Apprenticeship pathways are widely recognised as effective for earning while learning and acquiring practical and theoretical skills on the job. The rise of consortia-led apprenticeships reflects growing recognition by educational institutions, government bodies, and industry of the benefits of combining work, learning, and academic awards. However, there is a notable lack of published literature on the unique experiences of apprentices in third-level education, particularly in consortia-led supply chain programmes. This study seeks to fill that gap by offering insights to establish best practices and enhance the success of the supply chain apprenticeship programme. It examines experiences from 67 apprenticeship learners across various industries. Understanding apprentices' journeys, from their initial awareness of programmes to the support they receive, while identifying the challenges they face is essential. This research offers valuable insights for all stakeholders to positively influence the effectiveness of future consortia-led supply chain apprenticeships.

Assembly Systems assist the human in manufacturing by providing easy access to the assembly procedure. However, they are static systems that provide the same information to all users. Assembly Systems 4.0 are a relatively new concept that use data-driven insights to assist the human by providing context-specific information about the assembly process. Assembly Systems 4.0 should have a positive impact on reducing human error in manufacturing. In this research the utility of an Assembly System 4.0 is evaluated. Two experiments are conducted to investigate potential effects on human performance from the lens of error and failed quality parts. Through the first experiment, a laboratory simulation, it is proven that the Assembly System 4.0 can detect human error. In the second experiment, the system is compared against a traditional Assembly System. Four hundred assemblies are conducted, in a two-independent sample test. It is found that neither system prevents human error from occurring. However, how the error is treated is significant. The users of the Assembly System 4.0 detected, and corrected the errors, producing higher quality products.

Traditionally, efforts to ensure quality were grounded in a rigorous industrial engineering approach. However, there has been a dramatic shift on how manufacturing is carried out, due to the disruption inherent in Industry 4.0. The traditional methods of managing quality are not flexible enough to cope with the pace of change in modern manufacturing and the Factories of the Future. Discourse around modern quality management primarily focuses on the new digital tools available under Industry 4.0. Digital tools, in of themselves, will not address the complexity of the quality management problem. A new model for Quality 4.0 is required. In this research, a literature review was completed to investigate how quality is being managed in the era of Industry 4.0. A new model for Quality 4.0 is proposed based on the findings. The model has at its foundation three pillars of Process, Human, and Technology. The model integrates these pillars within the traditional rigorous industrial engineering approach and systems for quality management, which include Quality Management Systems, Total Quality Management, and Measurement and Analysis. The impact of this research is a generalizable model for real-world quality systems fit for the future. The model highlights the digitalization skills and quality structures required for a flexible data-driven modern Quality 4.0 system.

Purpose Maintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot. Design/methodology/approach A computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field. Findings Computer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R 2 is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences. Originality/value In this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.

This study aims to consolidate the fragmented knowledge dispersed across simple or incomplete (existing) frameworks on process innovation and identifies the research gaps in the management of the front-end stage. The outcomes contribute to the present knowledge by a comprehensive review (with the systematic literature review (SLR) methodology) and synthesis of twelve management frameworks of process innovation. The paper also presents a novel framework for management of the front-end of process innovation in manufacturing companies. This research identifies that the front-end in process innovation must integrate with activities linked to product innovation, if innovation projects are to be successful.

Error has always presented a challenge for the quality team, but never more so than under Industry 4.0. In Industry 4.0, the operator is required to work on more complex products, be flexible across processes, and be interconnected to machines, dealing with streams of in-process data. This change in work has led to higher error and, therefore, greater challenges for the quality function. In order to understand how best to apply the capacity available under Industry 4.0 to ease this burden, it is important first to understand the current approach to quality and where the research gaps lie. In this paper, an analysis of how error and quality has been managed in manufacturing, in the first 10 years of Industry 4.0, is completed, using a systematic literature review. Seven thematic clusters are identified in this research from 64 selected papers. They are Quality Management Systems, Total Quality Management, Measurement and Analysis, Human Factors, Technology, Human-System Integration, and Training. A synthesis of the thematic clusters reveals sustaining innovations across all activities. The summary of current approaches will assist quality teams better to improve their approach to reduce error and will identify where there are future research opportunities.

