In a recently published article, my co-authors and I examine how the firms embedded in supply networks engage in decision making over time. The supply networks as a complex adaptive system are simulated using cellular automata (CA) through a dynamic evolution of cooperation (i.e., “voice” decision) and defection (i.e., “exit” decision) among supply network agents (i.e., firms). Simple local rules of interaction among firms generate complex patterns of cooperation and defection decisions in the supply network. The incentive schemes underlying decision making are derived through different configurations of the payoff-matrix based on the game theory argument. The prisoner’s dilemma game allows capturing the localized decision-making process by rational agents, and the CA model allows the self-organizing outcome to emerge. By observing the evolution of decision making by cooperating and defecting agents, we offer testable propositions regarding relationship development and distributed nature of governance mechanisms for managing supply networks.
The results provide several theoretical and managerial implications. We find that supply networks form a complex web of relationships among various interdependent firms engaged in different activities to get the product or service to the end customer. Our results highlight the importance of managing interdependencies. Specifically, we capture the complex interdependencies involving simultaneous, ongoing relationships with multiple linkages among firms in supply network. The results of our study suggest that depending on the nature of the interdependence, incentive schemes and governance structures should be set up in supply chain relationships that aid in the formation of cooperative relationships throughout the network.
The simulation results reveal that if the incentive structure is such that the reward from mutual cooperation is lower than a threshold value, exit strategy on the part of even a single firm in the network can convert the supply network from a cooperative regime into a noncooperative regime. These findings highlight the importance of identifying cooperation-inducing incentive structures that have implications for the overall supply network. Particularly, instead of just focusing on the returns from cooperation, it makes sense to examine the relative payoff frommutual cooperation as compared to the payoff a firm may receive from not cooperating. As an example, consider the relationship between a retailer and a supplier. The supplier manufactures a product at a finite rate and incurs a fixed marginal production cost. The retailer purchases the product at a fixed wholesale price and sells at a fixed retail price. Both firms incur a fixed inventory-carrying cost and sales are lost if the product is unavailable. In this situation, it is possible that the supplier and the retailer may cooperate and increase the overall supply chain performance by collectively carrying more inventory relative to their optimal amounts. This will allow the firms to handle the opportunity costs created by stock-out situations. However, when a firm carries more inventory than the optimal amount, it would result in an additional cost for the firm. For instance, the retailer might behave opportunistically and impose the entire increase in inventory to be carried by the supplier—vendor managed inventory (VMI) is a case in point. While such an opportunistic behavior would certainly result in higher gains for the retailer, in designing incentive schemes it is important to understand how much higher this gain will be relative to the gains from mutual cooperation. Mutual cooperation among network members is the goal for developing a collaborative supply network and this goal is more likely to be realized if due consideration is given to other potential behavioral patterns (e.g., mutual defection, opportunism, etc.) and the consequent payoffs in the design of incentive schemes.
We observe that complex behavioral patterns can emerge in the supply network operating under simple rules of incentives. The results of our study show that for developing a collaborative supply network, the reward frommutual cooperation does not have to be the highest. Even in situations where firms are able to gain high rewards from opportunistic behavior, mutual cooperative behavior would survive and thrive as long as there is sufficient incentive to do so. The specific value of this incentive would vary depending upon the context of the industry and the supply chain processes. For instance, different components of order fulfillment cycle time are distributed across the network, and the variation of lead time at any stage affects the other stages and results in uncertainties for the whole order cycle time. Cooperative behavior (i.e., maintaining the variation of lead time within a specified range) can be induced if each component of the network can partake in the gains received from satisfied customers (e.g., revenue sharing). For instance, Denso as the major top-tier supplier to Toyota has had a history of showing comparable or sometimes higher earnings compared to Toyota. Therefore, an appropriate incentive structure is the key to smoothing the material flow within the supply network by ensuring a cooperative relationship among the network members engaged in the order fulfillment process.
Contrary to conventional thinking, we find that an increase in gains from opportunistic behavior does not necessarily translate to an increase in the number of defecting firms in the network. In fact, the results show that as long as the payoff from mutual cooperation is sufficiently high, within a certain range, increased incentives for opportunism can lead to an increased number of cooperating agents. It can be reasoned that in situations where firms are engaged in ongoing interactions, pursuing the strategy of cooperation in spite of the potential gains from opportunism provides a context for building trust. Notably, in the presence of trust, buyers and suppliers act in a risk-prone way by cooperating, rather than risk-averse way by defecting, even with the knowledge of the other party’s potential opportunistic behavior and potential losses. In essence, cooperation and trust are self-reinforcing. Initial cooperation results in trust building and the trust developed between supply chain partners enable subsequent cooperation. The aforementioned relationship between Toyota and Denso is a case in point. Even after Denso had broken away from the traditional Toyota keiretsu (a type of business group in which the set of companies have interlocking business relationships and shareholdings) and started to serve Toyota’s competitors, these two companies continue to trust each other and are continuing to cooperate, seemingly oblivious to potential opportunism by the other party. Under certain incentive schemes, firms can continue to pursue a cooperative strategy in spite of the dominance of exit strategy in the network. In other words, a cooperative strategy is capable of surviving in spite of adverse conditions, perhaps due to the interdependencies among suppliers within the network.
Our results suggest that, as the complex interdependencies increase with the increasing size of the supply network, the prospect of long-term survival of cooperative behavior among buyers and suppliers also increases. The expansion of the supply network due to continuous additions of new suppliers plays an important role in enhancing the prospects of the survival of a voice strategy. It can be reasoned that as the size of the supply network increases several additional interdependent relationships among suppliers are formed. For instance, as the automobile design moves toward the “drive-by-wire” concept and the electronics parts content increases, its supply network would necessarily expand to include additional hightech companies. These new interdependencies further increase the probability of formation of collaborative clusters among suppliers.
Source: Nair, A., Narasimhan, R., and Choi, T. 2009. Supply Networks as a Complex Adaptive System: Toward Simulation-Based Theory Building on Evolutionary Decision Making. Decision Sciences, 40(4): 783-815.