Industrial Case Research Articles

Exploring the expectations of the product manager role: the case of the Hungarian software industry

Exploring the expectations of the product manager role: the case of the Hungarian software industry

Life Cycle Analysis of the Production of Neutral Alcohol Obtained from Winemaking Byproducts for Use in Perfumery

As is the case in many other industries, major fragrance brands are now incorporating circular-economy in their product-development strategies. This is in response to growing consumer demand for more environmentally friendly products. In light of this objective, perfumery raw materials must be evaluated against environmental and societal criteria. This more critical context, coupled with the fact that alcohol can constitute up to 95% of a fragrance, means greater attention is being given the origin of this fragrance solvent. Industries are now becoming keenly interested in seeking out more environmentally friendly alternatives to alcohols derived from field crops (sugar beet, wheat, corn, sugarcane) so as to use a sustainable alcohol with excellent olfactory performance. To this end, this study entailed evaluating the environmental impact of an extra-neutral grape alcohol produced in France and obtained from winemaking residues. Its olfactory performance has been proven to meet the expectations and industrial needs of perfumers. The environmental impact was assessed by a life cycle analysis (LCA) carried out per the current NF EN ISO 14040 standard. The results show that extra-neutral grape alcohol is positively comparable to sugar beet alcohol, particularly as concerns greenhouse gas emissions. Upcycled grape alcohol is also produced through sustainable management of winemaking waste and does not conflict with land needs for food production. Lastly, unlike field crops, grapevines in the form of vineyards are a perennial crop and therefore serve as major carbon sinks, much the same way forests do.

Effects of Edge Radius and Coating Thickness on the Cutting Performance of AlCrN-Coated Tool

AbstractHigh-speed machining is a practical way to attain high productivity with lower costs. Under this condition, the tool geometry needs to be optimized to sustain high cutting forces and temperatures. The sharpness of the cutting edge and the coating thickness (CT) are two key parameters that affect the tool’s performance. While a sharp edge eases the cutting process, it causes a high stress concentration, which increases the wear rate and eventual edge fracture. In this study, we use a combination of finite element simulations and experimental testing to evaluate the effects of CT ( 1–3 μm), edge radius ($$r_{\beta }$$ r β , 6–15 μm), and coefficient of friction ($$\upmu = 0 - 0.2$$ μ = 0 - 0.2 ) on the stress distribution at the cutting edge. Our simulations showed that the larger the CT, the higher the stress magnitude inside the coating, but the lower the maximum stress depth percentile. Considering an industrial case of cutting steel workpieces using AlCrN-coated tungsten carbide tools under given cutting parameters, our simulations suggested an optimum CT of 3 μm. By manufacturing a series of milling tools with different CTs and edge radii, we validated the simulation results using a set of well-controlled milling experiments. Finally, the edge radius should be selected considering the size of rake/flank angle mainly to control stress distribution over the cutting edge.

Exploring coopetition and value‐based networks in business models for sustainability—A case study of the specialty coffee industry in Colombia

AbstractDrawing on coopetition‐based and values‐based business model networks, we explore competitors' coevolutionary interactions to drive sustainability. Using a specialty coffee industry case study in Colombia, we propose an analytical framework for collaboration, leveraging resources to transform neglected industry values. We apply the congruence analysis method to compare theoretical expectations with empirical findings, providing rigorous evidence. Our study reveals that developing values‐based networks through coopetition allows collective problem resolution and offers new opportunities for value creation. This research contributes to coopetition and values‐based network theories, emphasizing practical implications for sustainability‐focused business models. By highlighting competitor collaboration intricacies, our study enhances our understanding of the dynamics of local markets.

