Organizational Maturity and its Impacts in Product Development

The objective of this investigation was: (i) to develop a model that supports sustainable product development, considering the quality aspect and the environmental impact in the product life cycle, and (ii) to establish a framework to select the proportion of the share of these aspects during product development decisions. This research concentrates on achieving products that meet customer demand and have environmentally friendly life cycles. It also supports the implementation of design activities at an early stage of product development, positioning the share of quality in relation to environmental impact. The model is based on creating hypothetical prototypes of current products, and this approach concentrated on aggregating the quality (customer satisfaction) with life cycle environmental impacts (as in ISO 14040). The model was developed in five main stages, including: (i) defining product prototypes according to the modifications of quality criteria most desired by customers, (ii) assessing the quality of prototypes according to the Q quality index, (iii) prospective assessment of the environmental impacts of the life cycles of prototypes according to the LCA environmental index, (iv) methodical integration of the above-mentioned indicators into one quality and environmental indicator QLCA, and (v) analysis of possible production solutions and setting the direction of product development, taking into account both quality and environmental aspects. This research was extended with a sensitivity analysis of the QLCA indicator, after which a framework for selecting the proportion of the Q and LCA indicator’s share in product development decisions was established. The originality of this research is the ability of the developed model to facilitate eco-innovative product design and improvements while also selecting the share of qualitative and environmental aspects needed to develop sustainable products. The results provide a dynamic and effective tool for manufacturing companies; mainly designers and managers during qualitative and environmental prototyping of products commonly used by customers. The model will provide support in predicting a product that will be manufactured that will be satisfactory for customers and environmentally friendly based on LCA.

Change decisions in product development projects

PurposeSupplier involvement in cost reduction efforts has been concentrated mainly in the product development phase of the life cycle of the product. Often this concentration on the early phases of the product life cycle is defended with referral to the 80/20 rule that says that 80 per cent of the manufacturing costs are determined or committed during product design and development. The purpose of this paper is to look for empirical evidence that supports this rule and discuss the limited literature on joint buyer/supplier cost reduction programmes beyond the product development stage of the product life cycle.Design/methodology/approachAn extensive literature survey on both the cost commitment rule and collaborative cost reduction programmes beyond the product development stage was conducted.FindingsIt was found that empirical evidence in the literature for the 80/20 rule on cost commitment in product design is only anecdotal. Even more surprisingly, compared to the literature on cost reduction in design phases, the literature on cost reduction efforts in later stages of the product life cycle is rather limited and usually ignores possibilities for supplier/buyer collaboration.Research limitations/implicationsFurther empirical research (suggestions provided) should assess whether the identified lack of evidence is due to a gap in the empirical literature or to lower degrees of cost commitment and attention to cost efforts beyond product design indeed existing in practice.Practical implicationsThis paper calls practitioners to revisit the cost commitment rule and check its existence in their specific contexts. As a result of this exercise, practitioners may want to consider implementing some of the techniques reviewed in the paper that can assist in collaborative cost reduction beyond the product development stage.Originality/valueGiven the amount of money involved, the general applicability of the cost reduction techniques to various industries and relationships with suppliers, the opportunities for huge savings and the fact that most firms usually have many products and/or technologies at maturity stage, my research makes an important contribution by identifying both a lack of empirical evidence in the literature on the notion that 80 per cent of the costs are committed in the design phase and the limited attention given in the literature to supplier/buyer cost reduction programmes in further stages of the product life cycle.

