Learning from mistakes: A note on just-in-time systems

In this paper we seek to reconcile two competing frameworks for inventory management, namely the classical ‘optimization’ framework and JIT. While the first is predicted on the premise that the optimal level of inventory is obtained by balancing the costs and benefits of inventory, the latter claims that inventory is a form of waste and thus should be eliminated completely (but gradually). We show that some of the operational characteristics of JIT, as opposed to its rhetoric, can be obtained within the optimization framework provided that the relevant factors are taken into account. In particular, we show that in an environment which is subject to ‘learning from mistakes’, it is better to carry inventory levels which are lower than what is ‘operationally optimal’. We also study the (complex) relation between variability reduction, inventory levels, and overall cost. In general, reducing the variability of a system (in the sense of first- or second-order stochastic dominance) will not necessarily result in a lower level of inventory, although optimal cost will be reduced. We study conditions on variability improvement that guarantee that variability reductions will always result in reduced levels of optimal inventory.