What and big data are widely used in the whole supply chain. In the digital age, data analysis has gradually become one of the necessary skills for employees. So pay attention to data analysis. So what and big data are widely used in the whole supply chain?
What and big data are widely used in the whole supply chain? 1 Challenges and opportunities brought by the era of big data to procurement and supply chain.
1, the era of big data and its characteristics
Big data refers to the huge amount of data involved. With the continuous progress of the times and the rapid development of science and technology, technologies such as Internet, Internet of Things, mobile communication, management informationization, and e-commerce continue to penetrate each other, affecting all aspects of the country, enterprises, and people's livelihood. Today, people use big data to describe and define the massive data generated in the information explosion era, as well as the information and knowledge that can be captured, managed, processed and organized in a reasonable time to help people handle affairs and make decisions.
According to the data of American Internet Data Center, the data on the Internet will increase by 50% every year and double every two years. At present, more than 90% of the data in the world are only produced in recent years. In 2020, the scale of data generated globally will reach 44 times that of today. Judging from the increasing amount of data every day, the world has now entered the era of big data.
The era of big data highlights the importance of data resources. In 20 12, the Obama administration announced that it would invest 200 million dollars to promote the development of big data-related industries, upgrade the "big data strategy" to a national strategy, define big data as "the new oil of the future", and regard the possession and control of data as another national core asset besides land, sea and air rights. In 20 13, the French government issued its "digital road map", which listed five strategic high technologies, and "big data" was one of them.
In 20 12, the Ministry of Internal Affairs and Communications of Japan issued the 20 13 action plan, which clearly stated that "opening up new markets through big data and open data". The White Paper on Big Data Government released by the United Nations in 20 12 pointed out that big data is a historic opportunity for the United Nations and governments. China also regards the big data industry as a strategic industry and established the "Big Data Expert Committee".
20 14 "big data" ten trends forecast includes the alliance of data commercialization and data sharing, and the gradual development of big data ecological environment. At the same time, the expert committee of big data predicts that in 20 14 years, big data will have significant applications in the fields of Internet, e-commerce, finance (stock market forecasting, financial analysis) and health care (epidemic monitoring and forecasting, etc.). ), bioinformatics, pharmacy and so on.
The era of big data is an era in which the value of big data is fully exerted. According to Symantec's research report, the total information storage capacity of global enterprises has reached 2.2ZB( 1ZB= 1024EB, 1EB= 1024PB), with an annual growth rate of 67%. The world produces 1700TB of data every minute, but what attracts us is not only the huge numbers themselves, but what we do with them.
Big data can be applied to all walks of life. In terms of macro-economy, IBM Japan established a forecasting system of economic indicators, searched 480 economic data that affected the manufacturing industry from internet news, and calculated the predicted value of purchasing managers' index; Using the emotion analysis tool provided by Google, Indiana University summed up six emotions from the messages of nearly10 million netizens, and then predicted the changes of the Dow Jones Industrial Average, with an accuracy rate of 87%.
In manufacturing, Wall Street hedge funds analyze the sales of corporate products based on customer comments on shopping websites. Some enterprises use big data analysis to realize the management of purchasing and reasonable inventory, and understand customer needs and grasp market trends by analyzing online data, and so on.
According to McKinsey & Company, big data will bring US$ 300 billion in value to American medical service industry, increase the net profit of American retail industry by 60%, and reduce the product development and assembly cost of manufacturing industry by 50%. The new demand brought by big data will promote the innovation and development of the entire information industry; According to the latest research by the Center for Economic and Business Research, big data will increase the potential benefits of the British economy by more than 2 16 billion pounds (about 346.7 billion US dollars).
2. Challenges and opportunities brought by the era of big data to procurement and supply chain.
First of all, the business environment and business model are becoming more and more complex, turbulent, diversified and personalized. Secondly, the rapid development of e-commerce business model has broken national boundaries, leading to the rapid increase of cross-border business and frequent business activities, accompanied by a sharp increase in data volume. . Third, big data application processing has become an important focus of enterprise and social competition and development. Fourth, effectively mining big data has become an important issue facing the times. Finally, many enterprises do not fully understand the importance and value of big data.
What and big data are widely used in the whole supply chain? 2 Why is timely and accurate data so important in supply chain management?
1. Data types in the supply chain
There are many types of data, one of which is to divide it into static data and dynamic data. The former includes basic company information, product model, purchase price, BOM and other relatively fixed information.
The latter is mainly some transactional information, such as the daily output of the production line, the number of customer orders, the actual receiving quantity of the warehouse, the transportation location and so on.
As long as the static data is accurate, there is no real-time requirement. For example, the company name is generally not changed, as long as the company address, legal person and bank account number are correct.
The requirements for dynamic data are very high, not only accurate, but also able to reflect the actual situation at all times.
