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Founded in 1837, P&G is one of the world’s largest manufacturers of consumer goods and its headquarter is in Cincinnati, Ohio. By making customers’ everyday life more beautiful in a subtle yet significant way, P&G has been able to continue its growth in the past 180 years. P&G is now doing businesses in about 70 countries and regions around the world and it has factors or branch offices in more than 80 countries. Its more than 65 brands are popular in over 180 countries and regions, among which there are hairdressing, health and beauty, textiles and household care, baby, female and home health care products.
Requirement of the Project
- Rampant counterfeits;
- Diversified and complicated distribution channels make channel management very difficult;
- Widespread customers, hard to manually collect/analyze consumer data by traditional means
- Give every product a unique product ID. The company can accurately check and monitor cross-region sales of dealers at one level or multiple levels; consumers can verify the authenticity of their products in a straightforward and easy way, regulate the distribution market, ensure fair and orderly competition, maintain healthy market development; Gather and analyze data of daily chemical consumers, establish a database and achieve digital marketing; maintain enterprise’s brand image, increase consumers’ confidence in its products.
In order to turn its traditional marketing into digital marketing, P&G put together a cross-business department project team that’s under the direct management of P&G Global and in charge of P&G’s digital (QR code) application. In 2015, it hired WinSafe to upgrade the digital (QR code) management system for all of its products. Its oral health department, Safeguard, Pampers and shampoo department have all implemented “Product Digitization” management and accomplished functions such as anti-counterfeiting, tracing management, CRM management, channel dealer management, big data analysis and so forth.
- Intelligent material tracking and management: Run material recognition, positioning, tracking and monitoring through out the whole process, be it procurement and warehousing of raw materials, WIP management, warehousing of finished product or delivery.
- Production line tagging: Reasonable changes have been made based on customer demands and the system’s native defects without interfering with existing production; help to achieve OEE counting.
- Supply chain cooperative system: Seamless connection of various links, achieve whole-process monitoring of materials, total control of clients and reasonable allocation of funds.
- Product tracing and tracking: Built on the basis of various IOT technologies such as One-Item-One-Code, big data, etc. Modules involved include product management, channel control, logistics regulation and alarm handling.
- Member CRM and points management: Improve customer stickiness on the basis of function modules including member management, marketing, mobile payment, big-data analysis, etc.
- IDC operation and maintenance as well as management: The only enterprise that owns IDC in the industry, with great data management and operation competency.
- 1.Give every product a unique product ID. The company can accurately check and monitor cross-region sales of dealers at one level or multiple levels;
- 2.Consumers can verify the authenticity of their products in a straightforward and easy way, regulate the distribution market, and ensure fair and orderly competition;
- 3.Gather and analyze data of daily chemical consumers, establish a database and gradually achieve digital marketing;
- 4.Maintain enterprise’s brand image, increase consumers confidence in its products
- Pay To Store
- Tracking and tracing
Product tracing and tracking management
- Anti-counterfeiting management
Anti-counterfeiting check for consumers
Channel anti-counterfeiting (shippers, dealers)
- Big data and analysis
- Product customization
- Channel management
Channel inventory transparency
Build member loyalty through QR code scanning,
and achieve precision marketing through big-data analysis