The middleman as a panacea for supply chain coordination problems

The prevalence of intermediaries (middlemen) in supply chains is often seen as a dying remnant of less efficient times. Despite predictions that supply chains will rapidly “cut out the middleman” as technological advances have eased logistics, middlemen have continued to thrive. In this paper, we demonstrate a transaction role of middlemen that may help clarify their staying power. In a model with self-interested decision-making by both a manufacturer and a retailer, wherein incentive misalignment creates investment and production inefficiencies, we show that the integrated (first-best) outcome can be achieved with simple cost-based contracts if and only if a middleman is present. We further show that the approach of utilizing a middleman to fully coordinate the supply chain is robust in that it can be applied to a variety of circumstances discussed in the literature, including multilateral investment/effort choices, multiple product providers, and logistical investments made by the middleman.     

Cost of capital for incentives on capacity expansion investments

In this paper, the expression for the cost of capital is derived when capacity expansion investments and replacement investments exhibit differences in their effective prices. It is shown that the cost of capital derived by perturbing the optimal stock path should be constructed under an opportunity cost criterion.     

Determination of the portfolio selection for a property-liability insurance company

This paper presents an analysis of a portfolio model which can be used to assist a property-liability insurance company in determining the optimal composition of the insurance and investment portfolios. By introducing insurer's threshold risk and relaxing some non-realistic assumptions made in traditional chance constraint insurance and investment portfolio models, we propose a method for an insurer to maximize his return threshold for a given threshold risk level. This proposed model can be used to optimize the composition of underwriting and investment portfolios regarding the insurer's threshold risk level, as well as to generate the efficient frontier by adjusting insurer's threshold risk levels. A numerical example is given based on the industry's aggregated data for a sixteen year period.     

TO LEARN OR TO CHANGE: OPTIMAL R&D INVESTMENTS UNDER UNCERTAINTY IN THE CASE OF CLIMATE CHANGE

When studying R&D investments in technologies that address potential damage from climate change (termed as “research to change” or RTC), current literature overlooks the effects of purchased learning (i.e., learn through scientific research, termed as “research to learn” or RTL) about climate change. We investigate interactions between optimal R&D investments in RTC and RTL under uncertainty in climate change and research outcomes, while accounting for the positive impact that successful RTL may have on RTC outcome. We find that simultaneously investing in both RTL and RTC may be optimal when the probability that climate change imposes a specific level of damage is either moderate or very high and when RTL cost is relatively low. Whenever RTL and RTC are conducted simultaneously, then they substitute. However, when it is not optimal to conduct RTC and RTL simultaneously, then an increase in RTC cost decreases, at least weakly, RTL investment (i.e., RTL and RTC complement). When the probability that climate change imposes damage increases, then the optimal RTL investment may first decrease and then increase. Moreover, we identify conditions under which either the precautionary principle or the learn‐then‐act principle should be followed regarding R&D investments.     

Collaboration in tool development and capacity investments in high technology manufacturing networks

The procurement of capital intensive tools for hi-tech industries is one of the most complex tasks. Astronomical amounts of capital are invested in the processing equipment. Further, there is a large effort from the original equipment manufacturer (OEM) in customizing the high capital intensive equipment to suit their production process. This problem has received little attention from the quantitative decision making literature. For the first time we analyze the problem of OEM deciding on collaborating with the tool supplier via a special type of contract which we refer as “collaboration options”. We show that there are benefits to both the OEM and the tool suppliers from the collaboration.     

Capital budgeting and optimal timing of investments in flexible manufacturing systems

This paper investigates the financial-economic decision process for investments in flexible manufacturing systems (FMS). Contrary to popular belief, we show that conventional capital budgeting techniques can be used to make such investment decisions. First, we identify theoverall impact of installing an FMS and present guidelines for a cash flow forecasting model. We then present ways in which to incorporate uncertainty in these cash flows within a risk-adjusted discount rate. These expected cash flows and the discount rate are used in calculating the net present value (NPV). Once the capital budgeting analysis is completed, a critical issue facing the firm is the optimal timing of the installation. We reinterpret the general results on optimal timing of investments within the special context of an FMS project. Finally, we illustrate the above technique via a stylized example.     

Optimal investments for universal primary education

This paper describes the application of the discrete maximum principle to the problem of the introduction of universal primary education.The author is most indebted to the Ford Foundation for their financial support during most of the research reported here, under the Pakistan-Berkeley Project in Educational Planning.     

A Framework Algorithm to Compute Optimal Asset Allocation for Retirement with Behavioral Utilities

The question of optimal strategic asset allocation for investors with behavioural utilities saving for retirement is addressed. To date this problem has been studied assuming that an investor is rational in the sense when making investment decisions the preference relation of the investor satisfies all the axioms of choice. Research in behavioural science indicates that investment related decisions of many people do not satisfy the axioms of choice. Our interest is in developing a platform that allows the use of a broader class of utilities that may or may not satisfy the axioms of choice. Such utilities may not be convex. Our interest is in developing a framework algorithm that enables a user considerable flexibility in how their needs may be specified. For illustrative purposes a binomial tree is used to model asset returns, although the method developed can be used with more elaborate models.Work supported by the National Science Foundation grant CCR-9988205 and Office of Naval Research grant N00014-96-1-0274.     

Health shock risk,critical illness insurance,and housing services

This paper studies a consumption–investment problem involving health shock risk, perishable consumption, and consumption of housing services. Additionally to a risk-free asset and a stock index, the agent can invest in real estate. I analyze the impact of health shocks on the optimal consumption and investment decisions in model specifications with and without the possibility to buy critical illness insurance. I discuss the influence of critical illness insurance on the optimal strategy and analyze the drivers of the optimal critical illness insurance demand. The results indicate that health shock risk has potentially devastating consequences, especially for young agents. It turns out that critical illness insurance is an excellent instrument for hedging health shock risk and for consumption smoothing across different health states. Optimal critical illness insurance demand is decreasing in financial wealth and increasing in human wealth. Real estate prices have a minor influence on optimal critical illness insurance demand.     

Using mixture‐amount modeling to optimize the advertising media mix and quantify cross‐media synergy for specific target groups

One of the critical decisions in media planning is how to allocate advertising efforts across different media. While studies indicate that marketers can create positive synergy effects by spreading their effort across several media, there is little understanding of how much should be invested in each specific medium to optimize advertising results. In this study, we apply a novel methodology, mixture‐amount modeling, which allows advertisers to determine the optimal allocation of advertising effort across media as a function of the total advertising effort. Moreover, we test how the optimal allocation and the resulting response change for consumers with distinctive media usage patterns and varying degrees of product category experience. Based on these results, we quantify the potential synergy between media and calculate the synergistic capacity for specific target groups. We apply the model to data from 52 beauty care advertising campaigns that ran on TV and in magazines in the Netherlands and Belgium. We determine the optimal allocation of advertising investments (measured through Gross Rating Points) to maximize campaign recognition. Our findings support the existence of positive synergistic effects between magazine and TV advertising and illustrate that these effects depend on consumers' media usage and product category experience.