In this work, we provide an overview of our previously published works on incorporating demand uncertainty in midterm planning of multisite supply chains. A stochastic programming based approach is described to model the planning process as it reacts to demand realizations unfolding over time. In the proposed bilevel-framework, the manufacturing decisions are modeled as ‘here-and-now’ decisions...

How does demand uncertainty affect entry into skill-based competition? I investigate this question in a market entry experiment with skill-based payoffs by systematically varying two key elements of the market environment: demand risk and expected market size. Results show that people’s reactions to demand risk depend on the market size: in small markets people enter more when demand is risky, ...

A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. In contrast to comparing models based on the likelihood values with unknown sampling properties (as in, e.g., Egan et al, 2009), we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into the model and reflect the accompanyi...

This paper attempts to reconcile the risk-bearing characterization of entrepreneurs with the stylized fact that entrepreneurs exhibit conventional risk aversion profiles. We propose that the disparity arises from confounding two distinct dimensions of uncertainty: demand uncertainty and ability uncertainty. We further propose that entrepreneurs will be risk averse with respect to demand uncerta...

Di erences in the e ectiveness and side e ects of di erent drugs introduces uncertainty into pharmaceutical demand. This in turn introduces dynamic aspects to drug choice: patients have an incentive to experiment to gain information useful for future choices. Exploiting a rich panel dataset on anti-ulcer drug prescriptions, the purpose of this paper is to measure the importance of uncertainty a...

In industries like health care, consumer goods and agriculture, shortages are widely observed and the consequences can be costly. One of the main drivers of such shortages is the uncertain nature of supply and demand. To reduce uncertainties, sufficient information about supply and demand can be obtained by gathering relevant data (e.g., auditing suppliers and conducting market research). In th...

This paper presents a dual-objective facility programming model for a green supply chain network. The main objectives of the presented model are minimizing overall expenditure and negative environmental impacts of the supply chain. This study contributes to the existing literature by incorporating uncertainty in customer demand, suppliers, production, and casting capacity. An industrial case st...

many constants and coefficient effect on the modeling results. in many cases, it is possible these constants and coefficients differ from their real values in the environment, but the modeling results adapted with real boundary conditions due to other factors adjustment during modeling. uncertainty analysis is a tool for assessing the effect of uncertainty of various factors on the modeling res...

making capital and investment is the main driving forces of economic development. based on the investment sensitivity to the changes of some of macro-economic variables and risk and uncertainty, the present study evaluated the effective factors on investment in private sector in iran during 1980-2007. at first, the uncertainty variables of real informal exchange rate, nominal interest rate and ...

The revenue management concept and techniques are applied to model the coordination of supply chain elements. The fundamental premise of this approach is synchronization of a group of business entities consist-ing of a manufacturer and multiple suppliers to achieve an optimal supply chain capacity plans. The output of the supply chain can be various products and thus it is measured in terms of ...