Do Consumers Value Agri-Food Industries’ Environmental Commitment? The Case of the Table Olive Industry

Do Consumers Value Agri-Food Industries’ Environmental Commitment? The Case of the Table Olive Industry

Additive dynamic Bayesian networks for enhanced feature learning in soft sensor modeling

Due to the advantages of indicating variable structure and efficient reasoning, Bayesian Networks (BN) have been widely used in data-driven soft sensor applications. However, restricted to linear and conditional Gaussian property, BN-based soft sensors rarely achieve high prediction accuracy. In this paper, an ensemble learning framework – additive dynamic Bayesian networks (ADBN) is proposed for enhanced feature learning, in which Dynamic Bayesian networks are used to learn the conditional independencies among variables and construct feature sets for the following base learners. Additional DBNs are constructed upon the residual information from the past model, to carry out feature learning to fit the residuals. The procedure is repeated and a termination rule from the perspective of feature learning is proposed to end this process, and thus the model complexity can be well restricted. The proposed method is validated on two actual industrial cases. It reveals that the ADBN feature learning method has obtained great improvements. Compared to the single DBN feature engineering method, the root mean square error (RMSE) performance has been improved by 20% and 13% on the two cases, respectively.

Advancing Virtual Bioequivalence for Orally Administered Drug Products: Methodology, Real-World Applications and Future Outlook

Bioequivalence studies are pivotal in generic drug development wherein therapeutic equivalence is provided with an innovator product. However, bioequivalence studies represent significant complexities due to the interplay of multiple factors related to drug, formulation, physiology, and pharmacokinetics. Approaches such as physiologically based biopharmaceutics modeling (PBBM) can enable virtual bioequivalence (VBE) assessment through appropriately developed and validated models. Such models are now being extensively used for bioequivalence risk assessment, internal decision-making, and the evaluation of drug and formulation factors related to bioequivalence. Depiction of the above-mentioned factors through the incorporation of variability and development of a virtual population for bioequivalence assessment is of paramount importance in utilizing such models. In this manuscript, we have portrayed our current understanding of VBE. A detailed explanation was provided with respect to study designs, in vivo variability, and the impact of physiological, drug, and formulation factors on the development of the population for VBE. Furthermore, strategies are suggested to incorporate variability in GastroPlus with an emphasis on intra-subject and inter-occasion variability. Two industrial case studies pertaining to immediate and modified release formulation were portrayed wherein VBE was utilized for decision-making and regulatory justification. Finally, regulatory understanding in the area of VBE, along with future perspectives, was detailed.

A data-driven LSTMSCBLS model for soft sensor of industrial process

Abstract In the chemical industry, data-driven soft sensor modeling plays a crucial role in efficiently monitoring product quality and status. Industrial data in these applications typically exhibit significant temporal characteristics, meaning that current information is influenced by data from previous periods. Effectively extracting and utilizing these temporal features is essential for achieving accurate soft sensor modeling in complex chemical scenarios. To address this challenge, this study proposes a data-driven Broad Learning System (BLS) model, which combines Long Short-Term Memory (LSTM) networks with an adaptive algorithm known as the Stochastic Configuration Algorithm (SC), referred to as LSTMSCBLS. The model operates in two stages: temporal feature extraction and final prediction. In the temporal feature extraction stage, the integration of the LSTM network with a feature attention mechanism allows for efficient extraction of temporal features from high-dimensional time-series data. In the final prediction stage, the SC is integrated into the BLS, effectively mitigating issues related to node space redundancy and the determination of the number of nodes. The effectiveness and superiority of the proposed model are demonstrated through two industrial case studies involving a debutanizer column and a sulfur recovery unit.

Study on the Impact of Multi-brand Strategy on Perceived Value and Consumption Intention: A Case of the Chain Restaurant Industry

Abstract The restaurant service industry is highly competitive, and in recent years, multi-brand strategies have gained increasing importance. By differentiating various brands, businesses can cater to diverse consumer needs and preferences, thereby enhancing their market share. This study explores the impact of multi-brand strategies on the restaurant service industry, focusing on whether perceived value influences consumer purchase intentions within a multi-brand context. The findings aim to help businesses understand the advantages of developing multi-brand strategies and provide actionable recommendations. Using a survey methodology, this study collected and analyzed 238 valid samples. The results indicate that the development of multi-brand strategies positively impacts both the perceived value and the purchase intentions of consumers in the restaurant service industry, enhancing their perception and willingness to engage with the services offered. Keywords: Multi-brand, Perceived Value, Consumption Intention.