Everyone has the experience of online shopping. After the goods leave the warehouse, the courier company will refresh the location of the package every once in a while, which is achieved by GPS positioning on the car, and then according to the truck distribution plan, the delivery time can be roughly given. Through the GPS on a truck, the cargo of the whole vehicle can be tracked, which is the relationship between 1 and n, so the cost of realizing dynamic data is not high.
The situation of discrete manufacturing industry is much more complicated. A commodity needs to be traced back to the raw material supplier. After entering the factory, it needs to go through several different production and processing centers, and then complete assembly and inspection before it can be put into storage and distributed to downstream dealers or retailers.
We rarely put tracking and positioning devices on raw materials unless the goods are of high value or there are mandatory regulatory requirements in this regard, such as drugs.
If you want to track the production progress, you need to use the technology of Industry 4 and 0, install sensors on each device, and the system will automatically upload data after processing. If you want to install sensors on every production and internal handling equipment, it will be too heavy for a factory and cost-effective. Except for a few industry benchmark enterprises, the idea of doing real-time data is not strong for most factories.
2. Why does the supply chain need timely and accurate data?
Speaking of this, the supply chain has a strong demand for timely and accurate data, because we want to establish seamless links between all production, distribution, procurement and after-sales service. In addition, there are two key factors that make it necessary for us to obtain timeliness and accuracy.
2. 1 Enhance the visibility of the supply chain.
For the participants in the supply chain, the key visibility issues include the expected production and delivery time of goods. For example, the supplier promised to deliver the goods in 30 days, but in fact he needed 45 days. Because some raw materials have gone up in price, the supplier needs more time to find the source of goods in the market. He is unwilling to buy more expensive raw materials because it will increase the cost unless the customer is willing to accept the supplier's price adjustment request.
The location of raw materials and spare parts inventory also belongs to visibility, and customers need to arrange subsequent production and sales plans according to this information, which is very dependent on the accuracy of the information. When the supplier promises to deliver the goods to the customer's factory on a certain day, the supply chain will input this information into the system and make a production plan based on it. Sales will inform customers according to the production completion date, which is closely linked.
Once the supplier information is wrong and the goods arrive later than the promised time, the downstream arrangement of the supply chain will be affected, and the so-called "plan can't keep up with the changes" will occur.
Tracking the delivery date and inventory location is only the primary visibility, and the deeper demand is to be able to warn the risk of supply chain interruption. According to the available information, we need to judge when and where the shortage will occur and what impact it will have on production and sales.
For example, if some parts are missing in the production line, it will be shut down for 4 hours. If the output per hour is 100 sets of products, and the price of each set is 200 yuan, the loss is equal to 4 *100 * 200 = 80,000 yuan.
Of course, in the real world, the calculation method is more complicated, and the shortage of a certain raw material will involve more than N kinds of products and more than N customers. If visibility can be enhanced, we can foresee the potential supply shortage in the future and respond at the first time.
To do this, it is necessary to automatically transfer data between the upstream and downstream of the supply chain in time and accurately, and minimize human intervention.
2, 2 improve the effectiveness of the plan.
The important input of forecast plan is historical sales record. Based on the data, combined with the prediction model, the medium and long-term prediction is made.
For manufacturing enterprises, finance needs the input provided by the supply chain to formulate future business plans and various budgets, such as inventory, purchase volume, freight and so on.
The accuracy of the underlying data is very important. All plans are based on these data, match the data model and then "process". The supply chain will spend some time on data maintenance, which is to ensure the accuracy of basic data.
We know that the prediction is regular, and the short-term accuracy is higher than the long-term accuracy. Just like forecasting the weather, the weather forecast is the most accurate for tomorrow's weather, and the later, the lower the accuracy.
In order to enhance the accuracy of forecasting, the supply chain needs to get the latest data, so the higher the accuracy of planning. Now the demand fluctuates more and more frequently, which may be the same every day. In order to make the most accurate judgment, we must use the latest data.
3. Key issues of obtaining timely and accurate data
Considering the above two motives, the supply chain has been striving to obtain the most timely and accurate data. There are a few points that need special attention.
3. 1 Automatic data acquisition
If possible, we should try our best to collect and transmit data in real time. Data is stored in all nodes inside and outside the supply chain. In order to improve the reliability and timeliness of data, the best method is automatic collection.
IT is relatively easy to achieve this internally by investing in digital tools and implementing IT projects.
It is more difficult to realize it among external partners, and the biggest obstacle is the fear of trade secrets disclosure after * * * enjoys the data.
The supplier is worried that if the customer knows the information of his upstream supplier, he may skip the middleman and not let him continue to earn the difference. Therefore, when doing system docking, we must ensure that only data that can be shared, such as packaging specifications, can be shared.
3,2 Control access to related data
Give users specific data access rights according to their functions in the company. For example, purchase orders can only be created and modified by purchasing planners, and others in the company only have the right to view them.
