Agile Production Research Articles

Repurposing product development after COVID-19 by integrating Industry 4.0 and agile new product development practices in emerging economies

Repurposing product development after COVID-19 by integrating Industry 4.0 and agile new product development practices in emerging economies

Enhancing DevSecOps practice with Large Language Models and Security Chaos Engineering

Recently, the DevSecOps practice has improved companies’ agile production of secure software, reducing problems and improving return on investment. However, overreliance on security tools and traditional security techniques can facilitate the implementation of vulnerabilities in different stages of the software lifecycle.. Thus, this paper proposes the integration of a Large Language Model to help automate threat discovery at the design stage and Security Chaos Engineering to support the identification of security flaws that may be undetected by security tools. A specific use case is described to demonstrate how our proposal can be applied to a retail company that has the business need to produce rapidly secure software.

An integer mathematical model proposal for an agile external milkrun system

A milkrun system is a cyclical material delivery system that enables frequent small-batch deliveries with short lead times and low inventories. The literature classifies milkrun systems into external and internal categories. External milkrun systems in agile production environments, on the other hand, have not received much attention in the literature thus far. This study presents an integer mathematical model for the cost minimisation of external milkrun tours and discusses agile external milkrun systems. In addition to minimising costs, the proposed mathematical model can determine the types and number of milkrun vehicles. This is an important advantage for logistics professionals. The proposed model was coded using the Lingo software, and its effectiveness was shown using both a real-world problem and randomly generated test problems. The proposed model found the best solution in a few seconds for some real-world and most test problems. The results have shown that the proposed mathematical model for external milkrun systems in agile environments is useful.

Harmonising humans and technology: Exploring the dynamics of cognitive production, artificial intelligence and social communication in cybernetic systems

Agile cognitive production systems mark a manufacturing paradigm shift, propelled by the demand for accelerated product development and the adoption of digitalised production systems across extensive supply networks. Cognitive manufacturing emphasises the role of technology and automation in the learning and adaptation process. These systems independently analyse data, make real-time adjustments and optimise processes, sometimes minimising the need for human intervention. Based on a conceptual framework that draws on the diversity of living systems and cognitive processes, cybernetics provides a solid theoretical background. It explores the intricate connections between cognition, self-organising systems and the challenges arising from the autonomy of such systems. The concept of "cognition" in "agile cognitive systems" moves away from the conventional understanding of purely technical processes and towards human thought processes. This departure fosters a dynamic exchange where individual thoughts resonate in social communication. Addressing the role of artificial intelligence (AI), the article emphasises examining computers from a social science standpoint, exploring the relationship between computers and mental systems, capturing human faculties such as cognition, utterance, and understanding. The integration of AI into computer-mediated communication leads to the question how AI-equipped computers intersect with societal intelligence notions. The inherent intransparency of AI, often viewed as a black box, prompts queries about the potential black-box nature of an autonomously controlled AI factory or supply chain. In this hypothetical scenario, the idea of the supply chain as a communication network is challenged, emphasising the importance of human involvement. Research on human-centric cognitive production emphasises explainable AI and human-in-the-loop. This orientation goes beyond the technical dimensions and incorporates social science considerations, which emphasises the holistic nature of current research. In essence, research in the field of cognitive production is a comprehensive exploration of the complex interplay between human cognition, artificial intelligence and the evolving landscape of modern production systems.

Deployment options of AI components for network resource management in 5G‐enabled agile industrial production cell

SummaryOn‐demand manufacturing in Industry 4.0 requires flexibility of the networks which can be provided with the fifth generation (5G) of mobile communications wireless connectivity. A key component in the efficient utilization of the radio resources in a manufacturing scenario is network resource management (NRM). We show how NRM can be automated with artificial intelligence (AI). We introduce several futuristic industrial use cases that require AI in various parts of the process. We analyze the AI components' benefits and disadvantages in several deployment scenarios. The findings can be used by business stakeholders interested in deploying the 5G cellular wireless network to choose the best NRM and AI implementation strategy for a particular use case. We show that there are many viable options for the AI component in the process automation, but the cost of AI has to be considered in all cases. Also, we point out that an essential component, the standardized information flow on the status of the productivity key performance indicators (KPIs), is needed for the successful deployment and application of the 5G AI.

