Product Development Cycle Research Articles

ФОРМУВАННЯ ПРОДУКТОВОЇ СТРАТЕГІЇ ЯК ГОЛОВНОГО ЕЛЕМЕНТА МАРКЕТИНГОВОЇ ТОВАРНОЇ ПОЛІТИКИ

Introduction. Today, when the target market changes, competition increases or the marketing environment changes, the nature and pace of product development must also change. Different markets and competitive situations require different product development cycle lengths, degrees of standardization and support services. All of the above requires a rethinking of the role of marketing product policy, in particular marketing product strategy. The purpose of the article.To investigate the essence of the product strategy, the peculiarities of product marketing, to analyze the foreign experience of forming a product strategy and to build a model of the company's product marketing strategy. Metods. The use of the method of analysis and synthesis of foreign experience in the formation of product marketing made it possible to adapt the model of the company's product marketing strategy to domestic realities. Results A product strategy sets the direction for the business, unites everyone around common goals and reduces waste. Product marketers spend a lot of time solving problems – many of which could be avoided if product goals, priorities and strategies were clear and aligned. In order to better understand who should do what, a model of the company's marketing product strategy is proposed, which includes three components: a strategy for determining product activity (initial activity); product manufacturing strategy (input activity) and sales strategy (output activity). Conclusiоns The structure of product marketing and its functioning depends on the size of the enterprise, the markets in which it operates, as well as the skills and preferences of the people involved. However, in order to achieve success, it is necessary to realize three aspects: decide who and what should be done; understand who is ultimately responsible for the success of each product; a detailed understanding of existing and potential customers and markets. Knowing this helps ensure products are well positioned, customers want to buy them, and sales teams have what they need to sell.

Integrating Sustainability Goals into Innovation at Henkel

PRACTITIONER TAKEAWAYS To be meaningful, sustainability assessments need to account for the whole product life cycle. This requires strong partnerships with suppliers, customers, and end users, beginning already in the new product development cycle. Culture is an essential ingredient: We need to ensure that sustainability is not just a checklist but a core value for our employees and our business. Innovation and product management are both part of the product life cycle: How we assess sustainability impact for a new product in development needs to be consistent with how that product will be assessed once it is fully commercial. The methodology a company uses in its innovation process cannot be different from the one it uses in product line management.

Unified Platform for Automated Tests of Inverter-Based Resources with Harware-in-the-loop

Testing of inverter-based resources (IBRs) faces challenges in establishing standardized procedures across all product development stages. Most of the testing approaches use Hardware-in-the-Loop (HIL) during the early stages independently from the final product testing, which can lead to discrepant results. To solve these issues, this paper proposes a unified platform for automatically testing IBRs. It can be used during all product development stages, from pure simulation to Controller Hardware-in-the-Loop (CHIL) and finally on the complete commercial product, ensuring uniform test procedures with different test setups. A case study describing compliance tests of grid-connection codes of photovoltaic (PV) inverters are presented to demonstrate the proposed approach. Experimental tests were performed in accordance to Brazilian grid-connection codes in an ISO/IEC 17025 accredited laboratory. The results obtained with the proposed platform in CHIL and full-hardware showed high similarity with the ones obtained with the accredited laboratory, validating the proposed approach. In addition, the platform can be used to perform tests not specified in standards and to verify the performance of IBRs in operating conditions that are often difficult to be tested in laboratory. Ensuring consistent test procedures, the platform facilitates comparable results along all the testing stages, reducing the product development cycle.

COMPARING vaccine manufacturing technologies recombinant DNA vs in vitro transcribed (IVT) mRNA

Vaccine manufacturing fosters the prevention, control, and eradication of infectious diseases. Recombinant DNA and in vitro (IVT) mRNA vaccine manufacturing technologies were enforced to combat the recent pandemic. Despite the impact of these technologies, there exists no scientific announcement that compares them. Digital Shadows are employed in this study to simulate each technology, investigating root cause deviations, technical merits, and liabilities, evaluating cost scenarios. Under this lens we provide an unbiased, advanced comparative technoeconomic study, one that determines which of these manufacturing platforms are suited for the two types of vaccines considered (monoclonal antibodies or antigens). We find recombinant DNA technology to exhibit higher Profitability Index due to lower capital and starting material requirements, pertaining to lower Minimum Selling Price per Dose values, delivering products of established quality. However, the potency of the mRNA, the streamlined and scalable synthetic processes involved and the raw material availability, facilitate faster market penetration and product flexibility, constituting these vaccines preferable whenever short product development cycles become a necessity.

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.

