Selection of Production Reliability Indicators for Project Simulation Model

Pusztai, László Péter; Nagy, Lajos; Budai, István

doi:10.3390/app12105012

Cited by 4 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper presents a case study where workshops produce the same product in smaller quantities (100 pieces), but with slight differences: different machines and human resources for different workshops, and due to these changes, with different reliability values. The implementation of reliability-based indicators to the Stochastic CPM method considering one workshop was discussed earlier in an article [41], and that method was complemented with a production scheduling approach, which is a critical point when more than one unit produces the product. For this, a linear programming optimization method has been applied to the cost of production as well as the cost of inventory holding.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Risk Management Framework for Industry 4.0 Environment

2023

Self Cite

View full text Add to dashboard Cite

In past decades, manufacturing companies have paid considerable attention to using their available resources in the most efficient way to satisfy customer demands. This endeavor is supported by many Industry 4.0 methods. One of these is called MES (Manufacturing Execution System), which is applied for monitoring and controlling manufacturing by recording and processing production-related data. This article presents a possible method of implementation of a risk-adjusted production schedule in a data-rich environment. The framework is based on production datasets of multiple workshops, which is followed by statistical analysis, and its results are used in stochastic network models. The outcome of the simulation is implemented in a production scheduling model to determine how to assign the production among workshops. After collecting the necessary data, the reliability indicator-based stochastic critical path method was applied in the case study. Two cases were presented based on the importance of inventory cost and two different scheduling results were created and presented. With the objective of the least inventory cost, the production was postponed to the latest time possible, which means that workshops had more time to finish their previous work on the first day due to the small production quantity. When the cost was not relevant, the production started on the first day of each workshop, and the production was completed before the deadline. These are optimal solutions, but alternative solutions can also be performed by the decision maker based on the results. The use of the modified stochastic critical path method and its analysis shed light on the deficiency of the production, which is a merit in the continuous improvement process and the estimation of the total project time.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The risk and uncertain values are then added into the simulated cycle times. A detailed explanation of the model can be found in the article by the authors in [41].…”

Section: Stochastic Modeling and Simulationmentioning

confidence: 99%

A Risk Management Framework for Industry 4.0 Environment

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Figure 4 shows the MCDM methodologies most frequently used in QFD. AHP methodology is seen as the most frequently used in QFD to weigh the customer importance through a pairwise comparison [8,37,[49][50][51][52][53][54][55][56][57]. Fuzzy logic theory has been used in QFD to rank customer requirements in many recent case studies [58][59][60][61][62].…”

Section: Mcdm Methodologies In Qfd Identifiedmentioning

confidence: 99%

Using Multi-Criteria Decision Making in Quality Function Deployment for Offshore Renewable Energies

García-Orozco,

Vargas-Gutiérrez,

Ordóñez-Sánchez

et al. 2023

Energies

View full text Add to dashboard Cite

Quality function deployment (QFD) is now used in various fields, such as product development, design, manufacturing, planning, and quality management services, as a planning tool to achieve customer requirements and needs while improving performance and sustainability concerns. This paper presents a systematic literature review of multi-criteria decision-making (MCDM) methodologies integrated into QFD over the last year. In 2022, 56 research papers on planning strategies, the supply chain, and product development using QFD were published. Other fields such as energy, academia, and environment have also shown considerable interest in the integration of MCDM methodologies in QFD to improve decision-making processes. This research shows that the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solutions (TOPSIS) methodologies are mainly used to rank customer requirements and weigh their importance in the house of quality (HoQ) structure. The use of fuzzy logic has been incorporated into the correlation matrix to evaluate the relationships between customer requirements and technical requirements. Methodologies such as decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps are implemented to deal with contradictions, and they have also been used to rank engineering characteristics. In the field of energy and renewable technologies, only few studies related to the integration of MCDM methodologies in QFD were found, but it is forecasted that their use will be used more often as they offer improvements and benefits in the ocean energy sector.

show abstract

“…Robustness can be seen as a performance indicator as it measures the ability of a solution to maintain its performance relatively unchanged when exposed to uncertain conditions [7]. More specific KPIs when considering reliability only can be found in [18].…”

Section: Schedule Robustness As Performance Indicatormentioning

confidence: 99%

Robustness Evaluation Process for Scheduling under Uncertainties

et al. 2023

View full text Add to dashboard Cite

Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that characterize the new architecture of production systems. In this context, schedules must take into account an increasingly disrupted environment while maintaining a good performance level. This paper contributes to the identified field of smart manufacturing scheduling by proposing a complete process for assessing the robustness of schedule solutions: i.e., its ability to resist to uncertainties. This process focuses on helping the decision maker in choosing the best scheduling strategy to be implemented. It aims at considering the impact of uncertainties on the robustness performance of predictive schedules. Moreover, it is assumed that data upcoming from connected workshops are available, such that uncertainties can be identified and modelled by stochastic variables This process is supported by stochastic timed automata for modelling these uncertainties. The proposed approach is thus based on Stochastic Discrete Event Systems models and model checking techniques defining a highly reusable and modular process. The solution process is illustrated on an academic example and its performance (generecity and scalability) are deeply evaluated using statistical analysis. The proposed application of the evaluation process is based on the technological opportunities offered by the Industry 4.0.

show abstract

Selection of Production Reliability Indicators for Project Simulation Model

Cited by 4 publications

References 42 publications

A Risk Management Framework for Industry 4.0 Environment

A Risk Management Framework for Industry 4.0 Environment

Using Multi-Criteria Decision Making in Quality Function Deployment for Offshore Renewable Energies

Robustness Evaluation Process for Scheduling under Uncertainties

Contact Info

Product

Resources

About