Decision Support System In The Supermarket

Why Supermarket must choose OLAP System? The existing system is not able to meet the demand? OLAP system has advantages of better than now? In my position is right.

Because the supermarket is the emphasis on accurate data analysis to the marketing of goods, OLAP absolutely can meet the needs of the supermarket, the benefits include trend analysis, customer loyalty analysis, Carry out market analysis. These benefits for the future development of the supermarket have a great help.

INTRODUCTION

My project is forces on the actual characteristics of the supermarket business, Invoicing, Invoicing System analysis of the traditional shortcomings, is given to the data warehouse system as the core of environment the idea of building a Invoicing system, and focuses on the data warehouse system The design and implementation method for the establishment of supermarkets Invoicing system provides the benefit of their experience.

PROPOSED SOLUTION

OLAP System – is a technology that allows users to carry out complex data analyses with the help of a quick and interactive access to different viewpoints of the information in data warehouses. These different viewpoints are an important characteristic of OLAP, also called multidimensionality. Multidimensional means viewing the data in three or more dimensions. For a database of a Sales Department, these dimensions could be Product, Time, Store and Customer Age. Analyzing data in multiple dimensions is particularly helpful in discovering relationships that cannot be directly deduced from the data itself.

OLAP System Technology basics

To give the reader a feeling of how one should see OLAP, let us look at the following simple example:

Consider a Daily necessities retailer with many shops in different cities and many different styles of product, for example Furniture, Food, and Cleaning supplies. Each shop delivers data daily on quantities sold in Customs Behavior. These data are stored centrally. Now the business analyst wants to follow sales by month, outlet and behavior. These are called dimensions, for example month dimension. If we want to look at the data of these three dimensions and say something significant about them, what we are actually doing is looking at the data stored in a 3-dimensional cube.

Figure 4-1: A 3-dimensional OLAP cube

The following three cubes show us how we can look at, respectively: data on all product type sold in all months in the outlet Hong Kong, data on Potato chip sold in all months in all outlets, and data on all food sold in all outlets in the month April.

Figures 4-2: The OLAP cube looked at from 3 different dimensions

When we combine these three dimensions, we get data on the number of Potato chip sold in the outlet Hong Kong in the month April:

Figure 4-3: The 3 dimensions combined in the OLAP cube

Suppose we want information about the food of the Potato chip or the amount sold, we would have to define new dimensions. This would mean a 4-, 5- or even more-dimensional cube. Of course cubes like this are no longer ‘visible’ to the eye, but in an OLAP-application they are possible!

Components and their functionality

Description of goods subject classification and sale of corporate goods; customers topic describes the classification of business-to-customer and the customer contract management; theme describe the vendors of enterprise sales force selling of goods and sales of local conditions. Among them, of goods as a central theme, these three themes. Its specific contents include:

Product:

Goods intrinsic information (merchandise code, merchandise name, merchandise type, etc.)

Product inventory information (merchandise code, the Treasury numbers, inventory, date, etc.)

Commodity Marketing information (merchandise code, customer code, date of sale, sales price, sales volume, etc.)

Customer:

The inherent customer information (customer number, customer name, address, number, phone, etc.)

Customer contract information (customer number, contract code, start date, end date, quantity, price, etc.)

Customer purchase information (customer number, product code, unit price, quantity, date, etc.)

Vendor:

Vendor inherent information (vendor ID, selling merchandise, selling trade names, vendor address, etc.)

According to [C1], Product, Customer, Vendor data will be stored in the Database of three inside, Data through reorganization, transformation into useful information, the last available to the OLAP system.

Meet strategic objective for supermarket

Reduce inventory costs – through the data warehouse system will be tens of thousands of kinds of goods, sales data and inventory data together through data analysis and classification of knowledge, you can know the stock for some time, did not receive orders for goods, it is received fewer orders for goods and inventory turnover of goods quickly. To decision-makers can determine a corresponding change in the goods to ensure the proper inventory, thus speeding up cash flow, reduce inventory costs.

Carry out market analysis – The use of OLAP data analysis tools to examine data from a data warehouse to analyze customer buying habits, product composition, and other strategic information. System to the biggest-selling product analysis, then make sure that at the right time, right place at the correct inventory.

For trend analysis- The use of a data warehouse for product variety and inventory trends analysis to select the products need to be supplemented to study customer buying trends, analyze seasonal buying patterns, identify bargains, and its number to respond. To be able to predict seasonal sales, the system must retrieve the data warehouse, 1 million products in more than a year of sales data, and on this basis for analysis.

For subgroups of commodities, layout, purchase of the analysis, recommendation and merchandise-Excavated from the consignment record relevant information can be found to purchase a certain kind of product customers may buy other commodities. Mining market basket analysis is a typical example of such information. Customers through the discovery into their shopping basket in the relationship between the different commodities, analyze customer buying habits. Using Apriori algorithm, which can be found in products frequently purchased by customers at the same time.

The analysis of the effectiveness of promotional activities carried out-Supermarkets often through advertising, coupons, discounts and ones who enjoy a variety of ways engage in promotional activities in order to promote sales of products, the purpose of attracting customers. But only fully understand the customer, to locate promotional activities, improve customer response rates, reduce the cost of promotional activities. Promotion through the multi-dimensional analysis, we can compare the number of transactions during the sales and promotional activities with the situation before and after, correlation analysis can be excavated which commodities may be purchased along with the marketing of goods. Use of data mining techniques can also analyze what should be the time, in what locations, in what way and what kind of people engage in promotional activities, will truly achieve the marketing objective of avoiding unnecessary waste of corporate resources. At the same time, data mining can also be used in the past relating to promotional data to search for future Investment return on the largest users.

For customer loyalty analysis-Supermarkets are often all the way through the handle membership cards, the establishment of the customer membership system to track the customer’s consumer behavior. Members through the information to the customer data mining, can record a customer’s buying series, customer loyalty and purchasing trends can be analyzed by a systematic way. By the same customers to buy goods at different times can be grouped as a sequence. Sequential pattern mining can be used to analyze the customer’s consumption or changes in loyalty, whereby the pattern of price and merchandise to be adjusted and updated in order to retain existing customers and attract new customers.

Review

Successful Stories

Hypercity, a popular retail store format owned by the K Raheja Group offers a comprehensive product range – foods, hardware, home entertainment, high-tech products, appliances, furniture, sports, toys clothing and so on. The huge range also meant generation of vast amounts of data and more importantly, the need to analyze it.

OLAP benefits have already started pouring in. To start off with, the ease at which data is available to the business is very evident. The solution has helped the analytics team to publish numerous insights on customers, product category, buying behavior and so on, which has definitely helped the business in better planning and execution of promotions. Thus, the BI implementation is helping Hypercity in more ways than one.

conclusion

Data warehouse and multi-dimensional analysis with comprehensive data on capacity and can be fast and accurate analysis of the data to help managers make better business decisions, you can bring a competitive advantage for enterprises. The current data warehouse and data mining technology in domestic applications is not very extensive, but because of commercial enterprises have complex business structures, there are a large number of Invoicing business data, there is a specific need for decision analysis, the data warehouse technology in the business enterprise applications has broad prospects.

*Self-evaluation/limitation/improvement