Industrial Case Research Articles

Factor market distortion, total factor energy efficiency and energy shadow price: A case of Chinese manufacturing industry

The manufacturing industry is responsible for the highest share of energy consumption in China and improving its energy efficiency is important for reducing the dependence of economic growth on the growth of fossil energy consumption and carbon emissions. However, factor market distortion (FMD) has substantially inhibited effective resource allocation. This study proposes a metafrontier parametric linear programming method based on the Shephard energy distance function to measure the total factor energy efficiency (TFEE) of Chinese manufacturing industry. Then, we derive the energy shadow price by cost minimization. Finally, we compare and analyze the impact of different levels of FMD on TFEE and energy shadow price. The results showed that regions with lower FMD had higher TFEE from 2000 to 2021. FMD hindered TFEE mainly by affecting technical efficiency and scale efficiency. The energy shadow price within China's manufacturing industry exhibited a downward trend and regions with low FMD had a higher energy shadow price. This implies that the manufacturing industry in China is progressively diminishing its reliance on fossil fuels for production activities. The efficient distribution of production factors may enhance the actual marginal value of energy inputs.

Critical features of ecosystem practice empowering industrial sustainability transformation

AbstractThe pursuit of a sustainable industry is facilitated by the evolution of diverse cooperative networks. There is a limited understanding of how industrial actors can collectively foster the continuity in learning required to address wicked problems of sustainability. This study explores how industry stakeholders collectively address this wicked problem by fostering innovation in ecosystems and identifies the critical features of an ecosystem practice that supports empowerment in evolving multi‐actor network settings. Employing an abductive approach, we integrate insights from the innovation literature with analyses of three empirical industrial ecosystem cases to enhance understanding of the internal dynamics inherent in collaborative innovation. This study elucidates the critical features of ecosystem practice, underscoring four key characteristics essential for empowerment. These characteristics hinge on the adept balance of the four identified critical features throughout the process of continuous innovating in ecosystems. We argue that such practices serve as catalysts for cross‐industry innovation, thus expediting transformation. This research contributes to understanding the self‐organizing dynamics needed for sustained learning in evolving collaboration settings where actors, their motivations and their roles change. It also advances innovation theory by illustrating how wicked problems can be collectively addressed through the continuous exploration and exploitation processes of ecosystems.

The economic backstage of the landings of emigrants on the island of Lampedusa. An exemplary case of migration industry

The economic backstage of the landings of emigrants on the island of Lampedusa. An exemplary case of migration industry

Readiness in implementing green industry standard for SMEs: Case of Indonesia's batik industry

Green Industry Standard (GIS) acts as a guideline for industries, including the Small and Medium Enterprises (SMEs) to preserve the environment and economy in their production process. This study aims to assess the industry's readiness in the case of Indonesia's batik industry, to adopt the GIS and optimize its implementation. The method used in this study is survey and interviews. The survey and interviews involved 25 respondents, comprising owners or managers of the batik industry engaged in handcrafted, stamped, or combined batik production, from pattern-making to finishing stages. The analysis has been done using the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES) instrument to evaluate the industry's readiness and employing TOWS (Threats, Opportunities, Weaknesses, Strengths) analytical tool to formulate optimization strategies. The results of this study show that an assessment of the batik industries' compliance with Green Industry Standards revealed that they have not yet adopted sustainable practices in their production processes concerning materials, energy, water, products, and waste. The SOCRATES analysis also indicates that the batik industry's readiness to meet the technical requirements of the GIS is currently low. In conclusion, the primary approach in applying green industry principles should focus on rectifying existing weaknesses concerning craftsmen's awareness and understanding of green industry practices. After the industry's awareness emerges, utilizing GIS can boost production, improve raw material efficiency, and provide customers with greater "green" value.

SEMG-driven Hand Dynamics Estimation with Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller.

Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG's inherent variability, especially cross-session due to sensor repositioning; this limits the generalization of the Machine/Deep Learning (ML/DL) in charge of the signal-to-command mapping. The other hot front on the ML/DL side of sEMG-driven control is the shift from the classification of fixed hand positions to the regression of hand kinematics and dynamics, promising a more versatile and fluid control. We present an incremental online-training strategy for sEMG-based estimation of simultaneous multi-finger forces, using a small Temporal Convolutional Network suitable for embedded learning-on-device. We validate our method on the HYSER dataset, cross-day. Our incremental online training reaches a cross-day Mean Absolute Error (MAE) of (9.58 ± 3.89)% of the Maximum Voluntary Contraction on HYSER's RANDOM dataset of improvised, non-predefined force sequences, which is the most challenging and closest to real scenarios. This MAE is on par with an accuracy-oriented, non-embeddable offline training exploiting more epochs. Further, we demonstrate that our online training approach can be deployed on the GAP9 ultra-low power microcontroller, obtaining a latency of 1.49 ms and an energy draw of just 40.4 uJ per forward-backward-update step. These results show that our solution fits the requirements for accurate and real-time incremental training-on-device.

Self-organizing hierarchical incremental learning framework and universal approximation analysis based on stochastic configuration mechanism

Conventional machine learning algorithms face significant limitations when dealing with high-dimensional data. Besides, deep learning models often require substantial computational resources and have a high processing time despite their excellent performance. Hence, this paper proposes an expanded stochastic configuration network and a self-organizing hierarchical incremental learning (SHIL) framework to overcome these challenges. Specifically, this study introduces a novel supervised hierarchical clustering tree based on the minimum redundancy maximum correlation algorithm, which mines internal data structures to construct diverse hierarchies. Subsequently, by exploiting the parent-child node relationships in the tree structure, SHIL defines the maximum number of nodes as the switching condition between levels uses the supervisory mechanism as the parameter selection criterion, and adopts the tolerance error as the termination criterion for the training. Furthermore, the universal approximation property of the SHIL framework is provided. The proposed SHIL framework is validated on several benchmark datasets, image datasets, and industrial robot cases, with the corresponding experimental results demonstrating that SHIL significantly improves computational efficiency and ensures high accuracy.

Diffusion of Irresponsible Workplace Practices in Supply Chains: The Chinese Industrial Sector Case

Diffusion of Irresponsible Workplace Practices in Supply Chains: The Chinese Industrial Sector Case

ThermalTrend: a tool for organising and analysing thermal images

Infrared thermography (IRT), as a non-contact condition monitoring (CM) technique, measures the temperature distribution of machines and components to detect anomalous thermal behaviour. It is usually considered as a complementary technique and nowadays is used in a wide variety of industrial applications. In this paper, two commonly used criteria to assess the condition of machines based on the infrared thermography technique, ie absolute and relative temperature criteria, are represented. Then, the importance of the temperature trend in condition monitoring and fault diagnosis of machines and components is described. The ThermalTrend software, which was developed in the Condition Monitoring Unit, Smelting Department, Sarcheshmeh Copper Complex in Rafsanjan, Iran, is introduced and its most important and unique features are discussed. This software package helps thermographers to organise thermal images and provides the temperature trends of machines and components that are under regular monitoring. Finally, some industrial case studies on the usefulness and applicability of the infrared thermography technique and the ThermalTrend software are reported.

