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Decision Support System In The Supermarket

Why Supermarket must choose OLAP System? The prevailing system struggles to meet up with the demand? OLAP system has benefits of better than now? In my own position is right.

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

INTRODUCTION

My project is forces on the genuine characteristics of the supermarket business, Invoicing, Invoicing System analysis of the traditional shortcomings, is directed at the info warehouse system as the main of environment the thought of building a Invoicing system, and targets the info warehouse system The design and implementation way for the establishment of supermarkets Invoicing system supplies the good thing about their experience.

PROPOSED SOLUTION

OLAP System - is a technology which allows users to handle sophisticated data analyses by making use of an instant and interactive usage of different viewpoints of the information in data warehouses. These different viewpoints are an important feature of OLAP, also called multidimensionality. Multidimensional means observing the data in three or even more dimensions. For your database of the Sales Office, these sizes could be Product, Time, Store and Customer Get older. Analyzing data in multiple measurements is particularly helpful in exploring relationships that cannot be directly deduced from the data itself.

OLAP System Technology basics

To supply the reader a feeling of how one should see OLAP, why don't we go through the pursuing simple example:

Consider an everyday necessities retailer with many shops in several cities and a number of styles of product, for example Furniture, Food, and Cleaning supplies. Each shop gives data daily on volumes sold in Traditions Habit. These data are stored centrally. Now the business analyst wants to follow sales by month, outlet and behavior. They are called proportions, for example month dimensions. If we want to look at the data of the three dimensions and say something significant about them, whatever we are actually doing is looking at the data stored in a 3-dimensional cube.

Figure 4-1: A 3-dimensional OLAP cube

The pursuing three cubes show us how exactly we can look at, respectively: data on all product type sold in all months in the wall plug Hong Kong, data on Potato chip sold in all months in all retailers, and data on all food sold in all outlet stores 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 wall plug Hong Kong in the month April:

Figure 4-3: The 3 dimensions put together in the OLAP cube

Suppose we wish information about the food of the Potato chip or the total amount sold, we'd have to explain new dimensions. This might imply a 4-, 5- or even more-dimensional cube. Certainly cubes such as this are no longer 'noticeable' to the attention, but in an OLAP-application these are possible!

Components and their functionality

Description of goods subject classification and sales of commercial goods; customers issue identifies the classification of business-to-customer and the customer agreement management; theme identify the distributors of enterprise sales team selling of goods and sales of local conditions. Among them, of goods as a central theme, these three topics. Its specific contents include:

Product:

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

Product inventory information (merchandise code, the Treasury statistics, inventory, day, etc. )

Commodity Marketing information (goods code, customer code, night out of sales, sales price, sales quantity, etc. )

Customer:

The natural customer information (customer quantity, customer name, address, number, mobile phone, etc. )

Customer contract information (customer quantity, agreement code, start time, end date, volume, price, etc. )

Customer purchase information (customer amount, product code, product price, quantity, particular date, etc. )

Vendor:

Vendor natural information (seller ID, selling items, selling trade names, supplier address, etc. )

According to [C1], Product, Customer, Seller 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 aim for supermarket

Reduce inventory costs - through the info warehouse system will be tens of thousands of sorts of goods, sales data and inventory data together through data examination and classification of knowledge, you can know the stock for some time, didn't receive requests for goods, it is received fewer requests for goods and inventory turnover of goods quickly. To decision-makers can determine a equivalent change in the goods to guarantee the proper inventory, thus accelerating cashflow, reduce inventory costs.

Carry out market research - The usage of OLAP data analysis tools to examine data from a data warehouse to investigate customer buying patterns, product composition, and other proper information. System to the biggest-selling product research, then ensure that at the right time, right place at the correct inventory.

For trend research- The use of a data warehouse for product variety and inventory trends analysis to choose the products need to be supplemented to study customer buying styles, examine seasonal buying habits, identify bargains, and its own number to react. To be able to predict seasonal sales, the machine must retrieve the info warehouse, 1 million products in more than a year of sales data, and on this basis for analysis.

For subgroups of goods, design, purchase of the evaluation, 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 container analysis is an example of such information. Customers through the breakthrough to their shopping container in the relationship between different commodities, assess customer buying practices. Using Apriori algorithm, that can be found in products frequently purchased by customers at the same time.

The research of the potency of promotional activities transported out-Supermarkets often through advertising, coupons, savings and ones who like a variety of ways engage in promotional activities to be able to market sales of products, the purpose of attracting customers. But only fully understand the customer, to locate promotional activities, improve customer response rates, reduce the price of promotional activities. Advertising through the multi-dimensional examination, we can compare the amount of transactions during the sales and promotional activities with the problem before and after, correlation evaluation can be excavated which commodities may be purchased combined with the marketing of goods. Usage of data mining techniques can also assess what ought to be the time, in what locations, in what manner and what kind of people take part in promotional activities, will truly achieve the marketing purpose of avoiding needless waste of corporate resources. At exactly the same time, data mining can even be used in days gone by relating to promotional data to search for future Investment return on the major users.

For customer devotion analysis-Supermarkets are often completely the handle regular membership cards, the establishment of the client membership system to keep tabs on the customer's consumer patterns. Members through the information to the client data mining, can track record a customer's buying series, customer devotion and purchasing tendencies can be examined by a organized way. With the same customers to buy goods at differing times can be grouped as a series. Sequential style mining can be used to assess the customer's ingestion or changes in loyalty, whereby the design of price and merchandise to be changed and updated in order to sustain existing customers and appeal to new customers.

Review

Successful Stories

Hypercity, a popular shop format managed by the K Raheja Group offers a thorough product range - foods, hardware, entertainment, high-tech products, gadgets, furniture, sports, gadgets clothing and so forth. The huge range also intended generation of huge levels of data and moreover, the need to analyze it.

OLAP benefits have previously started pouring in. To begin with, the decrease at which data is open to the business enterprise is very noticeable. The answer has helped the analytics team to create numerous insights on customers, product category, buying habit and so forth, which has definitely helped the business enterprise in better planning and execution of campaigns. Thus, the BI execution is assisting Hypercity in more ways than one.

conclusion

Data warehouse and multi-dimensional evaluation with detailed data on capacity and can be fast and appropriate analysis of the data to help professionals make better business decisions, you can bring a competitive gain for enterprises. The existing data warehouse and data mining technology in domestic applications is not so comprehensive, but because of commercial companies have complex business buildings, there are a large volume of Invoicing business data, there's a specific dependence on decision analysis, the data warehouse technology in the business enterprise applications has wide-ranging prospects.

*Self-evaluation/limitation/improvement

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