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This study investigates a novel educational strategy in science, technology, engineering and mathematics (STEM) teaching that integrates the engineering design process (EDP) as a framework. The strategy aims to help teachers explain STEM concepts in a simplified way. We employed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to enable a systematic review that evaluated the effectiveness of this approach in improving both teaching and learning in STEM subjects.

In order to fulfill the objectives of the review, key data were extracted from each of the 400 articles that were reviewed from three databases: Scopus, ProQuest Central and EBSCO. Two types of analysis were conducted, namely descriptive analysis and literature classification.

This systematic review analyzed 44 articles on the EDP, focusing on 18 detailed studies mainly from ProQuest, SCOPUS and EBSCO. It revealed a limited focus on gender’s impact on EDP and a trend toward interdisciplinary use and integrated research approaches. The study underscores the need for further exploration of demographic influences and preparation programs in EDP across various disciplines, aiming to inform future research and educational policies.

The study’s value lies in its comprehensive assessment of engineering design (ED) research over the past decade, serving as a key reference point. It highlights progress in the field, consolidates findings and provides insights into the field’s evolution, guiding future research directions in ED.

The integration of sustainability, performance measurement and new product development (NPD) is key for aligning environmental and social objectives with business strategies. While previous research has initiated proposals for integrating sustainability into NPD or incorporating sustainability into corporate measurement systems, there is a notable deficiency in studies that comprehensively integrate these three perspectives. In this sense, this study proposes a performance framework (PF) to integrate sustainability performance indicators (PIs) into the measurement system considering the company’s NPD phases.

The PF was developed through a literature review and action research (AR). This resulting PF was positively evaluated by the practitioners in the company.

First, the review enabled the synthesis of an initial conceptual PF with 188 sustainability PIs and a five-step procedure. Then, the empirical results of the AR led to a new PF that presents the systematisation of the PIs database and a practice-based seven-stage approach.

This action-oriented research limits the extent to which this study’s findings can be generalised. Future research should apply the PF in different research designs to produce managerially relevant knowledge.

This PF may provide managers with actionable knowledge that best supports the measurement system integration with sustainability PIs considering the NPD phases.

Integrating sustainability, performance measurement and the NPD has been recognised as critical for supporting decision-making concerning the impact of processes and products. Compared with previous frameworks, the proposed PF extends the existing literature by introducing a systematised PIs database and a novel procedure for integrating sustainability measurement throughout the NDP.

This study aims to develop a model for coordination and communication overhead in distributed software development through case study analysis in the Indian outsourcing software industry. The model is based on business knowledge, which can be classified as domain, regulatory, strategic, business process and operation process knowledge as per existing literature.

Double case study method was used to verify an existing knowledge–management framework of software development from the literature. The stakeholders of both the cases were interviewed, and project documents were verified to reach conclusions.

The findings supported the business knowledge classification from the literature. The concept can be used to analyze the software project in a distributed environment.

The research work findings are based only on two case studies. The study findings cannot be generalized and should be used as a learning tool. There can be large variations of project characteristics with differences in business knowledge requirements. The research shows the importance of business knowledge transfer in global software development.

Projects managers in the distributed software development environment can use the findings in project planning and work allocation for better control over cost and schedule, etc.

There is little research works attempted to study the business knowledge classification in the global software industry making the research novel.

The main purpose of this study resides essentially in the development of a new tool to quantify the biomass in the bioreactor operating under steady state conditions.

Modeling is the most relevant tool for understanding the functioning of some complex processes such as biological wastewater treatment. A steady state model equation of activated sludge model 1 (ASM1) was developed, especially for autotrophic biomass (XBA) and for oxygen uptake rate (OUR). Furthermore, a respirometric measurement, under steady state and endogenous conditions, was used as a new tool for quantifying the viable biomass concentration in the bioreactor.

The developed steady state equations simplified the sensitivity analysis and allowed the autotrophic biomass (XBA) quantification. Indeed, the XBA concentration was approximately 212 mg COD/L and 454 mgCOD/L for SRT, equal to 20 and 40 d, respectively. Under the steady state condition, monitoring of endogenous OUR permitted biomass quantification in the bioreactor. Comparing XBA obtained by the steady state equation and respirometric tool indicated a percentage deviation of about 3 to 13%. Modeling bioreactor using GPS-X showed an excellent agreement between simulation and experimental measurements concerning the XBA evolution.

These results confirmed the importance of respirometric measurements as a simple and available tool for quantifying biomass.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

This research aims to develop an automated and optimization algorithms (OAs)-integrated 4D building information modeling (BIM) approach and a prototype and enable construction managers and practitioners to estimate the time of compound elements in building projects using the resource specification technique.

A 4D BIM estimation process was first developed by applying the resource specification and geometric information from the BIM model. A suite of OA including particle swarm optimization, ant colony, differential evolution and genetic algorithm were developed and compared in order to facilitate and automate the estimation process. The developed processes and porotypes were linked and integrated.

The OA-based automated 4D BIM estimation prototype was developed and validated through a real-life construction project. Different OAs were applied and compared, and the genetic algorithm was found as the best performing one. The prototype was successfully linked with BIM timeliner application. By using this approach, the start and finish dates of all object-based activities are developed, and the project completion time is automatically estimated.

