Towards intelligent and sustainable production systems with a zero-defect manufacturing approach in an Industry4.0 context

Lindström, John; Lejon, Erik; Kyösti, Petter; Mecella, Massimo; Heutelbeck, Dominic; Hemmje, Matthias; Sjödahl, Mikael; Birk, Wolfgang; Gunnarsson, Bengt

doi:10.1016/j.procir.2019.03.218

Cited by 30 publications

(23 citation statements)

References 18 publications

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“…The ZDM management model is often associated with I4.0 for presenting largely compatible objectives and providing synergistic and complementary approaches [17,93,94]. Beyond the most well-known ZDM objectives, such as minimizing failures and defects and their early online detection, this management model shares with I4.0 the purpose of minimizing production costs and making production more efficient and sustainable by reducing the number of failures, breakdowns and defective parts [95].…”

Section: Discussionmentioning

confidence: 99%

“…Although the ZDM model does not fully appear in the literature about SCs and the MPS, it should be emphasized that the effects of meeting its objectives entail certain benefits for SCs whose discussion is of interest in the present research: (i) minimization of defects on line, regardless of them being failures, breakdowns or defective quality parts, is a factor that, in turn, favors the minimization of the disturbances that usually affect the system [7,15,16,96]. Thus it is beneficial action for the automation of processes; and (ii) the sustainability of SCs is favored by the minimization and mitigation of defects in two of its dimensions, economic and environmental, because achieving the ZDM strategy favors the reduction of costs, but also the reduction of emissions and energy, and raw material use [17,94]. It should also be noted that ZDM and resilience are related in the literature [97] as they share some significant points.…”

Section: Discussionmentioning

confidence: 99%

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Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Serrano-Ruiz

Mula

2021

Computers

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Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Serrano-Ruiz

Mula

2021

Computers

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“…This urgent situation required medical equipment manufacturers and resource suppliers to adjust their production plans rapidly and maintain high product quality. According to Lindström et al (2019), the generic strategy to apply is the following: first, the scheduling solution has to be produced (predictive); then, when an unexpected event occurs, rescheduling should be conducted to generate a new feasible solution (reactive).…”

Section: Introductionmentioning

confidence: 99%

“…A previous study (Lindström et al, 2019) has proved that rescheduling a production system is one of the main challenges to be addressed to achieve ZDM. This is because the number of events in production is significantly increased, due to the addition of ZDM-oriented events, and their counter-actions must be integrated within the existing production with as little performance loss as possible.…”

Section: Introductionmentioning

confidence: 99%

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

et al. 2021

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Nowadays, the manufacturing industry is constantly changing. Production systems must operate in a highly dynamic environment where unexpected events could occur and create disruption, making rescheduling inevitable for manufacturing companies. Rescheduling models are fundamental to the robustness of production processes. This paper proposes a model to address rescheduling caused by unexpected events, aiming to achieve the zero-defect manufacturing (ZDM) concept. The goal of the model is to incorporate traditional and ZDM–oriented events into one methodology to calculate when the next rescheduling will be performed to effectively react to unexpected events. The methodology relies on the definition of two key time parameters for each event type: event response time (RT) and event delay response time (DRT). Based on these parameters, an event management algorithm is designed to identify the optimal rescheduling solution. The DRT parameter is calculated based on a multi-parametric dynamic formula to capture the dynamics of production. Moreover, ANOM, and ANOVA methods are used to analyse the behaviour of the developed method and to assess the level of robustness of the proposed approach. Finally, a case study based on real production scenarios is conducted, a series of simulation experiments are performed, and comparisons with other rescheduling policies are presented. The results demonstrate the effectiveness of the proposed event management algorithm for managing rescheduling.

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“…As stated in [8], the reason for combining and integrating the seven areas is that they together can achieve a better result. However, sometimes the issues may not be found, since all parameters and activities are not measured or monitored.…”

Section: Introductionmentioning

confidence: 99%

An Initial Model for Zero Defect Manufacturing

Lindström¹,

Kyösti

Birk

et al. 2020

Applied Sciences

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This paper investigates an initial model for Zero Defect Manufacturing (ZDM) using a cost function where the operation and condition of a production process are reflected, and the quality of the output/product and the production process (as well as safety aspects) can be considered. The outset of the study is based on empirical data collected from five manufacturing companies, and proposes an initial model for ZDM with an Industry 4.0 perspective. The initial ZDM model has a generic setup for a real-life system and its replication as a digital twin using system models based on a representation of a generic production process with its connected control system, and potential interconnections between unit processes. It is based on concepts from system theory of dynamic systems and principles from condition monitoring and fault detection. In that way the model is deemed as highly generalizable for manufacturing and process industry companies as well as for some critical infrastructures with production and distribution systems. The proposed model with its cost function setup is analyzed and discussed in the context of ZDM. It is concluded that production processes in the manufacturing and process industry can be made more intelligent and interoperable using this approach. Improved sustainability, competitiveness, efficiency and profitability of companies are foreseen welcomed secondary effects. Finally, the proposed ZDM model further develops the ZDM by adding to it a systematic approach based on a solid mathematical foundation.

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Towards intelligent and sustainable production systems with a zero-defect manufacturing approach in an Industry4.0 context

Cited by 30 publications

References 18 publications

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

An Initial Model for Zero Defect Manufacturing

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