A Design Flow Based on Docker and Kubernetes for ROS-based Robotic Software Applications

Human-centered robotic applications are becoming pervasive in the context of robotics and smart manufacturing, and such a pervasiveness is even more expected with the shift to Industry 5.0. The always increasing level of autonomy of modern robotic platforms requires the integration of software applications from different domains to implement artificial intelligence, cognition, and human-robot/robot-robot interaction. Developing and (re)configuring such a multi-domain software to meet functional constraints is a challenging task. Even more challenging is customizing the software to satisfy non-functional requirements such as real-time, reliability, and energy efficiency. In this context, the concept of Edge-Cloud continuum is gaining consensus as a solution to address functional and non-functional constraints in a seamless way. Containerization and orchestration are becoming a standard practice, as they allow for better information flow among different network levels as well as increased modularity in the use of multi-domain software components. Nevertheless, the adoption of such a practice along the design flow, from simulation to the deployment of complex robotic applications by addressing the de facto development standards (e.g., ROS - Robotic Operating System) is still an open problem. We present a design methodology based on Docker and Kubernetes that enables containerization and orchestration of ROS-based robotic SW applications for heterogeneous and hierarchical HW architectures. The methodology aims at (i) integrating and verifying multi-domain components since early in the design flow, (ii) mapping software tasks to containers to minimize the performance and memory footprint overhead, (iii) clustering containers to efficiently distribute load across the edge-cloud architecture by minimizing resource utilization, and (iv) enabling multi-domain verification of functional and non-functional constraints before deployment. The article presents the results obtained with a real case of study, in which the design methodology has been applied to program the mission of a Robotnik RB-Kairos mobile robot in an industrial agile production chain. We have obtained reduced load on the robot’s HW with minimal performance and network overhead, thanks to the optimized distributed system.

Smart calibration and monitoring: leveraging artificial intelligence to improve MEMS-based inertial sensor calibration

Micro-electro-mechanical systems (MEMS)-based sensors endure complex production processes that inherently include high variance. To meet rigorous client demands (such as sensitivity, offset noise, robustness against vibration, etc.). products must go through comprehensive calibration and testing procedures. All sensors undergo a standardized and sequential calibration process with a predetermined number of steps, even though some may reach the correct calibration value sooner. Moreover, the traditional sequential calibration method faces challenges due to specific operating conditions resulting from manufacturing discrepancies. This not only extends the calibration duration but also introduces rigidity and inefficiency. To tackle the issue of production variances and elongated calibration time and enhance efficiency, we provide a novel quasi-parallelized calibration framework aided by an artificial intelligence (AI) based solution. Our suggested method utilizes a supervised tree-based regression technique and statistical measures to dynamically identify and optimize the appropriate working point for each sensor. The objective is to decrease the total calibration duration while ensuring accuracy. The findings of our investigation show a time reduction of 23.8% for calibration, leading to substantial cost savings in the manufacturing process. In addition, we propose an end-to-end monitoring system to accelerate the incorporation of our framework into production. This not only guarantees the prompt execution of our solution but also enables the identification of process modifications or data irregularities, promoting a more agile and adaptable production process.

