Gıda Sektöründe Simit Satışlarına Yönelik Talep Tahmin Analizi

Yıl 2024, Sayı: 9, 57 - 66, 30.06.2024

Gökhan Özdemir , Semih Önüt

https://doi.org/10.52693/jsas.1447066

Öz

For all living things, including plants and animals, food has become the means of ensuring their growth, survival, and protection of health. Life requires eating enough food in a balanced manner in order to continue. As a result, the food industry is made up of all these food-related operations, from the lowest stages (fisheries, agriculture, and animal husbandry) to the final stages (production, execution, and maintenance). In 1995, this industry was valued at 680 billion dollars, while in 2018, it was valued at 1.5 trillion dollars. A major factor in the food industry's recent more than twofold growth in size is production and consumption. Like everything else, the food business has benefited from the unification and simpler extension of a worldwide transportation network. The goal of this programming is to make it possible for artificial intelligence to anticipate with ease both the demand for bagels for the upcoming month and the annual sales amounts of a company that manufactures, supplies, and sells bagels. The CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory) neural networks were used in the research as estimates of artificial intelligence. The prediction findings' accuracy was assessed using the Mean Squared Error (MSE) and Root Mean Square Error (RMSE). Software tests of the artificial intelligence techniques CNN and LSTM have shown nearly identical accuracy results. As a result, improvements in the findings of the precise estimation of the amount that may be sold will benefit sustainability, profitability, and market competition.

Anahtar Kelimeler

talep tahmini, yapay zeka, gıda sektörü, yazılım

Demand Forecast Analysis for Bagel Sales in the Food Industry

Öz

Food has become the solution for every living person, including plants and animals, to protect their health, sustain their lives and ensure their development. Consuming balanced and sufficient amounts of food is a necessity for the continuity of life. Therefore, all the processes of these food changes, starting from the lowest stage (agriculture, animal husbandry, fisheries), through activities such as production, execution and maintenance, to the final processes, constitute the food sector. The commercial value of this sector was 680 billion dollars in 1995 and 1.5 trillion dollars in 2018. Production and consumption play a big role in the fact that the volume of the food industry has more than doubled in a few years. The unification and easier expansion of a global transportation network has made a positive contribution to the food industry, as it does everywhere else. The purpose of this programming is to enable artificial intelligence to easily predict the annual sales amounts of a company that produces, supplies and sells bagels and the bagel demand for the next month. The research was carried out using the estimated artificial intelligence methods, LSTM (Long Short Term Memory) Neural Network and CNN (Convolutional Neural Network) Neural Network system. MSE (Mean Squared Error) and RMSE (Root Mean Square Error) were used to evaluate the accuracy of the prediction results. LSTM and CNN artificial intelligence methods have been tested in software and almost the same accuracy results are seen in both methods. Therefore, the change in the results of accurate estimation of the amount that can be sold will have a positive impact on profitability, competition with the market and sustainability.

