(s, S) Policies for a Dynamic Inventory Model with Stochastic Lead Times

This study analyzes a stochastic lead time inventory model under the assumptions that (a) replenishment orders do not cross in time and (b) the lead time distribution for a given order is independent of the number and sizes of outstanding orders. The study extends the existing literature on the finite horizon version of the model and yields an intuitively appealing dynamic program that is nearly identical to one that would apply in a transformed model with all lead times fixed at zero. Hence, many results that have been derived for fixed lead time models generalize easily. Conditions for the optimality of myopic base stock policies, and for the optimality of (s, S) policies are established for both finite and infinite planning horizons. The infinite-horizon model analysis is extended by adapting the fixed lead time results for the efficient computation of optimal and approximately optimal (s, S) policies

Computing (R, S) policies with correlated demand

This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by using off-theshelf optimisation software. Our modelling strategy can tackle a wide range of time-seriesbased demand processes, such as autoregressive (AR), moving average(MA), autoregressive moving average(ARMA), and autoregressive with autoregressive conditional heteroskedasticity process(AR-ARCH). In an extensive computational study, we compare the performance of our model against the optimal policy obtained via stochastic dynamic programming. Our results demonstrate that the optimality gap of our approach averages 2.28% and that computational performance is good

Inventories and the Business Cycle: An Equilibrium Analysis of (S,s) Policies

We develop an equilibrium business cycle model where nonconvex delivery costs lead producers of final goods to follow generalized (S,s) inventory policies with respect to intermediate goods. When calibrated to match the average inventory-to-sales ratio in postwar U.S. data, our model reproduces two-thirds of the cyclical variability of inventory investment. Moreover, inventory accumulation is strongly procyclical, and production is more volatile than sales, as in the data. The comovement between inventory investment and final sales is often interpreted as evidence that inventories amplify aggregate fluctuations. Our model contradicts this view. Despite the positive correlation between sales and inventory investment, we find that inventory accumulation has minimal consequence for the cyclical variability of GDP. In equilibrium, procyclical inventory investment diverts resources from the production of final goods; thus, it dampens cyclical changes in final sales, leaving GDP volatility essentially unaltered. Moreover, although business cycles arise solely from shocks to productivity and markets are perfectly competitive in our model, it nonetheless yields a countercyclical inventory-to-sales ratio.

Dynamic (S,s) Economies

In this paper we provide a framework to study the aggregate dynamic behavior of an economy where individual units follow (S,s) policies. We characterize structural and stochastic heterogeneities that ensure convergence of the economy's aggregate to that of its frictionless counterpart, determine the speed at which convergence takes place, and describe the transitional dynamics of this economy. In particular, we consider a dynamic economy where agents differ in their initial positions within their bands and face both stochastic and structural heterogeneity; where the former refers to the presence of (unit specific) idiosyncratic shocks, and the latter to differences in the widths of units' (S,s) bands and their response to aggregate shocks. We study the evolution of the economy's aggregate and the evolution of the difference between this aggregate and that of an economy without macroeconomic friction, where the latter pertains to a situation where individual units adjust with no delay to all shocks. We also examine the sensitivity of this difference to common shocks. For example, in the retail inventory problem the aggregate deviation and sensitivity to common shocks correspond to the aggregate inventory level and its sensitivity to aggregate demand shocks, respectively.

Stochastic dynamic programming heuristic for the (R, s, S) policy parameters computation

The (R, s, S) is a stochastic inventory control policy widely used by practitioners. In an inventory system managed according to this policy, the inventory is reviewed at instant R; if the observed inventory position is lower than the reorder level s an order is placed. The order's quantity is set to raise the inventory position to the order-up-to-level S. This paper introduces a new stochastic dynamic program (SDP) based heuristic to compute the (R, s, S) policy parameters for the non-stationary stochastic lot-sizing problem with backlogging of the excessive demand, fixed order and review costs, and linear holding and penalty costs. In a recent work, Visentin et al. (2021) present an approach to compute optimal policy parameters under these assumptions. Our model combines a greedy relaxation of the problem with a modified version of Scarf's (s, S) SDP. A simple implementation of the model requires a prohibitive computational effort to compute the parameters. However, we can speed up the computations by using K-convexity property and memorisation techniques. The resulting algorithm is considerably faster than the state-of-the-art, extending its adoptability by practitioners. An extensive computational study compares our approach with the algorithms available in the literature

Inventories and the business cycle: an equilibrium analysis of (S,s) policies.

The authors develop an equilibrium business cycle model in which final goods producers pursue generalized (S,s) inventory policies with respect to intermediate goods, a consequence of nonconvex factor adjustment costs. Calibrating their model to reproduce the average inventory-to-sales ratio in postwar U.S. data, the authors find that it explains half of the cyclical variability of inventory investment. Moreover, inventory accumulation is strongly procyclical, and production is more volatile than sales, as in the data. The comovement between inventory investment and final sales is often interpreted as evidence that inventories amplify aggregate fluctuations. However, the authors' model economy exhibits a business cycle similar to that of a comparable benchmark without inventories.Inventories ; Business cycles