An adoption framework for agile new product development using hybrid RBWM-ISM-Fuzzy MICMAC approach

An adoption framework for agile new product development using hybrid RBWM-ISM-Fuzzy MICMAC approach

Agile methodologies: Adapting product management to rapidly changing market conditions

This review paper explores the effectiveness of agile methodologies in adapting product management practices to the dynamics of rapidly changing market conditions. The paper synthesizes existing literature on the application of agile principles across different sectors, focusing on how these methodologies facilitate responsiveness and adaptability in product management. The core analysis centers on the comparison of traditional and agile product management approaches, drawing on case studies and expert opinions to highlight the benefits and challenges associated with agile adoption. Key findings demonstrate that agile methodologies, such as Scrum and Kanban, enable organizations to enhance product innovation and customer satisfaction by fostering a culture of continuous improvement and rapid response to market feedback. Despite these advantages, the review also identifies potential drawbacks, including the complexity of transitioning to agile frameworks and the scalability of agile practices in large organizations. The paper argues that agile methodologies are crucial for companies aiming to remain competitive in fluctuating markets, provided there is a strategic commitment to agile transformation and an investment in overcoming the initial hurdles of adoption. Recommendations for effective implementation of agile practices in product management are provided to guide organizations in their agile journey.

A systematic literature review with bibliometric analysis of Quality 4.0

PurposeThis study aims to investigate and provide comprehensive insights into the state of research on Quality 4.0 across various sectors. The investigation focusses on studies conducted in different countries and is motivated by the perceived benefits of adopting Quality 4.0 and the existing research gap in this domain. The literature review examines two main aspects of Quality 4.0: implementation ingredients and its applications.Design/methodology/approachThis study employed a comprehensive systematic literature review (SLR) and bibliometric analysis of Quality 4.0 publications to identify the benefits, challenges and trends of adopting Quality 4.0.FindingsThe applications of Quality 4.0 are centred on six key areas, including agile product development, predictive quality algorithms, cloud databases, automation, reduced cost of quality, streamlined quality processes, accurate demand forecasting and monitoring suppliers and quality metrics using digital dashboards. Geographical disparities in research were identified, with India and the United States of America emerging as the top publishing countries. The dominance of the subjects of business, management and accounting underscored the importance of quality management across diverse sectors.Research limitations/implicationsThe limitations of this study include the novelty of the Quality 4.0 topic, which posed a challenge in finding academic material, and the analysis period of six years, which could potentially limit the coverage of recent developments.Practical implicationsThe insights and observations from this research offer valuable guidance to academics, practitioners and professionals seeking to implement Quality 4.0 in diverse sectors.Originality/valueThis study contributes a novel perspective to the field by examining the application, utilisation and evaluation of Quality 4.0, providing valuable insights for effectively managing its implementation.

Product Innovation Using Agile Practices in Supply Chain Management Curriculum

The dynamic business landscape demands supply chain professionals with Agile project management skills. This study introduces a novel course project simulating a new product launch for a Fortune 500 company. We designed a theoretical multi-phase student team-based Agile project as part of an undergraduate Supply Chain Management course. This article describes implementing a project where teams are guided through the "Agile Way" using scaffolding of the Scrum framework. The project is designed using team-based learning of Agile methodologies. Agile methodologies often emphasize collaboration and open communication, which can foster psychological safety in teams. Using Agile frameworks and the designed templates, student teams work through theoretical Agile product innovation phases for a Fortune 500 company throughout the semester. Students simulate a product launch and apply Agile practices. A validated Agile survey assessed student learning outcomes across two semesters. Statistical analysis revealed a significant positive impact of using Agile templates on student understanding of Agile practices, team collaboration, and continuous improvement. This project offers a practical and adaptable framework and provides insights into using Agile management in the classroom. This study contributes to understanding student learning processes and outcomes using Agile project management and outlines teaching methods for a Supply Chain Management class that successfully implements Agile management projects.

