Foivos Psarommatis

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.

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.

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.