Monotone Forecasts

In this paper we provide necessary and sufficient conditions for the distribution of demand in the future to be stochastically increasing in the demand that has been observed in the past. We base our analysis on the multiperiod inventory model examined by Eppen and Iyer (1997). In the process of establishing the necessary and sufficient conditions we develop a new property called the sequential monotone likelihood ratio property

Supply Chain Operations in the Presence of a Spot Market: A Review with Discussion

We survey the recent literature on the use of spot market operations to manage procurement in supply chains. We present results in two categories: work that deals with optimal procurement strategies and work related to the valuation of procurement contracts. As an example of the latter, we provide new results on valuation of a supply contract with abandonment option. Based on our review, we also discuss the scope for doing further work

Optimization Under Supplier Portfolio Risk Considering Breach of Contract and Market Risks.

We consider a two-period sourcing and production problem. First, a firm (OEM) sources from multiple suppliers who have limited capacity and correlated disruption risk. After the supply is realized, the firm also has access to the spot market for the extra material needed for its production. The firm must decide (1) which suppliers to source from, (2) how much to source from them, and (3) how much to produce and how much to source from the spot market. We formulate this as a stochastic optimization problem to study the tradeoff the firm faces between costs and default risk. In order to incorporate the correlation of the supplier’s default risk, we use the t-copula dependence structure. A contract default is a rare event. Thus, in a Monte Carlo simulation, there is considerable variance around the optimal sourcing quantity. This variance leads to complexity in computing the optimal decision. We find that a diligent combination of importance sampling and conditional Monte Carlo schemes effectively reduces the variance in simulation estimates for the first-order conditions in the stochastic optimization problem. This paper shows that, for a supply chain with correlated default risks, the optimal sourcing problem can be solved by using importance sampling and a conditional Monte Carlo simulation