The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Analysis of the maturity of process monitoring in manufacturing companies
Open accessAbstract
The economic progress of recent years has contributed to the fact that both the quality of products and services offered and ISO standardization have become priority criterion that determines the success of manufacturing enterprises. Therefore, the monitoring and supervision of processes carried out in manufacturing companies is a key issue. These aspects support the achievement of key economic and quality objectives. The paper presents the results of a study on manufacturing enterprises in the context of process monitoring maturity. The research objective of the study was to determine the level of maturity in the use of process monitoring techniques and methods in manufacturing enterprises. The subject of the research were the techniques and methods used by the surveyed enterprises in such areas as: production management, machinery park management, warehouse management, transport management, inventory and supply management and IT tools. In order to determine the level of maturity, the author’s model was used, according to which the level of maturity of a manufacturing enterprise in the area of process monitoring depends on the instrumentation that is used in it.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The implication of inappropriate metrics can frustrate performance management initiatives and demotivate employees (Czerwinska and Pacana, 2022).”
From Implementation and Benefits of the 5S Method in Improving Workplace Organisation – A Case Study (2024) · cites this paper as Czerwinska and Pacana, 2022
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maturity models in business process management
Röglinger, M., Pöppelbuß, J., & Becker, J. (2012) · Business Process Management Journal · doi:10.1108/14637151211225225 · Cited by 601
Predictive Process Monitoring Methods: Which One Suits Me Best?
Francescomarino, C. D., Ghidini, C., Maggi, F. M., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98648-7_27 · Cited by 180
Predictive Business Process Monitoring with Structured and Unstructured Data
Teinemaa, I., Dumas, M., Maggi, F. M., & Francescomarino, C. D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45348-4_23 · Cited by 108
Comprehensive improvement of the surface quality of the diesel engine piston
Pacana, A., Czerwińska, K., & Bednárová, L. (2019) · Hrčak Portal of scientific journals of Croatia (University Computing Centre) · Cited by 46
Self-citationImproving the quality level in the automotive industry
Pacana, A., & Czerwińska, K. (2020) · Production Engineering Archives · doi:10.30657/pea.2020.26.29 · Cited by 44
Self-citationQuality Management of Aluminum Pistons with the Use of Quality Control Points
Ostasz, G., Czerwińska, K., & Pacana, A. (2020) · Management Systems in Production Engineering · doi:10.2478/mspe-2020-0005 · Cited by 43
Self-citationManagement optimizing the costs and duration time of the process in the production system
Krynke, M. (2021) · Production Engineering Archives · doi:10.30657/pea.2021.27.21 · Cited by 32
Modern Methods For Quality Control And Improvement
Wadsworth, H. M., Stephens, K. S., & Godfrey, A. B. (1986) · Cited by 32
Visually Monitoring Multiple Perspectives of Business Process Compliance
Knuplesch, D., Reichert, M., & Kumar, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23063-4_19 · Cited by 25
Multi-perspective Business Process Monitoring
Jalali, A., & Johannesson, P. (2013) · Lecture notes in business information processing · doi:10.1007/978-3-642-38484-4_15 · Cited by 16
Research works printed but not linked: 5
- Borkowski, S., Ingaldi, M., Jagusiak-Kocik, M., 2014. Quality Analysis and Technological Portfolio in Production of the Metal Screws. METAL 2014: 23rd International Conference On Metallurgy And Materials, 1716–1722.
- Fertsch, M., (ed.) 2016. Elementy inżynierii logistycznej, Poznan: ILIM, 11-52.
- Pacana, A., Bednarova, L., Liberko, I., et al., 2014. Effect of selected production factors of the stretch film on its extensibility. Przemysł Chemiczny. 93(7), 1139-1140.
- Pacana, A., et al., 2016. Decision support in the production of packaging films by cost-quality analysis. Przemysł Chemiczny, 93(12), 2263-2264.
- Sage, A.P., Rouse, W.B., 2009. Handbook of Systems Engineering and Management. Wiley-Interscience, New Jersey.
Cited by
Koplin, J., & Michalski, R. (2024) · System Safety: Human - Technical Facility - Environment · doi:10.2478/czoto-2024-0009 · Cited by 1
Self-citation
Metadata
- Authors
- Czerwińska, K., & Pacana, A.
- Affiliations
- Rzeszów University of Technology (PL)
- Corresponding author
- Pacana, Andrzej
- Venue
- Production Engineering Archives
- Published
- 2022-08-05
- Volume
- 28
- Issue
- 3
- Pages
- 246-251
- ISSN
- 2353-5156 (ISSN-L)
- 2353-7779
- OpenAlex
- W4290081816
- Cited by
- 16
- References
- 18
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 3 · 2023: 6
- FWCI
- 2.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Production Engineering Archives (publishedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Management and Optimization Techniques
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Business, Management and Accounting › Strategy and Management › Management Systems and Quality Improvement
- Keywords
- process monitoring, maturity levels, manufacturing companies, production management, warehouse management, transport management, software tools, instrumentation, ISO standardization, quality management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
