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Entering and exiting the Pandemic Recession, the author study the high-frequency real-activity signals provided by a leading nowcast, the ADS Index of Business Conditions produced and released in real time by the Federal Reserve Bank of Philadelphia. The author tracks the evolution of real-time vintage beliefs and compares them to a later-vintage chronology. Real-time ADS plunges and then swings as its underlying economic indicators swing, but the ADS paths quickly converge to indicate a return to brisk positive growth by mid-May. The author shows, moreover, that the daily real-activity path was highly correlated with the daily COVID-19 cases. Finally, the author provides a comparative assessment of the real-time ADS signals provided when exiting the Great Recession.

The purpose of this paper is to investigate the critical time window for pro-active construction accident prevention and response. Large to small organisations throughout the entire construction supply chain continue to be challenged to adequately prevent accidents. Construction worker injuries and fatalities represent significant waste of resources. Although the five C’s (culture, competency, communication, controls and contractors) have been focusing on compliance, good practices and best-in-class strategies, even industry leaders have only marginal improvements in recorded safety statistics for many years.

Right-time vs real-time construction safety and health identifies three major focus areas to aid in the development of a strategic, as opposed to tactical, response. Occupational safety and health by design, real-time safety and health monitoring and alerts and education, training and feedback leveraging state-of-the-art technology provide meaningful predictive, quantitative and qualitative measures to identify, correlate and eliminate hazards before workers get injured or incidents cause collateral damage.

The current state and development of existing innovative initiatives in the occupational construction safety and health domain are identified. A framework for right-time vs real-time construction safety and health presents the specific focus on automated safety and health data gathering, analysis and reporting to achieve better safety performance. The developed roadmap for right-time vs real-time safety and health is finally tested in selected application scenarios of high concern in the construction industry.

A strategic roadmap to eliminate hazards and accidents through right-time vs real-time automation is presented that has practical as well as social implications on conducting a rigorous safety culture and climate in a construction business and its entire supply chain.

Considers a system (machine) that is subject to failure (breakdown). Two characterizations are presented. In the first characterization, the state of the system is described by the real age of the machine and the number of failures incurred to date. In the second characterization, the state of the system is described by the real age of the machine and the virtual age of the machine. In either characterization, upon failure, the unit may undergo a repair which can partially reset the failure intensity of the unit. The degree of reset assumed by the repair is a function of the characterization utilized. The other alternative, at a failure, is to conduct a major overhaul that serves to refresh the failure intensity of the unit. General cost structures, depending upon (real age, number of failures) in characterization one or (real age, virtual age) in characterization two are permitted. The decision, on failure to repair or renew is formulated as a discrete semi‐Markov Decision process. Optimal decisions are of the threshold type. The threshold rules depend upon the characterization.

Rapid innovation and globalization have generated tremendous opportunities and choices in the marketplace for firms and customers. Competitive pressures have led to sourcing and manufacturing on a global scale resulting in a significant increase in products. The paper tries to identify the need for real time business intelligence (BI) in supply chain analytics.

The paper provides argument and analysis of the advantages and hurdles in BI.

The paper focuses on the necessity to revisit the traditional BI concept that integrates and consolidates information in an organization in order to support firms that are service oriented and seeking customer loyalty and retention. Enhancing effectiveness and efficiency of supply chain analytics using a BI approach is a critical component in a company's ability to achieve its competitive advantage.

This paper furthers understanding of the issues surrounding the use of BI systems in supply chains.

The purpose of this paper is to provide new insights into asset liquidity in direct commercial real estate investment in the UK. Transaction data provided by four institutional investors of commercial real estate are used to test for changes in asset liquidity as manifest in recorded times from price agreement to deal completion. Median times to completion by stage of the transaction are presented alongside industry estimates.

Stages of the transaction process are modelled and median times per stage calculated to track changes in asset liquidity over, and between, the two periods of the study (2000‐2002 and 2005‐2008). Real times to completion are considered in conjunction with estimated times compiled through interviews with senior level investment professionals. This paper applies the Wilcoxon rank‐sum test to determine the significance of variation in median times across the two study periods.

