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This chapter discusses the steps taken to access and use the ACS five-year data. The format of the data is discussed pointing out the fact that there is no requirement that an ACS five-year variable holds data for the same field year to year. The development of a cross-reference table is discussed allowing the data to be accessed by a common label.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

The aggressive competition in the real estate market forces real estate developers to tackle the challenge of selecting the best project construction phasing alternative. The real estate industry is characterized by high costs, high profit and high risks. The schedules of real estate projects are also characterized by having large number of repetitive activities that are executed over a long duration. The repetitiveness, long duration of execution, the high amounts of money involved and the high risk made it desirable to leverage the impact of changes in phasing plans on net present value of amounts incurred and received over the long execution and selling duration. This also changes the project progress, and delivery time as well as their respective impact on customer degree of satisfaction. This research addresses the problem of selecting the best phasing alternative for real estate development projects while maximizing customer satisfaction and project profit.

The research proposes a model that generates all construction phasing alternatives and performs decision-making to rank all possible phasing alternatives. The proposed model consists of five modules: (1) Phasing Sequencing module, (2) Customer Satisfaction module, (3) Cash-In calculation module, (4) Cost Estimation module and (5) Decision-making module. A case study was presented to demonstrate the practicality of the model.

The proposed model satisfies the real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model generates all construction phasing alternatives and performs multi-criteria decision making to rank all possible phasing alternatives. It quantifies the score of the two previously mentioned criteria and ranks all solutions according to their overall score.

The research proposes a model that assist real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model can be used to conclude general guidelines and common successful practices to be used by real estate developers when deciding the construction phasing plan. In this study the model is based on business models where all the project units are sold, rental cases are not considered. Also, the budget limitations that might exist when phasing is not considered in the model computations.

The model can be used as a complete platform that can hold all real estate project data, process revenues and cost information for estimating profit, plotting cash flow profiles, quantifying the degree of customer satisfaction attributable to each phasing alternative and providing recommendation showing the best one. The model can be used to conclude general guidelines and common successful practices to be used by real estate developers when tackling the challenge of selecting construction phasing plans.

This study aims to increase awareness and educate the reader about health-care fraud targeting seniors in the USA to help stakeholders better understand, recognize and prevent this type of fraud.

This paper collects statistics on the current state of health care frauds committed against seniors, and examines related cases and laws.

The authors find this type of fraud is highly prevalent and expected to increase. Current laws preventing this fraud from occurring are multifold and complex. While prevention strategies through law enforcement have been somewhat successful, a reduction in resources may put seniors at an increased risk in the years to come.

Without additional prevention strategies, the problem will likely escalate with a growing population of older adults. This study encourages further research into effective prevention strategies and methods to fight health-care fraud against seniors.

Health-care fraud and its associated costs pose a significant threat to the society and economy of the USA. Reducing this fraud will not only reduce the costs to the US economy but also improve the physical and mental well-being of senior victims, reduce their mortality and hospitalization rates and improve the public trust placed to health-care providers.

This study highlights how health-care fraud is committed against seniors. With the projected trend of an aging US population, educating stakeholders, increasing awareness and applying tools to protect seniors will be important to reduce the absolute scope of this problem in the future.

This study aims to demonstrate the evaluation of an incentive scheme to encourage pregnant people to set a quit-smoking date.

The paper outlines a collaborative approach, working with pregnant people, clinicians, tobacco dependency practitioners and academics to gain insights into their perspectives and experiences. Quantitative and qualitative data were analysed.

The incentive scheme and appropriate support from clinicians have been shown to encourage pregnant people to set a quit date. The tobacco dependency practitioners helped remove barriers, such as the perception of the stigmatisation of smoking when pregnant. The practitioners also helped pregnant people make informed decisions to support successful behaviour change. The impact of the scheme resulted in improved infant health indicators. The scheme’s evaluation also supported establishing stakeholder knowledge exchange and learning processes.

This is a single-site study among a relatively small group of people designed to achieve a specific evaluation objective. Caution in generalising to wider settings should be exercised.

This study highlights the efficacy of an incentive scheme, complemented with support from clinicians, and the significance of knowledge exchange and collaboration between stakeholders in health care with significance in similar settings.

The paper details the incentive scheme input, actions, output, outcomes and impact involving a wider range of stakeholders, including the emotional consequences for participants, clinicians and academics.

This paper introduces a novel quality measure, the percent-within-distribution, or PWD, for acceptance and payment in a quality control/quality assurance (QC/QA) performance specification (PS).

