Real-life Industry Research Articles

Building event management skills through authentic assessment

ABSTRACT With a greater focus on employability upon graduation, universities are increasingly considering how the course experience links to knowledge and skill development. To assist with skill development, industry practice and authentic activities should be embedded into the curriculum. Designing and including authentic assessment helps to replicate real-life industry scenarios which support students’ development of professional competencies. This study examines an authentic assessment of planning and operating an event within an event management topic. Data was collected through a survey of students, and additionally the reflective practitioner approach was used to consider the development of the curriculum. This study found that students gained a greater understanding of the event process by, for example, using stakeholder theory in order to prioritise event objectives, produce a project plan including key milestones, and develop risk management strategies. Communication, time management and content management were identified as key areas of skill development by students.

An Exploratory Study of the Use of ICT for Control Measures in Organization Across Industries

In business management, a control function refers to the process of monitoring, measuring, and regulating various aspects of an organization's activities to ensure that they align with the established goals and objectives. This function is crucial for achieving efficiency, effectiveness, and accountability within an organization. Information and Communication Technology (ICT) has had a profound impact on the control functions of various industries. These control functions encompass a range of activities including monitoring, automation, decision-making, and overall management of industrial processes. For the sake the research paper and better understanding for the readers we have divided Control Function into 5 components that are – Quality Control, Feedback, Finance, Supply Chain,Market and Competitor Analysis and Customer Relationship Management The paper will attempt to explain how each of the given components are impacted by ICT and provide a Real-life industry example for each.

Socio-technical dynamics of carbon dioxide capture and storage: A systems view on enablers and barriers at North Sea Port

Carbon dioxide capture and storage (CCS) is considered an option for energy-intensive industry to reduce its greenhouse gas emissions. Although it is well known that CCS faces technological, economic and societal challenges, how these challenges interact in a real-life industry has not yet been investigated collectively in a place-specific context. This study fills that gap by looking at the dynamic interactions between technological, economic and societal aspects, with the aim of clarifying enablers for and barriers to the implementation of industrial CCS in the North Sea Port industrial cluster, and identifying a course of action. The analysis was based on literature, interviews and group model building. By using group model building, expert stakeholders were brought together from industry, government and environmental non-governmental organizations. The participants built a qualitative model of the system dynamics of the implementation of industrial CCS in the North Sea Port industrial cluster jointly and on the spot. Enablers and barriers, such as costs, government's decisiveness and public support, are strongly interrelated. Public support plays a key role in multiple feedback loops in the system of industrial CCS implementation. The interdependence of societal and techno-economical elements needs to be acknowledged and responded to. There is need for transparent public engagement to build public support for CCS, and decisiveness and commitment from industry and government to transform that public support into successful and responsible CCS implementation.

Network science indicators and their relationship with performance during disruptions: a case study

Interest in supply chain (SC) resilience has increased in the wake of the pandemic and other crises, including those related to political and environmental instability. The literature offers some contributions to proactive indicators to assess the resilience of a system before a disruption occurs. Other studies provide metrics to assess resilience from the reactive perspective after the onset or end of a disruption. This paper examines the application of some proactive indicators from network science to some post-disruption measure of resilience, especially how these measure evolves as a function of time. We examine this by testing different supply chain designs against disrupted scenarios and using data from a real-life industry. The focus is on service level as a performance metric. The tested indicators correlate well with performance loss but show a limited ability to correlate with metrics representing SC dynamics. The practical contribution of this paper is an approach to measure SC resilience as an inherent property of the system, which can aid in designing future SCs, rather than measuring resilience as a response to a disruptive event. The paper also provides theoretical contributions, including the further validation of certain indicators from the literature and the identification of research areas in need of new metrics.