An architecture for model-based and intelligent automation in DevOps

The increasing complexity of modern systems poses numerous challenges at all stages of system development and operation. Continuous software and system engineering processes, e.g., DevOps, are increasingly adopted and spread across organizations. In parallel, many leading companies have begun to apply artificial intelligence (AI) principles and techniques, including Machine Learning (ML), to improve their products. However, there is no holistic approach that can support and enhance the growing challenges of DevOps. In this paper, we propose a software architecture that provides the foundations of a model-based framework for the development of AI-augmented solutions incorporating methods and tools for continuous software and system engineering and validation. The key characteristic of the proposed architecture is that it allows leveraging the advantages of both AI/ML and Model Driven Engineering (MDE) approaches and techniques in a DevOps context. This architecture has been designed, developed and applied in the context of the European large collaborative project named AIDOaRt. In this paper, we also report on the practical evaluation of this architecture. This evaluation is based on a significant set of technical solutions implemented and applied in the context of different real industrial case studies coming from the AIDOaRt project. Moreover, we analyze the collected results and discuss them according to both architectural and technical challenges we intend to tackle with the proposed architecture.

STRATEGIES FOR FINANCIAL GROWTH IN DENTISTRY: EXPLORING REVENUE STREAMS AND BUSINESS MODELS FOR MAXIMIZING PROFITABILITY

The Dental Industry is undergoing significant transformation as practitioners seek to enhance profitability amidst increasing competition and evolving patient expectations. Maximizing profitability in dentistry requires a multifaceted approach to financial management and strategic planning.This article explores various strategies for financial growth in dental practices, focusing on diverse revenue streams and innovative business models. It examines traditional methods such as expanding service offerings and optimizing billing practices, alongside emerging trends like implementing membership plans, utilizing advanced technologies and adopting value based pricing models.The study also highlights the importance of patient retention strategies including personalized care and effective communication in boosting long-term revenue [1,2].Through a detailed analysis of industry case studies and current trends, this article provides practical insights and actionable recommendations for dental practitioners. By leveraging these strategies, dental practices can enhance their financial performance, ensure sustainable growth and maintain high standards of patient care.

Contestation over a Profession's Memory: The UK Pharmacy Profession, 1880–1905

AbstractWe draw on the historical case of the UK pharmacy industry from 1880–1905 to examine how, in the face of a competitive threat to their survival, lower status professionals seek to reinvigorate the memory of their role in providing community service in the public interest. Derived from this, our study reveals how mnemonic work has a nuanced nature in professionalized settings. First, lower status actors enact certain types of mnemonic work because they need to maintain professional purity. Second, to maintain professional purity, lower status professionals also need to carefully sequence their mnemonic work and pay particular attention to the social context within which they are seeking to manipulate collective memory. Our study also shows how, within such a sequencing, for lower status professionals to successfully enact mnemonic work, they need to collectively mobilize their ranks and may engage in entryism to professional bodies dominated by their higher status peers.

Adopting automated bug assignment in practice — a longitudinal case study at Ericsson

[Context] The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in 2011-2016. The prototype evolved into an internal Ericsson product, TRR, in 2017-2018. TRR’s first bug assignment without human intervention happened in April 2019. [Objective] Our study evaluates the adoption of TRR within its industrial context at Ericsson, i.e., we provide lessons learned related to the productization of a research prototype within a company. Moreover, we investigate 1) how TRR performs in the field, 2) what value TRR provides to Ericsson, and 3) how TRR has influenced the ways of working. [Method] We conduct a preregistered industrial case study combining interviews with TRR stakeholders, minutes from sprint planning meetings, and bug-tracking data. The data analysis includes thematic analysis, descriptive statistics, and Bayesian causal analysis. [Results] TRR is now an incorporated part of the bug assignment process. Considering the abstraction levels of the telecommunications stack, high-level modules are more positive while low-level modules experienced some drawbacks. Most importantly, some bug reports directly reach low-level modules without first having passed through fundamental root-cause analysis steps at higher levels. On average, TRR automatically assigns 30% of the incoming bug reports with an accuracy of 75%. Auto-routed TRs are resolved around 21% faster within Ericsson, and TRR has saved highly seasoned engineers many hours of work. Indirect effects of adopting TRR include process improvements, process awareness, increased communication, and higher job satisfaction. [Conclusions] TRR has saved time at Ericsson, but the adoption of automated bug assignment was more intricate compared to similar endeavors reported from other companies. We primarily attribute the difference to the very large size of the organization and the complex products. Key facilitators in the successful adoption include a gradual introduction, product champions, and careful stakeholder analysis.