Water is an essential resource for humans, animals, and plants. Water is also necessary for the manufacture of many products such as milk, textiles, paper, and pharmaceutical composites. During manufacturing, some in-dustries generate a large amount of wastewater containing numerous contaminants. In the dairy industry, for each litre of drinking milk produced, about 10 L of wastewater is generated. Despite this environmental foot-print, the production of milk, butter, ice cream, baby formula, etc., are essential in many households. Common contaminants in dairy wastewater include high biological oxygen demand (BOD), chemical oxygen demand (COD), salts as well as nitrogen and phosphorus derivatives. Nitrogen and phosphorus discharges are one of the leading causes in the eutrophication of rivers and oceans. Porous materials have long held significant potential as a disruptive technology for wastewater treatment. However, thus far they have been understudied for use in dairy wastewater treatment. Ordered porous materials, such as zeolites and metal organic frameworks (MOFs), represent classes of porous materials with significant potential for the removal of nitrogen and phosphorus. This review explores the different zeolites and MOFs applied in the removal of nitrogen and phosphorus from wastewater and the prospect of their potential for use in wastewater management in the dairy industry.

PurposeThis 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). Design/methodology/approachThis 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. FindingsThe 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. Research limitations/implicationsThe 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. Originality/valueThe 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.

This study examines the experiences and impact COVID-19 has had on industry online learners. Exploring initially the term 'industry learner' and explaining the background to the design of the online modules the learners participated on. The design and delivery of this programme resulted in minimal impacts from the COVID-19 pandemic. The study explores qualitative reports of industry learners' experiences, and these are analysed using thematic analysis. The findings suggest that the design principle of maximising social interaction, at peer to peer and peer to educator levels, resulted in an enhanced student experience. Group based interactions were recognised by learners as both an occasional source of friction, but also as a valuable proxy for industry leadership roles and workplace group dynamics. This highlights the complex enhancing and inhibiting nature of group-based learning. Implications for further optimisation of online and blended learning environments are explored and associated future research priorities are identified.

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

There is a lack of clarity about how to augment the human in manufacturing. For practitioners, this creates challenges in understanding which technologies to invest in for specific automation goals, and where the value-add exists. A narrative review of the literature is conducted through which the relationship between augmentation and automation is clarified. Definitions for Augmentation, and the Augmented Human, and a new Taxonomy of Human Augmentation are proposed. Five classes of augmentation are identified: Physical, Collaborative Physical, Sensory, Embedded Intelligence, and Collaborative Social Intelligence. How the Taxonomy is applied to each goal of automation is illustrated. Finally the value-add of the classes is explored through industrial use cases, and the potential impact on manufacturing key performance indicators is summarised. This novel Taxonomy of Human Augmentation unifies the existing research, and provides a common description of each class of augmentation, which can assist practitioners in seeking and exploring augmentation solutions.

Collaborations between Industry partners and University researchers are common to address Industry 4.0 research projects. Such collaboration facilitate access to expertise across domains. However, there is often a mismatch of expectations around language, processes and deliverables. One of the reasons is that academics treat these type of research projects as traditional Knowledge Seeking Research. Treating the research as Solution Seeking Research is a better approach, which ensures that the needs of all participants in the project are met. Solution Seeking Research can be implemented using a Design Science approach. Collaboration must be encouraged, which requires effort around communication, coordination and cooperation. Using a Framework for Evaluation ensures that all guidelines of Design Science are achieved. In this paper, we outline how we managed successful Industry 4.0 collaboration projects. We extend the framework for evaluation by recommending suitable research methods for ex-ante and ex-post evaluation. In doing so, we provide a model for future researchers to deliver collaborative research projects to the satisfaction of all partners involved.

The authors would like to make the following corrections to the published paper [...]

The COVID-19 pandemic caused a shift in teaching practice towards blended learning for many higher education institutions. This led to the rapid adoption of certain digital technologies within existing teaching structures as a means to meet student access needs. This paper is an attempt to summarise and extend pre-COVID-19 pedagogical research to leverage digital immersive technologies for blended teaching in the post-pandemic era. This paper forms both a review of these methodologies and a case study of the I-Ulysses Virtual Learning Environment as an example of a platform that leverages such immersive digital technologies and employs instrumental use of VR. To further clarify, the purpose of the paper is to describe and propose a distance learning solution with immersive VR qualities; this is what the I-Ulysses environment represents, as the main obstacle to learners of site-specific information during the pandemic has been lack of on-site accessibility. Furthermore, this is of key importance, because Joyce’s novel takes place in historical Dublin, where access to the physical location of the story is indispensable to a reader.

