{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T06:26:59Z","timestamp":1773728819520,"version":"3.50.1"},"reference-count":52,"publisher":"Emerald","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,17]]},"abstract":"\n Purpose<\/jats:title>\n The purpose of this paper is to propose a novel model, to forecast demand for a third-party service by using the Grey Systems Theory (GST) and Markov Chains, where its forecast error performance is evaluated through mean percentage error, mean absolute percentage error, where it exceed other models' performance accuracy such as autoregressive integrated moving average.<\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n The model performs data characterization to qualify the GM (1,1) model and then applies a Markov Chain transition probability matrix and the GM (1,1) to forecast a time series with high degree of vagueness and imprecision and by providing a forecast kernel range \u2297A\u02c6.<\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n The MCGM (1,1) model integrates the GST GM (1,1) and Markov Chains in a novel hybrid model, that reduces the mathematical calculation complexity while provides practical forecast performance that exceed or it is equally good as other traditional methods.<\/jats:p>\n <\/jats:sec>\n \n Research limitations\/implications<\/jats:title>\n The model outperforms other non-stationary models but does not incorporate multiple variables and requires additional mathematical treatment or combined methods, where its data is stationary, seasonal or negative.<\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n This model can provide an accurate forecast projection of supply chain demand, for instance the space required in a third-party logistics services provider in Tijuana Mexico, it can be used to forecast complex supply chain systems with minimum, incomplete or poor data, to solve several practical application problems to forecast demand and resources.<\/jats:p>\n <\/jats:sec>\n \n Social implications<\/jats:title>\n The novel MCGM (1,1) hybrid forecasting model combines multiple predictive approaches, allowing for greater accuracy and adaptability. Its implementation enhances decision-making in key sectors such as health, energy and manufacturing, optimizing resources and reducing costs. This drives economic growth, increases sustainability and improves the quality of life in society.<\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n There are no MCGM (1,1) works applied in supply chain and current works have not established the model characterization criteria. The result of this investigation represents a novel proposal to solve uncertain models with poor information and small amounts of data (&gt;4 records), with higher forecast accuracy.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/gs-08-2024-0095","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T19:30:58Z","timestamp":1766172658000},"page":"225-244","source":"Crossref","is-referenced-by-count":0,"title":["Application of a grey model MCGM (1,1) for demand forecasting in a third-party logistics providers in maquila industry in Mexico"],"prefix":"10.1108","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2662-0609","authenticated-orcid":true,"given":"Francisco","family":"Trejo","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad An\u00e1huac M\u00e9xico , ,","place":["M\u00e9xico, Mexico"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8368-3948","authenticated-orcid":true,"given":"Rafael","family":"Torres Escobar","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad An\u00e1huac M\u00e9xico , ,","place":["M\u00e9xico, Mexico"]}]}],"member":"140","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"issue":"2016","key":"2026031700414766000_ref001","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.eswa.2016.06.032","article-title":"Short-term freeway traffic parameter prediction: application of grey system theoryes models","volume":"62","author":"Bezuglov","year":"2016","journal-title":"Expert Systems with Applications"},{"issue":"2008","key":"2026031700414766000_ref002","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.chaos.2006.08.024","article-title":"Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate","volume":"37","author":"Chen","year":"2008","journal-title":"Chaos, Solitons and Fractals"},{"issue":"2021","key":"2026031700414766000_ref003","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/1755-1315\/772\/1\/012009","article-title":"Gray system prediction in the alpine\u2013himalayan earthquake zone","volume":"772","author":"Chen","year":"2021","journal-title":"Earth and Environmental Science"},{"issue":"17","key":"2026031700414766000_ref004","doi-asserted-by":"publisher","first-page":"11339","DOI":"10.1007\/s00521-020-05658-0","article-title":"A hybrid rolling grey framework for short time series modelling","volume":"33","author":"Cui","year":"2021","journal-title":"Neural Computing and Applications"},{"issue":"5","key":"2026031700414766000_ref005","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/s0167-6911(82)80025-x","article-title":"Control problems of grey systems","volume":"1","author":"Deng","year":"1982","journal-title":"Systems and Control Letters"},{"issue":"2023","key":"2026031700414766000_ref006","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.energy.2023.127664","article-title":"Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model","volume":"277","author":"Ding","year":"2023","journal-title":"Energy"},{"key":"2026031700414766000_ref007","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1155\/2018\/3869619","article-title":"Forecasting crude oil consumption in China using a grey prediction model with an optimal fractional-order accumulating operator","volume":"2018","author":"Duan","year":"2018","journal-title":"Hindawi Complexity"},{"issue":"2022","key":"2026031700414766000_ref008","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.renene.2021.09.072","article-title":"A novel method for carbon emission forecasting based on Gompertz's law and fractional grey model: evidence from American industrial sector","volume":"181","author":"Gao","year":"2022","journal-title":"Renewable Energy"},{"issue":"2019","key":"2026031700414766000_ref009","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.engappai.2019.08.018","article-title":"Hybrid structures in time series modeling and forecasting: a review","volume":"86","author":"Hajirahimi","year":"2019","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1749","key":"2026031700414766000_ref010","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/pr12081749","article-title":"AHybrid GreySystem model based on stacked long short-term memory layers and its application in energy consumption forecasting","volume":"12","author":"Hao","year":"2024","journal-title":"Processes"},{"key":"2026031700414766000_ref011","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1007\/11579427_58","article-title":"A Grey-Markov forecasting model for the electric power requirement in China","volume":"3789","author":"He","year":"2005","journal-title":"MICAI 2005. 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