Enhancing agile product development with scrum methodologies: A detailed exploration of implementation practices and benefits

This study conducts a systematic literature review to examine the implementation, challenges, and ongoing evolution of Scrum methodologies within Agile product development frameworks. Aimed at understanding how Scrum can be effectively tailored to meet the needs of diverse organizational contexts, especially in scenarios involving remote and distributed teams, the research also seeks to uncover the trajectory of Agile practices' continuous evolution. Employing a rigorous methodology, the review analyzed peer-reviewed articles and conference papers from key academic databases, including IEEE Xplore, ACM Digital Library, and Scopus, published between 2013 and 2024. The selection was based on relevance to Scrum practices, empirical evidence, and theoretical contributions to the field of Agile development. Key insights from the study underscore the critical role of Scrum in enhancing collaboration, flexibility, and responsiveness in product development processes. Challenges identified include the necessity of overcoming organizational resistance and the importance of customizing Scrum practices to align with specific project and team dynamics. Notably, the research highlights Scrum's adaptability in supporting distributed teams through strategic communication and trust-building measures. Furthermore, the study points to the integration of Scrum with DevOps and its application beyond software development as indicators of its evolutionary potential within Agile methodologies. Finally, the study offers strategic recommendations for organizations adopting Scrum, emphasizing cultural adaptation, continuous training, and practice customization. It also suggests avenues for future research, particularly in exploring Scrum's integration with emerging technologies and broader industry applications, contributing valuable insights into enhancing Agile product development efficacy through Scrum methodologies. Keywords: Scrum Methodologies, Agile Product Development, Distributed Teams, Continuous Evolution.

Heat transfer optimisation using novel biomorphic pin-fin heat sinks: An integrated approach via design for manufacturing, numerical simulation, and machine learning

With the availability of advanced manufacturing techniques, non-conventional shapes and bio-inspired/biomorphic designs have shown to provide more efficient heat transfer. Consequently, this research investigates the heat transfer performance and fluid flow characteristics of novel biomorphic scutoid pin fins with varying volumes and top geometries. Numerical simulations were conducted using four hybrid designs for Reynolds Number 5500–13500. The impact of pin fin 'top' geometrical features on the heat transfer coefficient (HTC) was evaluated by combining computational fluid dynamics (CFD), experimental data, and machine learning. The results highlighted that the new pin fins saved 6.3 % to 14.3 % volume/material usage but produced around 1.5 to 1.7 times more heat transfer than conventional square/rectangular fins. Also, manipulating pin fins via the top geometrical properties can lead to more uniform velocity and temperature distributions while demonstrating the potential for increased thermal efficiency with reduced thermal resistance. Furthermore, six machine learning models accurately predict HTC using volume and surface area as key variables, achieving less than 5 % mean absolute percentage error (MAPE). Overall, this research introduces innovative biomorphic designs with unconventional geometries, emphasising resource optimisation and efficient HTC prediction using machine learning. It simplifies design processes, supports agile product development, calls for re-evaluation of conventional heat sink geometries, and provides promising directions for future research.

Agile product management as a catalyst for technological innovation

Agile Product Management (APM) has emerged as a critical driver of technological innovation, enabling organizations to rapidly adapt to changing market dynamics and customer needs. This abstract explores the key principles of APM and its role in fostering innovation in today's fast-paced business environment. At its core, APM is a collaborative, iterative approach to product development that emphasizes flexibility, customer feedback, and continuous improvement. By breaking down complex projects into manageable tasks and delivering value incrementally, APM enables teams to respond quickly to market feedback and evolving requirements. One of the key features of APM is its emphasis on customer collaboration. By involving customers early and often in the development process, organizations can ensure that their products meet customer needs and expectations. This customer-centric approach not only drives innovation but also helps organizations stay ahead of the competition. Another important aspect of APM is its focus on cross-functional teams. By bringing together individuals with diverse skills and backgrounds, APM fosters a culture of creativity and innovation. Cross-functional teams are able to quickly experiment with new ideas, iterate on designs, and deliver high-quality products to market faster. In addition to fostering innovation, APM also helps organizations manage risk more effectively. By breaking projects down into smaller, more manageable pieces, organizations can identify and address potential issues early in the development process, reducing the likelihood of costly delays or failures. Overall, APM has emerged as a powerful catalyst for technological innovation. By embracing the principles of flexibility, customer collaboration, and continuous improvement, organizations can not only drive innovation but also stay ahead of the competition in today's rapidly evolving business landscape.

