M. Doukas

Decentralized manufacturing systems review: challenges and outlook

During the last three decades, the economic landscape has abandoned its local characteristics and evolved into a global and highly competitive economy. The market demands toward high product variety, the low human labor costs in specific locations, the evolution of Information and Communication Technologies, and specific social and political forces are the principal reasons toward globalization. The main trend currently outlining the development of manufacturing paradigms is the ever-increasing tendency in the direction of decentralization of manufacturing functions toward decentralized entities. This has caused a fundamental reorganization process of the manufacturing organizations in order to cope with this trend. Several critical issues rise in the control and management of such organizations. These criticalities are further compounded by the need to achieve mass customization of industrial products, as this greatly complicates the manufacturing and supply activities. Moreover, the modalities for the configuration and implementation of each of the distributed manufacturing typologies are identified. The purpose of this paper is to specify the main trends, issues, and sensitive topics that characterize the behavior and performance of these production systems. Based on this review, a discussion over existing production concepts is performed.

Design of manufacturing networks for mass customisation using an intelligent search method

The increasing need towards higher product customisation in combination with demand volatility require efficient ways to design manufacturing network configurations. The vast number of alternative design configurations, however, affects production planners that cannot longer rely on experience in order to plan the network. This article presents a method for supporting decision-making in realistic manufacturing network design problems, which investigates the performance and viability of centralised and decentralised production networks under heavy product customisation. Simulation models of automotive networks are developed and their performance is evaluated. Two methods are used in the decision-making process, namely an exhaustive search and an intelligent search algorithm. Multiple conflicting user-defined criteria are used for the evaluation of the alternative manufacturing and transportation schemes, including lead time, production cost, flexibility, annual production volume and environmental impact. In addition, the performance of the intelligent search method is investigated using statistical design of experiments (SDoE). Moreover, a calibration procedure for the intelligent algorithm is presented. An assessment of the examined approaches, with respect to their responsiveness and suitability for highly customer-driven environments, is provided and can be used as a guideline for the manufacturing network planning. The proposed method is validated by utilising realistic data provided from a European automotive manufacturer.

Environmental Impact of Centralised and Decentralised Production Networks in the Era of Personalisation

The current trend of globalisation and decentralisation of the production activities has created a series of environment related issues. The increase of transportation distances, the escalated consumption of natural resources, and toxic emissions are among the generated challenges. Additionally, the manufacturing complexity, due to high product variety leads to increased energy consumption. Nevertheless, natural resources are limited and emission levels must be kept under the limits. This paper presents a methodology, implemented through a software tool, for the investigation of the environmental impact caused by centralised and decentralised manufacturing networks, under heavy product customisation. Simulation models of automotive manufacturing networks were developed, utilising real life industrial data, for the investigation of the impact of the production networks under highly diversified product demand, on environmental aspects. Multiple user-defined criteria have been used for the evaluation of the environmental footprint, including CO2 emissions and energy requirements in terms of depletion of natural resources. This paper aims at identifying optimal configurations of centralised and decentralised production networks, characterised by reduced energy requirements, low consumption of natural resources and reduced toxic emissions.

Smart mobile apps for supporting product design and decision-making in the era of mass customisation

Nowadays, manufacturing industries face the need to rapidly and effectively adapt to the fast changing market demands that are affected by globalisation, economic instability and customer needs towards higher product variety. Rigid centralised decision-making and IT infrastructures are no longer a viable solution for a company to withstand the globalised market pressure. Information on core activities of a company, such as the manufacturing network design, needs to be constantly available to managers and planners to increase awareness and supervision efficiency. Mobility and remote decision-making is steadily gaining ground as the standard practice in the inter-connected business world. Moreover, the personalisation of products tailored to the individual needs and preferences of the customers can now be achieved through mobile applications, namely apps. Yet, the establishment of mobile apps in the manufacturing domain is still premature. The work proposed in this article presents a set of mobile apps developed to support the customer integration in the product design phase and subsequently the design of the manufacturing network. The applicability of the developed mobile apps is demonstrated through a pilot case from the automotive sector, and specifically, from the customisation of accessories and car aesthetics.

