Application and Analysis of Retail Inventory Using Data Mining Techniques

Data mining is one of the most essential tools for gathering information from different datasets in almost all recent industries. In this 21st-century, data mining gained attention because of its significance in decision making, and it has become a key component in various industries such as retail. Inventory management requires pre-planned goals and attention to detail, and prioritizing items that require less attention can be a waste of time and resources. Learning indications about customers2019; shopping patterns by showing associations among various provides significant value in managing retail inventory. In the present research paper, popular data mining techniques have been applied and analyzed for multi-item inventory management in retail sales stores to show how data mining techniques can optimize and organize the retail inventory

Optimizing Inventory with Frequent Pattern Growth Algorithm for Small and Medium Enterprises

The success of a business heavily relies on its ability to compete and adapt to the ever-changing market dynamics, especially in the fiercely competitive retail sector. Amidst intensifying competition, retail business owners must strategically manage product placement and inventory to enhance customer service and meet consumer demand, considering the challenges of finding items. Poor inventory management often results in stock shortages or excess. To address this, adopting suitable inventory management techniques is crucial, including techniques from data mining, such as association rule mining. This research employed the FP-Growth algorithm to identify patterns in product placement and purchases, utilizing a dataset from clothing store sales. Analyzing 140 transactions revealed 24 association rules, comprising rules with 2-itemsets and frequently appearing 3-itemset rules. The highest support value in the final association rules with 2-itemsets was 10% with a confidence level of 56%, and the highest support value in the 3-itemsets was 67% with the same confidence level. Additionally, three rules had a confidence level of 100%. Thus, the association rules generated by the FP-Growth frequent itemset algorithm can serve as valuable decision support for sales of goods in small and medium-sized retail businesses

Consumer-driven innovation networks and e-business management systems

This paper examines the use of consumer-driven innovation networks within the UK food-retailing industry using qualitative interview-based research analysed within an economic framework. This perspective revealed that, by exploiting information gathered directly from their customers at point-of-sale and data mining, supermarkets are able to identify consumer preferences and co-ordinate new product development via innovation networks. This has been made possible through their information control of the supply-chain established through the use of transparent inventory management systems. As a result, supermarkets’ e-business systems have established new competitive processes in the UK food-processing and retailing industry and are an example of consumer-driven innovation networks. The informant-based qualitative approach also revealed that trust-based transacting relationships operated differently from those previously described in the literature

FAMOS: a size-class based financial analysis model

Beschrijving van het financieel model Financial Analysis Model of SMEs (FAMOS), dat de balansstructuur voor de niet-financiële sectoren en grootteklassen weergeeft. Het model geeft meer inzicht in de wijze waarop veranderingen in bijvoorbeeld de omzet, de financieringskosten en de winsten doorwerken op de balansstructuur van bedrijven en hun financiële indicatoren. Hierbij kan onderscheid worden gemaakt tussen de activastructuur (de verschillende vormen van bezittingen) en de vermogensstructuur (eigen of vreemd vermogen). De belangrijkste conclusie van het rapport is dat de balansstructuur slechts marginaal verandert bij een veranderde economische groei in het algemeen, en meer specifiek voor wijzigingen in betaalde rente en winsten. Er zijn wel grote verschillen mogelijk tussen sectoren en grootteklassen.

Designing appliances for mobile commerce and retailtainment

In the emerging world of the new consumer and the `anytime, anywhere' mobile commerce, appliances are located at the collision point of the retailer and consumer agendas. The consequence of this is twofold: on the one hand appliances that were previously considered plain and utilitarian become entertainment devices and on the other, for the effective design of consumer appliances it becomes paramount to employ multidisciplinary expertise. In this paper, we discuss consumer perceptions of a retailtainment commerce system developed in collaboration between interactivity designers, information systems engineers, hardware and application developers, marketing strategists, product development teams, social scientists and retail professionals. We discuss the approached employed for the design of the consumer experience and its implications for appliance design