This paper provides empirical evidence that liquidity increased from 2000 to 2008. Median times from price agreement to completion decreased significantly (p=0.015) from 2000‐2002 to 2005‐2008, indicating an increase in asset liquidity in step with an overall increase in transaction volume. Furthermore, senior investment actors were found to persistently over‐estimate transaction efficiency and underestimate liquidity risk when acquiring and disposing of commercial properties.

This work offers new insights into the changing nature of asset liquidity over the last decade based on a limited number of transactions. Additional studies involving larger samples of transactions would provide still greater insight into commercial real estate liquidity dimensions.

The paper presents evidence of pro‐cyclicality; asset liquidity varies positively with overall transaction volumes, and investment actors were found to overestimate asset liquidity suggesting a persistent underestimation of liquidity risk.

This paper addresses a gap in the extant literature offering real time on market‐time to completion observations alongside investor estimates. Median times to completion have been modelled and presented, together with time estimates provided by industry experts. Also, for first time in real estate research, median times to completion are shown to shorten significantly in‐line with increasing transaction volumes.

Purpose – This chapter provides details of research that attempts to relate traffic operational conditions on uninterrupted flow facilities (e.g., freeways and expressways) with real-time crash likelihood. Unlike incident detection, the purpose of this line of work is to proactively assess crash likelihood and potentially reduce the likelihood through proactive traffic management techniques, including variable speed limit and ramp metering among others.

Methodology – The chapter distinguishes between the traditional aggregate crash frequency-based approach to safety evaluation and the approach needed for real-time crash risk estimation. Key references from the literature are summarised in terms of the reported effect of different traffic characteristics that can be derived in near real-time, including average speed, temporal variation in speed, volume and lane-occupancy, on crash occurrence.

Findings – Traffic and weather parameters are among the real-time crash-contributing factors. Among the most significant traffic parameters is speed particularly in the form of coefficient of variation of speed.

Research implications – In the traffic safety field, traditional data sources are infrastructure-based traffic detection systems. In the future, if automatic traffic detection systems could provide reliable data at the vehicle level, new variables such as headway could be introduced. Transferability of real-time crash prediction models is also of interest. Also, the potential effects of different management strategies to reduce real-time crash risk could be evaluated in a simulation environment.

Practical implications – This line of research has been at the forefront of bringing data mining and other machine-learning techniques into the traffic management arena. We expect these analysis techniques to play a more important role in real-time traffic management, not just for safety evaluation but also for congestion pricing and alternate routing.

The study objective was to understand what components of organizational culture and capability combined with analytic skillsets are needed to allow organizations to exploit real-time analytic technologies to create supply chain performance improvements.

The authors relied on information processing theory to support a hypothesized model, which is empirically tested using an ordinary least squares equation model, and survey data from a sample of 208 supply chain executives across multiple industries.

The authors found strong support for the concept that real-time analytics will require specialized analytical skills for the managers who use them in their daily work, as well as an analytics-focused organizational culture that promotes data visibility and fact-based decision-making.

Based on the study model, the authors found that a cultural bias to embrace analytics and a strong background in statistical fluency can produce decision-makers who can make sense of a sea of data, and derive significant supply chain performance improvements.

The research was initiated through five workshops and presentations with supply chain executives leading real-time analytics initiatives within their organizations, which were then mapped onto survey items and tested. The authors complement our findings with direct observations from managers that lend unique insights into the field.

The heating, ventilation and air conditioning systems are responsible for a significant proportion of the energy consumption of the built environment, on which the occupant's pursuit of thermal comfort has a substantial impact. Regarding this concern, current software can assess and visualize the conditions. However; integration of existing technologies and real-time information could enhance the potential of the solution proposals. Therefore, the purpose of this research is to explore new possibilities of how to upgrade building information modeling (BIM) technology to be interactive; by using existing BIM data during the occupation phase. Moreover, the research discusses the potential of enhancing energy efficiency and comfort maximization together by using the existing BIM database and real-time information concomitantly.