FedGE: Break the scalability limitation of Graph Neural Network with Federated Graph Embedding

Neighborhood aggregation algorithms, represented as graph convolutional networks, have attained non-negligible success in numerous topological structure-based scenarios with the assumption that the topological structure of the given graph is pre-defined and relatively small. However, a real-world graph generally is a super large graph consisting of many small graphs that are interconnected and overlapping. Which makes graph embedding in real-life industries, by nature, fall into the federated learning scheme. While current graph-based algorithms are only able to capture the individual topology of each natural graph, learning the complete structural information of the merged large graph remains challenging due to the unsustainable computational cost of graph convolutional operations. We propose a tailored federated graph embedding framework to learn the intact structural information of the numerous inherently linked small-scale graphs and the embedding of each node. We leverage graphs with around two and a half million nodes to validate the effectiveness and the correctness of the proposed framework.

Synthetic load profiles of non-energy intensive industrial sites: A combined bottom-up and top-down approach

The heterogeneous nature of the industrial sector in terms of process and product variety often hinders assessments of its energy consumption. The generation of synthetic load profiles (LPs) however offers a key instrument to support energy suppliers, grid operators and industries themselves by quickly evaluating the impacts of energy efficiency measures, fuel switch, new technologies etc. Currently, such LP generators are only developed for single real-life industry plants or require comprehensive beforehand data. Within this study, we propose a new model for generating synthetic LPs of industrial process chains without the need for extensive, plant-specific data. This solution includes a series of bottom-up and top-down approaches. We analyse different data sources and develop algorithms based on underlying data science methods and technical and economical modelling paradigms like Markov chains or Economy of Scale. Here, we describe novel findings e.g., a method that proves the prediction of industrial shift models from a top-down perspective or effects on the energy demand of industrial plants for rising production capacities. Furthermore, we prove that only some production processes in industrial facilities are responsible for the main share of energy demand, as the residues can be modelled by numerical analyses. We validate the developed approach by generating synthetic, electrical LPs of different real-life industrial plants and comparing the results to measured LPs. This study contains five case studies, of which we found valid approximations of our synthetic LPs to the measured, real-life plants. However, our model’s results differ from measured LPs, especially when depicting lower energy demands (< 200 kW). Furthermore, long-term periodicities e.g., part-time working hours on Saturdays haven’t been incorporated into our model yet, which leads to certain inaccuracies. We quantified these effects within this study. This, nevertheless, leaves open areas for future research work.

PLAHS: A Partial Labelling Autonomous Hyper-heuristic System for Industry 4.0 with application on classification of cold stamping process

In real-life industry it is difficult to have fully-labelled datasets due to lack of time, resources or knowledge. In this sense, this paper proposes the design and development of a Partial Labelling Autonomous Hyper-heuristic System PLAHS, a solution that autonomously labels partially labelled databases and evaluates the yielded labelling solution by means of a novel Trustworthiness Metric (TM). The proposal combines a hyper-heuristic inspired approach with a Semi Supervised Learning Clustering (SSLC) methodology that optimizes the parameters of different clustering algorithms, based on a novel semi-supervised metric named Partially Supervised Optimization Metric (PSOM). The proposal has been tested with promising and excellent results on both a real use case for labelling work orders in a cold stamping press, and 13 databases from the UCI (multivariate data) and UCR (time series data) repositories.

The growing gap between lean production and digital lean tools

PurposeThis paper aims to gain empirical insights into the relationship between lean production and digitalization within Industry 4.0 from a process-theoretical perspective. Following an initial report at the European Lean Educators Conference 2021 conference, the authors searched for explanations as to why digital lean tools stagnate, whereas production improves continuously.Design/methodology/approachThis paper is based on a qualitative case study in a Norwegian processing industry company over a period of 18 months from 2020 to 2022.FindingsProcess theory offers explanations of why digitalization and lean can change over time. Despite agile development, digitalization is still characterized by centralization and programmatic planning. Lean production is decentralized, with long-term and continuous change processes. This creates challenges for coordination between digitalization and lean.Practical implicationsOrganizations should strive for coordination and collaboration between central and local decision makers and between digital and business process competence. Digital systems should have built-in flexibility for local setup, and local managers need sufficient competence to set up systems that are aligned with continuous improved production.Originality/valueThis study contributes empirical insights into real-life industry challenges to a literature that has until now been theoretical and focused on potential synergies.