Product Life Cycle Design for Sustainable Value Creation: Methods of Sustainable Product Development in the Context of High Value Engineering

Reaching a higher level of sustainability of a products life cycle became a strategic goal for many companies over the past years. To achieve that higher level of sustainability, the transformation of linear product life cycles (cradle to grave) into circular life cycles (cradle to cradle) in the context of a Circular Economy (CE) is one promising approach. However, implementing a circular life cycle results in higher requirements in defining the best fitted strategy at the end of each product use stage depending on economic, environmental and social factors.In order to cope these increased requirements, the whole product life cycle has to become more transparent. This transparency can be reached by a holistic digital representation and virtual quality control of all life cycle stages. To implement a virtual quality control in each stage and enable the monitoring and controlling of the whole life cycle, the concept of Virtual Quality Gates can be extended. Virtual Quality Gates divide a defined manufacturing chain into different virtual, quality-relevant decision points to check the product quality and ensure a defined quality state before further manufacturing steps are approved. This concept can be transferred to each life cycle stage to ensure a holistic optimization of the products life cycle quality and an optimized selection of a strategy at the end of each use stage.This paper presents a framework for the implementation of virtual quality gates into product life cycles. These extended quality gates are defined as Life Cycle Gates, which are virtual decision points to optimize a product life cycle. The demand and applicability of the concept is shown for a mechanical recycling process of automotive parts.

In modern realities, it is necessary to increase the efficiency of production and use of high-tech products of industrial enterprises. This becomes possible thanks to the implementation of a cost management system for the full life cycle of high-tech products. This system allows for a significant reduction in both financial and material costs at all stages of the life cycle of high-tech products. At the same time, the main attention is paid to the cost of the full life cycle of the sample. The purpose of the publication is to develop conceptual approaches to the construction of cost management systems at the stages of the life cycle of high-tech industrial products to minimize total (total) costs. Based on the analysis of theoretical and methodological approaches in the field of life cycle management of high-tech products, as well as foreign experience in managing the cost of high-tech products at various stages of its life cycle, the authors have developed a conceptual model of a cost management system for the life cycle of high-tech products. The proposed concept will contribute to the reform of the management system of the full life cycle of high-tech products. This will solve problems in the field of providing financial and material resources for all stages of the life cycle of high-tech products, starting with its development and ending with disposal, as well as increase the efficiency of using the life cycle management system of high-tech products of industrial enterprises. The novelty of the presented results lies in the fact that the authors for the first time from the standpoint of systematic and integrated approaches describe conceptual approaches to building a cost management system for the full life cycle of high-tech products, identify the problems of managing the operating costs of high-tech products and show the tools to solve them. This will allow a systematic approach to managing the full costs of all stages of the life cycle of high-tech products in order to minimize them.

Thinking in terms of product life cycles is one of the challenges facing manufacturers today: Efforts to increase efficiency throughout the life cycle do not only mean extended responsibility of the parties concerned. Economically successful business areas can also be explored. Whether new service concepts are required, new regulations have been passed or consumer values are changing, the differences between business areas are disappearing. Life cycle management (LCM) considers the product life cycle as a whole and optimizes the interaction of product design, manufacturing and life cycle activities. The goal of this approach is to protect resources and maximize effectiveness by means of life cycle assessment, product data management, technical support and, last but not least, life cycle costing. This paper shows the existing approaches of LCM and discusses their prospects and further development.

Sustainability in Product Life Cycle Management (PLCM) has emerged as a crucial factor in enhancing environmental stewardship and driving long-term business success. This review examines best practices and innovations in sustainable PLCM, focusing on strategies that integrate sustainability from product conception through to disposal. Key areas of exploration include sustainable design principles, such as eco-design and life cycle assessment (LCA), which aim to minimize environmental impact during product development. The review also highlights innovative approaches to material sourcing, emphasizing the shift towards renewable and recycled materials to reduce the carbon footprint and resource dependency. Moreover, the study delves into sustainable manufacturing practices, including energy-efficient production processes, waste reduction techniques, and the use of renewable energy sources. Another critical aspect discussed is the role of circular economy models in extending product life spans through repair, reuse, and recycling initiatives. These models not only contribute to resource conservation but also open new avenues for value creation in the post-consumer phase of the product life cycle. The review underscores the importance of supply chain transparency and ethical sourcing, which ensure that sustainability is upheld across all stages of the product life cycle. The challenges associated with implementing sustainable PLCM, such as balancing cost with environmental benefits and navigating complex regulatory environments, are also addressed. In conclusion, the review identifies key success factors, including stakeholder engagement, technological innovation, and a strong commitment to sustainability at the organizational level. These elements are essential for companies seeking to lead in sustainability while maintaining competitive advantage in an increasingly eco-conscious market. Keywords: Sustainability, Product Life Cycle Management, Best Practices, Innovations, Eco-design, Circular Economy, Sustainable Manufacturing, Supply Chain Transparency, Life Cycle Assessment, Environmental Stewardship.

