Forecasting Order Delays with Artificial Intelligence-Based Applications

Abstract

The digitalization trend in the world in the last two decades has begun to transform business processes. Departing from the traditional business approach, the use of technology in every field has changed business processes and, accordingly, the approach of managers. On the other hand, cloud computing, the internet of things, sensors and instant notification systems through these components brought by the last industrial revolution have transformed supply chains into a smart structure. Now, the information that a product sold by a retailer, which is the last link of a supply chain, is out of stock is transmitted to the raw material supplier in the first link of the chain. In this way, a perfect supply chain structure is formed and operations are carried out in lean working principles. The products prepared according to the reduction of the retailer's stocks are continuously supplied, which shows flawless operation. On the other hand, through digital components such as sensors and the Internet of Things, each step in workflows is instantly converted into data and stored in virtual environments called cloud computing. At this point, besides the problem of data storage and security, the main problem is to process the data in a meaningful way. Analyzing big data generated by AI-based components and systems is also possible with AI-based systems. Methods called machine learning have been developed for this situation and their application area is increasing day by day. In the light of this information, within the scope of the study, forecasts of order quantity delays for future periods using historical order data from customers for the products produced by an enterprise were analyzed using machine learning algorithms. The results of the analysis made through the Microsoft Azure Machine Learning Studio platform will contribute to the use of digital tools in the sector as well as increasing the application examples of machine learning. According to the results obtained as a result of the analyzes, recommendations were developed for the enterprise. Finally, suggestions for the application of machine learning in the field of business administration are presented.