Industrial Case Study Research Articles

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.

Theory of errors in innovation failure and strategic management of winning by failing

ABSTRACT The risk of failure in innovation projects affects organisational behaviour and the performance of firms. This study develops a theory of errors in innovation project-level failure. Error is a set of faults that generates deviations in phases of the R&D process in projects, decreasing the expected results. From a systemic perspective, innovation failure in a project is due to a set of errors that generates a negative outcome. The proposed theory clarifies the general determinants of innovation failure in projects that are due to errors in design, execution and market orientation. Case studies in the drug discovery industry and pharmaceuticals validate the consistency of the proposed theory, giving examples of errors in projects leading to innovation failures. The payoff matrix of success/failure occurrences predicts that the probability of failure in innovation projects can double by setting a difficult goal compared to an easy one. Inductive implications of the proposed theory are that organisations reduce innovation failure in projects with better adaptedness to changes that minimise errors and learn from them. The proposed theory can guide R&D managers, designers, etc. in detecting critical errors in the R&D process to apply strategies of problem solving based on a winning approach by learning from errors that lead to innovation failure in projects to enable the organisation to adapt, take advantage of important opportunities and cope with consequential environmental threats for achieving goals and sustaining the competitive advantage in turbulent markets.

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.

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.

Improving Lead Time through Lean Manufacturing: A Case Study

Lean manufacturing is a well-established methodology aimed at optimizing production by eliminating waste, enabling industries to thrive in a globally competitive environment. This paper presents a case study of a well-known automotive manufacturing industry on the axle process. This article aims the Value Stream Mapping (VSM) methodology was used in the axle process to reduce lead time by producing quality products with a reduction in non-value-added activities. The current state of the axle process was created using industry data from the past six months. Significant improvements were realized following the successful implementation of VSM, including a reduction in the lead time from 89.50 hours to 50.55 hours. The new state map was also created after implementing the improvements. The results illustrated that the VSM increased the effectiveness by 56.48% in the axle process.

Scenario‐Driven Metamorphic Testing for Autonomous Driving Simulators

ABSTRACTThe proliferation of driver‐assistance features in vehicles has resulted in a growing interest among the public in fully autonomous driving systems (ADSs). However, the integration of software and hardware in these complex systems presents significant testing challenges, particularly with respect to ensuring passenger safety. To address these challenges, simulation has emerged as a crucial step in the testing of ADSs. This paper presents a solution to the challenges faced in testing ADSs, with a focus on the validation of ADS simulators. The proposed approach involves using simulations and metamorphic testing (MT) to generate multiple concrete metamorphic relations (MRs) for testing ADS simulators. In order to accomplish this goal, we introduce three metamorphic relation patterns (MRPs). Each MRP is accompanied by a metamorphic relation input pattern (MRIP) that aids in generating detailed MRs. These MRs are designed to identify potential issues within the ADS simulator. To simplify the testing process and facilitate MT for testers, a self‐evolving scenario‐testing framework is also presented. The framework allows testers to improve test cases and MRs iteratively until issues detected are confirmed. The benefits and limitations of the framework are demonstrated using an industry case study. Overall, this study offers a practical solution to the challenges in testing ADSs and provides useful insights into improving testing efficiency for researchers and practitioners in the field.

Leveraging ICH M7 Control Options 3 and 4: Discussion and Clarification Using Industrial Case Studies

Leveraging ICH M7 Control Options 3 and 4: Discussion and Clarification Using Industrial Case Studies

Safety Design Criteria for the Emergency Discharge of Hazardous Substances in Small and Medium-Sized Polystyrene Polymerization Batch Reactor Processes: Case Study of the South Korean Chemical Industry

In small and medium-sized chemical plants, explosions constantly occur owing to runaway reactions because of equipment defects or human errors and so on. Accordingly, in this study, based on a case study of an explosion accident in a polystyrene reactor in South Korea, the dis-charge capacity of hazardous substances during a runaway reaction is reviewed and a method for safely disposing of hazardous substances is proposed. Using an acceleration rate calorimeter, the maximum temperature rise rate during the polystyrene reaction was determined, and it was determined that 355,643 kg/h can flow during a runaway reaction. A 30-inch header size was then selected to consider maximum flow rate, and two 81.4 m2 heat exchangers were selected to completely condense the hazardous substances. As a result, the facilities at the workplace were configured to condense all hazardous substances and discharge them into the atmosphere. If this method is used, it is believed that the lives of workers can be protected by preventing fires and explosions in small and medium-sized chemical plants in which runaway reactions may occur.