The platform is developed by designing and testing via prototyping method thanks to Internet of things technologies. The algorithm of the prototype uses real-time indoor thermal information and real-time weather information together with user's body temperature. Moreover, the platform processes the thermal values with specific material information from the existing BIM file. The final prototype is tested by a case study model.

The outcome of the study, “Symbiotic Data Platform” is an occupant-operated tool, that has a hardware, software and unique Revit-Dynamo definition that implies to all BIM files.

The paper explains the development of “Symbiotic Data Platform”, which presents an interactive phase for BIM, as creating a possibility to use the existing BIM database and real-time values during the occupation phase, which is operated by the occupants of the building; without requiring any prior knowledge upon any of the BIM software or IoT technology.

Creating a real-time data integration when developing an internet-of-things (IoT)-based warehouse is still faced with challenges. It involves a diverse knowledge of novel technology and skills. This study aims to identify the critical components of the real-time data integration processes in IoT-based warehousing. Then, design and apply a data integration framework, adopting the IoT concept to enable real-time data transfer and sharing.

The study used a pilot experiment to verify the data integration system configuration. Radio-frequency identification (RFID) technology was selected to support the integration process in this study, as it is one of the most recognized products of IoT.

The experimentations’ results proved that data integration plays a significant role in structuring a combination of assorted data on the IoT-based warehouse from various locations in a real-time manner. This study concluded that real-time data integration processes in IoT-based warehousing could be generated into three significant components: configuration, databasing and transmission.

While the framework in this research was carried out in one of the developing counties, this study’s findings could be used as a foundation for future research in a smart warehouse, IoT and related topics. The study provides guidelines for practitioners to design a low-cost IoT-based smart warehouse system to obtain more accurate and timely data to support the quick decision-making process.

The research at hand provides the groundwork for researchers to explore the proposed theoretical framework and develop it further to increase inventory management efficiency of warehouse operations. Besides, this study offers an economical alternate for an organization to implement the integration software reasonably.

The aim of this study is to identify the nodes where congestion occurs in the manoeuvring area of a large-scale airport and to provide appropriate suggestions for improvement.

To investigate the air traffic flow in a highly complex system such as an airport manoeuvring area, a two-stage method based on fast- and real-time simulation techniques is applied. The first stage involves the analysis with fast- and real-time simulations of a baseline model created to determine the congestion points. Based on the analysis, improvements to be performed in the layout of the manoeuvring area are proposed. In the second stage, alternative scenarios implementing these improvements are generated and evaluated in a fast-time simulation environment. Based on the results of simulations of different runway configurations, the main areas of congestion in the baseline airport model are determined. Congestion nodes are identified in the departure queue points and in the taxiway system. To mitigate congestion at these points, three alternative models comprising taxiway and fast-exit taxiway reconfigurations are tested using the fast-time simulation technique. The alternative solution found to be the best in these tests is selected for further testing in real-time simulations.

It is shown that the solution would result in an increase in the number of hourly operations and a significant decrease in total ground delays. When conducting the studies needed to identify congestion and design improvements, simulation techniques save both expense and time. Although fast-time simulations are usually adequate for identifying solutions, when critical configurations for the airport are considered, it is shown that it is necessary to also test the results of the fast-time simulations in real-time simulations.

The effects of meteorological events, such as rain, fog and snow, etc. are ignored in the simulations. Ground movements in manoeuvring areas are significantly affected by the runways used. Consequently, to enable a comprehensive evaluation in the study, three alternative runway use scenarios are examined.

This study utilizes a combination of fast- and real-time simulation techniques to identify the points where congestion occurs in the manoeuvring areas of large-scale airports and to find solutions to minimize the congestion. This approach attempts to combine advantages of both techniques while reducing their shortcomings. No study is found in the literature using both of these techniques together for the capacity analysis of airport manoeuvring areas.