Impacto da flexibilidade da indústria 4.0 nos indicadores de desempenho de um sistema de produção puxado

Orientador: Prof. Dr. Fabiano Oscar DrozdaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa : Curitiba, 27/02/2019Inclui referências: p.108-115Área de concentração: Inovação em Projetos, Produtos e ProcessosResumo: O desenvolvimento do projeto alemão Indústria 4.0 marca a ascensão de um novo contexto na gestão de operações, no qual consumidores exigem a personalização de seus produtos e uma maior flexibilização da produção. O efeito da adoção dessas políticas no desempenho dos populares sistemas de controle de produção puxado (SCPP) não foi endereçado por pesquisas anteriores, até onde consta para o autor. O objetivo deste trabalho é analisar o impacto da flexibilidade atrelada à Indústria 4.0 em diferentes SCPP, por meio de um planejamento de experimentos em um modelo de sistema de produção. O modelo foi exposto a diferentes cenários de flexibilidade a partir da variação dos níveis de 2 fatores experimentais: quantidade de tipos de produtos e heterogeneidade de tempo de processamento entre os tipos de produtos. Foram investigados 3 SCPP: Sistema de Controle Kanban (SCK), Sistema de Controle Kanban Estendido com política de cartões dedicada (SCKE-D) e Sistema de Controle Kanban Estendido com política de cartões compartilhada (SCKE-C). Os parâmetros ajustáveis dos SCPP foram otimizados, em cada cenário experimentado, com o objetivo de minimizar o nível de estoques e obter um nível de serviço mínimo de 95%. Os resultados mostram que os desempenhos dos 3 SCPP sofrem efeito significativo devido ao incremento de flexibilidade. O SCKE-C obteve o melhor desempenho em todos os cenários analisados, sendo superior especialmente em condições de alta quantidade de tipos de produtos. Apesar do desempenho inferior nos outros cenários, o SCK obteve desempenho estreitamente superior ao SCKE-D em condições de baixa quantidade de tipos de produtos e alta heterogeneidade de tempo de processamento. Palavras-chave: Flexibilidade. Indústria 4.0. Simulação. Sistema de Controle de Produção Puxado.Abstract: The development of Industry 4.0 marks the rise of a new context in operations management, in which consumers demand customization of their products and greater flexibility in production. The effect of the adoption of these policies on the performance of the popular pull production control systems (PPCS) was not addressed by previous research, as far as the author is concerned. This work aims to evaluate the impact of Industry 4.0 flexibility in different types of PPCS, through simulation experiments performed in a production system model. The model was exposed to different flexibility scenarios by varying the levels of 2 experimental factors: quantity of product types and processing time heterogeneity among product types. 3 PPCS were investigated: Kanban Control System (KCS), Extended Kanban Control System with dedicated card policy (EKCS-D) and Extended Kanban Control System with shared card policy (EKCS-C). The adjustable parameters of the PPCS were optimized in each experiment scenario in order to minimize the inventory level and obtain a minimum service level of 95%. The results show that the performances of the 3 PPCS are significantly affected by increased flexibility levels. EKCS-C achieved the best performance in all the analyzed scenarios, being especially superior in environments exposed to a high quantity of product types. Despite the inferior performance in the other scenarios, KCS obtained a better performance than EKCS-D in conditions of low product types quantity and high processing time heterogeneity. Keywords: Flexibility. Industry 4.0. Simulation. Pull Production Control Syste

Robust production & inventory control systems for multi-product manufacturing flow lines