Scheduling parallel batch processing machines: A case study in the semiconductor industry

This paper presents a mathematical programming-based solution approach for the scheduling problem of batch-processing parallel machines with eligibility constraints. A case study has been presented in the semiconductor industry, where the ovens are scheduled for the underfill cure operation of products. The case includes constraints, such as oven-product eligibility restrictions, loading constraints for the batching of products for ovens, daily production requirements, and oven capacity constraints. In this study, we also assess the difference between creating batches of a single product type or different product types to be allocated to the ovens. The case study results have shown that the proposed models, in comparison to the current situation, increase the occupancy rate of ovens. The execution of the models aids the company in gaining visibility on the scheduling of ovens and successfully managing the production plan and order commitment. The proposed models have been effective and supportive of the semiconductor company.

Similarity coefficient and TOPSIS methods for designing flexible machine cell layout: a case study of electrical and electronic industries

Similarity coefficient and TOPSIS methods for designing flexible machine cell layout: a case study of electrical and electronic industries

Efficiency evaluation of intellectual capital for value creation: a case study of the semiconductor industry in Taiwan

PurposeThis study constructs an efficiency evaluation framework for assessing the human, structural and relational capital in the semiconductor industry of Taiwan. Furthermore, we analyze whether there are significant differences in efficiency across different levels concerning the industry supply chain (upstream, midstream and downstream), employee service tenure, capital scale and company establishment years.Design/methodology/approachThis study focuses on Taiwanese semiconductor companies, utilizing data sourced from the Taiwan Economic Journal (TEJ) Database for the period spanning 2017 to 2021, encompassing a total of five years. Due to the nondisclosure of intangible asset values by all companies, an effort was made to ensure a comparable baseline by excluding companies with incomplete or missing data. Finally, empirical analysis was conducted on a sample of 64 companies using the dynamic network data envelopment analysis method.Findings(1) Overall efficiency demonstrates structural capital as the most prominent, followed by relational capital, while human capital shows relatively poorer efficiency. (2) To enhance the efficiency of intellectual capital, priority should be given to improving the efficiency of outputs related to intellectual property rights such as patents. (3) The midstream segment exhibits the best efficiency in both structural and relational capital. (4) Companies with longer employee service tenures exhibit superior efficiency in human capital in the long run. (5) Companies with extended establishment years and larger capital scales demonstrate superior efficiency in both human and structural capital.Originality/valueReflecting on past literature, scholars have primarily focused on the relationship between intellectual capital and firm efficiency, often emphasizing the overall efficiency of intellectual capital. However, within organizations, human capital, structural capital, and relational capital are interrelated. This study, for the first time, assesses the efficiency of these three components within an organization. The research addresses the challenges in analyzing the efficiency of intellectual capital and introduces a highly contemporary approach – dynamic network data envelopment analysis (DNDEA). Using the semiconductor industry in Taiwan as a case study, this paper conducts empirical analysis in a captivating and worthy industry. Therefore, the ideas presented in this paper are original.

Combining the analytical hierarchy process, fuzzy expert systems, and the exponential risk priority number for the holistic evaluation of innovation projects in manufacturing

ABSTRACT Manufacturing companies operate in a complex environment and need efficient manufacturing processes to remain competitive. Therefore, evaluation methods are essential for decision makers when selecting manufacturing innovation projects (MIPs). However, most approaches are not suitable for strategic use and do not consider all relevant evaluation dimensions. To address this issue, this work presents an approach to evaluate and select MIPs holistically, considering potential, effort, and risk. The approach enables the analysis of the strategic impact of an MIP using a fuzzy expert system, and further evaluates the implementation effort and risk using a combination of the Analytical Hierarchy Process and the Exponential Risk Priority Number. The approach was developed using the results of a systematic literature review and expert-based methods. Finally, the approach was validated in an industrial case study and enabled a transparent evaluation of the strategic potential, effort, and risk of two MIPs, leading to informed project selection.

