in strategic management, and Hotelling’s competition model (Wilensky 2013) in economics.

Simulation in social science, including ABM in strategic management, is employed as a methodology rather than as a tool to solve a problem (Gilbert and Terna 2000). It helps social scientists to develop a theory, which is more complex than predicting the future of a system. This perspective of the use of simulation is opposite to engineering and operational research field, which more focuses more on prediction than theory development. ABM has also been considered as a sensible approach to model a market (Onggo 2016). This is because a market is formed by interactions among individual - whether it is customers or individual firms. The result of individual behaviour creates market behaviour that emerges at system level.

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Table 3.1 Previous work of supply chain competition and collaboration that employ ABM

Author(s) Topic Scope

Coll Comp Plan Bull Netw Sche Trus Inve Pro Log Risk Info Fina Pric

Forrester (1962) ! ! !

Swaminathan et al. (1998) ! ! !

Ahn et al. (2003) ! !

Xue et al. (2005) !

Arunachalam and Sadeh (2005) ! ! !

Caridi et al. (2005) ! ! ! !

Zhang et al. (2006) ! !

Jiao et al. (2006) ! ! !

Kwon and Lee (2007) !

Zarandi et al. (2008) ! !

Zhu (2008) ! !

Dimitriou et al. (2009) and

Dimitriou (2010) ! !

Cheng (2011) ! ! ! !

Kwon et al. (2011) ! !

Fu and Fu (2012) ! !

Chen et al. (2013) ! ! !

He et al. (2013) ! ! !

Santos et al. (2013) ! !

Hsieh and Lin (2014) ! !

Mohamed et al. (2015) ! !

Note:

Coll : Collaboration/coordination Sche : Scheduling Risk : Supply chain risks

Comp : Competition Trus : Trust Info : Information sharing

Plan : Supply chain planning Inve : Inventory Fina : Supply chain financial aspect Bull : Bullwhip effect Pro : Product development Pric : Pricing

Netw : Network/supply chain configuration Log : Logistics

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As ABM is used to understand the real world rather than to solve a problem, the studies are mostly theory-driven works (Siebers and Onggo 2014). Also, the model is used for learning and understanding the problem rather than implementing the findings in the real world. It means no empirical data is required to the modelling approach so that dynamic hypotheses play a major role in the model development.

Moreover, ABM allows social researchers, including strategic management researchers, to undertake inductive and deductive analysis. Inductive finds patterns from empirical data and deductive derives conclusions from particular axioms, ABM enables both approaches in order to undertake what-if analysis (Axelrod 1997a). If these approaches are applied for ethnography observation, it may need 30-40 years to complete (Watts and Gilbert 2014).

Compared to mathematical modelling, ABM has many benefits in social science modelling (Axtell 2007; Zenobia et al. 2009); it does not need assumption of equilibrium and is able to incorporate the process dynamics and feedback, which are essential in analysing an emergent behaviour (Pavón et al. 2008; Robertson and Caldart 2009; Farmer and Foley 2009). Thus, simulation has been considered as a promising contribution to social science (Louie and Carley 2008).

Nonetheless, little mention is made of business competition and collaboration in ABM literature. When the issues are considered, most previous work separates it into two different research topics. Only a few studies incorporate these problems in a single research, such as Axelrod (1997a) who models competition and cooperation interaction by adopting game theory. Nevertheless, when competition and cooperation are taken into account, the study focuses on the emergence of coopetition - a term to define cooperative competition. In reality, this pattern typically occurs in horizontal supply chains, such as coopetition among Toyota’s suppliers (Wilhelm 2011).

For ABM competition models, most previous research combines a traditional competition model with other natural models, such as the NK model in Lenox et al.

(2006) and Caldart and Ricart (2007), and the forest fire model in Robertson and

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Caldart (2008). The NK model is a biological model introduced by Kauffman (1993) to describe adaptive evolution as opposed to Darwinian 'selectionist' theory, while the forest fire model is a theoretical physics model. In Lenox et al. (2006), the NK model is employed to investigate the coordination of interdependence activities among enterprises under competition situation. It is incorporated with a classical economic model of competition to generate the competitive behaviour.