The production line of modern multi-product manufacturing with erratic demand profiles shows that the selection and implementation of appropriate production control strategy are an important challenge. Organisations that adopt pull-type production control strategies, such as Kanban control strategy, for multi-product production lines find that is necessary to plan high Kanban card allocations in order to maintain volume flexibility to manage demand variability. This can result in line congestion, long lead times and low throughput rate. A recently proposed shared Kanban allocation policy has the benefit of minimising inventories in the line by allocating Kanbans accordingly and therefore maintains volume flexibility. However, many pull production control strategies that have been shown to be successful in single product manufacturing environments, for instance Kanban, CONWIP and Basestock cannot operate the shared Kanban allocation policy naturally. This Thesis presents a practically applicable modification approach to enable pull production control strategies that are naturally unable to operate in a shared Kanban allocation policy mode to operate it. Furthermore, the approach enables the development of a new pull production control strategy referred to as Basestock Kanban CONWIP control strategy that has the capability to operate the shared Kanban allocation policy, minimising inventory and backlog while maintaining volume flexibility. To investigate the performance of the pull production control strategies and policies, discrete event simulation and evolutionary multi-objective optimisation approach were adopted to develop sets of non-dominated optimal solutions for the experiments. Nelson’s screening and selection procedure were used to select the best pull control strategy and Kanban allocation policy when robustness are not considered. Additionally, Latin hypercube sampling technique and stochastic dominance test were employed for selection of a superior policy and strategy under environmental and system variability. Under non-robust conditions (anticipated environmental and system variability), pull control strategies combined with the shared Kanban allocation policy outperforms pull control strategies combined with dedicated Kanban allocation policy. Conversely, pull control strategies combined with the dedicated Kanban allocation policy outperforms pull control strategies combined with shared Kanban allocation policy when the system is prone to environmental and system variabilities. Furthermore Basestock Kanban CONWIP control strategy outperforms the alternatives in both robust and non-robust conditions

Performance analysis and development of pull-type production control strategies for evolutionary optimisation of closed-loop supply chains

The objective of this thesis is to establish a Closed-Loop Supply Chain (CLSC) design that is analysed through a series of simulation models, aimed at defining the highest performing production control strategy, whilst considering multiple related variables on both the forward and reverse flow of materials in manufacturing environments. Due to its stochastic nature, the reverse logistics side of the CLSC represents an increased source of variance for the inventory management and control strategies as it implies the erratic supply of returned materials, in addition to the very random customer demand, hence with highly variable inputs on both sides of the productive system, intrinsically inherent to this line of research. To test the operational performance of several pull-type production control strategies, a simulation-based research method was designed. The strategies experimented were: Hybrid Extended Kanban CONWIP special case (HEKC-II), Hybrid Kanban CONWIP (HKC), Dynamic Allocation Hybrid Extended Kanban CONWIP special case (DNC HEKC-II) and Dynamic Allocation Hybrid Kanban CONWIP (DNC HKC). All were tested in scenarios with high and low processing time variability and with 90% returned products and 40% returns from an open market system, therefore totaling 16 simulation models. Multi-objective evolutionary algorithms were utilised to generate the Pareto-optimum performance frontier with the objective of simultaneously minimising both performance metrics: The overall average work in progress (WIP) and the average backlog queue length (BL) for the entire CLSC. Processes used in the recovery and recycling of end of life manufactured goods were examined. This research method structures leading factors towards improved economic viability and sustainability of technologies required for the effective implementation of inventory control strategies on highly complex closed-loop supply chains with the focus on the performance metrics and optimum utilisation of resources available for the industry. The dynamic allocation strategies proved significant performance improvement, shifting the entire Pareto frontier forward with major advances on both metrics. Furthermore, it happened on all scenarios tested. The modified HEKC-II, with an optimisable parameter that enables it to be overwritten in a way that it can match the well-established HKC, also performed as originally intended and had better results than HKC in some cases, especially with the higher variability level. It also provided grounds for the suggested improvements and flexibilisation of the HEKC strategy. A major contribution of this thesis was the successful implementation of another advanced control methodology, entitled here the Intelligent Self-Designing Production Control Strategy, which provided maximum control performance. It consisted essentially of DNC HEKC-II with the following modifications: I) Extensive increase of dynamically allocated authorisation cards; II) Further anticipation of the time to trigger the change in the number of cards according to the finished goods buffer level, plus an acceleration/deceleration factor of this change; III) The capability of downsizing itself to become similar to HKC in an optimisation process if diverse production system conditions and variability would require. It displayed a very significant shift of the performance frontier