Knowledge that Transforms

The tendency to rely on accounting earnings as the primary metric of corporate performance has been subject to criticism in recent times. A key concern is that earnings misrepresent changes in value in that cash outlays occur upfront but expenses are recognized only over time. While recognized expenses indeed add up to the initial cash outflow, the equality holds only in undiscounted (nominal), not discounted (real), terms. Accordingly, corporate earnings figures suffer from a form of money illusion. In this paper, we demonstrate that such money illusion can have an upside when it is present in vertical relationships subject to self‐interest. In particular, a buyer who focuses on earnings has incentives to increase purchases since it does not immediately encounter the full cost of cash outflows. These added incentives can promote more efficient trade. We also show that the increased incentives to buy can also lead to Pareto improvements by spurring the supplier to invest more in developing technology. Finally, we demonstrate that judiciously chosen inventory valuation rules can lead to efficient supply chain outcomes. Thus, efficiency can be achieved when supply chain parties freely trade and regulators specify only the accounting rules under which they operate.

The economic approach to determining the optimal control limits of control charts requires estimating the gradient of the expected cost function. Simulation is a very general methodology for estimating the expected costs, but for estimating the gradient, straightforward finite difference estimators can be inefficient. We demonstrate an alternative approach based on smoothed perturbation analysis (SPA), also known as conditional Monte Carlo. Numerical results and consequent design insights are obtained in determining the optimal control limits for exponentially weighted moving average and Bayes charts. The results indicate that the SPA gradient estimators can be significantly more efficient than finite difference estimators, and that a simulation approach using these estimators provides a viable alternative to other numerical solution techniques for the economic design problem.

Information delays exist in an inventory system when it takes time to collect, process, validate, and transmit inventory/demand data. A general framework is developed in this paper to describe information flows in an inventory system with information delays. We characterize the sufficient statistics for making optimal decisions. When the ordering cost is linear, the optimality of a state‐dependent base‐stock policy is established even when information flows are allowed to cross over time. Additional insights into the problem are obtained via a comparison between our models and the models with stochastic order lead times. We also show that inventory can substitute for information and vice versa.

This paper investigates the impacts of competition and market uncertainty on airlines' network structures and capacity investment. The airlines choose their network structures and construct capacities while demands are unknown. After uncertainty is resolved, they determine the total number of seats to offer in each leg constrained by their capacities built earlier. We conclude that market uncertainty is the driving force of hub‐and‐spoke networks, whereas the market mean is the driving force of point‐to‐point networks. Which of the two countervailing forces dominates determines the equilibrium network structures. Moreover, we find that the airlines' total expected profits in the mixed equilibrium in which the airlines employ different networks are larger than in the pure hub‐and‐spoke network equilibrium in which each airline employs the hub‐and‐spoke network. However, the mixed equilibrium does not necessarily yield larger profits than the pure point‐to‐point equilibrium in which each airline employs the point‐to‐point network.

We consider a single‐period assemble‐to‐order system that produces two types of end products to satisfy two independent and stochastic customer orders. Each type of product is used to fulfill a particular customer order and these two products share a common component. Furthermore, one customer may confirm her order before the other one, and the manufacturer needs to make a commitment immediately upon the receipt of each customer order on how many products to be delivered. We propose a model for optimizing the inventory and production decisions under the above ATO environment. We also extend our model to the situation where the manufacturer can fulfill the unsatisfied low‐priority demand using the left‐over inventories after fulfilling the high‐priority demand, in case the low‐priority customer arrives first. Numerical experiments are conducted, which provide some interesting insights on the impact of uncertain demand pattern.

Make‐to‐order (MTO) manufacturers face a common problem of maintaining a desired service level for delivery at a reasonable cost while dealing with irregular customer orders. This research considers a MTO manufacturer who produces a product consisting of several custom parts to be ordered from multiple suppliers. We develop procedures to allocate orders to each supplier for each custom part and calculate the associated replenishment cost as well as the probability of meeting the delivery date, based on the suppliers' jobs on hand, availability, process speed, and defective rate. For a given delivery due date, a frontier of service level and a replenishment cost frontier are created to provide a range of options to meet customer requirements. This method can be further extended to the case when the delivery due date is not fixed and the manufacturer must “crash” its delivery time to compete for customers.