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PurposeThe overall contribution of this work is to provide a usable maturity model for collaborative scheduling (CS) that extends the literature, identifies inconsistencies in schedule development, and improves collaboration in the construction industry.Design/methodology/approachVia subject matter expert elicitation and focus groups, the maturity model establishes five pillars of collaboration—scheduling significance, planners and schedulers, scheduling representation, goal alignment with owner, and communication. The maturity model is then validated through iterative feedback and chi-squared statistical analysis of data obtained from a survey. The five pillars are tied to the literature and previous work in CS.FindingsThe analysis shows that current industry projects are not consistent in collaboration practice implementation, and the maturity model identifies areas for collaboration improvement. The study's contributions to the body of knowledge are (1) developing a maturity model-based approach to define and measure the current level of collaboration and (2) discovering the level of consistency in scheduling collaboration practice implementation.Practical implicationsThe findings provide a benchmark for self-evaluation and peer-to-peer comparison for project managers. The model is also useful for project managers to develop effective strategies for improvement on targeted dimensions and metrics.Originality/valueThe construction engineering and management (CEM) literature does not contain targeted models for scheduling collaboration in the context of maturity and, broadly speaking, neither does the literature at large. The literature also lacks actionable items as presented for the maturity model for collaborative scheduling (MMCS).

Publisher Copyright: © 2022 American Society for Engineering Management. The Critical Path Method (CPM), the Last Planner System (LPS) and location-based methods, such as the Line of Balance (LB), are discussed extensively in the technical literature about schedules. However, no discussion exists focusing on the differences and similarities of these methods in terms of their use in different countries. Using chi-squared and Fisher’s exact tests, this research compared three countries (Brazil, Finland, and United States) and the methods to evaluate both intra- and inter-country implementation to gain additional insights about their use. Results suggest statistically significant intra- and inter-country differences regarding how these methods are used, with a specific focus on mechanics in the countries, offering important information to address their various scheduling needs. The results reflect the current state of practice; engineering and construction managers should understand different ways of understanding scheduling. Such understanding can lead to more efficient communication with collaborators and when incorporating foreign teams in projects. The study identifies the need for further scientific explanation as to why these methods are used in the manner they are intra-country as well as adaptions made in inter-country relationships.

This research examines how the construction industry addresses the roles of schedulers and schedules, with the goal of improving current practices. We use a mixed-mode approach of qualitative and q...

This perspectives article addresses risk in cyber defense and identifies opportunities to incorporate risk analysis principles into the cybersecurity field. The Science of Security (SoS) initiative at the National Security Agency seeks to further and promote interdisciplinary research in cybersecurity. SoS organizes its research into the Five Hard Problems (5HP): (1) scalability and composability; (2) policy-governed secure collaboration; (3) security-metrics-driven evaluation, design, development, and deployment; (4) resilient architectures; and (5) understanding and accounting for human behavior. However, a vast majority of the research sponsored by SoS does not consider risk and when it does so, only implicitly. Therefore, we identify opportunities for risk analysis in each hard problem and propose approaches to address these objectives. Such collaborations between risk and cybersecurity researchers will enable growth and insight in both fields, as risk analysts may apply existing methodology in a new realm, while the cybersecurity community benefits from accepted practices for describing, quantifying, working with, and mitigating risk.

The practice of threat modeling to identify and mitigate, or eliminate, vulnerabilities, has a long history in military operations research. More recently, these practices are applied to the cyber domain to give a defender an increased advantage. Most cybersecurity research, however, is performed in the computer science and network infrastructure fields where the focus is on theory and infrastructure management. Consequently, the work is lacking traditional operations research perspectives in risk analysis, assessment, and decision support that may lead to a greater overall defensive posture. This chapter outlines several operations research techniques used in modeling cybersecurity threats and proposes a value model framework for security metrics and best practices that is supported by data and interviews with subject matter experts. The value model, which includes a framework that can be customized for any organization, is illustrated using notional data. Finally, through a survey of cyber professionals with both military and corporate industry experience, an inventory of valued attributes for a secure cyber system is provided, along with potential differences in values based on an organization’s history of attacks and/or breaches. This model, grounded in the principles of military operations research, will enable organizations to assess the performance of their respective cyber systems, manage risk, and continuously improve their processes.

A value model is developed for military logistics that fulfill emergent requests for tailored resupply packages from the sea. Asset tracking technologies, including radio frequency identification, barcoding, internal positioning systems (IPS), and camera-aided technology, are considered as alternatives to a multi-objective decision model. Model measures include registration of inventory in the system, stowage factor enablement, storage location precision, retrieval identification accuracy, system compatibility, and security. The decision model is presented using insights from subject matter experts. Given the requirements of selective offloading in dense storage environments, IPS is the preferred asset tracking technology. Sensitivity analysis and recommendations for engineering managers are provided.

Supplier quality management (SQM) is an important function in the construction industry. Many construction organizations place high importance on using quantitative analyses to select effective SQM practices that ensure that materials, assemblies, and fabricated equipment for the construction project are within quality specifications. However, traditional quantitative analysis methods may be limited because the process of acquiring enough data to conduct the analyses is time consuming and costly. This article discusses the use of principal component analysis (PCA) to analyze a number of SQM practices from construction organizations known for their effective SQM. PCA is useful in this study because the data available for analysis are small in size and multivariate. SQM practices are discussed extensively and validated with subject matter experts (SMEs) using the analytical hierarchy process (AHP). We show that supplier's work observation, supplier performance rating, inspection effort tracking, and...

With group judgments in the context of the Analytic Hierarchy Process (AHP) one would hope for broad consensus among the decision makers. However, in practice this will not always be the case, and significant dispersion may exist among the judgments. Too much dispersion violates the principle of Pareto Optimality at the comparison level and/or matrix level, and if this happens, then the group may be homogenous in some comparisons and heterogeneous in others. The question then arises as to what would be an appropriate aggregation scheme when a consensus cannot be reached and the decision makers are either unwilling or unable to revise their judgments. In particular, the traditional aggregation via the geometric mean has been shown to be inappropriate in such situations. In this paper, we propose a new method for aggregating judgments when the raw geometric mean cannot be used. Our work is motivated by a supply chain problem of managing spare parts in the nuclear power generation sector and can be applied whenever the AHP is used with judgments from multiple decision makers. The method makes use of principal components analysis (PCA) to combine the judgments into one aggregated value for each pairwise comparison. We show that this approach is equivalent to using a weighted geometric mean with the weights obtained from the PCA.

This paper presents a methodology for developing a spare parts inventory management system with a focus on the nuclear power sector. Often, demand for spare parts is highly intermittent and cannot be accurately forecasted through traditional methods. Examples include nuclear power generation equipment, ground space systems, and aircraft engine parts. We take a data-driven engineering management approach and develop a four-step methodology for spare parts management in such environments. These steps comprise an influence diagram for identifying relevant factors, weighting of influences through the analytic hierarchy process, grouping parts according to inventory criticality indices, and the development of base stock inventory policies for each group. This approach allows the system to be actively managed within a continuous improvement framework through employee engagement and input, and mathematical assumptions are not made in the models. To our knowledge, no such integrated, comprehensive methodology for spare parts has been developed. The techniques employed in this research can be effectively used together to holistically manage the entire spare parts process, or they may be used separately to manage portions of the process. This paper provides an overview of the methodology, and the entire approach is illustrated via a test bed nuclear power generation facility.