Framework to Identify Fast Moving Items in the Service Automation Division of ABC Company

Abstract

Identification of fast moving consumable items of photocopiers is the main research area of this project. Mainly there are four main consumable item types has been discussed in the document. Nowadays new businesses are emerging and the competitiveness in the business industry is very high. It is very important to identify business opportunities in-order to survive in the business industry. Identifying new business opportunities and come up with new strategies for business make the business solid in the industry. New strategies always make the business to keep on track. In-order to come up with new strategies it is essential to identify trends of item moving for that industry. In-order to identify market trends or item moving patterns in the industry need to analyze the company data. These company data help to make effective decision makings. Decisions that made by a company decides the future of that company. Day by day data been stacked up in company databases. Large amount of data need to be analyzed properly for make effective decisions like regular customers, customers buying patterns, seasonal trends, fast moving items etc. These decisions evolve with increase company sales. In- order to analysis, large amount of data and complex data need to use techniques which are suitable for that such as, business intelligence (BI) tools, data mining techniques etc. This research has been carried out for a rental business in-order to identify fast moving items and rental business’s business trends and forecast trends in the company. Because of large amount of data, data mining techniques have been used to analyze the data sets. Drill down methods has used to identify variations of number of copies have been taken from each photocopier machine and the impact of customers for the high amount of usage of photocopies. K- means cluster analysis used to identify items with similar characteristics. Hence that, K-means data mining technique has applied to identify fast moving items in the dataset. From the identification of fast moving items and high usages (no of copies) suggest new business strategies and forecast for usage volumes of photocopiers and fast moving items. Key Words: Pattern Recognition, Clustering, Fast Moving Items, Forecasting, Business Trends, Data Mining