Replacement decisions with multiple stochastic values and depreciation

YesWe develop an analytical real-option solution to the after-tax optimal timing boundary for a replaceable asset whose operating cost and salvage value deteriorate stochastically. We construct a general replacement model, from which seven other particular models can be derived, along with deterministic versions. We show that the presence of salvage value and tax depreciation significantly lowers the operating cost threshold that justifies (and thus hastens) replacement. Although operating cost volatility increases defer replacement, increases in the salvage value volatility hasten replacement, albeit modestly, while increases in the correlation between costs and salvage value defer replacement. Reducing the tax rate or depreciation lifetime, or allowing an investment tax credit, yield mixed results. These results are also compared with those of less complete models, and deterministic versions, showing that failure to consider several stochastic variables and taxation in the replacement process may lead to sub-optimal decisions

Industrial Flexibility in Theory and Practice

At the heart of any decision problem is some degree of "flexibility" in how to act. Most often, we aim to extract greatest possible value from this inherent flexibility. The three essays compiled here are aligned with this same general aim, but we have an important secondary concern: to highlight the value of flexibility itself in the various situations we study. In the first essay, we consider the timing of an action: when to replace obsolete subsystems within an extensive, complex infrastructure. Such replacement action, known as capital renewal, must balance uncertainty about future profitability against uncertainty about future renewal costs. Treating renewal investments as real options, we derive the unique, closed-form optimal solution to the infinite horizon version of this problem and determine the total present value of an institution's capital renewal options. We investigate the sensitivity of the solution to variations in key problem parameters. The second essay addresses the promising of lead times in a make-to-order environment, complicated by the need to serve multiple customer classes with differing priority levels. We tackle this problem with a "model free" approach: after preparing a discrete-event simulation of a make-to-order production system, we determine a policy for lead time promising through application of a reinforcement learning algorithm. The third essay presents an empirical analysis of new product launches in the automotive industry, showing that manufacturing flexibility is one key indicator of superior productivity during launch. We explore the financial dimensions of the apparent productivity differences and show that the use of flexible manufacturing increases an automobile plant's likelihood of being chosen to host a new product launch