On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG Market
Despite the growing popularity of machine learning (ML), such solutions are often incomprehensible to employees and difficult to control. Addressing this issue, we discuss some essential problems of explainable ML applications in the fast-moving consumer goods (FMCG) market. This research puts forwa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2025-03-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
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| Online Access: | https://doi.org/10.61822/amcs-2025-0002 |
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| author | Grzegorowski Marek Janusz Andrzej Marcinowski Łukasz Skowron Andrzej Ślęzak Dominik Śliwa Grzegorz |
| author_facet | Grzegorowski Marek Janusz Andrzej Marcinowski Łukasz Skowron Andrzej Ślęzak Dominik Śliwa Grzegorz |
| author_sort | Grzegorowski Marek |
| collection | DOAJ |
| description | Despite the growing popularity of machine learning (ML), such solutions are often incomprehensible to employees and difficult to control. Addressing this issue, we discuss some essential problems of explainable ML applications in the fast-moving consumer goods (FMCG) market. This research puts forward a new approach to effective supply management by utilizing rough sets (RST), distance-based clustering, and dimensionality reduction techniques. In the presented case study, we aim to reduce the work done by experts by applying a single delivery plan to many similar points of sale (PoS). We achieve this objective by clustering vending machines based on historical sales patterns. To verify the feasibility of such an approach, we performed a series of experiments related to demand prediction on two data representations with various clustering techniques. The conducted experiments confirmed that, without losing quality in terms of MAE and RMSE, we could operate on PoS in an aggregate manner, thus reducing the workload of preparing delivery plans. |
| format | Article |
| id | doaj-art-ebbb3ee236774b06b44e600d4a06fa36 |
| institution | OA Journals |
| issn | 2083-8492 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj-art-ebbb3ee236774b06b44e600d4a06fa362025-08-20T01:54:12ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922025-03-01351193110.61822/amcs-2025-0002On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG MarketGrzegorowski Marek0Janusz Andrzej1Marcinowski Łukasz2Skowron Andrzej3Ślęzak Dominik4Śliwa Grzegorz51Institute of InformaticsUniversity of WarsawBanacha 2, 02-097Warsaw, Poland1Institute of InformaticsUniversity of WarsawBanacha 2, 02-097Warsaw, Poland3FitFood Solskiego11/28, 31-216Cracow, Poland4Systems Research Institute Polish Academy of SciencesNewelska 6, 01-447Warsaw, Poland1Institute of InformaticsUniversity of WarsawBanacha 2, 02-097Warsaw, Poland3FitFood Solskiego11/28, 31-216Cracow, PolandDespite the growing popularity of machine learning (ML), such solutions are often incomprehensible to employees and difficult to control. Addressing this issue, we discuss some essential problems of explainable ML applications in the fast-moving consumer goods (FMCG) market. This research puts forward a new approach to effective supply management by utilizing rough sets (RST), distance-based clustering, and dimensionality reduction techniques. In the presented case study, we aim to reduce the work done by experts by applying a single delivery plan to many similar points of sale (PoS). We achieve this objective by clustering vending machines based on historical sales patterns. To verify the feasibility of such an approach, we performed a series of experiments related to demand prediction on two data representations with various clustering techniques. The conducted experiments confirmed that, without losing quality in terms of MAE and RMSE, we could operate on PoS in an aggregate manner, thus reducing the workload of preparing delivery plans.https://doi.org/10.61822/amcs-2025-0002rstclusteringpcaumapxaillmtrismfmcgsupply management |
| spellingShingle | Grzegorowski Marek Janusz Andrzej Marcinowski Łukasz Skowron Andrzej Ślęzak Dominik Śliwa Grzegorz On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG Market International Journal of Applied Mathematics and Computer Science rst clustering pca umap xai llm trism fmcg supply management |
| title | On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG Market |
| title_full | On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG Market |
| title_fullStr | On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG Market |
| title_full_unstemmed | On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG Market |
| title_short | On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG Market |
| title_sort | on explainability of cluster prototypes with rough sets a case study in the fmcg market |
| topic | rst clustering pca umap xai llm trism fmcg supply management |
| url | https://doi.org/10.61822/amcs-2025-0002 |
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