Logistics 4.0 – digital transformation with smart connected tracking and tracing devices

Tracking and tracing devices can provide real-time information from the supply chain and enable digital transformation in the logistics and supply chain industry. In this connection, Logistics 4.0 refers to the potential for information technology and smart connected assets to be used in logistics in the same way as the Industry 4.0 concept which has been applied in operations and manufacturing. This paper analyses current tracking and tracing-focused applications that can provide value for logistics operations through a case study approach.This study employs an exploratory multiple-case study approach, which is based on interviews with development project stakeholders. The paper analyses three industrial case studies and how tracking/tracing applications are connected to value processes.The findings of this study show that the value of Logistics 4.0, through the deployment of tracking/tracing applications, is delivered in terms of operational efficiency, visibility, transparency, and safety/security. The payback depends on the volume of transactions, the possibility of reusing the tracking tags, the duration of trips, and the supply chain structure. The paper provides insight into how Logistics 4.0 technology can enhance logistics performance value. Based on the analysis, the study proposes the following potential application domains: (1) intermodal tracking of the shipments for operations control, (2) asset management of containers, and (3) certification of the process steps and authenticity.

Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study

Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study

Comparative Study of an Industrial Building Made of Cold formed steel versus Hot Rolled Steel Elements

Industrial buildings based on Cold Formed Steel (CFS) members have gained an extent of construction all over the world. It becomes an alternative process of construction to the Hot Rolled Steel (HRS) elements, allowing it to respond to the requirements in a short time. The aim of this work is to present a technical and economic comparative study of an industrial building case, made of CFS versus HRS elements, and designed according to the European and American codes. Relying on numerical modeling, the design according to Eurocode 3 demonstrates that the industrial building made of CFS is more economical compared to the HRS members building by 43% in terms of weight and 28% in terms of cost. In addition, based on the Average Capacity Design (ACD) ratio of the elements of a CFS, the design according to Eurocode 3 part 1-3 is less conservative than the same building designed according to American Iron and Steel Institute (AISI) code. Moreover, the American code privileges the safety aspect of the designed CFS building with the ASD method over the LRFD and LSD methods. Otherwise, a CFS building designed according to Eurocode 3 part 1-3 with truss columns and beams reveals a weight saving of 25% and 14%, respectively, compared to a building with a full web of beams and columns, and a building with a full web of columns and steel trusses.

C-SLA-MLO: Enhancing SLA Compliance in Industrial Wi-Fi through Cooperative Multilink Operation

In the dynamic landscape of Industry 4.0, robust wireless connectivity emerges as a critical enabler for Industrial Internet of Things (IIoT) applications. With the advent of Wi-Fi 7’s multilink operation (MLO), the demand for schedulers capable of meeting diverse Service Level Agreement (SLA) requirements within industrial environments becomes paramount. Addressing this need, this paper introduces Cooperative SLA-MLO (C-SLA-MLO), a novel distributed multilink scheduler that balances data across MLO channels to fulfill the SLA of STAs operating within the same BSS. We provide a simulation-based evaluation where we compare C-SLA-MLO with other benchmark approaches, in terms of SLA compliance when varying the SLA characteristics, the level of QoS, or background traffic, showcasing an average reduction of 90% in SLA deviation in the industrial use case. This study contributes practical insights for enhancing wireless connectivity in IIoT, offering potential benefits for industrial network optimization by exploiting the MLO feature brought by the new IEEE 802.11be.