In 2020 Ireland missed its EU climate emissions target and without additional measures will not be on the right trajectory towards decarbonisation in the longer 2030 and 2050 challenges. Agriculture remains the single most significant contributor to overall emissions in Ireland. In the absence of effective mitigating strategies, agricultural emissions have continued to rise. The purpose of the review is to explore current research conducted in Ireland regarding environmental modelling within agriculture to identify research gap areas for further research. 10 models were selected and reviewed regarding modelling carbon emissions from agriculture in Ireland, the GAINS (Air pollution Interactions and Synergies) model used for air pollutants, the JRC-EU-TIMES, (Joint Research Council-European Union-The Integrated MARKAL-EFOM System) and the Irish TIMES model used for energy, the integrated modelling project Ireland (GAINS & TIMES), the environmental, economic model ENV-Linkages and ENV-Growth along with the IE3 and AGRI-I models. The review found that data on greenhouse gas emissions for 2019 reveals that emissions can be efficiently lowered if the right initiatives are taken. More precise emission factors and adaptable inventories are urgently needed to improve national CO2 reporting and minimise the agricultural sector’s emissions profile in Ireland. The Climate Action Delivery Act is a centrally driven monitoring and reporting system for climate action delivery that will help in determining optimal decarbonisation from agriculture in Ireland. Multi-modelling approaches will give a better understanding of the technology pathways that will be required to meet decarbonisation ambitions.

The Covid-19 pandemic caused a shift in teaching practice towards blended learning for many Higher Education institutions. This led to the rapid adoption of certain digital technologies within existing teaching structures as a means to meet student access needs and facilitate learning. Integration of these technologies caused numerous challenges for practitioners and often provided mixed results. This paper is an attempt to summarise and extend pre-Covid pedagogical research to leverage digital immersive technologies for blended teaching in the post-pandemic era. Focus is given towards the evolution of Virtual Learning Environments through elements of immersive audio-visual technologies, which are shown to be effective when coupled in a blended approach. It is both a review of these methodologies and a case study of the I-Ulysses: Virtual Learning Environment as a point of comparison for evaluating the review.

There may be unrecognised environmental and economic benefits in cultivating industrial hemp for CO2 sequestration in Ireland. By using a Systems Thinking approach, this study aims to answer how industrial hemp, which can sequester between 10 tonnes (t) to 22 t of CO2 emissions per hectare, has been helpful towards carbon sequestration efforts in Ireland. A mixed-methods design combining qualitative and quantitative secondary material is used to inform Behaviour over Time Graphs (BoTGs) to illustrate the data from 2017 to 2021. In 2019 at its peak of hemp cultivation in Ireland the total CO2 emissions from agriculture was 21,156.92 kilotonnes, and the total land cultivated with hemp was 547 hectares which represented an estimated 0.0079% of total land use and 0.011% of agricultural land use. Based on a sequestration rate of between 10 t and 22 t of CO2, industrial hemp had the potential to remove between 5470 t and 24,068 t of CO2 in 2019. The total amount of estimated CO2 sequestrated between 2017 and 2021 was between 14,660 t and 64,504 t of CO2. This represents an estimated contribution in carbon tax equivalent of between €348,805 and €1,534,742, respectively.

The transformation of new paradigms for online learning delivery has evolved rapidly over the past few years. For postgraduate programmes, the thesis module is regularly the capstone and significant in terms of academic credits. In reality, this module can be just an ‘add on’ set of resources with no dedicated online learning space. Industry students undertaking postgraduate programmes online traditionally feel overwhelmed while embarking on a thesis. Notably, too, they face the challenge of not being on campus and having the same learning opportunities as their on-campus counterparts. This paper highlights the importance of supporting and assisting online industry learners in participating fully with their thesis. The authors identify the challenges that face these learners at postgraduate level, recognising that a new way to help and prepare them to carry out and write a good thesis is essential. By focusing on a dedicated online module for ‘all things thesis’, the paper presents the positive experiences learners can have when participating in this module. The findings emphasise the need for educational providers to offer as part of their programmes a high-quality thesis module designed to support the postgraduate online industry learner.

Research method and data returned to investigate what is being done in the era of Industry 4.0 to reduce human error in manufacturing.