Innovative online learning in entrepreneurship education: The impact of embedding real-life industry practice in the virtual learning environment

Applied areas of entrepreneurship are growing in popularity due to the ongoing need for students to gain specialised skills to find employment after graduation. This study explores how educational scaffolding can facilitate the delivery of online experiential activities to improve the entrepreneurial learning experience and the performance of music management students by using information and communications technology tools within the virtual learning environment. A case study methodology was adopted to present two scenarios of innovative online learning delivered in a postgraduate programme in music management. This research takes into consideration the difficult transitional journey for some students from studying to being immediately thrust into the workplace, which often requires different types of knowledge, experience, skills and competences. The findings show that, by strengthening industry links, tutors can build a portfolio of online practical activities that represent the ‘real-life’ industry, helping to forge long-term, collaborative partnerships to facilitate learners’ acquisition of more meaningful knowledge and opportunities.

Design and Development of an Edge-Computing Platform Towards 5G Technology Adoption for Improving Equipment Predictive Maintenance

During the last decades numerous innovations and inventions have been presented under the framework of Industry 4.0. The ultimate goal of the latest industrial revolution is the digitalization of modern manufacturing systems. Therefore, several techniques and technologies have been investigated by the academia and the industrial practitioners, such as Edge Computing. The above-mentioned paradigm adheres to the distributed computing paradigm, which is an analogous to the trend of decentralization of manufacturing systems. However, there are still several challenges to be addressed in the integration of such decentralized computing systems in real-life industries. Therefore, in this paper, the design and preliminary development of an Edge-computing platform is proposed, in order to distribute the computational load to the nodes and by extension to enable the utilization of machine learning techniques for the calculation of Remaining Useful Life (RUL) of critical machine tool components. Moreover, the proposed framework promotes the utilization of 5G cellular networks, in order to take advantage of the ultra-low latency and increased bandwidth offered by this technology.

Physical Internet deployment in industry: literature review and research opportunities

PurposeThe Physical Internet (PI) provides solutions to improve the current unsustainable economically logistics systems. This paper aims to identify the key themes and point out the gaps to demonstrate the current state of the PI deployment in the industry. Furthermore, this paper attempts to provide possible opportunities for future research to lead research directions of PI deployment in the industry.Design/methodology/approachThis paper follows the methodology of systematic literature review (SLR) including five processes of (1) identifying the search process; (2) selecting publications following the criteria that include and/or exclude a publication; (3) evaluating and analyzing the chosen literature; (4) reporting findings and (5) synthesizing future research opportunities. A total of 88 papers ranging from 2011 to 2021 were selected and coded for analysis.FindingsThis paper has identified 7 themes, including the PI concepts, assessments of the PI, components of the PI, innovative facilities applied in the PI, collaboration in the PI, the PI implementation and literature review. Meanwhile, five key methodologies including experimental, exploratory, review, design science research as well as mixed-methods have been classified. A framework has been proposed to summarize the results incorporating the real-life industries involved in the existing research. The authors also present the research gaps and future research opportunities for the PI deployment in industries.Originality/valueThis study is one of the first research focusing on the PI real-life implementation and thus contributes to the PI literature differing from other literature review papers. The proposed framework is useful to learn about the current core research themes in the PI. The discussion of future research opportunities may inspire interesting research delving into the field.

A Semantic Similarity Model to Compare Heterogeneous Data Sources to Augment Engineering Data with New Failure modes in Automotive Industry