Coordination dynamics between fuel cell and battery technologies in the transition to clean cars

Significant progress reconciling economic activities with a stable climate requires radical and rapid technological change in multiple sectors. Here, we study the case of the automotive industry's transition to electric vehicles, which involved choosing between two different technologies: fuel cell electric vehicles (FCEVs) or battery electric vehicles (BEVs). We know very little about the role that such technological uncertainty plays in shaping the strategies of firms, the efficacy of technological and climate policies, and the speed of technological transitions. Here, we explain that the choice between these two technologies posed a global and multisectoral coordination game, due to technological complementarities and the global organization of the industry's markets and supply chains. We use data on patents, supply-chain relationships, and national policies to document historical trends and industry dynamics for these two technologies. While the industry initially focused on FCEVs, around 2008, the technological paradigm shifted to BEVs. National-level policies had a limited ability to coordinate global players around a type of clean car technology. Instead, exogenous innovation spillovers from outside the automotive sector played a critical role in solving this coordination game in favor of BEVs. Our results suggest that global and cross-sectoral technology policies may be needed to accelerate low-carbon technological change in other sectors, such as shipping or aviation. This enriches the existing theoretical paradigm, which ignores the scale of interdependencies between technologies and firms.

Projecting hydrological response to climate change and urbanization using WEAP model: A case study for the main watersheds of Bicol River Basin, Philippines

Study regionBicol River Basin, Philippines Study focusThe study investigated the impact of climate change and urbanization on water resources in the Philippines. It used Water Evaluation and Planning (WEAP) software and climate forecasts from eight Global Circulation Models (GCMs) to assess the nation's water balance, focusing on two sensitive watersheds: Libmanan-Pulantuna (LPW) and Quinali (QW). The study projects water availability in these areas by creating climate-urbanization scenarios. Furthermore, our endeavor aims to provide insight into the complexities of hydrological processes within traditionally under-observed regions, serving as a blueprint for future environmental planning and sustainable water management in the Philippines and beyond. New hydrological insightsThe study's simulated streamflow matches observed data (R2: 0.70–0.85, NSE: 0.57–0.67) and indicates warming trends and variable precipitation in both watersheds. Rising water demand in domestic and industrial sectors contrasts with a decline in agriculture due to land conversion. QW faces increased water demands from urbanization, while LPW has lower unmet water demand. Low Land Decline scenarios indicate consistent water scarcity in agriculture, while High Land Decline scenarios predict a decrease by 2100. Urbanization significantly impacts future water stress more than climate change, with reduced agricultural land generally associated with lower water stress. Conversely, climate change exacerbates unmet demand issues in domestic and industrial development cases. Due to diminishing agricultural land, both watersheds are expected to transition from water-deficient to water-surplus regions by the end of the century. However, immediate action is crucial to address current water demands, particularly in the severely water-deficient Quinali Watershed.

The Challenges in Electrical and Electronic Technology Courses in Vocational Colleges

This paper addresses the challenges in the teaching of electrical and electronic technology in higher vocational colleges and proposes specific countermeasures to improve teaching quality and effectiveness. The countermeasures include optimizing teaching content, emphasizing practical application and innovation, innovating teaching methods, introducing modern instructional approaches, strengthening the teaching team, enhancing teacher quality and practical expertise, upgrading experimental equipment and facilities, enriching curriculum resources, and incorporating digital teaching materials. These measures aim to cultivate high-quality skilled talents, promote social and economic development, and enhance national competitiveness. By adjusting the course structure, incorporating real-world industry cases, and fostering collaboration with enterprises, students can better understand and apply electrical and electronic technology. The introduction of project-based teaching, flipped classrooms, and multimedia technology can enhance student engagement and facilitate independent learning. Furthermore, the improvement of experimental resources and the establishment of online teaching platforms can enhance students’ practical skills and provide a variety of learning resources. These measures contribute to the overall improvement of electrical and electronic technology teaching in higher vocational colleges.