Sustainable fashion: Mapping waste streams and life cycle management

Affected by the Covid-19, the 2021 International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM 2021) is scheduled to be held online on November 26-28, 2021. The conference will focus on the themes of Artificial Intelligence, Intelligent Manufacturing, Intelligent Control System and Machine Learning. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence and Intelligent Manufacturing and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence and Intelligent Manufacturing and related areas.Committee, Committee, Conference Agenda, Keynote Speaker, conference paper Virtual Room 1, conference paper Virtual Room 1, conference paper Virtual Room 1, conference paper Virtual Room 1, conference paper Virtual Room 2, conference paper Virtual Room 2, conference paper Virtual Room 2, conference paper Virtual Room 2, conference paper Virtual Room 2 are available in the pdf.List of titles Committee, Conference Agenda, Keynote Speaker, Conference Paper Virtual Room 1, Conference Paper Virtual Room 2 are available in this Pdf.

The circular economy (CE) has emerged as a promising paradigm to address environmental challenges through resource efficiency, product life cycle transformation, and business model innovation. Yet, implementing circular business models remains challenging due to the complexity of coordinating stakeholders and managing large volumes of data, especially across the different life cycle phases. Artificial intelligence (AI) offers great potential to address these challenges. However, prior research has predominantly focused on a generic and undifferentiated application of AI within the CE, neglecting the complexity and diversity across life cycle phases. Our study seeks to address this. Drawing on the external enablement framework, we conduct a qualitative study comprising 57 semi‐structured interviews with domain experts and AI‐based ventures operating in the CE. In demonstrating how AI facilitates business model innovation by enabling navigation through circular product life cycles, our findings advance three key areas. We extend the external enablement framework by showing that external enablers can interact synergistically. Specifically, we develop a model that illustrates how the underlying dynamics of the CE and AI jointly enable business model innovation and subsequent new venture creation through complementary mechanisms, a dynamic we term dual enablement . We show how AI enhances life cycle efficiency, fosters systems interconnectivity, and supports holistic decision‐making, engendering two novel categories of business models, which we coin AI‐based business models for complexity navigation and dual enablement .

Additive manufacturing, characterized by its inherent layer by layer fabrication methodology has been coined by many as the latest revolution in the manufacturing industry. Due to its diversification of Materials, processes, system technology and applications, Additive Manufacturing has been synonymized with terminology such as Rapid prototyping, 3D printing, free-form fabrication, Additive Layer Manufacturing, etc. A huge media and public interest in the technology has led to an innovative attempt of exploring the technology for applications beyond the scope of the traditional engineering industry. Nevertheless, it is believed that a critical factor for the long-term success of Additive Manufacturing would be its ability to fulfill the requirements defined by the traditional manufacturing industry. A parallel development in market trends and product requirements has also lead to a wider scope of opportunities for Additive Manufacturing. The presented paper discusses some of the key challenges which are critical to ensure that Additive Manufacturing is truly accepted as a mainstream production technology in the industry. These challenges would highlight on various aspects of production such as product requirements, process management, data management, intellectual property, work flow management, quality assurance, resource planning, etc. In Addition, changing market trends such as product life cycle, mass customization, sustainability, environmental impact and localized production will form the foundation for the follow up discussion on the current limitations and the corresponding research opportunities. A discussion on ongoing research to address these challenges would include topics like process monitoring, design complexity, process standardization, multi-material and hybrid fabrication, new material development, etc.