Design and Construction of Bamboo Products and Rattan Accessories by the Local Artisans of Lahore Pakistan

Purpose: Bamboo and rattan are two materials that greatly affect all products, fixtures, and items designed and made by artisans in Lahore, Pakistan. The aims of this research encompass the study of the traditional and contemporary practices of design and the construction methodologies adopted by the people in the area regarding the formation of basic bamboo products and rattan accessories significantly basketry manufacture in Lahore. Coming from a backdrop of knowledge transfer within families, global resources and design trends, strong vectors for sustainability, minimalism, functionality, significant development in technique and aesthetics was taken up for analysis in the study. Further, it will discuss the economic stature of local artisans amidst the economic challenges. Methodology: For this purpose, a comprehensive research has been conducted into the bamboo and rattan market in Lahore, Pakistan. The study documented insights from the interviews with local artisans specializing in bamboo and rattan craftsmanship and all their observations. The research has gathered rich first-hand data on design practices, material uses and infusion of modern influence in their work, as it engaged actively with the craftspeople. Findings: The handicraft industry of bamboo and rattan in Lahore is feeling both challenges and opportunities. The industry had then begun to bear strained supplies, with growing costs towards the case of Pakistan's timber industry. Reduced spending by consumers has turned out to disrupt the supply line and eventually the livelihoods of those artisans, since rising material costs and government taxing were on the rise. It is strategic adaptations that will help the industry to prosper with government support and sustainable practice. Unique Contribution to Theory, Policy and Practice: This study presented a number of recommendations for creating an awareness, developing frameworks, improving practices, and encouraging authorities to take major actions in order to support bamboo and rattan artisans in Lahore, Pakistan. It could be focusing on historic handcrafts, modern design concepts and construction techniques. Hence, through this process, the art of producing accessories will be revived, thereby harnessing the economic feasibility of the products and accessories industry of Pakistan.

The Viability of Quick Service Restaurant (QSR) in the Township Market: A Fast Food Industry Case in South Africa (SA)

The Viability of Quick Service Restaurant (QSR) in the Township Market: A Fast Food Industry Case in South Africa (SA)

Temporal Refinement of Major Primary Air Pollutant Emissions Based on Electric Power Big Data: A Case of the Cement Industry in Tangshan City

Temporal Refinement of Major Primary Air Pollutant Emissions Based on Electric Power Big Data: A Case of the Cement Industry in Tangshan City

A mixture of shallow neural networks for virtual sensing: Could perform better than deep neural networks

Owing to outstanding representation abilities, deep neural networks (DNNs) have recently been extensively studied and attracted increasing attention in virtual sensing of key industrial variables. Although various learning algorithms have been developed to train the DNNs, the complexities of industrial data, such as scarcity of labeled samples, inevitable measurement noise, and multi-modality, may prevent the DNNs from achieving the best performance. This paper aims to deal with the limitations of the DNNs in an alternative way, by fully exploiting the potentials of shallow NNs. Specifically, a Bayesian model structure of a semi-supervised mixture of multiple single-hidden layer NNs (BSsMSLNN), each of which serves as an expert for a local region, is first proposed. Then, a variational inference- and gradient ascent-based training algorithm is developed, which allows parameter learning and mode identification to be completed in a unified framework. The performance of the BSsMSLNN is evaluated using both synthetic and real industrial cases for virtual sensing, demonstrating the effectiveness and superiority of the proposed schemes.