OIL: an industrial case study in language engineering with Spoofax

AbstractDomain-specific languages (DSLs) promise to improve the software engineering process, e.g., by reducing software development and maintenance effort and by improving communication, and are therefore seeing increased use in industry. To support the creation and deployment of DSLs, language workbenches have been developed. However, little is published about the actual added value of a language workbench in an industrial setting, compared to not using a language workbench. In this paper, we evaluate the productivity of using the Spoofax language workbench by comparing two implementations of an industrial DSL, one in Spoofax and one in Python, that already existed before the evaluation. The subject is the Open Interaction Language (OIL): a complex DSL for implementing control software with requirements imposed by its industrial context at Canon Production Printing. Our findings indicate that it is more productive to implement OIL using Spoofax compared to using Python, especially if editor services are desired. Although Spoofax was sufficient to implement OIL, we find that Spoofax should especially improve on practical aspects to increase its adoptability in industry.

An Ergonomic Risk Assessment System Based on 3D Human Pose Estimation and Collaborative Robot

Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development.

Strategies and Tools for Small- and Medium-Sized Enterprises (SMEs) to Move toward Green Operations: The Case of the Taiwan Metal Industry

Strategies and Tools for Small- and Medium-Sized Enterprises (SMEs) to Move toward Green Operations: The Case of the Taiwan Metal Industry

Empirical investigation of drivers, motivations and barriers for implementation of integrated continuous improvement system: a case of Ethiopian manufacturing industries

PurposeContinuous improvement in an integrated approach is a philosophy developed over decades based on a set of management practices. It comprises enhancement methodologies that escalate success as well as diminishes letdowns. The state-of-the-art literature finds a variety of practices for the execution of continuous improvement (CI) system. However, it is rare to find an empirical study with an inclusive system that considers various practices in one frame for improving competitiveness. With this outlook, therefore, this study aims originally to identify drivers, motivations and barriers for the application of integrated CI system, and then conduct an empirical investigation within the context of Ethiopian manufacturing industries.Design/methodology/approachJustification of the problem, extensive review of literature, identification of practices, developing research framework, investigational analysis of the empirical study using reliability and descriptive statistical analysis and identifying leading drivers (unique practices and common factors), motivations and barriers are the research approaches used in this study. Furthermore, the primary data were collected through a self-administered questionnaire and were analyzed using a statistical package for social science (SPSS) 23 and the findings were triangulated to relate to the existing state-of-the-art literature.FindingsThe findings indicate 17 common factors associated with human, strategic, operational, technology, structure, resource and information factors; 21 unique practices of just-in-time (JIT), lean-six sigma (LSS), supply chain management (SCM), total productive maintenance (TPM), and total quality management (TQM) methodologies; 20 barriers connected with internal and external issues and 17 motivations linked to operational, innovation and business results are identified. In general, the empirical analysis discovers the practices are noteworthy and commonly supported by a least of 52% of the respondents. As a result, the first five prominent common factors, barriers, unique practices and motivations are also supported by a minimum of 72%, 73.9%, 65.8% and 75% of the respondents, respectively.Research limitations/implicationsEven though this is the first-ever study in the Ethiopian manufacturing sector with the focus on integrated CI practices of JIT, TQM, TPM, SCM and LSS initiatives, some limitations have existed and the major limitations of the study are, targeted merely small number manufacturing companies despite the fact that there are hundreds of companies implementing CI system in Ethiopia, and the data collected were only based on the perception of the respondents, and other information’s like annual reports was not employed to support the findings.Practical implicationsThe findings of this study underlined that the implementation of drivers in relation to common factors and unique practices supported by exterminating barriers in manufacturing industries of Ethiopia enables to enhance competitiveness through attaining operational, innovation and business results though it requires a practical case application to prove this perception-based analysis.Originality/valueThough there are a number of studies published on integrated continuous improvement, currently it is found that there is no literature focused on the identification and empirical investigation of drivers (common factors and unique practices), motivations and barriers for application of integrated CI system with the focus of JIT, TQM, TPM, SCM and LSS initiatives. Therefore, this is the first-ever study, and the empirical analysis of the study discovered that identified practices are providing valuable insights for manufacturing industries which will be on board on this voyage including institutions, practitioners and other sectors.