Unlike conventional construction estimation methods which need various tools and are error prone and time-consuming, the developed method bypasses the existing time estimation tools and provides the integrated and automated process with BIM and machine learning algorithms. Furthermore, this approach integrates 4D BIM applications into construction design procedures, connected with OA automation.

The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications through the lens of cumulative capability theory.

Adopting a multiple-case design, we examine four cases of digitalization initiatives within lean manufacturing firms. We collected data through semi-structured interviews and direct observations during site visits.

The study uncovers the development of learning capabilities as a result of integrating lean and digitalization. We find that digitalization in lean manufacturing firms contributes to the development of both routinized and evolutionary learning capabilities in a cumulative fashion.

The study adds nuance to the limited theoretical understanding of the integration of lean and digitalization by showing how it cumulatively develops the learning capabilities of lean manufacturing firms. As such, the study supports the robustness of cumulative capability theory. We further contribute to research by offering empirical support for the cumulative nature of learning.

Managing project completion within the stipulated time is significant to all firms' sustainability. Especially for software start-up firms, it is of utmost importance. For any schedule variation, these firms must spend 25 to 40 percent of the development cost reworking quality defects. Significantly, the existing literature does not support defect rework opportunities under quality aspects among Indian IT start-ups. The present study aims to fill this niche by proposing a unique mathematical model of the defect rework aligned with the Six Sigma quality approach.

An optimization model was formulated, comprising the two objectives: rework “time” and rework “cost.” A case study was developed in relevance, and for the model solution, we used MATLAB and an elitist, Nondominated Sorting Genetic Algorithm (NSGA-II).

The output of the proposed approach reduced the “time” by 31 percent at a minimum “cost”. The derived “Pareto Optimal” front can be used to estimate the “cost” for a pre-determined rework “time” and vice versa, thus adding value to the existing literature.

This work has deployed a decision tree for defect prediction, but it is often criticized for overfitting. This is one of the limitations of this paper. Apart from this, comparing the predicted defect count with other prediction models hasn’t been attempted. NSGA-II has been applied to solve the optimization problem; however, the optimal results obtained have yet to be compared with other algorithms. Further study is envisaged.

The Pareto front provides an effective visual aid for managers to compare multiple strategies to decide the best possible rework “cost” and “time” for their projects. It is beneficial for cost-sensitive start-ups to estimate the rework “cost” and “time” to negotiate with their customers effectively.

This paper proposes a novel quality management framework under the Six Sigma approach, which integrates optimization of critical metrics. As part of this study, a unique mathematical model of the software defect rework process was developed (combined with the proposed framework) to obtain the optimal solution for the perennial problem of schedule slippage in the rework process of software development.

This study focuses on identifying key principles for implementing strategic changes in design and construction companies interested in successfully using offsite construction (OSC) in multifamily housing projects, considering the need for more affordable and sustainable multifamily housing in the United States of America (USA).

Using mixed methods, the study involved three phases of data collection and data analysis: (1) delphi survey, (2) online survey, (3) proposition of principles and validation interviews.

The key principles identified by the present research are (1) develop product-oriented business model, (2) promote leadership and mindset engagement with OSC principles, (3) engage in partnerships and more innovative contractual models, (4) shift toward digital transformation, (5) develop training and knowledge management strategies, (6) adopt lean construction (LC) practices, (7) develop training and knowledge management strategies and (8) integrate logistics and supply chain management with AEC (architecture, engineering and construction) processes.

The implementation of the principles and strategic changes identified in this study aims to prepare design and construction companies, especially small and medium-sized enterprises (SMEs), to embrace the increasing use of OSC in multifamily projects in the USA, which will make them more efficient and resilient and, ultimately, will contribute to the construction of more affordable and sustainable multifamily housing projects in the USA.

This is the first research to address holistic strategies to support design and construction companies in adopting OSC.

The application of lean and quality improvement methods is very common in process improvement projects at organisational levels. The purpose of this research is to assess the adoption of Lean Six Sigma™ approaches for addressing a complex process-related issue in the coal industry.

The sticky coal problem was investigated from the perspective of process-related issues. Issues were addressed using a blended Lean value stream of supply chain interfaces and waste minimisation through the Six Sigma™ DMAIC problem-solving approach, taking into consideration cross-organisational processes.

It was found that the tendency to “solve the problem” at the receiving location without communication to the upstream was, and is still, a common practice that led to the main problem of downstream issues. The application of DMAIC Six Sigma™ helped to address the broader problem. The overall operations were improved significantly, showing the reduction of sticky coal/wagon hang-up in the downstream coal handling terminal.

The Lean Six Sigma approaches were adopted using DMAIC across cross-organisational supply chain processes. However, blending Lean and Six Sigma methods needs to be empirically tested across other sectors.

The proposed methodology, using a framework of Lean Six Sigma approaches, could be used to guide practitioners in addressing similar complex and recurring issues in the manufacturing sector.

This research introduces a novel approach to process analysis, selection and contextualised improvement using a combination of Lean Six Sigma™ tools, techniques and methodologies sustained within a supply chain with certified ISO 9001 quality management systems.