Anahtar Kelimeler

demand forecasting, artificial intelligence, food industry, software

Kaynakça

• [1] Ünsal A., “Susamlı Halkanın Tılsımı”, 2010.
• [2] Özüdoğru A.G., Görener A., “Sağlık Sektöründe Talep Tahmini Üzerine Bir Uygulama”, İstanbul Ticaret Üniversitesi Sosyal Bilimleri Dergisi, 2015.
• [3] Sarı M., “Yapay Sinir Ağları Ve Bir Otomotiv Firmasında Satış Talep Tahmini Uygulaması”, 2016
• [4] Bayramoğlu T., Pabuçcu H., Boz F.Ç., “Türkiye İçin Anfis Modeli İle Birincil Enerji Talep Tahmini”, Ege Akademik Bakış, 2017.
• [5] Yiğiter Ş.Y., Sarı S.S., Başakın E.E., “Hisse Senedi Kapanış Fiyatlarının Yapay Sinir Ağları Ve Bulanık Mantık Çıkarım Sistemleri İle Tahmin Edilmesi”, Kahramanmaraş Sütçü İmam Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2017
• [6] Haliloğlu E.Y., Tutu B.E., “Türkiye İçin Kısa Vadeli Elektrik Enerjisi Talep Tahmini”, Journal of Yasar University, 2018.
• [7] Yıldırım A., “Talep Tahmin Yöntemlerinin Karşılaştırmalı Analizi: Gıda Sektöründe Bir Uygulama”, 2019.
• [8] Nasuhoğlu H., “Eczacılık Sektöründe Yapay Sinir Ağları Ve Zaman Serileri Analizi İle Talep Tahmini”, 2019
• [9] Nebati E.E., Taş M., Ertaş G., “Türkiye’de Elektrik Tüketiminde Talep Tahmini: Zaman Serisi Ve Regresyon Analizi İle Karşılaştırma”, Avrupa Bilim ve Teknoloji Dergisi, 2021.
• [10] Çoban F., Demir L., “Yapay Sinir Ağları ve Destek Vektör Regresyonu ile Talep Tahmini: Gıda İşletmesinde Bir Uygulama”, Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 2021
• [11] Bilişik M.T., “Gıda Sektöründe Talep Tahmininde Yapay Sinir Ağları, Regresyon, Hareketli Ortalamalar Ve Winters Üstel Düzeltme Metodlarının Karşılaştırılması”, Eurasian Academy Of Sciences Eurasian Business & Economics Journal, 2021
• [12] Sarı M., “Yapay Sinir Ağları Ve Bir Otomotiv Firmasında Satış Talep Tahmini Uygulaması”, 2016.
• [13] Tsai P., Huang Y., Tai J., “Estimating Soil Water Content From Thermal Images With An Artificial Neural Network”, CATENA, 2024.
• [14] Shafie M. R., Khosravi H., Farhadpour S., Das S., Ahmed I., “A Cluster-Based Human Resources Analytics For Predicting Employee Turnover Using Optimized Artificial Neural Networks And Data Augmentation”, Decision Analytics Journal, 2024.
• [15] Lu C., Lee T., Lian C., “Sales Forecasting For Computer Wholesalers: A Comparison Of Multivariate Adaptive Regression Splines And Artificial Neural Networks”, Decision Support Systems, 2012. [16] Vhatkar S., Dias J., “Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model”, Procedia Computer Science, 2016.
• [17] Zhou S., Lu W., Li W., Wang S., “Forecasting The Temperature Of A Building-Integrated Photovoltaic Panel Equipped With Phase Change Material Using Artificial Neural Network”, Case Studies in Thermal Engineering, 2024.
• [18] Ali Y., Aly H. H., “Short Term Wind Speed Forecasting Using Artificial And Wavelet Neural Networks With And Without Wavelet Filtered Data Based On Feature Selections Technique”, Engineering Applications of Artificial Intelligence, 2024.
• [19] Alam M. S., Deb J. B., Amin A. A., Chowdhury S., “An Artificial Neural Network For Predicting Air Traffic Demand Based On Socio-Economic Parameters”, Decision Analytics Journal, 2024.
• [20] Shi H., Wei A., Xu X., Zhu Y., Hu H., Tang S., “A CNN-LSTM Based Deep Learning Model With High Accuracy And Robustness For Carbon Price Forecasting: A Case Of Shenzhen's Carbon Market In China”, Journal of Environmental Management, 2024.
• [21] Yılmaz M.C., Orman Z., “LSTM Derin Öğrenme Yaklaşımı İle Covid-19 Pandemi Sürecinde Twitter Verilerinden Duygu Analizi”, Istanbul University Press, 2021
• [22] Dao F., Zeng Y., Qian J., “Fault Diagnosis Of Hydro-Turbine Via The Incorporation Of Bayesian Algorithm Optimized Cnn-Lstm Neural Network”, Energy, 2024.