The new quality measure takes any sample size or distribution and uses a Bayesian updating process to re-estimate parameters of a design distribution as sample observations are fed through the algorithm. This methodology can be employed in a wide range of applications, but the authors demonstrate the use of the measure for a QC/QA PS with upper and lower bounds on 28-day compressive strength of in-place concrete for bridge decks.

The authors demonstrate the use of this new quality measure to illustrate how it addresses the shortcomings of the percent-within-limits (PWL), which is the current industry standard quality measure. The authors then use the PWD to develop initial pay factors through simulation regimes. The PWD is shown to function better than the PWL with realistic sample lots simulated to represent a variety of industry responses to a new QC/QA PS.

The analytical contribution of this work is the introduction of the new quality measure. However, the practical and managerial contributions of this work are of equal significance.

An important phenomenon often observed in supply chain, known as the bullwhip effect, implies that demand variability increases as we move up in the supply chain. On the other hand, the cross-docking is a distribution strategy that eliminates the inventory holding function of the retailer distribution center, where this latter functions as a transfer point rather than a storage point. The purpose of this paper is to analyze the impact of cross-docking strategy compared to traditional warehousing on the bullwhip effect.

The authors quantify this effect in a three-echelon supply chain consisting of stores, retailer and supplier. They assume that each participant adopts an order up to level policy with an exponential smoothing forecasting scheme. This paper demonstrates mathematically the lower bound of the bullwhip effect reduction in the cross-docking strategy compared to traditional warehousing.

By simulation, this paper demonstrates that cross-docking reduces the bullwhip effect upstream the chain. This reduction depends on the lead-times, the review periods and the smoothing factor.

A mathematical demonstration cannot be highly generalizable, and this paper should be extended to an empirical investigation where real data can be incorporated in the model. However, the findings of this paper form a foundation for further understanding of the cross-docking strategy and its impact on the bullwhip effect.

This paper fills a gap by proposing a mathematical demonstration and a simulation, to investigate the benefits of implementing cross-docking strategy on the bullwhip effect. This impact has not been studied in the literature.

This study aims to analyse the influence of digitalisation on the management control function of small and medium-sized enterprises (SMEs). In particular, it aims to illuminate how digitalisation influences management control elements, organisation and roles/competencies and to identify obstacles to digitalisation of management control in SMEs and measures taken to overcome them.

The study is based on guideline-supported expert interviews conducted with 14 financial managers from SMEs in Germany, Austria and Switzerland.

This study reveals the influence of digitalisation on management control elements, organisation, and roles/competencies. The automation and standardisation of management control processes result in new elements for management control, such as strategic support for management. In addition, the increased availability and transparency of data enable the use of instruments within a company that allow for quick analyses of the company's development. Digitalisation leads to the integration of management control into the corporate network and, thus, a change in the organisation of management control. It also triggers the expansion of management control competencies, especially IT competencies. A shortage of internal digitalisation resources, unclear corporate roadmaps, and a lack of managerial experience loom as central challenges for digitalising the management control function. Measures derived from the interviews can help SMEs overcome the obstacles to the digitalisation of management control.

This research is the first interview-based study of the impact of digitalisation on management control in SMEs, potential obstacles to that digitalisation, and measures to overcome those obstacles. Thus, it contributes to the emerging debate on factors that may explain why SMEs lag in terms of the digitalisation of their internal processes.

This study addresses the prevailing complexities and limitations in estimating and managing construction overhead costs (COCs) in the existing literature, with the purpose of enhancing the accuracy of cost performance indicators in construction project management.

An innovative approach is proposed, employing the activity-based costing (ABC) accounting method combined with building information modelling (BIM) to assign real overhead costs to project activities. This study, distinguished by its incorporation of a real case study, focuses on an administrative building with a four-story concrete structure. It establishes an automated method for evaluating project cost performance through the detailed analysis of earned value management (EVM) cost indicators derived from ABC results and BIM data.

The results show that the ABC integration improves the accuracy of cost performance indicators by over 9%, revealing the project's true cost index for the first time and demonstrating the substantial value of the approach in construction engineering and management.

The current study highlights a notable gap in the existing literature, addressing the challenges in onsite overhead cost estimation and offering a solution that incorporates the state-of-the-art techniques.

The proposed method has significant implications for project managers and practitioners, enabling better-informed decisions based on precise cost data, ultimately leading to enhanced project outcomes.

This research uniquely combines ABC and BIM, presenting a pioneering solution for the accurate estimation and management of COCs in construction projects, adding significant value to the current body of knowledge in this field.