Nicolas Jonard

Measuring the performance of lot-sizing techniques in uncertain environments

Cost-effectiveness and computational time are the traditional criteria for evaluating lot-sizing techniques. However, in evolving environments, frequent revisions of demand forecasts may induce various degrees of instability in planned orders, depending on the selected lot-sizing technique. In poorly flexible production systems, the cost of implementing these alterations may overcome the benefits from using a cost-efficient technique. In this paper, we evaluate lot-sizing techniques on the basis of two criteria. The first is the traditional cost-effectiveness criterion. The second, that we call robustness, is designed to capture some of the characteristic features of decision-making in uncertain environments. Robustness is related to the stability of the set-up streams when demand fluctuates. We propose and discuss several alternative measures of robustness. The simulation results clearly show an inverse relationship between cost-effectiveness and instability. Therefore, managers should take into account these two “opposite” dimensions in their decision process, under quite unforeseeable environments.

Knowledge Creation, Knowledge Diffusion and Network Structure

This paper models knowledge creation and diffusion as processes involving many agents located on a network. Knowledge diffusion takes place when an agent broadcasts his knowledge to the agents to whom he is directly connected. Knowledge creation arises when agents receive new knowledge which is combined with their existing knowledge stocks. Thus both creation and diffusion are network-dependent activities. This paper examines the relationship between network architecture and aggregate knowledge levels. We find that knowledge growth is fastest in a “small world”, that is, when the underlying network structure is relatively cliquish (dense at a local level) yet has short paths. This corresponds to a locally-connected graph which includes a few long-distance connections or shortcuts.

Interaction of Local Interactions: Localized Learning and Network Externalities

We extend the model of Nelson and Winter with a lattice-based spatial structure in order to study the effects of localized learning and increasing returns to adoption on the emergence and persistence of technological diversity. We break down the global effects analyzed in Jonard and Yildizoglu (1998) in order to understand the mechanism that is behind the complementarity between localized learning and externalities in the creation and persistence of diversity. We show that two configurations, which are commonly studied in the literature, with very localized learning on the one hand, and global externalities on the other hand, are actually very peculiar cases. They correspond to very stylized dynamics which cannot be obtained as limits of more general cases.

Networks Competition under Local Interaction and Behavioral Learning

Diffusion is modelled as a repeated coordination game between a large number of locally interacting heterogeneous agents. Agents are represented with stochastic learning algorithms that generate robust path-dependent patterns of behavior. Formal analyses of such locally interacting systems encounter many technical difficulties, hence we run numerical simulations. We find that lock-in is positively correlated to the interaction distance. Diversity, i.e. simultaneous coexistence of networks, appears for small interaction distances but vanishes as the size of neighborhoods increases. We also find an inverse relationship between the interaction distance and the speed of standardization.