In real life industry, in-time exposure of symptoms and their failure modes observed during fault events provide key signals to system engineers to take necessary corrective actions for effectively arresting product defects. A real-life ontology-based semantic similarity system is employed for automatic comparison of engineering data (comes in the form of design failure mode effect analysis, DFMEA data) with the field repair data collected during product warranty period. Given the complexity of engineering data and the overwhelming volume (hundreds of millions of data points) of field repair data makes identification of new symptoms and failure modes from the first principles an impractical task. Typically, the engineering data is recorded by using technical vocabulary, e.g. unstable electric contact, Seat Belt comfort per MVSS208, whereas the field repair data is highly unstructured in nature. Consequently, we observe following types of noises in the field repair data – abbreviated text entries, inconsistent use of vocabulary (‘seat buckle is damaged’ vs ‘buckle unlatching’), and finally the incomplete text entries. More importantly, limited mental mapping capacity of a human agent limits discovery of new symptoms and failure modes from the industrial scale data. Not surprisingly, the text mining and semantic similarity are gaining a serious attention due to their ability to automatically discover the knowledge assets buried in unstructured text by training machines to compare and link high volume of data. In our approach, initially the key constructs (e.g. symptoms, failure modes) from the data are annotated by using the domain ontology. These constructs are then used to construct pairs of terms and pairs of tuples, which are used to compute pair-to-pair and tuple-to-tuple semantic similarity respectively. Finally, the text-to-text semantic similarity is calculated by combining other two semantic similarity scores. It is used to determine whether new symptoms or failure modes from the field repair data can be used to augment the DFMEA data. The proposed method is implemented as a prototype tool and its performance is validated by using real-life data from automobile domain. On an average, our system has F1 score of 0.75 and 0.78 in discovering and identifying new symptoms and synonym symptoms respectively, whereas it achieved the F1 score of 0.72 and 0.68 in discovering new failure modes and in identifying synonym failure modes respectively. The fault detection rate is improved by 35%, whereas the fault isolation rate is improved by 40.5%.

Blockchain-integrated technologies for solving supply chain challenges

PurposeBlockchain technology was developed to synchronize the data and transactions over the supply chain network and connected nodes. This paper aims to show how blockchain technology can enhance flexibility and agility in supply chain operations. The integration of blockchain and other recently developed technology can help deal with supply chain uncertainties and other challenges being faced by the industry.Design/methodology/approachThrough an extensive literature review of existing research papers and conversation with supply chain managers, barriers and challenges in the supply chain were identified. Some elements were researched of blockchain technology that can be used to resolve some challenges. Blockchain technology and other technologies integration is developed for implementation in supply chain for better visibility and efficiency of supply chain.FindingsThe challenges in the supply chain are categorized, and the solution is given through the integration of blockchain and other technologies like Internet of Things and artificial intelligence. The integration shows the execution of tasks through blockchain and various technologies in supply chain.Research limitations/implicationsBlockchain in supply chain is finding its strong place in India when compared to developing nations. There is a need for technology experts, supply chain managers and consumers to understand blockchain’s importance. Challenges faced by industries to use blockchain may be analyzed further with real-life industry case studies.Practical implicationsThis research helps enterprises in successful execution of smart technologies in their supply chains. This research helps enterprises in successful execution of smart technologies in their supply chains. Managers and practitioners may use the models developed in real-time implementation. The technologies are described in detail to help the practitioners select the best suitable for their organization.Social implicationsDigital supply chains are finding the way in industries due to lean and efficient nature. It is beneficial to use the smart technologies to make supply chain green and sustainable.Originality/valueThe implementation of the digital supply chain and its challenges are discussed in the research paper. This will work as a platform for research in the area of technologies for supply chain.

The cooperation between suppliers and an agro-industrial slaughterhouse: a measurement tool

Paper aims To identify factors to measure the cooperation between an agro-industrial pork slaughterhouse cooperative and input suppliers. Originality A performance measurement tool applicable in real-life industry practices related to input suppliers, based on critical success factors. Research method The performance measurement tool was calculated based on the analytic hierarchy process (AHP) procedure, constituted by two essential elements, the critical success factors and the key performance indicators, organized in a hierarchical structure. The key performance indicators are used to detail the critical success factors, measuring the supplier’s performance in relation to specific cooperation characteristics. Main findings In general, 77% of suppliers are fully or potentially cooperative with an established performance in risk sharing and information sharing factors. For the remaining 23% of suppliers classified as partially cooperatives, suggestions were proposed for changes to be implemented which improve their cooperation level performance. Finally, a direct relation between a low proportion of input discards and a established cooperative performance between suppliers and the analyzed agro-industrial pork cooperative was not verified. Implications for theory and practice To provide a performance measurement tool developed with methodological rigor to be used by researchers and agro-industrial pork slaughterhouse cooperative managers to improve the Brazilian pork slaughterhouse supply chain context.