Enhancing factory data integration through the development of an ontology: from the reference models reuse to the semantic conversion of the legacy models

Data integration is one of the most crucial challenges for current manufacturing companies. Indeed, while the extended use of software tools generate more and more data about products, processes, and production resources, still this huge amount of data is represented using different formats and non-aligned structures. This issue is worsened by the fact that data can be found scattered in not linked databases and hosted in mutually incompatible systems. The growing need to access these data and the knowledge that they encapsulate on a global view from different perspectives is addressed by various approaches in order to support the integration between involved systems. This paper deals with the applicability of Semantic Web technologies in industrial context to enhance semantic interoperability. In particular, it proposes a systematic approach to support the development of a semantic model, focusing on the combination of two main critical aspects: the reuse of existing reference models and the semantic migration of the existing legacy models. Even both the aspects have been separately studied in previous research works and even their overlap is recurring during the stage of ontology development, to the best of our knowledge the literature is missing studies covering the synergistic and automatic combination of these two aspects within a holistic approach. The application possibilities of the approach are also investigated within a real case study from a high-precision mould-making company; thus demonstrating its feasibility for use in complex manufacturing contexts.

Simulation-Based Design of Production Networks for Manufacturing of Personalised Products

This paper presents a method for the design of manufacturing networks focused on the production of personalised goods. The method, which is implemented to a software tool, comprises of a mechanism for the generation and evaluation of manufacturing network alternative configurations. An exhaustive search and an intelligent search algorithm are used, for the identification of efficient configurations. Multiple conflicting user-defined criteria are used in the evaluation, including cost, time, CO2 emissions, energy consumption and quality. Discrete Event Simulation models of manufacturing networks are simulated for the calculation of performance indicators of flexibility, throughput and work-in-process, and are used for assessing the performance of centralised and decentralised networks. The results obtained through the exhaustive and intelligent search methods are compared. The applicability of the method is tested on a real-life industrial pilot case utilising data from an automotive manufacturer.

A Knowledge-Based Decision-Making Framework for the Design of Manufacturing Networks for Custom-Made Products

Efficient design of manufacturing networks is paramount for a sustainable growth. The establishment of mass customization and the transition to personalization complicates design activities and leads to vast amounts of unexploited data. This research work aims to exploit existing knowledge for enhancing decision-making during the initial manufacturing networks design, which carry out custom orders of industrial equipment. A method developed into software is proposed, comprising a Genetic Algorithm with knowledge-enriched operators and an intelligent initialization algorithm that exploits existing planning knowledge. The validation of the method is performed using data from a high-precision mold-making manufacturer and its network of first-tier suppliers.

Design of Multi-Stage Manufacturing Networks for Personalized Products Using Metaheuristics

Manufacturers are nowadays highly affected by the ever-increasing number of product variants, under the product personalization trend. The large number of cooperating manufacturing network partners leads to enormous search spaces of alternative manufacturing network configurations. This obstructs effective decision-making towards configuring efficient network structures, a nonetheless crucial decision for a company. Exact methods guarantee that the identified solution is the optimum, with regards to the objectives set in the specified problem. However, in real life cases the magnitude of the solution space is such that these methods cannot be utilized due to computational constraints. For tackling such NP-hard problems, meta-heuristics can be utilized that provide a trade-off between the quality of solution and the computation time. This research work describes the modeling and solving of a manufacturing network design problem using the meta-heuristic methods of simulated annealing and tabu search. The quality of the results identified by these methods is compared with the results obtained from an intelligent search algorithm and an exhaustive enumerative method, which are implemented into a web-based platform for the design and planning of manufacturing networks. The approach is validated through its application to a real life case study with data acquired from the automotive industry.