The same is true for external partners. Customers can check the inventory information of suppliers, but they are not allowed to contact commercial secrets such as commodity cost analysis.
3, 3 efforts to improve and maintain the accuracy of data.
We need to constantly improve the accuracy of data, the key of which is data collection and input. We need to maintain data regularly. For example, there is a negative number in the inventory or backflush account in the system, indicating that there are problems with the data in some places and there may be loopholes in the process. We need to find the problem and deal with it as soon as possible.
Data is the basis of supply chain, which provides a basis for us to make various plans. Although it is a bit expensive to realize accurate and timely data, investment will inevitably bring corresponding returns during the period of great damage to the supply chain.
What and big data are widely used in the whole supply chain, and the three big data have become the weapon of the supply chain.
Retail, manufacturing, service (non-financial) and medical industries account for the largest share of big data in China's supply chain, accounting for about 83% of the market share, while energy only accounts for 1%. According to the forecast of Analysys think tank, the market size of supply chain big data in China will reach about 6 billion in 20 16 (excluding supply chain finance).
The report divides supply chain big data into four types: structured data, unstructured data, sensor data and new data, covering transaction data, time period data, inventory data, customer service data and location data. The report shows that at present, big data has been widely used in supply chain links including logistics, services and finance.
Effectively promote the reform of logistics mode
In the supply chain, the role of big data is first reflected in logistics. 20 14 12.26 According to the data released by China Logistics Information Center, 11month, the total social logistics in China 196.9 trillion yuan, an increase of 8.3% at comparable prices, a decrease compared with the same period last year/. From the situation in the past five years, the asset growth rate of logistics enterprises has gradually slowed down, and the operating efficiency of logistics enterprises is weak.
In this case, logistics enterprises need to provide services that exceed customers' expectations from the perspective of value extension, and develop with the idea of efficient logistics and value-added services, and big data is the basic element for logistics enterprises to provide value-added services. In addition, with the rise of many specialized logistics modes, the core of reducing supply chain costs will be the use of data assets. Big data can effectively promote the transformation of efficient logistics mode and is an effective means to reduce logistics costs.
With the help of big data, enterprises can cooperate with China Meteorological Service Center to collect highway information, provide weather forecast and road live service for national highways, optimize driving routes, monitor, evaluate and warn the status of vehicles and goods in real time, and intelligently trace product transportation.
Through big data, enterprises can effectively predict and evaluate risks and make reasonable, accurate and scientific decisions according to decision-making factors such as logistics time, cost, service, logistics data and customer demand. Using logistics data, enterprises can make detailed regional and online store forecasts to help e-commerce platforms and express delivery companies make quick decisions.
For example, Amazon's patent "Predicting Logistics" is a model that uses big data to gain insight into user needs. "Forecast Logistics" will detect the mouse stay time of users on goods, and then comprehensively consider users' purchase history, search records, wish lists and so on.
Therefore, according to these massive data, we can predict the purchase behavior of users, and transport these goods out of the warehouse in advance and store them in the consignment center. When the user really places an order, it can be delivered immediately. By using big data, Amazon has greatly shortened the delivery time of goods.
Constructing forecasting model to improve synergistic effect
According to the analysis of big data, logistics enterprises can establish a forecasting model, realize the accurate prediction of product sales, and then realize the accurate calculation of future inventory, so as to make the inventory distribution of factories, regional markets and local markets more reasonable, thus improving the synergy effect. Enterprises can control and supervise the whole supply chain by comprehensively grasping all the basic data in the process of supply chain logistics and combining their own resources and capabilities.
For example, CAR Inc' s rental rate once reached a certain level, and some cars were vacant. By using SAPHana, CAR Inc, a database platform launched by SAP, the process was optimized, and the vehicle utilization rate was increased by 65,438+05% again.
Provide accurate financial services
Conduct industry analysis and price fluctuation analysis through big data technology, put forward early warning, avoid credit risk, conduct credit evaluation on target customers, approve short-term small loans, and provide loans for precise financial and logistics services.
For example, in order to realize the docking between banks and small and medium-sized foreign trade enterprises and break the state of unequal information, Yitong Company, a subsidiary of Alibaba, uses its own system processing capabilities to integrate financing work such as supervision, application, delivery, repayment and lending into a unified information network processing platform, and controls the whole transaction process.
Obtain the detailed data and information of the transaction link, verify the authenticity of the enterprise trade with the help of the third-party service platform, realize the information interaction, business collaboration and transaction transparency of all parties, and find a feasible solution for the financing problem of small and medium-sized enterprises.
In supply chain finance, big data can also provide many value-added services. Use big data to obtain user demand information from the source, gain insight into potential demand, and provide information consultation for the supply chain; It can carry out all-round credit management for upstream and downstream customers of supply chain finance, form an interactive supervision and control mechanism, and reduce transaction costs and risks; Analyze and predict supply chain performance, guide supply chain management, especially the operation of supply chain collaborative data.