A Value Modeling Approach for Product Development Processes and Supply Chain Management

Chapter 8 - Production planning and supply chain management under the conditions of Industry 4.0

Effective implementation of the new product development (NPD) process not only enables management to coordinate the efficient flow of new products, but will also assist to support ramp-up of various supply chain activities and other related activities supporting the commercialisation of the product. Thus, companies need to address all these issues in parallel to be successful and this requires some kind of integrative product development approach. The purpose of this research is to increase the understanding of how NPD is connected to supply chain management (SCM) by investigating how the NPD process is structured and executed in two international manufacturing companies seeking to realise an innovative, predictable and efficient product development. Several essential linkages between NPD and SCM have been derived from case study findings concerning important issues for successful product development.

While Industry 4.0 targets are theoretically set from various aspects, the approaches for covering some of its key areas are heterogeneous. The main pillars for Productive4.0 – a European research initiative with the industrial drive – are Digital Production, Product Lifecycle Management and Supply Chain Management. These are also key areas for Industry 4.0, and so far handled with heterogeneous approaches, toolsets – and success, indeed.This paper has two aims. First, to provide an overview of the state-of-the-art concepts regarding these three areas, filtered through the actual industrial requirements. Second, to survey the Arrowhead Framework concerning these areas, and suggest a homogeneous approach that can support not only the main vision of these three domains but the actual design-time and operational-time tasks needed to make the vision of Industry 4.0 reality.

The advent of the fourth industrial revolution (industry 4.0) has sparked a transformation in manufacturing through the integration of deep learning (DL) and big data technologies. These innovations have enhanced the ability of manufacturers to process and analyze vast amount of data, providing insights that improve decision-making, efficiency, and overall productivity. Deep learning, a subset of artificial intelligence (AI), offers advanced capabilities in data pattern recognition and predictive modeling, while big data facilitates the management of large and complex datasets from various sources. However, literature on the integration of deep learning, big data and industry 4.0 is still limited in the manufacturing context. This paper provides a detailed overview of big data, deep learning and industry 4.0 in manufacturing. It explores the applications of deep learning and big data in manufacturing, highlighting their role in optimizing production processes, predictive maintenance, quality control, and supply chain management. Furthermore, the paper addresses the key challenges and solutions associated with the integration of these technologies, such as data privacy, security, computational complexity, and the need for skilled labor. KEYWORDS: Deep learning, Industry 4.0, Supply chain management, Big data, Predictive maintenance, Artificial intelligence, ERP systems.

With the increasing frequency of marine development activities, the market demand for underwater equipment continues to grow, and the enhancement of its product competitiveness has become the key to the development of the industry. This paper first analyzes the supply chain structure of underwater equipment products. It identifies the supply chain risk factors in five dimensions: external environment, planning process, procurement process, manufacturing process, and delivery process. Secondly, taking Yunnan WW Company as an example, a sharing factor evaluation index system for the underwater equipment product chain and supply chain was constructed, and the index risk evaluation value was calculated using the Fuzzy Comprehensive Evaluation Method. The risk level of the underwater product supply chain management process was obtained, and the direction for enterprises to optimize supply chain management was determined. Finally, suggestions are put forward for the optimization and improvement of the underwater product supply chain management, and the implementation effect of procurement process optimization countermeasures in supply chain management is verified, proving that optimizing the underwater product supply chain management can further enhance the company's competitiveness and better face opportunities and challenges.

This research work is focused towards identification of application of components of industry 4.0 such as Internet of Things (IoT), machine learning, big data, and blockchain to supply chain management (SCM) to address the current research gaps as well as to identify the potential of industry 4.0 in developing real time decision making smart production process. In this paper a discussion is presented about problems that are being faced in existing systems such as system complexity, scalability issues, operational cost and difficulty in decision making in dynamic condition of demand. This paper also presented the impacts of components of industry 4.0 in SCM. Along with that, a six-layer architecture integrating these components are also presented for smart production planning and decision making.

