Contents<\/strong><\/p>\n\n\n\n Order processing, especially in such a diversified network, is a complex challenge. Until now, the ordering process for stores and the central warehouse required extensive specialist knowledge and a lot of time, as orders had to be created manually. This task is complicated by the need to minimize costs per supplier, maintain optimal stock levels and ensure that products are reordered on time. The importance of an efficient ordering system is underlined by studies that show that customers switch to the competition if products are not available, which can lead to declining customer loyalty and dissatisfaction in the long term.<\/p>\n<\/div>\n\n\n\n This article focuses on a retail company in Austria that shares characteristics with other medium-sized retailers. The company operates around 165 stores in Europe and offers its products online and in stores. Each store sells special products, with the product range being tailored to the respective country. Some products are shared by the stores and ordered in larger quantities from a central warehouse. Until now, stores and the central warehouse have had to order products manually. Creating orders requires a lot of expertise and time. This is compounded by the need to minimize costs per supplier, maintain good stock levels and ensure that products are ordered on time when required. Getting to grips with the problem is particularly important for online retailers because, according to a study by Dadzie and Winston [2], many customers will buy a product from a competitor if it is not available. Anand and Chadha [3] point to long-term negative effects such as declining customer loyalty and dissatisfaction with substitute products. Zinn and Liu [4] also show that stock shortages can damage a store’s image.<\/p>\n\n\n\n The main objective of this project is to improve this process and support it by calculating order proposals per supplier. This is done by combining business rules (per supplier and warehouse) with a system that estimates demand based on historical data and related events. Aastrup and Kotzab [1] provide an overview of the current possibilities for tackling this problem.<\/p>\n\n\n\n Below is a strategy that aims to ensure that the right amount of stock is available by forecasting customer demand.<\/p>\n\n\n\n In order to accomplish this task, all suppliers are regularly taken into consideration. Each supplier delivers a certain number of products, and the key question is which products need to be reordered. At the lowest level, heuristics and forecasts are used to determine the time at which a product is likely to be out of stock. Based on this assessment, a preliminary requirement is determined, which leads to the creation of a “shopping basket” for the respective supplier. This shopping cart can then be customized using specific rules. For example, some suppliers require a minimum order quantity or certain order units (e.g. pallets instead of boxes). Here, too, attempts are made to achieve discounts by possibly ordering more than is necessary. Next, the procedure at the lowest level is explained in more detail.<\/p>\n\n\n\n Heuristics are methods for finding solutions to problems quickly and with practical means. People often rely on simple rules or experience instead of analyzing everything down to the smallest detail. They help to make quick decisions when there is no perfect solution.<\/p>\n<\/div>\n\n\n\n\n
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<\/figure><\/div>\n\nThe importance of efficient order processing in the digital age<\/strong><\/h3>\n\n\n\n
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<\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>Challenges in retail order processing<\/h3>\n\n\n\n
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