Even though the study model activities coordination beyond a single firm, it does not represent particular operations that can be related to SCM. Meanwhile, Caldart and Ricart (2007) adopt the NK model to mainly investigate competition issue, particularly in studying exploitation and exploration in corporate strategy.

Robertson and Caldart (2008) introduce the adoption of the forest fire model to simulate firms’ behaviour in business strategy implementation. The forest fire logic is employed to represent the effect of advertising or diffusion of innovation as a result of a competition strategy implementation. However, these studies tend to produce a complex model as it adopts a complicated behavioural rule from the logic of natural models. To some extent, it may not be possible to generalise the emergent pattern from a simple behaviour in the real problem situation.

In ABM platforms, several classical economic models of competition have also been developed as a part of the software’s library, such as Hotelling’s competition model (Hotelling 1929) developed by Wilensky (2013) in NetLogo. Hotelling’s model is often illustrated as a competition between two ice cream stalls located in along the street on a beach (i.e. one-dimensional competition). As both stalls always attempt to optimise their market share, they keep changing their location until they come with the right to each other at the same halfway point (Robertson and Caldart 2009). However, Wilensky (2013) allows more than two firms to compete, and the competition space can be set into two dimensions.

There are still many other ABM studies in competition, but they are limited in one layer of competition. None of them considers multiple layers of competition, such as competition among firms that emerges in each stage of supply chains.

However, compared to ABM studies in collaboration or cooperation issue, ABM

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model in business competition is more numerous. ABM research that is related to collaboration topic mostly corresponds to SCM context, and cooperation is typically attributed to game theory to model coopetition problems.

3.4!ABM validation

ABM still has a challenge, particularly in terms of validation. This is because the resulting emergent result of the agent-based simulation model is sometimes difficult to compare with the real world. Several ABM models that are developed based on theories, such as the Hotelling’s competition models (Wilensky 2013), are easier to validate compared to the non-theory-based models. Nevertheless, no theory is precise and complete even though it has been well proven (Gross and Strand 2000;

Zenobia et al. 2009).

Some researchers argue that ABM is not a better approach than mathematical models, such as Casti (1997), Louie and Carley (2008), Gross and Strand (2000), and Casti (1997). The reason for this is that all variables are still under control in simulation, whereas system should not be isolated once developing theory, particularly in social science (Louie and Carley 2008). These debates mostly emerged in social science domain, including strategic management, where theory generation is the main outcome of research.

On the other hand, ABM tends to produce theoretical models. With respect to this, Heath and Hill (2010) suggest the system dynamics validation approaches to determine the plausibility of ABM results. They propose the use of system thinking to understand and model the problem situation in ABM. It allows modellers to structure the interdependencies, understand the properties and the limitations, and analyse the emergent behaviour. The beer game simulation is an example of a model that can be validated by this approach. The time delays in receiving and responding information, which reflects the human bounded rationality, is

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considered as the main cause of the ‘misperceptions of feedback’ that causes the bullwhip effect (Diehl and Sterman 1995).

Gilbert (2008) also proposed two validation methods in ABM: fitting it with the theory and with the real-world phenomenon. The first comparison is called as a theory-based explanation, and the latter is a based explanation. For the case-based explanation, it corresponds to the comparison of the resulting behaviour with the empirical behaviour of the real-world, known as a phenomenon. This test does not necessarily need a quantitative match of the model results with the real world;

the qualitative similarity between model outputs and the real world is sufficient to be the basis of model validity.

There are still many validation approaches that have been employed in validating theoretical or hypothetical models in ABM studies. They includes biological behaviour explanation as conducted by Levinthal (1997), empirical validation through, for example, case studies (Zenobia et al. 2009), parameter calibration with the real world (LeBaron 2001; Zenobia et al. 2009), model docking by developing two models and comparing the results (Burton 2003), and empirical validation for the micro level behaviour (Zenobia et al. 2009). However, these validation approaches are difficult to perform when the model is hypothetical and not developed to explain a theory or phenomenon. The approaches would also be impossible to apply if the ABM study aims to understand and explore several behavioural rules as the empirical data is hard to obtain.