The adoption of the lean concept as a company strategy to creates competitive advantages and provides important savings opportunities has been increasing recently. Its application has been extended from manufacturing to almost all types of industry including coal mining under the realization of the need for productivity improvement and the need for efficient operation. However, published information and articles showing the real application of lean in the mining sectors are still scarce. The objective of this paper is to show the application of lean production method in coal hauling operations in one of coal mining company in Indonesia. The lean method with Plan, Do, Check, Act (PDCA) cycle has been utilized to identify, analyze and improve productivity. The main result of this study reveals that implementing lean increase the productivity of coal hauling operations from 115 to 129 tons per hour. Statistical test using two sample t-test confirmed the significance of the result. This paper demonstrates lean application in coal hauling operations and bring significant result.

The integration of artificial intelligence (AI) in fashion retail is reshaping industry dynamics and enhancing efficiency and innovation across product development, marketing, and supply chain management. This study examines how different capabilities of AI--operational, cognitive, and affective--influence employee creativity within the U.S. fashion retail industry. Guided by Dynamic Capability Theory, Social Cognitive Theory, and Conservation of Resources Theory, the study explores proactive and responsive creativity as critical outcomes of AI integration. Using a quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 302 fashion retail employees were analyzed. The results reveal that cognitive and affective AI capabilities significantly enhance both proactive and responsive creativity, with affective capabilities exhibiting the strongest influence. Operational AI capabilities, however, show no significant positive impact on employees' creative responses. Furthermore, creative self-efficacy positively moderates specific AI-creativity relationships, while AI-enhanced job replacement anxiety did not have a significant moderating effect. The findings offer key managerial implications, highlighting the critical role of selecting appropriate AI capabilities and developing formal training initiatives to maximize creative collaboration between employees and AI in fashion retail organizations.

<i>Decision Sciences Journal</i> – Call for Papers <b>Focused Issue on “The Interaction of Product Modularity and Supply Chain Management”</b>

This paper documents the fact that the research in the two areas of holonic manufacturing systems and multi-agent systems contains substantial overlap. Many technologies and results achieved in the multi-agent area can be applied with advantage in the holonic field. This is especially true when talking about standardisation, which should enable interoperability of systems. The FIPA international consortium has introduced a systematic approach to development and maintenance of specifications and standards for the multi-agent domain. The principles of the FIPA Abstract Architecture are briefly described. Two examples of applications of FIPA standards in different areas within the scope of holonic systems (control, and production planning and supply chain management) are presented. FIPA standards have been recognised as a suitable candidate for ensuring complete interoperability in the holonic field.

Beside single cases and research on very particular issues, cultural aspects are mostly neglected within the planning process as well as during the operation of global supply chains and networks. In this article a conceptual framework is developed that integrates cultural issues into the research and practice of supply chain planning and operating. The framework is based on theories of culture, psychology, decision processes and on concepts of SCM and networks. The ideas are supplemented with empirical evidence from case studies. This framework presents an extended view on supply chains and networks and contributes to the field of SCM research.

<i>Decision Sciences Journal</i> – Call for Papers Focused Issue on “The Interaction of Product Modularity and Supply Chain Management”

A Research Agenda for the Nexus of Product Development and Supply Chain Management Processes

Contributors

PurposeProduct portfolio management is a strategic concern. Product portfolio management includes decisions associated with adding new products, maintaining existing products and deleting or phasing out problematic products. This paper first introduces a framework to identify risks of product deletion along supply chain activities. It utilizes failure mode and effects analysis (FMEA) to identify, analyse and evaluate product deletion risks on supply chains and proposes managerial implications for risk management in dynamic business scenarios. It is meant to build upon and address a gap in the product deletion and supply chain linkage literature.Design/methodology/approachFMEA is utilized in this study to structure and manage potential risks in product deletion decision-making on supply chains. FMEA is based on an analysis of severity, occurrence and detectability of failure modes. FMEA provides methods to help identify managerial preventive solutions to avoid and mitigate risk consequences of such decisions.FindingsTen top product deletion risks are identified in this study; discussions of their negative impact on supply chain performance, and possible managerial recommendations are followed for risk control, monitor and elimination.Practical implicationsFindings help managers to predict, avoid and mitigate risk consequences of product deletion decisions; especially those related to the supply chain. A framework to structure various risks of product deletion in the supply chain can be useful to both practitioners and researchers.Originality/valueThis study advances product portfolio management through enhanced understanding of product deletion decision-making in organizations; and especially contributes to a broader investigation of such decisions in supply chain management. It also structures the factors that play a role in identifying risks.