The interaction of lean and building information modeling in construction

Lean construction and Building Information Modeling are quite different initiatives, but both are having profound impacts on the construction industry. A rigorous analysis of the myriad specific interactions between them indicates that a synergy exists which, if properly understood in theoretical terms, can be exploited to improve construction processes beyond the degree to which it might be improved by application of either of these paradigms independently. Using a matrix that juxtaposes BIM functionalities with prescriptive lean construction principles, fifty-six interactions have been identified, all but four of which represent constructive interaction. Although evidence for the majority of these has been found, the matrix is not considered complete, but rather a framework for research to explore the degree of validity of the interactions. Construction executives, managers, designers and developers of IT systems for construction can also benefit from the framework as an aid to recognizing the potential synergies when planning their lean and BIM adoption strategies

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

The Role of GIS to Enable Public-Sector Decision Making Under Conditions of Uncertainty

Uncertainty is inherent in environmental planning and decision making. For example, water managers in arid regions are attuned to the uncertainty of water supply due to prolonged periods of drought. To contend with multiple sources and forms of uncertainty, resource managers implement strategies and tools to aid in the exploration and interpretation of data and scenarios. Various GIS capabilities, such as statistical analysis, modeling and visualization are available to decision makers who face the challenge of making decisions under conditions of deep uncertainty. While significant research has lead to the inclusion and representation of uncertainty in GIS, existing GIS literature does not address how decision makers implement and utilize GIS as an assistive technology to contend with deep uncertainty. We address this gap through a case study of water managers in the Phoenix Metropolitan Area, examining how they engage with GIS in making decisions and coping with uncertainty. Findings of a qualitative analysis of water mangers reveal the need to distinguish between implicit and explicit uncertainty. Implicit uncertainty is linked to the decision-making process, and while understood, it is not displayed or revealed separately from the data. In contrast, explicit uncertainty is conceived as separate from the process and is something that can be described or displayed. Developed from twelve interviews with Phoenix-area water managers in 2005, these distinctions of uncertainty clarify the use of GIS in decision making. Findings show that managers use the products of GIS for exploring uncertainty (e.g., cartographic products). Uncertainty visualization emerged as a current practice, but definitions of what constitutes such visualizations were not consistent across decision makers. Additionally, uncertainty was a common and even sometimes helpful element of decision making; rather than being a hindrance, it is seen as an essential component of the process. These findings contradict prior research relating to uncertainty visualization where decision makers often express discomfort with the presence of uncertainty.

Functional Data Analysis in Electronic Commerce Research

This paper describes opportunities and challenges of using functional data analysis (FDA) for the exploration and analysis of data originating from electronic commerce (eCommerce). We discuss the special data structures that arise in the online environment and why FDA is a natural approach for representing and analyzing such data. The paper reviews several FDA methods and motivates their usefulness in eCommerce research by providing a glimpse into new domain insights that they allow. We argue that the wedding of eCommerce with FDA leads to innovations both in statistical methodology, due to the challenges and complications that arise in eCommerce data, and in online research, by being able to ask (and subsequently answer) new research questions that classical statistical methods are not able to address, and also by expanding on research questions beyond the ones traditionally asked in the offline environment. We describe several applications originating from online transactions which are new to the statistics literature, and point out statistical challenges accompanied by some solutions. We also discuss some promising future directions for joint research efforts between researchers in eCommerce and statistics.Comment: Published at http://dx.doi.org/10.1214/088342306000000132 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org