Quality by Design Considerations for Drop-on-Demand Point-of-Care Pharmaceutical Manufacturing of Precision Medicine.

Distributed and point-of-care (POC) manufacturing facilities enable an agile pharmaceutical production paradigm that can respond to localized needs, providing personalized and precision medicine. These capabilities are critical for narrow therapeutic index drugs and pediatric or geriatric dosing, among other specialized needs. Advanced additive manufacturing, three-dimensional (3D) printing, and drop-on-demand (DoD) dispensing technologies have begun to expand into pharmaceutical production. We employed a quality by design (QbD) approach to identify critical quality attributes (CQAs), critical material attributes (CMAs), and critical process parameters (CPPs) of a POC pharmaceutical manufacturing paradigm. This theoretical framework encompasses the production of active pharmaceutical ingredient (API) "inks" at a centralized facility, which are distributed to POC sites for DoD dispensing into/onto delivery vehicles (e.g., orodispersible films, capsules, single liquid dose vials). Focusing on the POC dispensing/dosing processes, QbD considerations and cause-and-effect analyses identified the dispensed API quantity and solid-state form (CQAs), as well as the nozzle diameter, system pressure channel, and number of drops dispensed (CPPs) for detailed investigation. Final assay quantification and content uniformity CQAs were measured from demonstrative levothyroxine sodium single-dose liquid vials of glycerin/water, meeting the standard acceptance values. Each POC facility is unlikely to maintain full quality control laboratory capabilities, requiring the development of appropriate atline or inline methods to ensure quality control. We developed control strategies, including atline ultraviolet-visible (UV-vis) verification of the API ink prior to dispensing, inline drop counting during dispensing, intermediate atline-dispensed volume checks, and offline batch confirmation by liquid chromatography-tandem mass spectrometry (LC-MS/MS) following production.

Modelling the enablers of productivity of enterprise-level Agile software development process using modified TISM approach

Purpose This study aims to examine the enablers of productivity of enterprise-level Agile development process using modified total interpretative structural modeling (TISM). The two main objectives of the current study are to determine the variables influencing enterprise-level agile development productivity and to develop modified TISM for the corresponding components. Design/methodology/approach To identify enablers of the productivity of enterprise-level agile software development process a literature review and opinions of domain experts were collected. A hierarchical relationship among variables that show direct and indirect influence is created using the modified TISM (M-TISM) technique with Cross Impact Matrix-Multiplication Applied to Classification analysis. This study examined and analyzed the relationships between the determinants within the enterprise using a M-TISM technique. Findings With the literature review, the study could identify ten enabling factors of the productivity of Agile development process at the enterprise level. Results depict that program increment (PI) planning and scalable backlog management, continuous integration and continuous delivery (CI/CD), agile release trains (ART), agile work culture, delivery excellence, lean and DevOps practices, value stream mapping (VMS), team skills and expertise, collaborative culture, agile coaching, customer engagement have an impact on the productivity of enterprise-level Agile development process. The results show that team collaboration, agile ways of working and customer engagement have a greater impact on productivity improvement for enterprise-level Agile development process. Research limitations/implications The developed model is useful for organizations employing scaled Agile development processes in software development. This study provides a recommended listing of key enablers, that may enable productivity improvements in the Agile development process at the enterprise level. Strategists should focus on team collaboration and Agile project management. This study offers a modified TISM model to academicians to help them understand the effects of numerous variables on maintaining the productivity of an enterprise-level Agile. The identified characteristics and their hierarchical structure can help project managers during the execution of Agile projects at the enterprise level, more effectively, increasing their success and productivity. Originality/value The study addresses the gap in the literature by interpretative relationships between the identified enabling factors. The model validation is carried out by a panel of nine experts from several information technology organizations deploying Agile software development at the enterprise level. This unique method broadens the knowledge base in Agile software development at scale and provides project managers and practitioners with a practical foundation.

