Artificial Intelligence(AI), Machine Learning (ML) in The Demand Forecasting Process
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| Format: | Thesis |
| Kulcsszavak: | analysis forecast kereslet mesterséges intelligencia multinacionális vállalatok network |
| Online Access: | http://dolgozattar.uni-bge.hu/47231 |
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| 040 | |a BGE Dolgozattár Repozitórium |b hun | ||
| 041 | |a en | ||
| 100 | 1 | |a Abusultan Rafiq | |
| 245 | 1 | 0 | |a Artificial Intelligence(AI), Machine Learning (ML) in The Demand Forecasting Process |c Abusultan Rafiq |h [elektronikus dokumentum] |
| 520 | 3 | |a In most business entities, demand forecasting has become of great concern to help reduce operational costs, improve revenue, and increase customer satisfaction. Yet obtaining a more accurate and reliable demand forecast has been a great challenge for many business organisations. Most business organizations consider sharing data with their partners in the supply chain to enhance planning, efficiency, and accuracy. However, errors associated with forecasting can still lead to a significant rise in operational costs and a reduction in profits. Demand forecasting helps reduce the losses associated with the unpredictability of future demands. Many supply chains face challenges, especially the firms staged upstream of the chain. This suffering emanates from the variance amplification resulting from distortion in the information about demand from multi-stage supply chains, impacting operational efficiency. This study analyzed the various forms of demand forecasting and conducted a comparative analysis of the traditional and Artificial Intelligence (AI) techniques that can be used to predict future demands to assist business organisations minimize operational costs and improving revenue collection. The methodology employed by this study was a comparative analysis of the traditional forecasting method, and the AI or Machine Learning (ML) approaches. The study was guided by three research questions: 1. What improvements are expected from AI /Machine learning in the demand forecasting process 2 . How can machine learning be applied in demand forecasting 3. Which ML technique is more accurate and reliable in demand forecasting The study concludes that a hybrid system based on AI/ML is the best demand forecasting technique for most businesses to consider using to reduce operational costs, ensure customer satisfaction and improve revenue through accurate and efficient planning. | |
| 695 | |a analysis | ||
| 695 | |a forecast | ||
| 695 | |a kereslet | ||
| 695 | |a mesterséges intelligencia | ||
| 695 | |a multinacionális vállalatok | ||
| 695 | |a network | ||
| 700 | 1 | |a Csonka Dr. László |e ths | |
| 700 | 1 | |a Keresztes Dr. Éva Réka |e ths | |
| 856 | 4 | 0 | |u http://dolgozattar.uni-bge.hu/47231/1/ARTIFICAL%20INTELLIGENCE%28AI%29%2C%20MACHINE%20LEARNING%20%28ML%29%20IN%20THE%20DEMAND%20FORECASTING%20PROCESS%20_Final%20.v.5.pdf |z Dokumentum-elérés |
| 856 | 4 | 0 | |u http://dolgozattar.uni-bge.hu/47231/2/Csonka%204%20dissertation_report_external_RAbusultan_2023jan.pdf |z Dokumentum-elérés |
| 856 | 4 | 0 | |u http://dolgozattar.uni-bge.hu/47231/3/Kerseztes%205%20Abusultan%2C%20Rafiq%20dissertation_report_for_internal_advisors.pdf |z Dokumentum-elérés |