Co-creating innovations with users: A systematic literature review and future research agenda for project management

This study aimed to systematically review the extensive literature on user innovation co-creation and connect the findings to the project management domain. It focused specifically on new product development, offering a domain-based systematic review of methods, tools, and user types involved in the co-creation process. Analyzing a total of 266 articles, the authors synthesize the types of users and methods discussed in the domain of user innovation, aligning them across the new product development cycle and specific phases of co-innovation. Additionally, the authors formulate research questions and propositions that may inspire the project management domain. This study provides insights into innovation-oriented, exploratory project management and enhances the configuration approach to project management. From a practical perspective, it provides a comprehensive overview of methods that can enrich a managerial toolkit for leading innovative projects.

Development of a Fast Running Equivalent Circuit Model with Thermal Predictions for Battery Management Applications

Equivalent circuit modelling (ECM) is a powerful tool to study the dynamic and non-linear characteristics of Li-ion cells and is widely used for the development of the battery management system (BMS) of electric vehicles. The dynamic parameters described by the ECM are used by the BMS to estimate the battery state of charge (SOC), which is crucial for efficient charging/discharging, range calculations, and the overall safe operation of electric vehicles. Typically, the ECM approach represents the dynamic characteristics of the battery in a mathematical form with a limited number of unknown parameters. Then, the parameters are calculated from voltage and current information of the lithium-ion cell obtained from controlled experiments. In the current work, a faster and simplified first-order resistance–capacitance (RC) equivalent circuit model was developed for a commercial cylindrical cell (LGM50 21700). An analytical solution was developed for the equivalent circuit model incorporating SOC and temperature-dependent RC parameters. The solution to the RC circuit model was derived using multiple expressions for different components like open circuit voltage (OCV), instantaneous resistance (R0), and diffusional parameters (R1 and C1) as a function of the SOC and operating temperature. The derived parameters were validated against the virtual HPPC test results of a validated physics-based electrochemical model for the voltage behavior. Using the developed RC circuit model, a polynomial expression is derived to estimate the temperature increase of the cell including both irreversible and reversible heat generation components. The temperature predicted by the proposed RC circuit model at different battery operating temperatures is in good agreement with the values obtained from the validated physics model. The developed method can find applications in (i) onboard energy management by the BMS and (ii) quicker evaluation of cell performance early in the product development cycle.

Efficiency and Cost-Effectiveness in Agile DevOps with Cloud Computing

Abstract This article explores how Agile methodology, DevOps principles, and Cloud Computing fit into the software industry. The synergy study focuses on Axes Software, the oldest logistics software company in Romania. The effects of the association of these technologies during the development cycle of a software product are explored. Both their impact in the operational activities and the transformation of the development mode, as well as the financial impact, are evaluated. In the first part, theoretical aspects of Agile and DevOps ways of working and cloud technologies are explored. The amplification of the potential of the association of the three concepts is examined. With the basis formed, the paper continues with a detailed case study of Axes Software. It looks at the adoption of new technologies and way of working. Challenges and successes encountered are also considered. Key performance indicators such as release frequency, bug incidence (dropped from 72 to 4), and development velocity are examined to validate the effectiveness of this integration. The financial aspects, including initial investment and savings, are also analysed, leading to an assessment of the return on investment. Notably, deployments of new software versions now take two days instead of eleven, while the market witnessed a 275% increase in clients preferring the company’s cloud-hosted solutions over on-premises options. Finally, the effects of the association for software developers are distinguished, emphasizing continuous learning, leadership vision and supporting digital transformation.

Integration of soft tooling by additive manufacturing in polymer profile extrusion process chain

Integrating additive manufacturing (AM) into polymer extrusion offers process chain flexibility and design freedom. It reduces the need for time-consuming iterations and trial-and-error in the die design process. Consequently, polymer AM of extrusion (i.e., soft tooling) allows for a shorter product development cycle and cost-effectiveness for small-scale production and highly customized products. In this study, carbon fibre (CF)-polyether-ether-ketone (PEEK) dies were manufactured using the fused filament fabrication (FFF) AM process, employing a streamlined die design achieved through freeform transition planes. Calibration slides were produced using masked stereolithography (MSLA), FFF, and conventional manufacturing techniques (i.e., machining) to preserve the final product’s cross-section during the cooling process. The dimensional and surface characteristics of these calibration slides were evaluated to assess the dimensional accuracy and surface topography of various materials and manufacturing processes. The dimensional evaluation reveals that MSLA-printed parts exhibit deviation from the nominal dimension closer to the conventionally manufactured part. The integrated soft tooling in the polymer extrusion line was tested with polypropylene (PP) and acrylonitrile butadiene styrene (ABS) as extruded materials. The surface topography of the CF-PEEK die displays distinctive ripple features resulting from the FFF process, identified by relatively flat peaks and deep valleys. The overall surface texture parameter values of CF-PEEK were higher due to the presence of deep valleys. Considering that the areas around the peaks interacted more with polymer molecules during the extrusion, the surface texture parameters of the extrudates were closer to the value observed in 90 µm region areas around the peaks. Notably, extrudates of AM calibration slides have lower surface texture parameters than extrudates of conventionally manufactured calibration slides, even though surface defects in the form of dimple sink marks were observed in extruded material of PP from extrudates using AM calibration slides.

