Input variables: the experimental design or the behaviour space

The input variables play important roles in the behaviour space, particularly in achieving objective 3 – exploring the effect of firm competition and collaboration strategy on supply chains. They include the:

1.! customer loyalty,

2.! manufacturer survivability to work with undesired supplier/s 3.! duration of collaboration

4.! maximum number of partnerships 5.! manufacturer trust

6.! manufacturer strategic movement 7.! supplier survivability

8.! supplier trust

The idea of behaviour space construction is by varying the agent’s attributes and behaviour. However, the agent’s attributes and behaviour are not only characterised by the input parameters, but also by the non-input variables in the model setup. The non-input defines the agent’s attributes and behaviour that have a fixed or constant value in all experiments, while the input refers to the experimental factor. A list of the variables in the simulation setup which affect the agent’s attributes and behaviour is provided in Table 5.5.

The experimental design is set into two parts: the base run and the behaviour space. The base run represents the default behaviour when most of the experimental factors are adjusted to their lowest value to represent the conventional business relationships, except the manufacturer survivability to work with undesired supplier/s. In the base run, the manufacturer survivability is set at medium value or level because this factor is sensitive to the outputs, so the medium level of this experimental factor is considered to be the realistic point to represent the average manufacturer’s ability to survive when it works with the undesired suppliers.

Meanwhile, the behaviour space characterises the what-if analysis to test the hypotheses proposed in this study. Each experiment in the behaviour space consists

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of 5 scenarios to represent 5 levels of expected influence of each experimental factor on the model outputs.

Table 5.5 Variables’ feature in the simulation setup Agent’s attributes or behaviour

1. Manufacturer’s willingness to compromise

3. Duration of collaboration !

4. Maximum number of sourcing !

5. Manufacturer trust !

6. Manufacturer strategic movement !

Supplier

1. Supplier’s maximum number of partnerships

!

2. Supplier survivability !

3. Supplier strategic movement !

4. Supplier trust !

Each scenario of the experiment is described as the following:

-! the lowest extreme level of the experimental factor (scenario 1) -! the low level of the experimental factor (scenario 2)

-! the medium level of the experimental factor (scenario 3) -! the high level of the experimental factor (scenario 4)

-! the highest extreme level of the experimental factor (scenario 5)

The values of each experimental factor are determined hypothetically according to the practical experience towards the implementation of the experimental factor.

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In other words, empirical judgement is adopted to set the experiments in the behaviour space. These experiments are run after the computer model had been verified and validated.

Driven by the main hypothesis in each research objective, the exploration process for each experimental factor was conducted dynamically. It means that the behaviour space was also defined in a dynamic approach. For example, for the duration of collaboration, it was presumed that the results would be different when the suppliers could link with more than one manufacturer, so the duration of collaboration was run under two levels of the supplier number of partnerships:

single-link supplier and dual-link-supplier. A discussion of behavioural space is presented in the next chapter. Each scenario in the behaviour space was run for 1000 time units with 50 replications. A description of the experimental factor setup for the base run and the behaviour space is provided in Table 5.6. A detail of experimental design or behaviour space is presented in Table 5.7.

Table 5.6 The base run and the behaviour space

Experimental factor Base run Behaviour space

1 Duration of collaboration

The shortest duration, to represent the no collaboration approach.

5 scenarios, including the base run, with 2 levels of supplier number of partnerships:

5 scenarios, including the base run. The level of each scenario is varied

proportionally and combined with the behaviour space of the supplier number of partnerships.

Each scenario was run under 2 levels of duration of collaboration:

- extremely short duration, and

- extremely long duration.

3 Trust

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Experimental factor Base run Behaviour space

3.1 Manufacturer trust

No trust. 5 scenarios, including the base run. The scenarios were run under 3 levels of duration of collaboration:

- extremely short duration,

- extremely long duration.

3.2 Supplier trust No trust. 5 scenarios, including the base run, with 2 levels of duration of collaboration

- extremely short duration, and

- extremely long duration.

3.3 Customer trust/loyalty

No trust/loyalty. 5 scenarios, including the base run.

4 Individual firm

5 scenarios, including the base run, with several low levels of manufacturer strategic movements.

4.2 Supplier survivability

The medium level of survivability.

5 scenarios, including the base run.

5 Probability of making extreme strategic changes

(manufacturer strategic movements)

No extreme strategic change. 5 scenarios, including the base run.

Table 5.7 The scenarios for the manufacturer collaborative and competitive behaviour Experimental

factor Scenario Computer setup Scale representation Duration of

collaboration (D)

D-1 4 time units Extremely short duration D-2 20 time units Short-medium duration D-3 40 time units Medium-long duration D-4 60 time units Long duration

D-5 80 time units Extremely long duration

Number of partnerships (P)

P-1 1 link Single sourcing with a

single-link supplier

P-2 2 links Dual sourcing with

dual-link suppliers

P-3 3 links Multi sourcing with 3-link suppliers

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Experimental

factor Scenario Computer setup Scale representation P-4 4 links Multi sourcing with 4-link

suppliers

P-5 5 links Multi sourcing with 5-link suppliers

SM-1 12 time units Extremely low survivability S_M -2 16 time units Low survivability

S_M -3 20 time units Average survivability S_M -4 24 time units High survivability

SM -5 28 time units Extremely high survivability

Supplier

S_S -5 8 time units Extremely high survivability

Probability of

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The main goal of the experimental design is to analyse the level of experimental factors (the scenario) that result in a better market performance than the base run.

Despite the complexity caused by the agents’ interactions, a higher level of most of the experimental factors is expected to improve the supply chains performance, as observed from the market-level perspective. In particular, higher levels of the duration of collaboration, the maximum number of manufacturer’s partnerships, the manufacturer trust, the manufacturer survivability to work with undesired supplier/s, the supplier number of partnerships, the supplier trust, the supplier survivability, and the customer loyalty are expected to improve the agent’s existence in the long-term; whereas a higher likelihood of the manufacturers making big leaps (represented by higher manufacturer strategic movement) would lead to a shorter agent’s life. When each firm can exist longer – regarding the manufacturer and supplier agents, the number of supply chains in the market which can survive would be higher for a long run competition. As a result, the more customers able to be served by the available supply chains and the market performance (indicated by the supply chain fill rate) would be higher. Nonetheless, as previously discussed in Chapter 2, this expectation is difficult to realise due to the complexity in the real world. Hence, most of the hypotheses of this study are constructed against this static expectation. An illustration of the expected static effect is presented in Figure 5.7.

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MARKET PERFORMANCE:

The supply chains fill rate

The number of supply chains in the market

Figure 5.7 The expected effect of the individual experimental factor to the outputs