Search results

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

Due to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.

This study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.

This study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.

APMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

Industrial robots are of great significance to the long-term development of family firms. Drawing on the lens of the principal–principal conflict, this paper aims to investigate the influence of family non-executive directors on robot adoption in Chinese family firms.

This paper selects the family firms in China from 2011 to 2019 as the sample. Furthermore, the authors manually collected the family non-executive directors and constructed the robot adoption variable utilizing data sourced from the International Federation of Robotics. In brief, this paper constructs a comprehensive framework of the mechanisms and additional tests pertaining to the influence of family non-executive directors on robot adoption.

This paper finds that family non-executive directors can promote robot adoption in family firms. The underlying mechanism analysis shows that family non-executive directors promote robot adoption by exerting financial and human effects. This paper further finds that the characteristics of family non-executive directors, such as kinship, differential shareholding and excessive directors, affect the role of family non-executive directors. Finally, robot adoption can improve future performance, and the promotional effect is more evident when family members are non-executive directors.

This paper contributes to the related literature from the following two aspects. Firstly, this paper decomposes the types of family directors to understand the role of family non-executive directors, which challenges the assumption that family board members are homogeneous in family firms. Second, this paper expands the research on the factors that influence robot adoption in emerging economies from the micro-enterprise level. In addition, the findings in this paper have managerial implications for family firms to optimize their strategic decisions with the help of the mode of board right allocation.

This study aims to improve a construction company's overall project delivery by utilising lean six sigma (LSS) methods combined with building information modelling (BIM) to design, modularise and manufacture various building elements in a controlled factory environment off-site.

A case study in a construction company utilised lean six sigma (LSS) methodology and BIM to identify non-value add waste in the construction process and improve sustainability.

An Irish-based construction company manufacturing modular pipe racks for the pharmaceutical industry utilised LSS to optimise and standardise their off-site manufacturing (OSM) partners process and leverage BIM to design skids which could be manufactured offsite and transported easily with minimal on-site installation and rework required. Productivity was improved, waste was reduced, less energy was consumed, defects were reduced and the project schedule for completion was reduced.

The case study was carried out on one construction company and one construction product type. Further case studies would ensure more generalisability. However, the implementation was tested on a modular construction company, and the methods used indicate that the generic framework could be applied and customized to any offsite company.

This is one of the few studies on implementing offsite manufacturing (OSM) utilising LSS and BIM in an Irish construction company. The detailed quantitative benefits and cost savings calculations presented as well as the use of the LSM methods and BIM in designing an OSM process can be leveraged by other construction organisations to understand the benefits of OSM. This study can help demonstrate how LSS and BIM can aid the construction industry to be more environmentally friendly.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

This study aims to examine the challenges in the implementation of energy management systems in residential buildings to lower the running cost and achieve a better energy-efficient building.

This study adopted a mixed research method. Quantitative data was gathered by issuing a research questionnaire to 20 Delphi experts, while qualitative data was acquired through a Systematic Literature Review. Data received was analyzed using the descriptive analysis method.

The findings revealed that the main barriers to incorporating energy management systems (EMSs) in residential buildings consist of a lack of awareness of energy management systems, lack of management commitment to energy management, lack of knowledge about energy management systems, lack of funds for energy management systems, resistance to energy management technology by the property owners and property managers, distrust and resistance to energy management technology by the property owners, high initial cost of energy management technologies, shortage of technicians for energy management technologies, the nonexistence of local manufacturers of energy management equipment, lack of incentives for efficient energy management and high repair costs of energy management technologies.

The specific focus on residential buildings may limit the applicability of findings to commercial or industrial sectors. Further research is warranted to accommodate other energy-consuming sectors.

People’s perceptions, either wrong or correct, affect their ability to make an informed decision to adopt energy management systems, denying them the opportunity to reap the associated benefits. Therefore, there is an urgent need for the residential industry stakeholders and the government to increase educational opportunities for property owners, managers and property tenants on the importance of energy management systems.

This research presents the potential obstacles and problematic areas that residents may encounter while using these energy management systems. Consequently, they will be able to make a well-informed choice when installing energy management systems. Moreover, the research elucidates the identification of novel perspectives and also unexamined obstacles that impede the widespread use of energy management systems in residential buildings.

This paper aims to introduce a model using a digital twin concept in a cold heading manufacturing and develop a digital visual management (VM) system using Lean overall equipment effectiveness (OEE) tool to enhance the process performance and establish Fourth Industrial Revolution (I4.0) platform in small and medium enterprises (SMEs).

This work utilised plan, do, check, act Lean methodology to create a digital twin of each machine in a smart manufacturing facility by taking the Lean tool OEE and digitally transforming it in the context of I4.0. To demonstrate the effectiveness of process digitisation, a case study was carried out at a manufacturing department to provide the data to the model and later validate synergy between Lean and I4.0 platform.

The OEE parameter can be increased by 10% using a proposed digital twin model with the introduction of a Level 0 into VM platform to clearly define the purpose of each data point gathered further replicate in projects across the value stream.

The findings suggest that researchers should look beyond conversion of stored data into visualisations and predictive analytics to improve the model connectivity. The development of strong big data analytics capabilities in SMEs can be achieved by shortening the time between data gathering and impact on the model performance.

The novelty of this study is the application of OEE Lean tool in the smart manufacturing sector to allow SME organisations to introduce digitalisation on the back of structured and streamlined principles with well-defined end goals to reach the optimal OEE.

The aim of the article was to compare the learning outcomes of the same content in the form of a traditional analogue lesson and in the form of a virtual reality (VR) lesson with the use of head-mounted display (HMD).

The study included one biology lesson conducted in 4 groups of a dozen people and one biology lesson in VR carried out individually on 75 people. The respondents completed the knowledge test, a questionnaire before and after the class regarding the attitude to new technologies, and feelings after the lesson. The researchers used detailed observation sheets (subjects' behaviour and the dynamics of the lesson). The obtained results were analysed statistically through lesson type (traditional/VR), respondent type (technology enthusiast/non-enthusiast) and question type. The Mann–Whitney U test, t-student and chi-squared (?²) test were used.

The average of the overall results in the knowledge test was similar in both groups (16 points; ±SD 2.13), slightly better for the analogue group and for the non-enthusiast group. It was found that VR hinders the acquisition of knowledge by tech enthusiasts, who perceive it primarily in the play paradigm. However, it encourages the learning of technology sceptics, who quickly discover a passion for exploring the virtual world. It was clearly indicated, quantitatively and qualitatively, how the technology modalities directly influenced the learning outcomes.

The article offers fresh insights into how students' perceptions of the educational process can be transformed through the integration of VR. The compelling findings and nuanced analysis provide a robust foundation for exploring new frontiers in educational technology.