Structuring postponement strategies in the supply chain by analytical modeling

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Design and Planning of Manufacturing Networks for Mass Customisation and Personalisation: Challenges and Outlook

AbstractManufacturers and service providers are called to design, plan and operate globalized manufacturing networks, addressing to challenges such as ever-decreasing lifecycles and increased product complexity. These factors, caused primarily by mass customisation and demand volatility, generate a number of issues related to the design and planning of manufacturing systems and networks, which are not holistically tackled in industrial and academic practices. The mapping of production performance requirements to process and production planning requires automated closed-loop control systems, which current systems fail to deliver. Technology-based business approaches are an enabler for increased enterprise performance. Towards that end, the issues discussed in this paper focus on challenges in the design and planning of manufacturing networks in a mass customization and personalization landscape. The development of methods and tools for supporting the dynamic configuration and optimal routing of manufacturing networks and facilities under cost, time, complexity and environmental constraints to support product-service personalization are promoted

Study and Prospects: Adaptive Planning and Control of Supply Chain in One-of-a-kind Production

Based on the research project titled “Adaptive Planning and Control of Supply Chain in One-of-a-kind Production”, the research group performed a systematic review of supply chain integration, risk prediction and control and trace ability. Studies of a computer-aided and integrated production system for cost-effective OKP systemare included. Our efforts relevant to integration of supply chain in OKP, modeling &control of ripple effects in OKP supply chain and the trace ability of the OKP supply chain are introduced in this paper

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Variety Steering Concept for Mass Customization

In this paper we make the distinction between subjective and objective customer needs. The subjective needs are the individually realized and articulated requirements, whereas the objective needs are the real ones perceived by a fictive neutral perspective. We show that variety in mass customization has to be orientated on the objective needs. In order to help mass customizers better evaluate the degree to which they can fulfill the objective needs as well as their internal complexity level we have developed a key metrics system model. We also present a conceptual application showing how to use this model to support decision making related to the introduction or reduction of product variants.Variety Management; Complexity; Production/Operations Management

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Quantitative Research on Hotspots and Frontiers of Mass Customization Research

Mass customization (MC) has become an important means for enterprises to gain competitiveness, but there is a lack of systematic analysis of recent research on MC. The study conducts quantitative research on 1018 valid documents in the Web of Science database in the past ten years using bibliometric analysis and the CiteSpace software to understand MC's research hotspots and frontiers. Firstly, the data results show that keywords such as mass customization and design appear the most frequently, representing the high attention paid to them by academia. Secondly, keywords such as big data and information technology have the highest centrality value, indicating their relatively important position in MC. Finally, keywords such as industry 4.0, smart manufacturing and 3d printing are the keywords of recent MC research. This study will provide some reference for researchers to comprehensively understand the hotspots and frontiers of MC research

A Framework of Implementation of Collaborative Product Service in Virtual Enterprise

To satisfy new market requirements, manufacturing industry has shifted from mass production that takes advantage of the scale of production, to quality management that optimizes the internal enterprise functions, to e-manufacturing era that leverage intellectual capital via collaborative innovation. In the same time, the product itself is becoming the most important asset for sustainable business success. Consequently, the effectiveness, efficiency and innovation for the development of the product across the whole product lifecycle are becoming key business factors for manufacturing enterprise to obtain competitive advantages for survival. To tackle such challenges, a new business model called collaborative product services in virtual enterprise is proposed in this paper. The architecture of this new model is developed based on the framework and the application of web service and process management for collaboration product service in virtual enterprise. Indeed, it is hoped that this architecture will lay the foundation for further research and development of effective product lifecycle management in virtually collaborative enterprise environment.Singapore-MIT Alliance (SMA

A review of the meanings and the implications of the Industry 4.0 concept

The global industrial landscape has changed deeply in the last few years due to successive technological developments and innovations in manufacturing processes. The Industry 4.0 concept has emerged and the academic literature has paid an increased attention to this topic, which remains non-consensual or ill defined. In this research, a literature review is made to understand this concept in its technological dimension, and to comprehend its impacts. This new industrial paradigm brings together the digital and physical worlds through the Cyber-Physical Systems enhanced by Internet of Things and it is expected that this novel has consequences on industry, markets and economy, improving production processes and increasing productivity, affecting the whole product lifecycle, creating new business models, changing the work environment and restructuring the labor market. Therefore, this paper focuses on Industry 4.0 concept and contributes for its clarification and further understanding about the importance and implications of this complex technological system.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio