Browsing by Author "Kabadurmus, Ozgur"

Now showing 1 - 19 of 19

Citation - WoS: 36
Citation - Scopus: 51
A big data analytics based methodology for strategic decision making
(EMERALD GROUP PUBLISHING LTD, 2020) Murat Ozemre; Ozgur Kabadurmus; Kabadurmus, Ozgur; Ozemre, Murat
Purpose The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology. Design/methodology/approach In this study two different machine learning algorithms Random Forest (RF) and Artificial Neural Networks (ANN) are employed to forecast export volumes using an extensive amount of open trade data. The forecasted values are included in the Boston Consulting Group (BCG) Matrix to conduct strategic market analysis. Findings The proposed methodology is validated using a hypothetical case study of a Chinese company exporting refrigerators and freezers. The results show that the proposed methodology makes accurate trade forecasts and helps to conduct strategic market analysis effectively. Also the RF performs better than the ANN in terms of forecast accuracy. Research limitations/implications This study presents only one case study to test the proposed methodology. In future studies the validity of the proposed method can be further generalized in different product groups and countries. Practical implications In today's highly competitive business environment an effective strategic market analysis requires importers or exporters to make better predictions and strategic decisions. Using the proposed BDA based methodology companies can effectively identify new business opportunities and adjust their strategic decisions accordingly. Originality/value This is the first study to present a holistic methodology for strategic market analysis using BDA. The proposed methodology accurately forecasts international trade volumes and facilitates the strategic decision-making process by providing future insights into global markets.
Citation - WoS: 35
Citation - Scopus: 25
A circular food supply chain network model to reduce food waste
(Springer, 2022) Ozgur Kabadurmus; Yigit Kazancoglu; Damla Yüksel; Melisa Ozbiltekin-Pala; Yüksel, Damla; Pala, Melisa Özbiltekin; Kabadurmus, Ozgur; Kazançoğlu, Yiğit
Food loss and waste (FLW) is a growing global problem throughout the world. The rapid increase in food waste and deficiencies in treatment processes have led to greater harm to the environment. A circular food supply chain (FSC) is now an essential means of encouraging circular economy. Proper food waste treatment and recycling operations can not only benefit the environment but these wastes can also be used as raw material for production in a circular economy. In this study a circular food supply chain network model is designed to reduce the food waste generated in the circular food supply chain systems of municipalities. Then a mixed-integer linear programming model is generated to model the proposed circular food supply chain network model. The MILP model is a network model aimed at reducing the food waste generated. To do so two objectives are considered: the overall cost of the network is minimized and the amount of distributed food waste from the generation nodes to the end nodes is maximized. Due to the bi-objective nature of the proposed mathematical model the Improved Augmented Epsilon Constraint method (AUGMECON2) is implemented to solve the problem optimally. To illustrate the applicability and effectiveness of the proposed mathematical model two real-life case studies were carried out in Izmir the third largest city in Turkey. The computational results demonstrate that the proposed model is beneficial for both small and large municipalities since it provides the Pareto-optimal set where the total amount of distributed food waste is maximized and the total cost is minimized. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 60
Citation - Scopus: 78
A simulation-based methodology for the analysis of the effect of lean tools on energy efficiency: An application in power distribution industry
(ELSEVIER SCI LTD, 2019) Serdar Baysan; Ozgur Kabadurmus; Emre Cevikcan; Sule Itir Satoglu; Mehmet Bulent Durmusoglu; Durmusoglu, Mehmet Bulent; Cevikcan, Emre; Baysan, Serdar; Kabadurmus, Ozgur; Satoglu, Sule Itir
In recent years there has been an increasing interest in energy consumption in manufacturing systems due to the rising environmental awareness in society as well as the necessity of being cost-effective in the global competitive environment. Companies have to take measures to decrease their unnecessary energy consumption while maintaining their throughput. In this context lean manufacturing is regarded as one of the most significant management initiatives for energy management since it intensifies the effective utilization of resources via the identification and elimination of wastes. In this paper a holistic methodology which integrates Energy Value Stream Mapping experimental design and simulation is developed with the aim of analyzing and reducing the energy consumption within Lean Transformation. Moreover the effects of unevenness (mura) and overburden (muri) as root causes of waste (muda) on energy consumption has been addressed for the first time. The proposed methodology is applied to a real-life cable ladder manufacturing system and its feasibility is demonstrated. The results of the application showed that the adaptation of cellular manufacturing pull system and mistake-proofing yielded approximately 72.37% reduction in energy consumption. (C) 2018 Elsevier Ltd. All rights reserved.
ALİAĞA VE İZMİR LİMANLARININ LOJİSTİK POTANSİYELİ VE BÖLGENİN KONTEYNER TAŞIMACILIĞI AÇISINDAN GELECEĞİ
(2020) Ozgur Kabadurmus; Ugur Orhan Karakopru; Hazar Dorduncu; Karakopru, Ugur Orhan; Kabadurmus, Ozgur; Dorduncu, Hazar
Günümüzde artan küreselleşmenin bir sonucu olarak dış ticarette en yaygın kullanılan taşımacılık modu olan denizyolu taşımacılığının önemi de son derece artmıştır. Üretimin Batı pazarlarından Doğu pazarlarına kaymasıyla birlikte Doğu pazarlarında üretilen ürünlerin Batı pazarlarına ulaştırılması için yeni yollar arayışına girilmiştir. Türkiye Doğu ve Batı arasında bir köprü görevi gördüğünden doğru stratejik adımlarla gelecekte bir lojistik üssü olma potansiyeline sahiptir. Dünya konteyner ticaretinin giderek artması ile birlikte Türkiye de konteyner taşımacılığı pazarından önemli miktarda pay almaya başlamış ve konteyner ticaretindeki payını artırmak için çalışmalar ve yatırımlar yapmaya başlamıştır. Budoğrultuda bu çalışmanın amacı Türkiye’nin en önemli limanlarından olan Aliağa ve İzmir Limanlarını ve bölgenin gelecekteki lojistik potansiyelini özellikle konteyner taşımaları açısından incelemektir.
Analitik Hiyerarşi Prosesi Yöntemi ile Lojistik Sektöründeki İdeal Satış Personeli Profiline Ulaşmak: Uluslararası Bir Lojistik Firma Örneği
(2020) Ozgur Kabadurmus; Duygu Demirelöz Demir; Demir, Duygu Demirelöz; Kabadurmus, Ozgur
Uluslararası lojistik sektöründe yoğun bir rekabetin yaşanması ulusal ve uluslararası faaliyet gösteren lojistik şirketlerini insan kaynaklarını optimal şekilde yönetme stratejisine yönlendirmektedir. Bu bağlamda lojistik şirketlerinde nakit akış döngüsünde önemli rollere sahip olan satış personellerinin yetkinliklerinin belirlenmesi şirketlerdeki başarıyı sürdürülebilir hale getirebilmek adına büyük önem taşımaktadır. Bu çalışmanın amacı insan kaynağının optimal yönetim stratejisinin başarılı bir şekilde uygulanması kapsamında lojistik şirketlerindeki satış personelinin yetkinliklerinin belirlenmesi ve işe alımda kullanılmasıdır. Çalışmada önerilen modeli test etmek üzere vaka çalışması olarak ulusal ve uluslararası alanlarda faaliyet gösteren bir şirketin lojistik operasyonlarında çalışacak satış personelinin yetkinlikleri belirlendikten sonra çok kriterli karar verme yöntemi olan Analitik Hiyerarşi Prosesi uygulanarak şirket için en uygun aday seçilmiş ve sonuçlar tartışılmıştır.
Citation - WoS: 33
Citation - Scopus: 36
Antecedents to supply chain innovation
(EMERALD GROUP PUBLISHING LTD, 2020) Fatma Nur Karaman Kabadurmus; Kabadurmus, Ozgur; Karaman Kabadurmus, Fatma Nur; Kabadurmuş, Fatma Nur Karaman
Purpose The purpose of this study is to examine organizational and environmental (competition capital scarcity and organization of labor) factors that affect firms' innovation activities within the supply chain. Design/methodology/approach This study empirically examines the factors that affect firms' innovation activities using firm-level data from the last round of Business Environment Enterprise Performance Surveys (BEEPS). The analysis covers major supply chain functions: production delivery and support systems. Findings The study shows that drivers of innovation vary with the type of innovation activity, as such innovation efforts across supply chain functions should prioritize strategic resources that will create competitive advantages. Our results also reveal that sustainability efforts in the Eastern Europe and Central Asia (EECA) region should prioritize labor market reforms over capital market reforms. Originality/value Current research on innovation and supply chain issues does not explicitly analyze innovations that occur in different sustainable supply chain functions and empirical studies that focus on the determinants of innovations in the supply chain network are very limited. The data used in this study cover 30 economies in EECA many of which are low- and middle-income countries and thus contribute to the implementation of sustainable practices in developing countries.
Citation - WoS: 4
Citation - Scopus: 10
Bi-Objective green vehicle routing problem minimizing carbon emissions and maximizing service level
(GAZI UNIV FAC ENGINEERING ARCHITECTURE, 2023) Ozgur Kabadurmus; Mehmet Serdar Erdogan; Erdoğan, Mehmet Serdar; Kabadurmus, Ozgur
In this study a bi-objective Green Vehicle Routing Problem is presented as an extension of the well-known Vehicle Routing Problem. Green Vehicle Routing Problem aims to improve routing decisions of companies using Alternative Fuel Vehicles to reduce carbon emissions. The presented problem herein has two objectives that are the minimization of total carbon emissions and the maximization of service level. While total carbon emission is assumed to be proportional to total distance cargo delivery time window violations of customers are considered as an indicator of service level. The problem was modeled as Mixed-Integer Linear Programming and epsilon-constraint method which is a multi-objective optimization method is developed to solve it. To effectively solve large problem instances a clustering-based heuristic method is proposed. The heuristic method achieved a good performance by finding near Pareto-optimal solutions that are found by the MILP model. Our proposed mathematical model and heuristic method are tested on seven realistically designed hypothetical case studies. According to the results the minimization of carbon emission and maximization of service level are two conflicting objectives. As the service level increases the number of vehicles and carbon emissions also increase. As carbon emission increases and time windows violation decreases more vehicles and alternative fuel stations are used.
Çok Kriterli Karar Verme Yöntemleri Kullanarak Spor Kulüplerinde Lojistik Kararların Verilmesi
(2019) UĞUR ORHAN KARAKÖPRÜ; Ozgur Kabadurmus; Karaköprü, Uğur Orhan; Kabadurmus, Ozgur
Son yıllarda spor daha profesyonel ve endüstriyel bir sektör haline gelmiş ve spor kulüplerinde analitik karar verme yöntemlerinin kullanımı çok daha önemli hale gelmiştir. Spor kulüplerinde takımın maçlara taşınmasına karar verilmesi dikkatle çözülmesi gereken karmaşık bir problemdir. Bu çalışmada spor kulüplerinin maçlara ulaştırılması probleminin çözümü için bir çok kriterli karar verme yöntemi önerilmiştir. Bu problem spor kulüpleri için literatürde daha önce ele alınmamıştır. Bu çalışmada problemin çözümü için iki aşamalı bir yöntem önerilmiştir. Analitik Hiyerarşi Süreci kriterlerin ağırlıklarının belirlenmesinde kullanılmış ve ELECTRE de taşıma alternatiflerinin en iyiden en kötüye sıralanmasında kullanılmıştır. Önerilen yöntemi test etmek üzere gerçek bir vaka çalışması sunulmuştur. Bu gerçek vaka çalışmasında bir Türk spor kulübünün voleybol branşındaki üst yaş takımı için en iyi taşıma alternatifi büyük orta boy veya küçük otobüslerin dış kaynak kullanımı veya satın alımı gibi bir çok alternatif arasından seçilmiştir. Kriterler maliyet konfor düzeyi zaman ve prestij olarak belirlenmiştir. Bu vaka çalışması en iyi kararın otobüsleri maç başına kiralamak yerine büyük otobüs satın almak olması nedeniyle kulüpteki karar vericilerin lojistik kararlarını değiştirmelerinin gerektiğini ortaya çıkarmıştır.
Citation - WoS: 1
Citation - Scopus: 2
Design of multi-product multi-period two-echelon supply chain network to minimize bullwhip effect through differential evolution
(IEEE, 2017) Ozgur Kabadurmus; M. Serdar Erdogan; M. Fatih Tasgetiren; Erdogan, M. Serdar; Tasgetiren, M. Fatih; Kabadurmus, Ozgur
A supply chain network consists of facilities located in dispersed geographical locations. This network structure can be optimized to minimize total cost or total inventory by deciding the order quantities and distribution of links connecting the facilities. However bullwhip effect (i.e. amplification of order fluctuations) is an important performance metric for supply chains because as the order variance increases in the downstream of the supply chain (e.g. distributors) the demand variance in the upstream (e.g. manufacturer) amplifies and causes inefficiencies in the supply chain. In this study we optimize supply chain network structure for multi-product multi-period two-echelon supply chain networks to minimize bullwhip. Due to nonlinear structure of the objective function i.e. bullwhip effect this paper proposes a differential evolution (DE) algorithms employing variable neighborhood search (VNS) and constraint handling methods to optimize supply chain network structure. The proposed algorithm is tested over randomly generated test instances and its effectiveness is demonstrated.
Citation - WoS: 11
Citation - Scopus: 11
Design of pull production control systems using axiomatic design principles
(Emerald Group Holdings Ltd., 2020) Ozgur Kabadurmus; Mehmet Bǔlent Durmuşoǧlu; Kabadurmus, Ozgur; Durmusoglu, Mehmet Bulent
Purpose: The purpose of this paper is to contribute to the lean manufacturing literature by providing a roadmap for pull production control system (PCS) implementation. Design/methodology/approach: Axiomatic Design (AD) methodology is used to develop the proposed pull PCS transformation roadmap. Findings: The proposed design methodology is validated in a real-life manufacturing system. The results show that the proposed methodology significantly reduces the design efforts. The methodology effectively helps to choose the most appropriate pull PCS and determine its operational settings with respect to the manufacturing system characteristics. Research limitations/implications: This study presents only one case study to test the proposed methodology. In future studies the validity of the proposed method can be further generalized in different manufacturing sectors by real-life implementations. Practical implications: In many real-life lean production projects companies do not know where to start or how to proceed which leads to repetitive design efforts and inefficient designs. The developed roadmap of this study minimizes incorrect or imperfect design trials and increases the success of pull production transformation projects. Originality/value: The implementation of pull PCS requires extensive design knowledge and expertise. Therefore many real-life applications fail due to costly and time-consuming trial-and-error-based design efforts. In the literature there is no comprehensive guideline or roadmap for pull PCS implementation. To address this issue this study provides a novel holistic roadmap to transform an existing push PCS to pull. The proposed methodology uses AD principles and combines fragmentary studies of the pull production literature. © 2021 Elsevier B.V. All rights reserved.
DOĞU AVRUPA VE MERKEZ ASYA’DA YENİLİK: BİR ÇOK KRİTERLİ KARAR VERME YAKLAŞIMI
(2019) FATMA NUR KARAMAN KABADURMUŞ; Ozgur Kabadurmus; Kabadurmus, Ozgur; Kabadurmuş, Fatma Nur Karaman
Günümüzün yüksek rekabet ortamında ülkelerin inovasyon düzeyleri rekabetçi avantajlarını da belirlemektedir. Bu çalışma Doğu Avrupa ve Orta Asya ülkelerinin inovayon düzeylerini çok kriterli karar verme yöntemleri ile karşılaştırmaktadır. Dünya Bankası’nın firma düzeyindeki inovasyon veri seti (BEEPS) kullanılarak ülkelerin inovasyon düzeyleri ve yetenekleri değerlendirilmiştir. Bu çalışmada geliştirilen TOPSIS tabanlı yöntemle dört inovasyon türü (Ürün Organizasyonel Pazarlama ve Süreç Yeniliği) kullanılarak ülkeler karşılaştırılmıştır. Ayrıca ülkelerin inovasyon sıralamasının farklı kriter ağırlıklarında nasıl değiştiğini gösterecek şekilde bir duyarlılık analizi yapılmıştır.
Citation - WoS: 30
Citation - Scopus: 34
Evaluating Reliability/Survivability of Capacitated Wireless Networks
(Institute of Electrical and Electronics Engineers Inc., 2018) Ozgur Kabadurmus; Alice E. Smith; Kabadurmus, Ozgur; Smith, Alice E.
In telecommunication network design problems survivability and reliability are often used to evaluate quality of service while usually ignoring link capacity. In this paper a new metric that combines network reliability with network resilience is presented to measure reliability/survivability effectively for capacitated networks. Capacitated resilience is compared with well-known network reliability/survivability metrics ( K-terminal reliability all-terminal reliability traffic efficiency and K-connectivity) and its benefits and computational efficiency are discussed. An application is shown using heterogeneous wireless networks (HetNets). With the growing use of new telecommunication technologies such as 4G and wireless hotspots HetNets are gaining more attention. The source of heterogeneity of a HetNet can either be the differences in nodes (such as transmission ranges failure rates and energy levels) or the differences in services offered in the network (such as GSM and WiFi). © 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 5
Evaluation of Stadium Locations Using AHP and TOPSIS Methods
(ESKISEHIR OSMANGAZI UNIV FAC EDUCATION, 2020) Ugur Orhan Karakopru; Ozgur Kabadurmus; Karakopru, Ugur Orhan; Kabadurmus, Ozgur
With the industrialization of sports stadiums started to mean more than a venue for playing games. Therefore choosing the best location for a stadium is an important problem for sports clubs. In this paper a two-step hierarchical multi-criteria decision-making model has been developed to evaluate stadium locations. In the first step Analytic Hierarchy Process (AHP) has been used to determine the criteria weights. In the second step TOPSIS has been used to evaluate the alternatives. Four main criteria have been identified to evaluate alternatives: (1) stadium capacity (2) construction cost (3) accessibility and (4) distances. As a case study the proposed model has been applied to three new stadium constructions in Izmir Turkey.
Citation - WoS: 1
Citation - Scopus: 1
Forecasting Damaged Containers with Machine Learning Methods
(Springer Science and Business Media Deutschland GmbH, 2022) Mihra Güler; Onur Adak; Mehmet Serdar Erdoğan; Ozgur Kabadurmus; Güler, Mihra; Adak, Onur; Erdogan, Mehmet Serdar; Kabadurmus, Ozgur; N.M. Durakbasa , M.G. Gençyılmaz
Forecasting the number of damaged containers is crucial for a maritime company to effectively plan future port operations. The purpose of this study is to forecast damaged container entries and exits. In this paper we worked with a global logistics company in Turkey. Comparisons between ports were made using the company’s internal port operations data and externally available data. The external data that we used are Turkey’s GDP exchange rates (USD/EUR) import and export data TEU of Mersin port and the total TEU of Turkey’s ports (2015–2020). Our aim is to forecast the number of damaged containers at a specific port (Mersin Turkey) using different machine learning methods and find the best method. We used Linear Regression Boosted Decision Tree Regression Decision Forest Regression and Artificial Neural Network Regression algorithms. The performances of these methods were evaluated according to various metrics such as R2 MAE RMSE RAE and RSE. According to our results machine learning methods can forecast container demand effectively and the best performing method is Boosted Decision Tree regression. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Citation - Scopus: 12
Multi-commodity k-splittable survivable network design problems with relays
(SPRINGER, 2016) Ozgur Kabadurmus; Alice E. Smith; Kabadurmus, Ozgur; Smith, Alice E.
The network design problem is a well known optimization problem with applications in telecommunication infrastructure designs and military operations. This paper devises the first formulation and solution methodology for the multi-commodity k-splittable two-edge disjoint survivable network design problem with capacitated edges and relays. This problem realistically portrays telecommunications network design but has not been solved previously due to its computational difficulty. Edge capacity is considered as either a discrete or a continuous variable. An exact method and a practical heuristic method are presented and computational results are discussed.
