Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems

Ferrer, Borja Ramis; Muhammad, Usman; Mohammed, Wael M.; Lastra, José L. Martínez

doi:10.3390/machines6030039

Cited by 32 publications

(14 citation statements)

References 31 publications

(30 reference statements)

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“…Economic Success Criteria: It is best practice in business monitoring [46] and manufacturing [47] to add an economic success criterion in the form of a Key Performance Indicator (KPI) to the project. A KPI is an economic measure for the current and future business relevance of an application and requires a precise definition ( [48], ISO 22400).…”

Section: Resource Demandmentioning

confidence: 99%

Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Studer

Bui

Drescher

et al. 2021

MAKE

114

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Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.

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Section: Resource Demandmentioning

confidence: 99%

Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Studer

Bui

Drescher

et al. 2021

MAKE

114

View full text Add to dashboard Cite

show abstract

“…Due to the high competitiveness environment in which the industry finds itself in, the interest in techniques and tools that enable better control and supervision of the companies' performance has been steadily growing [24]. The research for techniques and tools for controlling and visualizing performance allows companies to identify gaps in their processes, allowing an improved management of their resources, enhancing possible improvements in them [11].…”

Section: Performance Evaluation and Performance Measuresmentioning

confidence: 99%

Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry

2021

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In the last few decades, the fast technological development has caused high competitiveness among companies, encouraging a pursuit for strategies that allow them to gain competitive advantage, such as the monitoring of performance by using key performance indicators (KPIs). However, its selection process is complex since there are several KPIs available to evaluate performance and different relationships between them. To overcome this challenge, the use of a multiple criteria decision-making model (MCDM) was proposed, namely the analytic network process (ANP) through which a reduced number of them are prioritized. To identify which KPIs are suitable for the press cast and die manufacturing industry, a literature review was made, and 58 unique KPIs were identified. Thus, to validate the proposed methodology, a case study was carried out in an automotive press molding industry. With the implementation of the proposed ANP model it was possible to identify 9 KPIs that ensure the correct molding process monitoring, while being aligned with the Balanced Scorecard criteria. The results show that the proposed model is suitable for selecting KPIs for the molding industry.

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“…In the published works of research results reasonably: -the need to understand the order and algorithm of calculation, the role and value of a specific KPI in evaluating the processes and results of the opera tion of the enterprise [2]. That is why for inclusion in the KPI system it was proposed [3,4] to test each individual analytical indicator; -the expediency of a significant amount of contextual information to ensure the usefulness of each KPI and causativeness with management functions (planning, organizing, controlling, looking, regulating) [5]; -the feasibility of visualizing the KPI results [2]. In [6], it was established that it is appropriate to combine actual state graphs with trend graphs, which will allow the most profitable identify deviations and track the gap.…”

Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

“…At the same time, it was noted that trend charts are the best way to control the unstable deviation of the actual values of the KPI from target/planned. The position [2] on the complexity of the task of for ming a common KPI system for business entities of various sectors of the economy deserves support. Correct is the conclusion [7] about the feasibility of developing a unique KPI system by adapting the indicators recommended in ISO 22400 [8,9] with the needs of a particular enter prise.…”

Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

Formation of the key performance indicators system for lean management of enterprises

Kolos

2018

TAPR

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Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems

Cited by 32 publications

References 31 publications

Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry

Formation of the key performance indicators system for lean management of enterprises

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