Preface

The proceedings contain the scholarly papers presented in two reputable joint conferences, the 7th International Conference on Mechanical, Automotive and Materials Engineering (CMAME 2019) with its workshop the 2nd International Conference on Progress in Mechanical and Aerospace Engineering (PMAE 2019). Both conferences have been held in different parts of the world for some years, as indicated by the number sequence. This year (2019), the conferences are held in Melbourne, Australia.The conferences have invited keynote speakers who are professors from renowned universities in Australia, England and China. Together with the assistance of the International Technical Committee (members from universities and research organisations around the world), the conference attracted many submissions of high quality papers. Hence, the papers in these proceedings have passed the vigorous review process and amended to the satisfaction of reviewers and have undergone critiques in a face-to-face presentation with expert audience. It is on this basis that these proceedings are good references to future research investigations and publications.The proceedings have four sections. Section 1 (Engineering Material Design and Analysis) has papers on different treatment of materials or related processes. Study of materials properties is essential to develop efficient machining and formation processes for cost effective manufacturing. The papers are good reflection of the ultimate goals of materials engineering research.Section 2 (Mechanical Engineering and Intelligent Robots) papers focus on robotics and kinematics. Applications range from inspection to localisation and sensors. The broad application areas of robots in industry are highlighted in the short conference version of the papers. It is interesting to note that authors of this section come from countries such as Australia, China and South Korea.Section 3 (Applied Mechanics and Engineering) papers are related to more fundamental treatment of fluid mechanics, mechanical design, drive systems and residual strain problems. These studies have no immediate industry applications but the theoretical knowledge outcomes form the more foundation level research will contribute to future development of products requiring these technologies.Section 4 (Advanced Manufacturing Systems and Technologies) papers are more application oriented investigating real-life industry problems in ship building, machining discrepancies, automotive crash, product life cycle analysis, energy consumption and non-linear control systems. These applications are researched in universities from 4 countries around the world, illustrating the benefits of a face-to-face conference allowing exchange of ideas and latest research findings.The variety of research topics presented in the conference and novelty exhibited in the papers published in these proceedings once again demonstrated the value of the two conferences jointly organised enabling researchers of different nationalities and different engineering fields to participate in a live research conversation. I would like to thank the conference committee, the conference secretariat, keynote speakers and all participants for their hard work making this conference a success as well as publishing a valuable knowledge literature contributing to state-of-the-art developments in mechanical, automotive, materials and aerospace engineering.

EXPLORATION OF E-LEARNING VS TRADITIONAL LEARNING IN INDIA

Purpose of Study: The purpose of the study is to carry out the comparison of traditional learning and e-learning with reference to university education.&#x0D; Methodology: A comparative analysis method that has been selected to fulfill the purpose of the study. The research data has been collected by various sources internet, previous research studies published in the Journal, universities which offer different technical and nontechnical programs .and further analyzed by in-depth understanding approach.&#x0D; Result: The outcome of this study will show the effect of e-learning in the current era. As we have compared e-learning with traditional learning and the result shows us that e-learning has filled the gap between the universities and the real-life industries' demands.&#x0D; Main finding: The result of this study showed that e-learning is the need of the present era in order to fulfill the gap between the universities and the real-life industries' demands. Additionally, e-learning based courses will have more impact and provide more skill and exposure to students as compared to traditional learning mode.&#x0D; Implications/Applications: This article can help the different universities and students to introduce more e-learning courses in their curriculum and they can fill the real-time industries' demands.&#x0D; Novelty/originality of the study: Our research can ensure that e-learning has a huge impact on our students and can help to increase the skills and exposure of them according to the current demands.