Citation - Scopus: 5
Solving 0-1 Bi-Objective Multi-dimensional Knapsack Problems Using Binary Genetic Algorithm
(Springer Science and Business Media Deutschland GmbH, 2021) Ozgur Kabadurmus; M. Fatih Tasgetiren; Hande Oztop; Mehmet Serdar Erdoğan; Tasgetiren, M. Fatih; Erdogan, M. Serdar; Oztop, Hande; Kabadurmus, Ozgur
The multi-dimensional knapsack problem (MDKP) is a well-known NP-hard problem in combinatorial optimization. As it has various real-life applications the MDKP has been intensively studied in the literature. On the other hand far too little attention has been paid to the multi-objective version of the MDKP. In this chapter we consider the bi-objective multi-dimensional knapsack problem (BOMDKP). We propose a Binary Genetic Algorithm (BGA) with an external archive for the problem. Our proposed BGA algorithm also employs a binary local search. The non-dominated solution sets are obtained for various bi-objective benchmark instances with 100 250 500 and 750 items by employing the proposed BGA. Then the performance of the BGA is compared with other multi-objective algorithms from the literature i.e. MOEA/D and MOFPA. Furthermore it is observed that the Pareto-optimal solution set provided by Zitzler and Laumans for 500 items and 2 knapsacks includes 30 dominated solutions. Also the Pareto-optimal solutions for the scenario with 750 items are not reported in Zitzler and Thiele [43]. Hence the true Pareto-optimal solution sets are found for all benchmark problem instances using Improved Augmented Epsilon Constraint (AUGMECON2) method. The non-dominated solution sets of the BGA MOEA/D and MOFPA are compared with the Pareto-optimal solution sets for all test instances. The computational results indicate that the proposed BGA is more effective to solve the BOMDKP than the best-performing algorithms from the literature. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 41
Citation - Scopus: 52
Sustainable multimodal and reliable supply chain design
(Springer, 2020) Ozgur Kabadurmus; Mehmet Serdar Erdoğan; Erdogan, Mehmet S.; Kabadurmus, Ozgur
Emerging issues and new challenges of globalization have forced companies to design their supply chains for not only minimizing cost but also considering other factors. Supply chains are exposed to new environmental regulations to reduce their carbon emissions and compelled to consider other overlooked factors such as risk. In this paper we consider a multi-echelon multimodal supply chain network design problem with multiple products and components that take economic environmental and risk factors into account. The problem is modeled as a Mixed Integer Linear Programming model and constrained by a carbon cap-and-trade scheme and a risk threshold. This novel problem realistically portrays the supply chain network design considering sustainability and reliability factors simultaneously. The proposed model has been tested on randomly generated hypothetical but realistic test instances. The impacts of different risk thresholds and unit carbon prices on the supply chain cost risk and emissions are analyzed. The effects of multimodal transportation modes on cost risk and emissions are also tested. Results prove that using multimodal transportation decreases supply chain cost and carbon emission. In addition the total supply chain cost and carbon emission increase if the decision maker is risk-averse. The choice of transportation modes is sensitive only to emission levels. © 2020 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Using big data analytics to forecast trade volumes in global supply chain management
(IGI Global, 2019) Murat Özemre; Ozgur Kabadurmus; Kabadurmus, Ozgur; Özemre, Murat
As the supply chains become more global the operations (such as procurement production warehousing sales and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However in today's competitive and complex global supply chain environments making accurate forecasts has become significantly difficult. In this chapter the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts. © 2020 Elsevier B.V. All rights reserved.
Using Big Data Analytics to Forecast Trade Volumes in Global Supply Chain Management
(IGI Global, 2022) Murat Özemre; Ozgur Kabadurmus; Kabadurmus, Ozgur; Ozemre, Murat
As the supply chains become more global the operations (such as procurement production warehousing sales and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However in today’s competitive and complex global supply chain environments making accurate forecasts has become significantly difficult. In this chapter the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts. © 2022 Elsevier B.V. All rights reserved.