From Supply Chain Efficiency to Customer Segmentation Focus
Because of this focus on supply chain forecasting accuracy and efficiency, the need for capturing very specific customer data becomes critical. The case study portrays the capturing of segmentation data as focused on growing each of the brands mentioned that VF relies on this data to base marketing, location development and store introductions, and pricing strategies on. In reality, the data delivered for these marketing programs and location-based analyses is also providing an agile and scalable platform for VF to more effectively manage and mitigate its supply chain risk as well.
Relying on Alteryx for data analysis as it has superior capability to Microsoft Access and Excel in conjunction with the use of SRC Software for geo-demographic analysis, VF has created a workflow for translating data warehouses into the basis of marketing and supply chain strategies. The strategic goal of getting the right product on the right floor at the right time is further supported by secondary objectives of making data warehouses more efficiently integrated into the VF data warehousing and analysis tools. A secondary objective of more effectively creating an effective retail network is also shown in how the geo-demographic analysis is used for selecting, investing in and launching store locations (Thompson, Walker, 2005). Geo-demographic analysis can illustrate where the best possible income and age demographics exist to support a new store (Lee, Trim, 2006). In addition to these customer-centric measures of performance, geo-demographics can effectively be used to optimize a distribution network to mitigate supply chain costs and inefficiencies (Lewis, Hornyak, Patnayakuni, Rai, 2008).
Another factor that shows how VF is attempting to unify its entire business model with analytics is how focused the organization is becoming on making analytics real-time in nature to measure store, brand and location performance, which is an emerging best practice in the retail industry (Adnan, Longley, Singleton, Brunsdon, 2010). This focus on using geo-demographics to accelerate their entire business model is also seen in how the company is working to streamline the new market forecast or market...
Data Warehousing Data Warehouse technology has changed the way that global organizations conduct business. Many have found it impossible to create a business strategy without a data warehouse. The purpose of this discussion is to research and explain the importance of data warehouse management. We will begin by defining data warehouse and describing the business uses for the technology. Our discussion will then focus of data warehouse management. We will examine the
Data Warehousing and Data Mining Executive Overview Analytics, Business Intelligence (BI) and the exponential increase of insight and decision making accuracy and quality in many enterprises today can be directly attributed to the successful implementation of Enterprise Data Warehouse (EDW) and data mining systems. The examples of how Continental Airlines (Watson, Wixom, Hoffer, 2006) and Toyota (Dyer, Nobeoka, 2000) continue to use advanced EDW and data mining systems and processes to streamline
Similarly, the Air Force needed no only some intelligent reporting capabilities, but a way that Air Force personnel, government employees, and civilian IT contractors would work together in the evaluation of applications and reports in a more robust and real-time manner. "The intent was to provide the Keystone user community the ability to do more complex financial analysis and reporting on a "self-service" basis to reduce overall system maintenance and
because the system is designed to be able to handle complex queries for information much faster than are traditional databases, designing and implementing such an attack becomes more difficult and complex (Warigon, 1997). At the same time, the ease with which information in a data warehouse can be manipulated creates more significant problems than a traditional database should unauthorized access be obtained (TechFaq, 2010). While no database or information
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Since poor data quality within a system often results in poor business decisions being made from this data, it is very important that each administrator or system architect look at each customer or end-user differently, in their own unique light. Since each end-user is different, and the needs of the customer often stem from the warehouse's ability to accurately store quality information, a system that dates back to the 1970's
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