Enhancing Injection Molding Optimization for SFRPs Through Multi‐Fidelity Data‐Driven Approaches Incorporating Prior Information in Limited Data Environments

AbstractInjection molding is one of the dominant methods for mass‐producing short fiber reinforced plastics renowned for their exceptional specific properties. In the utilization of such composite components, optimization of process parameters significantly influences material characteristics and part performance. However, in industrial practice, this process often relies on intuition and iterative experimentation. Prior studies have introduced data‐efficient optimization methods but faced limitations in adopting minor variations in the product development cycle. This study introduces a multi‐fidelity optimization framework aimed at efficiently addressing new problems by leveraging previously acquired knowledge from analogous domains, particularly accommodating alterations in material scenarios. Two data‐driven frameworks are explored: 1) Gaussian process‐based and 2) neural network‐based, each employing distinct information‐transferring techniques, hierarchical Kriging and transfer learning, respectively. Bayesian optimization of process parameters under limited data budget, which is typical in realistic industrial settings, is performed. The results highlight the efficiency of the proposed framework, demonstrating superior performance not only in data‐driven modeling but also in optimization efficiency compared to conventional single‐fidelity approaches. The Pearson correlation coefficient is utilized to assess the applicability of the multi‐fidelity framework in handling the inherent ambiguity of the similarity of problem scenarios. The proposed method is believed to be adaptable and versatile, offering potential application across various challenges in process optimization.

Boeing: Reclaiming the lost ground?

Boeing is a leading American aerospace and defense company that has a long history dating back to 1916. William Boeing, in Seattle, Washington, founded the company and focused initially on building seaplanes for commercial and military use. Throughout its history, Boeing has taken several risks that have helped to shape the company into the global powerhouse it is today. One of the earliest examples of this is the company's decision to shift its focus from building seaplanes to building land-based aircraft in the 1920s. This move was risky at the time, as the market for land-based aircraft was still relatively untested, but it ultimately paid off as the demand for air travel proliferated in the following years. Another significant risk that Boeing took was the development of the 747, which was the first wide-body commercial airliner. This massive project required a significant investment in time and money, which later proved to be a huge success and established Boeing as the leader in the commercial aircraft industry. Boeing has traditionally adopted this risky business strategy, popularly known as “bet the business strategy,” which is characterized by a high risk of product failure in every new product development cycle. So far, Boeing has successfully navigated each cycle, allowing the company to maintain its market leadership and generate good financial returns.

Research on and Application of Feature Recognition and Intelligent Retrieval Method for Multi-Component Alloy Powder Injection Molding Gear Based on Partition Templates

The forming process of multi-alloy gears by metal powder injection molding is tedious, and the current design process mainly depends on the experience of designers, which seriously affects the product development cycle and forming quality. In order to solve the problem of the gear feature expression being missing, which hinders the automatic retrieval of similar parts in the analogical design process, a feature recognition and intelligent retrieval method for a multi-alloy powder injection molding gear based on partition templates is proposed in this paper. The partition templates of the gear are defined, and gear digitization is completed by using the automatic recognition algorithm. Searching for similar gear parts in the knowledge base, designers can analogically design the forming process for new parts according to the mature process of the parts in the knowledge base. The automatic identification and intelligent retrieval system developed according to this method has been implemented in two MIM (metal injection molding) product manufacturing enterprises. Case studies and industrial applications have proved the effectiveness of the system, the efficiency of identification and retrieval has been improved by more than 97%, and the number of mold tests has been reduced by 60%.