Toward a policy landscape to support the product‐as‐a‐service design process for a circular economy

AbstractDespite the widely recognized potential of product‐as‐a‐service (PaaS) business models to contribute to the circular economy, many industrial case studies have reported that several systemic factors tend to hinder product manufacturers from effectively designing such solutions. More specifically, factors such as linear value chains, organizational structures, and market and financial conditions tend to restrict the freedom of product manufacturers to design circular PaaS solutions effectively. In this article, we take a novel approach to advance the body of knowledge concerning the interactions between policies and circular PaaS designing. We first explore the potential of the existing policy landscape to support designing for a circular economy in the manufacturing industry. The results suggest that the existing policy landscape is primarily focused on regulating product design outcomes and is thus deemed unsuitable for supporting circular PaaS designing. Subsequently, we investigate the literature to uncover how internal and external systemic factors may hinder the implementation of the key facets of circular PaaS designing in the manufacturing industry. Based on these findings, we propose a prospective policy framework that includes a mix of complementary policy interventions that systemically target the identified internal and external factors to support the implementation of the key facets of circular PaaS designing.

Strategic labour inspection in fissured workplaces and transnational employment relations: Lessons from co-enforcement approaches and transgovernmental cooperation

This paper examines strategic enforcement approaches relying on co-enforcement and transgovernmentalist. It examines three cases in the construction industry in Austria, Asturias (Spain), and Poland, as well as three cases in maritime shipping in Finland, Spain, and Poland, focussing on Labour Inspection’s (LI)s motivations for engaging in co-enforcement and transnational cooperation. Data collection involved desk research, semi-structured interviews (39 construction and 14 in maritime), observation of inspections, and participant observation in EU seminars we organised together with regulatory actors (specific details are provided in the appendix). Findings show that LI’s selection of co-enforcement and transnational alliances are driven by the specific and highly contingent challenges each organisation faces. LI organisations are seeking to exploit synergies, but both the material reality and perception of these depends on diverse pre-existing infrastructures and organisation-specific assets.

The interplay among trust, reciprocity and commitment factors in facilitating supply chain collaboration: the case of Vietnamese fishery industry

PurposeSupply chain collaboration is increasingly important in today’s competitive business environment. While prior research has acknowledged the role of relationships in facilitating collaboration, few studies have considered inter-personal and inter-organisational attributes simultaneously. This study addresses this gap by investigating these two levels of the manufacturer-supplier relationship in Vietnam’s fishery industry and their impacts on supply chain collaboration.Design/methodology/approachThe study employed a quantitative approach. A questionnaire was distributed to 635 fishery manufacturers in Vietnam by the drop-and-collect method. EFA and CFA were used to assess the reliability and validity of the measurement model, while CB-SEM was employed for structural model assessment and hypothesis testing.FindingsSupply chain collaboration encompasses incentive alignment, collaborative communication, decision synchronisation, and information sharing. All antecedents, including commitment, inter-organisational trust, reciprocity, and inter-personal trust, positively affect collaboration. Inter-personal trust positively influences the other antecedents, and inter-organisational trust enhances commitment. Furthermore, there are partial mediating effects among these relationship attributes. However, no significant moderating effect regarding firm size is observed.Originality/valueThe study extends the social exchange theory to encompass both inter-personal and inter-organisational relationship attributes. Additionally, it pioneers in investigating the interrelatedness of these antecedents. By addressing the research gap in the Vietnamese fishery supply chain, it offers valuable insights for both academics and practitioners, contributing to theoretical understanding and practical implications in supply chain collaboration.

Multivariate range Value-at-Risk and covariance risk measures for elliptical and log-elliptical distributions

In this article, we propose the multivariate range Value-at-Risk (MRVaR) and the multivariate range covariance (MRCov) as two risk measures and explore their desirable properties in risk management. To facilitate analytical analyses, we derive explicit expressions of the MRVaR and the MRCov in the context of the multivariate (log-)elliptical distribution family. Frequently-used cases in industry, such as normal, student-t, logistic, Laplace, and Pearson type VII distributions, are presented with numerical examples. As an application, we propose a range-based mean-variance framework for optimal portfolio selection.