How Do Supply Chain Management Practices Influence Organizational Performance? A Case Study of Bangladesh's Agro-Food Industry

Introduction: Supply chain management practices are essential for enhancing organizational performance, particularly in the Agro-food industry. This study examines the impact of specific SCM factors—Strategic Supplier Partnership, Customer Relationship, Level of Information Sharing, and Quality of Information Sharing—on organizational performance in Bangladesh. Methodology: Data were collected via a structured questionnaire targeting key stakeholders in Bangladesh's Agro-food industry, using a 5-point Likert scale to measure responses. Structural equation modeling (SEM) was utilized to analyze the relationships between SCM practice factors and organizational performance factors such as marketing and financial performance. Findings: The analysis revealed that customer relationships, quality of information sharing, and strategic supplier partnerships have significant positive relationships with organizational performance, particularly in terms of financial and marketing outcomes. However, the level of information sharing did not show a significant impact on both financial and marketing performance. Managerial Implications: The study emphasizes the significance of prioritizing strategic supplier partnerships, customer relationships, and high-quality information sharing for improving organizational performance in Bangladesh's Agro-food industry. Strengthening these SCM practices can lead to better financial and marketing performance, providing valuable insights for managers to develop targeted strategies.

Line Balancing Study Using Value Stream Mapping Tool on Lean Manufacturing: A Case Study in an Electronic Industry

XY Electronics, a leading international company in electronic components manufacturing, is confronting significant production constraints that adversely affect output, lead times, and operational expenses. This study examines the manufacturing line for product A using Value Stream Mapping to analyze process times and identify bottlenecks where takt times are exceeded. It focuses on areas surpassing production cycle times and aims to enhance line utilization through better line balancing and waste reduction. The results reveal that the header assembly, along with coplanarity and pre-testing 3, are major bottlenecks, which significantly impact productivity. By optimizing task allocation, refining workforce distribution, and employing cross-training, the production line efficiency improved significantly. In addition, strategic workforce reallocation and station optimization were crucial in addressing resource underutilization and enhancing overall operational efficiency

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.

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.

Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA–WISP approach

Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers’ models, and the industry’s intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decision-making, IVFF–PIPRECIA and IVFF–WISP techniques. IVFF–PIPRECIA determines criteria importance weights, whereas IVFF–WISP identifies optimal alternatives. A case study in the textile industry validates the framework’s practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model’s credibility. The approach effectively handles decision-making uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.

Gender, Race and Sectoral Inequality in Megacities: Case Study of Banking and Financial Services Industry

This article explores the issue of inequality by using the example of the banking and financial services industry workers’ wages in 2022 in the most famous American metropolitan areas, focusing on 3 of its dimensions: gender inequality, racial inequality, and pay gaps in this sector compared to others. The study is based on data from the American Community Survey (ACS) conducted by the Census Bureau. Population-level figures are derived using the replicate weights calculated in the article using STATA 16.1 software (the information in the ACS is contained in raw microdata form, the ACS is a probability sample, so the sampling weights derived by factoring in the non-response bias are essential to arrive at the population-level estimates). The financial services sector plays a significant role in the economies of these metropolitan areas, employing an average of 7.77% of adult workers (the highest in Phoenix (10.41%) and Dallas (9.77%)). The study showed that in these megacities, financial services workers earned 64.77% more in 2022 than workers in other sectors of the local economy, with the highest gap in New York (111.24%) and Boston (80.81%). Women in the financial sector of these cities earn, on average, 65.59% less than men, with the largest gender gap in Chicago (71.84%) and Dallas (71.26%). The earnings gap for black and non-white workers compared to their white counterparts in the cities studied averaged 36.16%, with the largest sectoral gaps in Houston (60.14%) and Riverside (55.06%). The findings can inform the development of policies that promote equitable pay practices, enforce stronger anti-discrimination laws, and support diversity and inclusion initiatives within the industry.

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.