Green Analytical HPLC Method Development of Acyclovir Loaded Floating Raft Formulation in Spiked Rabbit Plasma

Objective: The need of this investigation is to design eco-friendly, sensitive, and precise bioanalytical high-performance liquid chromatography (HPLC) techniques that leverage green analytical tools such as green analytical procedure index (GAPI), analytical eco scale (AES), and analytical greenness metric (AGREE) to analyze ACV-spiked rabbit plasma and ACV-loaded floating raft formulation (FRF) because The literature survey revealed insufficient data for the HPLC approach to FRF. Method: Methods involving plasma spikes mirroring the in-vivo models provide insights into potential real-life scenarios and the functionality of floating raft formulations loaded with ACV. The Design Expert tool optimized critical process parameters and identified significant factors influencing the chromatographic technique. Results: The CCD was used to optimize the flow rate (0.6 mL/min) and buffer solvent ratio (99:1) for separation. The C18 column (250 X 4.6 mm) was utilized to achieve the elution of ACV. The developed method was thoroughly validated through ICH guidelines. The ACV, lower limit of detection (LLoD), and lower limit of quantitation (LLoQ) retention times were established at 2.3 minutes, 15, and 20 ng/mL, respectively. Discussion: In summary, the study demonstrated the exceptional performance sensitivity, specificity, and robustness of the bioanalytical method. The method proved highly effective in quantifying ACV in plasma samples in terms of revalidatability, transferability, reproducibility, and the product development cycle. The developed method proves that using both design of experiments (DoE) and green analytical chemistry together creates a valid, eco-friendly, and reliable way to analyze the making of an ACV-loaded floating raft formulation.

PROCESS DEVELOPMENT IN MECHANICAL ENGINEERING: INNOVATIONS, CHALLENGES, AND OPPORTUNITIES

Process development in mechanical engineering encompasses a broad spectrum of innovations, challenges, and opportunities that drive advancements in various industries. This review delves into the multifaceted landscape of process development within mechanical engineering, highlighting key themes and trends. Innovations in process development are propelled by the continuous quest for efficiency, sustainability, and enhanced performance. Advances in additive manufacturing, automation, and digitalization have revolutionized traditional manufacturing processes, enabling the creation of complex geometries with unprecedented precision and speed. Additive manufacturing techniques such as 3D printing offer versatility in material selection and design freedom, facilitating rapid prototyping and customization while minimizing material wastage. Automation technologies, including robotics and AI-driven systems, optimize production workflows, augmenting productivity and reducing human error. Moreover, digital twins and simulation tools empower engineers to simulate and optimize processes virtually, accelerating product development cycles and minimizing costly experimentation. However, alongside these innovations, mechanical engineers face a myriad of challenges. The integration of new technologies often requires substantial investment in infrastructure and workforce training. Quality control and assurance remain critical concerns, particularly in additive manufacturing, where ensuring part integrity and repeatability is paramount. Furthermore, the sustainability implications of emerging processes must be carefully evaluated to mitigate environmental impact and resource depletion. Despite these challenges, process development in mechanical engineering presents abundant opportunities for growth and advancement. The rise of Industry 4.0 initiatives fosters collaboration between academia, industry, and government, driving research and development efforts towards sustainable and interconnected manufacturing ecosystems. The advent of advanced materials, including composites and biomaterials, unlocks new possibilities for lightweight structures, enhanced performance, and tailored functionalities. Additionally, the pursuit of circular economy principles promotes resource efficiency and waste reduction throughout the product lifecycle. In conclusion, process development in mechanical engineering is characterized by a dynamic interplay of innovation, challenges, and opportunities. By embracing emerging technologies, addressing key challenges, and leveraging collaborative partnerships, mechanical engineers can drive transformative change across industries, paving the way for a more efficient, sustainable, and interconnected future.
 Keywords: Process, Development, Mechanical, Engineering, Innovation, Review.

Package Assembly Design Kits - The Future of Advanced Package Design

HDFO design layout and system-level performance require a range of dynamic design rule definitions. Package Assembly Design Kits can capture and implement these design rule contraints. These PADKs offer a special type of implementation by validating the design of layout with a benchmark that supports the manufacturing and assembly processes performed by the OSATs. Partnering with an OSAT to utilize PADKs early in the life cycle of product development is critical to reducing the number of design layout spins sometimes required to meet manufacturing and assembly best practices. As the HDFO signal I/O counts increase proportionally with the package complexity, the only practical solution is to leverage the strength of a PADK to drive the package design layout from day one. Establishing a PADK into an existing desgin workflow does take some investment; howeverk the rewards can be exponential, with sequential or parallel projects commencing. Areas requiring an investment will depend on the current design environment and established workflows. The following are the four pillars that lead to a successful PADK implementation: high-end computer hardware, compatible software, advanced training, and a qualified support team. The scale of each will additionally depend on the scope of each package being developed. One especially positive feature of the elements that make up the package assembly design kit is that each item can be implemented into existing design flows. The impact on the design flow is minimal, but the results are immense. Pulling all the benefits together is like opening the doors into tomorrow, and they remain open with the potential and opportunities offered by the advancements in Package Assembly Design Kits.

