Case-based validation: the shakeout phenomenon

The second validation approach performed in this study is case-based validation.

This validity test explains the emergent pattern of the model by using a real case study found in the literature. The case that was confirmed by this approach is the decreasing number of supply chains in the market, which can reflect the supply chain failures. This outcome emerged consistently from the simulation run, not only in the base run but also in the behaviour space. Hence, it can be interpreted as the general effect of long-term competition - without considering new entrants to the market.

The model shows that competition reduces the number of firms both in extreme and non-extreme ways, and the extreme way is known as a shakeout. Shakeout is a term popularly used in business and management analysis to describe a phenomenon whereby there are massive exits of a number of companies in a market due to competition (Bonaccorsi and Giuri 2000). The failures of firms can be an effect of profit loss, declining demand, or acquisition by a competitor. This situation is very likely to intimidate all the firms in a market (Day 1997). It is also often

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interpreted as the decline of industries due to decreasing demand, or the decline of interest of the customers in buying the product (Lieberman 1990).

However, the results of the simulation indicate that a decrease in the number of supply chains and the supply chain fill rate in the market are solely caused by the competition, not by the customer. Assuming that the customer’s preference is fixed, the demand seems to decrease because the firms (the manufacturers) converge in particular strategic locations. When the strategic position of the manufacturers is similar to that of each other, the manufacturers serve a similar market segment; so the manufacturers share the market with each other. Moreover, as explained in section 6.2, the number of market clusters or market concentrations is very likely to decline during the competition. This leads to a lower supply chain fill rate as the firms become more similar to each other. In reality, this behaviour may often be interpreted as declining demand, as explained by Lieberman (1990).

In this model, the firms who exit from the market are represented by the dead agents. A manufacturer agent will die if it has one of these following states:

-! it does not have any link with the customer until it overs a particular time limit, which is assumed to be similar as manufacturer survivability without supplier.

it does not have a supplier until it reaches the end of the length of the manufacturer survivability without supplier.

-! it links with less efficient and/or responsive supplier for several ticks until it spans the limit of the manufacturer survivability to work with an undesired supplier.

The first condition is much less likely to occur in the base run as the customer is set to be not loyal to the manufacturers; so the customers always choose the closest manufacturer to them as long as the manufacturer has a supplier. In other words, the manufacturers in the base run always have customers as long as they have links with a supplier. Meanwhile, a supplier agent would die if it cannot manage to find a manufacturer to collaborate with before it reaches the limit of the supplier survivability period.

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An example of an extreme shakeout resulted by the model is illustrated in Figure 5.11. The figure shows the emergence of a monopoly obtained from the base run.

The monopoly occurs in just 88 time units of the simulation. As 1 time unit can be interpreted as between 3 and 18 months (see section 5.2.1.5), the 88 time units can be implied to be between 22 and 132 years. Hence, it can be considered as a very short duration of the competition period.

a) b)

Figure 5.11 Competition can lead to one supply chain moving to domination:

a) 10 supply chains exist at the initial condition (time unit 1);

b) monopoly emerges at the end (time unit 88).

The monopoly occurs when most manufacturers, who stay in less efficient and responsive positions, select suppliers who are far more efficient and responsive than them; whereas, these suppliers approach manufacturers who stay in more efficient and responsive locations. This means that these manufacturers (who are in more efficient and responsive positions) could not manage to find an appropriate supplier with whom they can collaborate. On the other hand, the other suppliers, who are in less efficient and responsive positions, move closer to the manufacturers. However, they fail to attract the target manufacturers as the firms have been linked with other suppliers who are far more efficient and responsive than them. These less efficient and responsive suppliers also stay too far beyond the willingness to compromise of the manufacturers who stay in in more efficient and responsive region and still have

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no supplier to work with them. If this situation occurs continuously for a long time, it would potentially lead to a monopoly. Moreover, this situation would be likely to emerge when most of the suppliers are far more efficient and/or responsive than the manufacturers.

In the base run, the occurrence of a monopoly is in 6 out of 50 cases (12%) of the results. The dominant result of the number of supply chains in the market at the end of the simulation is two supply chains (13 out of 50 replications), followed by three supply chains (12 out of 50 replications). The other results for the number of supply chains in the market are 5 and 6 supply chains, which for each of them occurs 5 times out of 50 replications. These numbers resulting from the base run are presented in Figure 5.12.

However, most simulation outcomes in this study very likely result in a high reduction in the number of supply chains in the market, which can be interpreted as the potential occurrence of a shakeout. This shakeout phenomenon reflects several case studies in business competition. This is likely to occur in an innovation-based competition strategy (Bonaccorsi and Giuri, 2000; Klepper and Simons, 2005).

