Manufacturing and consumer spending are still the backbone of economic growth in several countries and represent important sectors. While manufacturing has evolved significantly in past decades, there is still much opportunity in supply chain optimization particularly through shared data between partners and investment in technology and operations resiliency. Another major trend is “Servitization" which refers to the increased role of services to future profitable revenue growth of leading manufacturers.
The retail sector on the other hand is undergoing a sea of changes in attitudes and operations, with the biggest changes happening in "brick & mortar". Companies now clearly realize how the power has shifted considerably away from brands and towards the consumer fuelled by the mobile, digital and social revolution. At the same time some companies have also become quite savvy in using the power of information technology to reshape omni-channel customer experience, customer loyalty, contextual marketing, merchandising and pricing optimization.
So whether it is operational streamlining and service optimization, warranty and product failure analytics driven by telematics data in manufacturing, or supporting the double-digit increase in use of Big Data analytics for customer acquisition, pricing optimization and market intelligence in retail, companies need a faster way to access data, more agility to change and be far more cost-effective than traditional replication-based integration.
Learn how data virtualization for retail and manufacturing can help you to improve your business. It enables companies to squeeze value out of all data, and do it efficiently across operational and analytical uses. In fact the creation of a common data layer that can deliver the same logical views of integrated data in different format for analytics and other formats for operational integration has many benefits including accuracy and reuse. Below are some of the specific applications of data virtualization for agile data integration and data services in manufacturing and retail:
One of the world's leading heavy construction equipment maker has made big investments in servitization. By using telematics Big Data from its customer’s equipment, and combining it with other customer, product, warranty and service data they deliver optimal recommendations for services to customers and dealers that both save money for customers and increase profitability for vendors. They use data virtualization to integrate Big Data with other sources to feed analytics and also to deliver the results of analytics as operational alerts into dealer and customer systems through data services.
Servitization is also very important in the high-tech sector. A leading network equipment maker uses data virtualization for delivering the underlying data platform for smart services analytics through customer and reseller portals and is realizing significant profit realization from services while the product margins continue to erode.
Manufacturers who have grown through M&A have to make acquisitions accretive within months. On the one hand this requires realizing cost efficiencies and on the other to be able to cross-sell customers through enlarged dealer networks and product catalogs. While separate PLM, ERP and CRM systems may take months or years to consolidate, data virtualization has been used to create unified product catalogs, pricing and discount tables, and accelerate the rationalization of information delivery to dealer networks for maximized revenue. The virtual catalog is then used as an abstraction layer between existing sources and cross-platform sales systems and dealer portals.
Retailers have long known that an empty shelf is lost revenue and excess inventory is excess cost. To optimize the movement of the right goods to the right shelves at the right times and support them with the right promotions yields the elusive profitability in retail. Data virtualization has helped create virtual inventory views across manufacturing, warehousing, transportation and retail locations and systems to provide a real-time accurate view of where there is inventory of each product to allow merchandisers and store managers to make hourly order and stocking decisions
As discussed in the above example, in addition to having the right goods on the shelves companies must promote them appropriately - this is contextual marketing which requires deep customer understanding and the agility to act on it. Data virtualization helps drive the data towards big data analytics and takes actionable insights back to points of engagement. For example, a major fashion retailer is capturing data from traditional sources such as POS and transaction systems along with social media indicating customer preferences and intentions, market trends and events and competitive pricing information. This is used for various predictive analytics on what products will sell where and to whom and at what price point or discount level. Data virtualization also enables these complex analytics to be disseminated as data services that can be consumed by web, mobile and merchandizing systems without additional programming.
A high-tech manufacturer of storage disks and memory chips has multiple manufacturing and testing facilities across the globe and yet has global quality and warranty standards regardless of which plant makes the products. The data from testing is aggregated in near-real time and combined using data virtualization to feed the statistical analytics for both production quality control and to grade the output disks into appropriate categories for warranty. This common data layer is also used for all types of Quality control reporting across the company.