Accurately evaluating strategic commitments to remanufacturing requires a comprehensive understanding and consideration of the simultaneous effect of uncertainty on supply and demand. This paper presents a model that supports improved remanufacturing decision-making under stochastic demand and supply components and various cost factor considerations. The model is formulated as a newsvendor model extension that considers the simultaneous effects of probabilistic supply and demand. This characterisation assesses the effects of critical operational constraints on several strategic performance measures including cost, revenue and profit. The paper employs a Monte Carlo approach to assess the performance and extent of engagement in strategic remanufacturing supply chain operations, and illustrates results via a case study application. Results indicate that the technique can assist decision-makers in strategically determining the potential of future engagements in remanufacturing activities, and extends research findings by theoretically examining strategic decision-making for remanufacturing activity.

Severe catastrophic natural disasters adversely affect housing stock and regional capacity to build and repair houses, with unfavorable impacts on the business continuity of industrial and business organizations resting on that affected community. Restoring industrial capacity and business continuity is crucial for the organizations’ survival in the aftermath of a disaster but the process takes time while the affected region faces an unexpected surge in the demand for housing. Modeling approaches largely used in the supply chain management realm may support humanitarian logistics and policy-making by providing a testbed in which supply chain elements of the recovery process can be examined before implementing. This research presents a system dynamics model that considers the problem of housing recovery from the demand and supply perspective and provides significant insights for policymakers into how the production of permanent housing depends upon the uncertainties and feedback effects of material and labor. In order to highlight its utility, a hypothetical scenario in which a hurricane hits the U.S. Hampton Roads region, which is a major population center along the mid-Atlantic coast, has been configured by using actual data collected from both governmental and academic sources.

Introduction This article demonstrates the utility of the system dynamics approach to model and simulate US demand for ambulatory health care service both for the general population and for specific cohort subpopulations over the 5-year period, from 2003 to 2008. A system dynamics approach that is shown to meaningfully project demand for services has implications for health resource planning and for generating knowledge that is critical to assessing interventions. Methods The study uses a cohort-component method in combination with structural modeling to simulate ambulatory health care utilization. Data are drawn from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey. Results The simulation of the total population requiring ambulatory services between 2003 and 2008 is performed to test the functionality and validate the model. Results show a close agreement between the simulated and actual data; the percent error between the two is relatively low, 1.5% on average. In addition, simulations of purposively selected population subsets are executed (men, 18–24 years of age, white, African American, Hispanic, and insurance coverage), resulting in error between simulated and actual data, which is 7.05% on average. Conclusions The proposed model demonstrates that it is possible to represent and mimic, with reasonable accuracy, the demand for health care services by the total ambulatory population and the demand by selected population subsets. This model and its simulation demonstrate how these techniques can be used to identify disparities among population subsets and a vehicle to test the impact of health care interventions on ambulatory utilization. A system dynamics approach may be a useful tool for policy and strategic planners.

Drug manufacturing firms that operate in a globalized ecosystem are subject to a myriad of complex and simultaneous uncertainties. Such uncertainties may have a detrimental impact on firms' operational performance if not appropriately addressed. A firm's ability to react to different degrees of risk can be enhanced by reconfiguring its logistics components. However, selecting the appropriate combination of logistics components that preserve service levels in an unpredictable environment is challenging for global pharmaceutical manufacturing firms. The effects of various levels of supply and demand uncertainties interacting concurrently in a multi-echelon capital-intensive supply chain environment restricted by different regulatory frameworks further exacerbate these factors' cumulative effects. In this paper, we develop a simulation framework capable of capturing these complexities and exploring various combinations of logistics levers to examine the impact of different policy variables on these firms' operational performance subject to varying volatility levels. The simulation framework developed in this paper considers standard logistic practices employed by one of the world's leading biopharmaceutical manufacturing companies. Guided by Subject Matter Expert theoretical information, we have structured experiment designs using the Taguchi Orthogonal methodology and analyzed the statistical significance of different policy variables using ANOVA tests.