Posted: May 24th, 2022
Data Warehouse Case Study
VF Corporation Data Warehouse Case Study
VF Corporation (NYSE:VF) is a global leader in the development of branded lifestyle apparel including women’s and men’s jeans, outwear, backpacks, footwear, sportswear and occupational uniforms and apparel. The company operates in six different business segments globally including contemporary brands, jeanswear, imagewear, sportswear, and outdoor action sports brands (VF, 2011). The most successful product division is outdoor and action sports, which contributes 38.1% (2009), followed by jeanswear which contributed 34.9% of revenue in 2009. Additional contributions by product division include imagewear, which generates 12% of total revenue, sportswear that generates 6.9%, contemporary brands that contribute 6.5%, and other services and revenue streams that delivered 1.5% of total revenue in 2009. What is unique about the company’s structure is the division by vertical clothing line dispersed throughout the Asian, Canadian, European, Latin American and U.S. markets. At present VF employs 46,600 throughout its global operations. At the close of its latest fiscal year, the company had generated revenues of $7.2B during FY2009 and a net income of $736M. This profit figures represented a 21.5% reduction from 2008 as slowed customer spending and a higher textile costs significantly slowed sales growth.
Figure 1, VF Corporation Stock Performance illustrates how well the company’s stock has appreciated relative to competitors over the last ten years. The competitors included in the analysis include Cherokee (CHKE), Dussault Apparel (DUSS), VOLV (VLOV), and the Warnaco Group (WRC). Comparing the stock performance of these companies over the last ten years to VFC shows how effective the use of advanced information systems are in rejuvenating a brand, as can be seen in the 2008 — 2010 timeframe in Figure 1 below.
Figure 1: VF Corporation Stock Performance
VF Corporation is well positioned to continue growing both in terms of market share and valuation as it is the only competitor based in the U.,S. market with such a widely diverse product line and branding positions across the spectrum of the market. VF also has the unique approaches to branding and segmentation based on geo-demographics as the case indicates. The use of geo-demographics for analyzing large data sets is differentiating the sales and profit performance of retailers (Adnan, Longley, Singleton, Brunsdon, 2010).
There are many factors that contribute to the success of VF Corporation standardizing on a series of applications that enable them to complete geo-demographics and advanced market analysis. The legacy data, burgeoning in size, has begun to dwarf the scalability and performance of its existing personal productivity applications including Microsoft Access and Excel. There are many, many organizations still relying on these personal productivity applications to do data warehousing analysis when more advanced applications are necessary to support strategic initiatives (Weller, 2007).
The case study shows the success of VF Corporation in meeting their data management and analysis challenges by putting a challenge and very clear objective at the center of their efforts. This was the decision to integrate supply chain management, forecasting, and store expansion all into the efforts to gain greater insights into their customer base. This overriding objective of having the right product on the right floor at the right time illustrates how VF perceived the problem as not isolated, but as systemic to the entire business model of the company. Using analytics to create a unified, demand-driven supply network that could enable greater levels of business agility became the highest priority goal, which corresponds to the successful use of analytics to streamline retailing operations from other studies (Lewis, Hornyak, Patnayakuni, Rai, 2008). With the focus on how to optimize supply chain performance over time and make VF orders of magnitude more agile than it had been in the past, the company also moves in the direction of anticipating demand through forecasting more effectively than its competitors. This is because when a demand-driven supply work is created each source of demand and its variation must be anticipated (Lewis, Hornyak, Patnayakuni, Rai, 2008).
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 potential insights as well. The use of geo-demographics to more effectively mitigate risk across customer segmentation, location selection and performance strategies, while developing more effective forecasts is critical to the profitable growth of retailing businesses (Foote, Krishnamurthy, 2001).
At first glance, it appears VF Corporation is being successful with geo-demographics due to its focus on using the insights gained to better manage branding, marketing and location-based analysis. In reality, the use of real-time geo-demographic data generated from their SRC systems are serving to unify its business model by providing greater direction and focus to its supply chain. This in turn creating a demand driven supply network (Lewis, Hornyak, Patnayakuni, Rai, 2008). The result is that VF Corporation is made more competitive using these techniques and translating data warehousing into a competitive advantage over time (Thompson, Walker, 2005).
Adnan, M., Longley, P., Singleton, a., & Brunsdon, C. (2010). Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases. Transactions in GIS, 14(3), 283-297.
Paul Sheldon Foote, & Malini Krishnamurthi. (2001). Forecasting using data warehousing model: Wal-Mart’s experience. The Journal of Business Forecasting Methods & Systems, 20(3), 13-17.
Yang-Im Lee, & Peter R.J. Trim. (2006). Retail marketing strategy: The role of marketing intelligence, relationship marketing and trust. Marketing Intelligence & Planning, 24(7), 730-745.
Lewis, M., Hornyak, R., Patnayakuni, R., & Rai, a.. (2008). Business Network Agility for Global Demand-Supply Synchronization: A Comparative Case Study in the Apparel Industry. Journal of Global Information Technology Management, 11(2), 5-29.
Andy Thompson, & Jonathan Walker. (2005). Retail network planning – Achieving competitive advantage through geographical analysis. Journal of Targeting, Measurement and Analysis for Marketing, 13(3), 250-257.
VF, Inc. (1 February). Hoover’s Company Records,112083. Retrieved February 9, 2011, from Hoover’s Company Records.
Sally Weller. (2007). Fashion as viscous knowledge: fashion’s role in shaping trans-national garment production. Journal of Economic Geography, 7(1), 39.
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