Figure 5.12 The number of supply chains in the market of the base run (9 = 3.18, s = 3.01)

Several shakeout cases have been documented in the academic literature. In the PC industries, the number of PC manufacturers decreases from 832 to 435 firms in

6

No. supply chains in the market

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the late 1950s. This figure is estimated to be as few as five firms for the long-term winners. For the television picture-tube industries, 40 television manufacturers existed and 74 tube manufacturers operated in 1955. In 1959, 52 picture-tube manufacturers remained, and this decreased to 7 picture-picture-tube manufacturers in 1997. In Magnetic Resonance Imaging (MRI) equipment, 28 MRI manufacturers operated in 1982 and 20 MRI manufacturers remained in 1993. It is predicted that only two manufacturers would survive in the future (Day 1997). The UK steel casting industry also experienced the shakeout phenomenon; about 60 firms operated with about 90 plants in 1975, but 70,100 tonnes of capacity had been closed by the end of 1983 (Baden-Fuller 1989).

Shakeout also emerged to an extreme degree in innovation or technology-based competition. As reviewed by Klepper and Simons (2005), the manufacturers of automobile, tyres, televisions, and penicillin have experienced extreme shakeouts.

The number of manufacturers of each product fell by 70% to 97% over three decades or more after it reached the peak. For automobile industries, the highest number of producers was in 1909 with about 270 automobile manufacturers. Then, it dramatically decreased to about ten manufacturers in 1967. For the tyre industries, the number of manufacturers peaked at about 275 firms in 1922 and then fell sharply to about 30 manufacturers in 1980. Meanwhile, for the television and penicillin industries, the highest number of manufacturers occurred in 1951 with about 90 manufacturers and in 1952 with about 30 firms respectively. Both industries then fell dramatically, with 20 manufacturers in 1989 for the television industries and 10 manufacturers in 1992 for the penicillin producers. Regarding the conformity of the results with real case studies, it can be suggested that the model developed in this study is valid to represent the real world. The declining number of firms in the market in the model can be seen in the actual cases.

Nevertheless, these shakeout cases can hardly be explained from the supply chain perspective. This is because the existence of the SCM paradigm is still relatively new compared to the period required for understanding the shakeout phenomenon. It has only been known for 34 years since it first appeared in the

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academic literature in 1982 (Gibson et al. 2005). Moreover, most studies and analysis in SCM are conducted for analysing a particular supply chain, not for market analysis. Most of them only address the issue of the supply chain failures that occur in particular companies.

On the other hand, the recent phenomenon of declining companies, such as Japanese electronics firms (Sony, Sharp, and Panasonic), is generally considered not to be related to supply chain failures. A widely held view is that this phenomenon regards marketing failure as the main causes rather than viewing the problem from the SCM perspective. For example, many business news stories point out that the reasons for the profit loss in Japanese electronics companies are the uncompetitive marketing approach (Fingleton 2012; Morris 2012; Wingfield-Hayes 2013), which is considered the result of a mistake in taking a strategic movement (Hall 2009), and the rigidity of Japanese corporate culture which hinders the response speed of the firm (Ihlwan 2009; Hall 2009). The weakening Japanese economy is also regarded as a cause of rising costs in innovation and the manufacturing processes (Ihlwan 2009; Wakabayashi 2012). Even though their market share is decreasing, their supply chain practices are still regarded as successful. They even also have an excellent supply chain risk management which prevents them from suffering from supply disruptions, as caused by disasters or earthquakes.

In addition to this, Nokia was also seen to experience lower market shares. It is widely understood that the factor that led Nokia to its past successes was its supply chain (McCray et al. 2011). Nokia was referred to as having the best supply chain practices by Reeves and Deimler (2011). It even won the Supply Chain Management Award for Excellence in Supply Chain Operations at the EXCHAiNGE conference in 2015 (Fourtane 2015). Nevertheless, Nokia was overtaken by its competitors, e.g. Apple and Samsung.

Regarding its competition with Apple, Nokia was deemed to be in crisis by 2011 (McCray et al. 2011). While Nokia claimed that their supply chain was strong, by

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contrast, Satariano and Burrows (2011) found that Apple’s relationships with its supplier were not really congenial; the suppliers experienced high pressure in supporting Apple’s success.

These findings imply that collaboration success may not guarantee the long-term survivability of a supply chain. It also indicates that supply chain failures may not be solely caused by the inappropriate implementation of supply chain collaboration strategies. Because there is still no literature which incorporates the supply chain perspective in analysing industry shakeouts, this study initiates a new insight into understanding the issue, which considering SCM and strategic management perspective.