Artificial Intelligence (AI) has emerged as a transformative force in the realm of industrial predictive maintenance (PdM), enabling businesses to anticipate equipment failures and optimize maintenance schedules. This paper examines the integration of AI technologies such as machine learning, deep learning, and IoT systems into predictive maintenance frameworks. Drawing on case studies and current implementations across various sectors, including manufacturing, steel production, and ICT infrastructure, the research examines key processes such as data acquisition, preprocessing, model development, and decision-making. It also evaluates the use of advanced sensors and wireless networks, the architecture of predictive models, and practical implementation strategies. Benefits, including reduced operational costs, minimized downtime, and enhanced safety, are discussed alongside challenges such as data quality, model accuracy, and organizational maturity. Ultimately, this paper underscores AI’s pivotal role in advancing Industry 4.0 by facilitating data-driven maintenance that is both intelligent and proactive. Keywords: Predictive Maintenance (PdM), Artificial Intelligence (AI), Industry 4.0, Machine Learning (ML), Internet of Things (IoT), Deep Learning, Remaining Useful Life (RUL), Condition Monitoring.

The aim of the article. In the current market conditions, the main objectives of the product policy of the enterprise are to manage the competitiveness of products, adjusting their life cycles and assortment. For this purpose, the commodity strategy of the enterprise which is directed on achievement by the enterprise of target positions in the market by means of the products should be developed. The product is the subject of many management decisions: it is created, brought to market with the support of various marketing activities, if necessary modified and, if economically feasible, withdrawn from production. The aim of the article is to form marketing strategies for industrial products on the market on the basis of the developed classification of innovative goods depending on the level of their efficiency at separate stages of the product life cycle. Analyses results. The article develops the classification of innovative industrial products depending on the level of their efficiency at the separate stages of the product life cycle. The authors formed the recommended marketing strategies based on the proposed classification of products, depending on the range of levels of efficiency in which the product is. The article also presents the practical use of the developed recommendations on the example of the life cycle of the innovative electric motor of PJSC «Electromotor», which confirms their practical significance and suitability, allowing industrial enterprises to effectively manage their range. The article also builds a graph-analytical model of comprehensive efficiency indicators of an innovative electric motor, which allows to clearly track their dynamics over the entire period of its life cycle. Conclusions and directions for further research. In market conditions, each enterprise must determine the directions of formation and implementation of marketing product policy, taking into account the characteristics of each product. Forming a marketing product policy, the company must work on updating the range and bringing new products to market, because the right strategy for product range management will provide the desired volume of sales and corresponding profits. Thus, due to the proposed classification of goods at different stages of the life cycle, depending on the level of efficiency in which the product is or will be, the company can develop specific marketing strategies and tactics, including research and development (R&D), development of production, selection of sales channels, improvement of forms and methods of advertising, price regulation, development of new ones, modernization of existing and removal of obsolete inefficient products, etc. The use of effective marketing solutions can affect the duration and course of the product life cycle and consequently to achieve maximum profits. Keywords: efficiency, innovative product, product life cycle, marketing strategy, assortment management.

Since the cycle of development of new products gets shorter and shorter, managing Product Life Cycle becomes more important and complex. The new generation products have more functionalities and sub-assemblies compared to the previous ones on applying enabling technologies. However, machine learning techniques have limitations in the initial phases of the development of new generation products as Middle-Of-Life (MOL) data is insufficient. Guided by this challenge, the study addresses predictive maintenance issues of new generation products and presents a conceptual model providing the main elements of a data sampling approach to overcome the lack of MOL data at the initial phases of predictive maintenance applications. Doing this, we aim at facilitating the information search relevant to the maintenance recommendation and providing an optimized maintenance management considering insufficient data of further functionalities of new generation products. In that regard, the implementation of the proposed approach could help manufacturing companies (i) address knowledge representation, exploitation, openness and diffusion in the maintenance domain, (ii) overcome insufficient MOL data using manufacturing intelligence, (iii) enable predictive maintenance strategies at the initial phase of application, and (iv) improve innovative maintenance plan using enabling technologies. Accordingly, the study paves the way for predictive manufacturing to early-predict equipment condition and make optimized recommendations for adjustments and maintenance to ensure normal operations.