Research on the Application of Digital Twins Technology in the R and D Stage of Automobiles Enterprises

As a pillar industry of the country, the digital transformation of the automobile industry is of great significance to optimize the upgrading of the automobile industry structure, promote the long-term development of automobile enterprises, and accelerate the construction of smart cities and smart transportation. However, for all automobile enterprises to promote the digital transformation of automobile industry, it is necessary to formulate a complete set of digital transformation plan for the whole life cycle of automobile product development, among which Digital twins technology can be used as the first choice of digital transformation plan. Only by promoting digital transformation can enterprises be competitive in the future development.

Tolerance Specifications Management Integrated into the Product Development Cycle

In tolerancing activities focusing on the allocation of geometrical tolerances, many critical issues originate from the non-optimal assignment of responsibilities among the organization units involved. This paper aims to depict relations between different tolerancing activities and relevant specifications, assigning them to the proper actor and, therefore, expanding the ISO 8015:2011 “responsibility principle”. A classification among tolerancing activities, specifications, and media is proposed; a horizontal hierarchical framework among functional, manufacturing, and verification specifications and a vertical hierarchical framework along the supply chain are discussed. Examples of both hierarchical structures are presented.

Process parametric optimization of fused deposition modeling for manufacturing of acrylonitrile butadiene styrene parts

Additive manufacturing has been a revolution in the last decade and has led to a number of product innovations. Rapid prototyping (RP) has significantly reduced the product development cycle time and RP techniques are now replacing many conventional plastic processing techniques where production volume is not an issue. In order to ascertain the utility of additive manufacturing techniques for the development of fully functional parts, it’s important to establish the optimal set of process parameters for achieving maximum mechanical performance. In the current experimental investigation, the dogbone-shaped parts of acrylonitrile butadiene styrene (ABS) were fabricated by the fused deposition modeling (FDM) process. The process parameters were optimized for dogbone-shaped specimens fabricated by the FDM process. Among the parameters, it has been found that orientation and infill density are the most dominant factors affecting the tensile strength of FDM parts printed with ABS material.

A Deep Learning-Based CAE Approach for Simulating 3D Vehicle Wheels Under Real-World Conditions

The implementation of deep learning (DL) in computer-aided engineering (CAE) can significantly improve the accuracy and efficiency of simulating 3D vehicle wheels under real-world conditions. While traditional CAE methods can be time-consuming and computationally expensive, DL can reduce simulation time and development cycles across all industries. This work explores the role of DL and AI in virtual manufacturing and CAE and investigates how they can be used to improve the accuracy and efficiency of simulations for 3D vehicle wheels. Deep learning models can learn the complex relationships between different wheel design parameters, such as tire load distribution, stress distribution, and fatigue life. Once trained, these models can be embedded into CAE software, allowing for faster and more accurate simulations of wheel performance. This interdisciplinary study uses various deep learning techniques, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and recurrent neural networks (RNNs), to create a more efficient and accurate relationship between CAD modeling and CAE simulation. The research aims to leverage the potential of deep learning models to automate 3D CAD design, accurately predict CAE results, and provide in-depth explanations and verifications. The benefits of this research are expected to extend to the automotive industry's pursuit of more robust and resilient wheel designs. By streamlining the product development process from conceptual design to engineering performance evaluation, this study has the potential to revolutionize the automotive industry's product development cycle.

Digital Twin and Generative AI for Product Development

Optimisation of the product design and development cycle is crucial for maintaining product quality and ensuring lower production costs. It is necessary to intervene at early stages to prevent future expenses. Leveraging rapid digitalisation to achieve the desired goals and bringing in ‘smart manufacturing’ will benefit industries with large-scale productions. This paper proposes a novel approach to enhance ‘design for manufacturability (DfM)’ paradigm. Integrating digital twins and generative artificial intelligence (AI), a software is proposed that not only simulates real-world environments for testing and visualisation of potential processes but also provides designs to optimise the manufacturing process, maintain cost, and enhance the product’s appeal to the target market. The proposed model uses sensors to replicate the product in a digital environment to run a simulation. Meanwhile, a generative AI model embedded in the software provides creative and effective solutions based on user requirements and market data. When incorporated into the product development cycle, this process will ensure cost efficiency and improve the time required to develop quality products, enabling quicker launches. Thus, combining these emerging technologies yields a powerful, innovative model that enhances design for manufacturability.