Agile logistics challenges of petroleum product distribution in Nigeria

Agile distribution of petroleum product is a strategy towards uninterrupted supply of quality products at affordable prices. Industry and economic productivity as well as transportation in Nigeria is largely driven by petroleum energy due to low investments in other sources of energy, yet the petroleum energy supply and distribution is marred by lingering history of scarcity, high prices, and distribution bottlenecks to end users all over the nation. The study through a descriptive survey following literature view, primary and secondary data collection sought to determine at granular level the limiting factors of agile petroleum product distribution in Nigeria. Petroleum supply, consumption and pricing are significant aspects of the study survey. Study findings shows limiting factors of distribution agility cut across pricing, supply, technology, infrastructure, accessibility and lead time, storage, management, and Human Resource issues. Significantly lack of local refining capacity and poor distribution network are notorious for supply limitations and rising price of products. Addressing the challenges would require strong political will, driven by genuine commitment and focused attention towards economic growth and improvement in the welfare of the citizens.

Retraction notice: Improving supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development

Retraction notice: Improving supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development

A REVIEW OF AGILE METHODOLOGIES IN PRODUCT LIFECYCLE MANAGEMENT: BRIDGING THEORY AND PRACTICE FOR ENHANCED DIGITAL TECHNOLOGY INTEGRATION

This study explores the intersection of agile methodologies and Product Lifecycle Management (PLM), aiming to bridge theoretical insights with practical applications for an improved integration of digital technologies. Agile methodologies, originally developed for software development, have gained prominence for their adaptive and collaborative approach. This paper investigates their applicability and impact within the broader context of PLM, a strategic process encompassing the entire lifecycle of a product from conception to disposal. The integration of agile practices in PLM promises enhanced flexibility, faster response to changing requirements, and improved collaboration among cross-functional teams. The review encompasses a comprehensive examination of existing literature, synthesizing insights from both agile and PLM domains. The analysis delves into key principles of agile methodologies, such as iterative development, continuous feedback, and adaptive planning, and evaluates their potential contributions to various stages of the product lifecycle. Additionally, the paper scrutinizes real-world case studies and industry examples to illustrate successful implementations and identify challenges in adopting agile practices within PLM frameworks. Theoretical frameworks are explored to establish a foundation for the practical integration of agile methodologies, addressing potential conflicts and synergies. The review highlights the significance of aligning agile principles with PLM objectives to foster digital technology integration. The proposed synthesis of theory and practice aims to guide organizations in leveraging agile methodologies for optimizing their product development processes within the dynamic landscape of digital technology. This review contributes valuable insights to academia and industry professionals seeking a nuanced understanding of the synergy between agile methodologies and PLM for enhanced digital technology integration.
 Keywords: Project Management; Product Cycle; Agile Methodologies; Management; Resources; Review.

A hybrid ANN-MILP model for agile recovery production planning for PPE products under sharp demands

Today, supply chains (SCs) have been struggling with a new type of disruption known as outbreaks such as epidemics or pandemics. This type of disruption has features such as long-term and uncertain lifespan and leads to severe fluctuations in product demand. This paper elaborates on a hybrid approach based on artificial neural networks (ANN) and mathematical programming techniques to efficiently deal with this type of disruption in the production planning of SCs. In the first phase of the hybrid approach, a multi-layer perceptron ANN model with an optimised structure is developed to efficiently predict the demand and its peak points. In the second phase, the predicted demands are considered as input in a new multiobjective agile recovery production planning model. The proposed model minimises total costs and delivery times and maximises responsiveness. A real case study in Iran is conducted to verify and validate the proposed hybrid approach. The prediction error of the ANN method is about 1 percent. According to the predicted demand, optimal decisions are determined by the proposed model. The impact of under-estimation and over-estimation of demand is evaluated in terms of total costs, delivery time, responsiveness and shortage costs in the SCs.