Validation process in any research should be related to the purpose of the model (Robinson 1997; Robinson 2014). The validity of a model should represent plausibility related to the related problem domain (Sargent 2013). If the problem is hypothetical and does not have a strong relevance with any previous theories, the models can be validated only according to its plausibility, such as the Schelling’s segregation model, the Hotelling’s competition model, and the beer game.

Despite these contradictory opinions, ABM still offers some advantages compared to other approaches. It can incorporate the concept of complexity to

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produce and understand an emergent behaviour (Robertson and Caldart 2008).

Moreover, ABM is known as an effective approach to study simple individual rules that lead to an emergent behaviour at the macro level system. Several studies that employ this approach have been used as the main reference for other studies, such as Schelling’s segregation model and the beer game. It means that the benefit of ABM outweighs the challenge in validation.

3.5! Conclusions and summary

This chapter shows that ABM approach has been well implemented in both SCM and strategic management, particularly in modelling competition and collaboration in business issues. Although the application of ABM has significantly increased in recent years, the ABM studies in SCM are still limited, particularly in modelling competition and collaboration issue. No study attempts to benefit the system perspective analysis in ABM for analysing the long-term demand fulfilment and survivability of supply chains in the market. In other words, there is an opportunity to apply ABM in modelling the issue in SCM.

Furthermore, compared to other simulation approaches, ABM has a unique feature to model and observe a problem. While DES and SD have a top-down approach, ABM employs a bottom-up approach. It enables researchers to understand an emergent behaviour at macro level by investigating the behaviour at the micro level of individual agent. This approach is appropriate to model a phenomenon that is difficult to explain empirically and analytically. It is also suitable to explore the emergent outcome of what-if experiments on the individual agents. In short, ABM is the best approach where the problem situation requires analysis from two level of point of views: from the agent-level and the system-level.

Although ABM still has an issue to validate theoretical models, the advantages of the use of ABM still outweigh the drawback, particularly when the problem is not possible to study by using an empirical approach.

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This research benefits the unique features of ABM to bridge the literature gap in the SCM and strategic management. SCM has operations-level perspective, which is close to the agent-level view. Meanwhile, strategic management tends to employ market-level perspective that can be similar as a system-level standpoint.

Moreover, according to the literature that is reviewed in this study, ABM has been implemented both in SCM and strategic management to model competition and collaboration even though the problem is still examined separately. It means that ABM is the most appropriate approach to bridge the literature gap in supply chain competition and collaboration, as defined in Chapter 2.

The use of ABM in this research is essential to provide new insight about competition and collaboration in SCM. It also offers a contemporary approach to strategic management in modelling and understanding the emergent outcome of multi-layer competition driven by decision makings at operations level. The ABM role in bridging the gap that is identified in this study is illustrated in Figure 3.1. A rough estimation of the application of quantitative and qualitative approach to each research domain (SCM and strategic management) is presented, and the shaded area represents the main domain of the problem proposed in this study. The use of ABM also allows operational research (OR) feature to the modelling approach and operations management (OM) approach to the analysis. The methodology of the ABM application in this Thesis is presented in the next chapter, which is Chapter 4.

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SUPPLY CHAIN MANAGEMENT

Qualitative approach (Operations Management

-OM)

Quantitative approach (Operational Research - OR)

STRATEGIC MANAGEMENT

Qualitative approach

The use of ABM in this research (OR & OM):

“Competition and collaboration in supply chains”

SCM perspective:

applying competitive and collaborative behaviour at individual firm- level Strategic management perspective:

analysing the supply chains long-term performance and survivability from market-level perspective

Quantitative approach

ABM ABM

Figure 3.1 ABM role in this research: to merge the gap between the related research domains.

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