1. Inventory optimization. For example, SAS's unique and powerful inventory optimization model enables minimizing supply costs and improving supply chain responsiveness while maintaining a high level of customer satisfaction. Its inventory costs can fall by 15% to 30% in the first year, and its accuracy in predicting the future rises by 20%, resulting in a 7% to 10% rise in its overall revenue. Of course there are other potential benefits such as increased market share. In addition, the quality of the product will be significantly improved by utilizing the SAS system, and the defective rate will be reduced by 10% to 20% as a result.
2. Creating operational benefits, a large amount of data is collected from supply chain channels, as well as from a network of instruments or sensors at the production site. Tighter integration and analysis of these databases using big data can help improve inventory management, the efficiency of sales and distribution processes, and the continuous monitoring of equipment. For manufacturing to thrive, companies must understand the cost benefits that big data can produce. Predictive maintenance of equipment is in a position to adopt big data technology now. Manufacturing will be a major source of Big Data business revenue.
3, B2B e-commerce supply chain integration. Strong e-commerce will lead the upstream downstream production plan - downstream sales docking, this docking trend is the upstream manufacturing outsourcing supply chain management Supply-Chain, focusing only on the production Manufacturing, ProductionChain (R&D). Logistics outsourcing up to supply chain outsourcing is a huge leap, reflecting the strong competitiveness of e-commerce and integration capabilities, massive data support and cross-platform, cross-company docking has become possible.B-B supply chain integration has a strong market space to improve China's industrial layout, industry chain optimization, optimize the distribution of production capacity, reduce inventory, reduce supply chain costs, improve supply chain efficiency.
4, logistics platform scale development, B-C business model integration has become a reality, but the construction of logistics execution platform is dragging the bottleneck. The integration of the sales supply chain of multiple products has great technical difficulties, such as the supply cycle, inventory cycle, distribution time, logistics operation requirements, etc., such a logistics center is very difficult, the construction of big data platform will drive the overall sales supply chain integration; China's there is the reality of the problem of inter-regional logistics and distribution, urban-rural differences, etc., the government's control is a major difficulty/troubleshooting, the big data platform to help the government's functions The big data platform will help the government to adjust its functions in place.
5, product co-design, in the past we are most concerned about product design. But now, in the product design and development process, the relevant personnel collaborate with each other, the factory and manufacturing capabilities are also in synchronized design and development. The current pressure is to deliver to the market more competitive, higher configuration, lower price, higher quality products, and at the same time to meet all these requirements, is the next major value of manufacturing and engineering companies. That's where big data comes in.