Spatial-Temporal Finite Element Analytics for Cyber-Physical System-enabled Smart Factory: Application in Hybrid Flow Shop

Cyber-Physical System (CPS) is a crucial direction in realizing the next-generation smart manufacturing. With the deployment of IoT technologies, massive production data and information are real-timely accessible for decision-makers. However, the enemies: complexity and uncertainty, that researchers and practitioners have been fighting for decades still hinder the development of smart manufacturing. In the realm of manufacturing optimization like production planning and scheduling, considerable research efforts have been made. However, more or less similar results are generated, which are theoretically optimal but lack the ability to cope with disturbances in the real-life industry. This study develops a framework of the Cyber-Physical System (CPS) and proposes a smart digitalization solution to achieve real-time visibility, traceability, and information sharing. Also, a novel Spatial-Temporal Finite Element Analytics (ST-FEA) based on real-time cyber-physical visibility and traceability is innovated to achieve real-time advanced production planning and scheduling in the Hybrid Flow Shop. By discretizing the space and time of the original problem in meshing, the complexity and uncertainty are substantially reduced. The simpler models can thus be established and solved with a more straightforward method. Case studies will be conducted to verify the practicability and effectiveness of the proposed method in future research.

Time minimization in flexible robotic cells considering intermediate input buffers: a comparative study of three well-known problems

ABSTRACTFlexible manufacturing cells are widely used in many real-life industries. These cells contain a group of highly automated machines, the input station, and the output station that are interconnected by a robot which handles the materials, automatically. The system repeats a cycle in its long run, and the cycle time depends on the order of the loading/unloading activities which are performed by the robot. To achieve the most efficient use of these cells, order of the robot activities must be determined. This study is motivated by such a real life system with intermediate machine buffers. First, a mathematical model is presented. Then, to decrease the solution time, a modification of the model is proposed. Additionally, another model is proposed to find the maximum processing time of the machines when the cycle time is known. An assignment problem method is utilized to optimize the cyclic production, and performances of all the proposed methods are examined using several numerical instances. Finally, the performance of the considered system is compared with no intermediate machine buffers system, and also it is compared with the system utilizing a dual-gripper robot. The results show that performances of the three robotic cells are non-identical in practice.

Optimal Production Scheduling in the Dairy Industries

This work presents the application of a Mixed-Integer Linear Programming (MILP) model that was previously developed in our group for the lot-sizing and production scheduling problem in a real-life industry. In particular, the case of yoghurt production, a representative food process, in a large-scale dairy facility from Greece is studied in detail. The model takes into account all constraints typically met in production scheduling (inventory limitations, material balances, equipment capacity, etc.) having as major objective the minimization of cost. New information regarding new orders, order modifications and/or cancellations and deviations of actual to planned production is considered by utilizing a rolling horizon algorithm. The suggested solution strategy has been successfully integrated with the industrial partner’s system to facilitate the decision-making process for the production scheduling of the facility.

Engineering Technology Curriculum Development: Bridging the Gap Between Academia-Industry Through Undergraduate Final Year Project

The Malaysia Higher Education sector has experienced major growth in the 60 years since its independence. In order for Malaysia to keep up with the increasingly challenging and competitive global economy, higher education must be sustainably transformed. One of the key shifts highlighted in the Malaysia Higher Education Blueprint 2015-2025 is through empowering Technology, Vocational, Education and Training (TVET) programmes. Although the graduates from the Malaysian Technical University Network (MTUN) have shown significant achievements through the Graduate Employability rates, there are some pressing issues that still need to be addressed. The industry practitioners have raised concerns about graduates that does not meet employers’ expectations and is not well-prepared to enter the workforce. As part of the initiative to address the gap above, the Engineering Technology Infrastructure Program (ETIM) at Universiti Malaysia Pahang (UMP) has taken the effort to appoint the industry practitioners as its Undergraduate Final Year Project (FYP) co-supervisors. These industry co-supervisors plays an important role since the beginning of the course by providing real-life industry problems for the students to propose solutions. This method have seen tremendous improvement towards students’ soft skills such as problem-solving and decision-making. This helps the graduates to better prepare themselves upon entering the workforce and simultaneously fulfilling the industry needs in being exposed to real-life industry problems. The industry and academia should continuously work together to ensure that the courses and curriculum are current and in accordance to the requirements posed by the industry.