Impact of big data on procurement and supply chain
“At present, the application of big data in the supply chain focuses more on the demand side and marketing field close to the market. Compared with the field of procurement and supply, the field of market demand develops the application of big data in the first place, and many enterprises have gained a lot. Therefore, in the field of procurement and supply, efforts should be made to catch up with the pace of The Times, and use big data to make greater contributions to the supply of enterprises and supply chains. With enough data, good methods and sophisticated tools are needed to turn it into value.
一、 Big data era and its characteristics
Big Data refers to the large scale of Data involved. As the progress of The Times and the rapid development of science and technology, the Internet, the Internet, mobile communications, information management, e-commerce technology such as mutual penetration, and applied to all aspects of the state, enterprises and people’s livelihood, today, people use big data to describe and define the information explosion times produce huge amounts of data, and within a reasonable period of time to capture, management, processing, and to help people deal with affairs and decision-making information and knowledge and so on a more positive purpose.
America’s Internet data centre points out that Internet data will grow by 50 per cent a year, doubling every two years, while more than 90 per cent of the world’s data is now produced in recent years. The world will generate 44 times as much data in 2020 as it does today. Judging from the increase of these data every day, the world has entered the era of big data.
The era of big data highlights the importance of data resources. In 2012, the Obama administration announced an investment of $200 million to drive the development of big data-related industries, upgraded the “big data strategy” to a national strategy, defined big data as “new oil in the future”, and regarded the possession and control of data as another national core asset besides land, sea and air rights. “Big data” is one of five strategic new technologies that the French government announced in 2013 as part of its digital road map. In 2012, Japan’s general affairs ministry released the 2013 action plan, clearly proposing to “open up new markets through big data and open data”. The United Nations pointed out in its 2012 white paper on big data governance that big data is a historic opportunity for the United Nations and governments. China also regards big data industry as a strategic industry and has established “big data expert committee”. In the top 10 trend forecast of “big data” in 2014, it includes data commercialization and data sharing alliance, and the gradual development of big data ecological environment. Meanwhile, the expert committee of big data predicted that big data will have remarkable applications in Internet and e-commerce, finance (stock market prediction, financial analysis), health care (epidemic monitoring and prediction, etc.), biological information and pharmacy in 2014.
The era of big data is an era in which the value of big data is given full play. According to symantec research, the total amount of information storage in global enterprises has reached 2.2zb (1ZB=1024EB, 1EB=1024PB), with an annual increase of 67%. The world produces about 1, 700 terabytes of data per minute, but it’s not just the sheer size of the Numbers that interests us, it’s how we use that data to do something about it. Big data can be applied to all walks of life. In terms of the macro economy, IBM Japan company has established the economic indicator prediction system, searched 480 economic data affecting the manufacturing industry from the Internet news, and calculated the predicted value of the purchasing managers’ index. Indiana university used the mood analysis tool provided by Google to conclude six moods from nearly 10 million netizens’ comments, and then predicted the changes of dow Jones industrial index with an accuracy rate of 87%. On the manufacturing side, Wall Street hedge funds use customer reviews on the shopping website to analyze corporate product sales. Some enterprises use big data analysis to manage procurement and reasonable inventory, understand customer demand and grasp market trend by analyzing online data. According to the calculation of McKinsey, big data will bring us $300 billion of value to the us medical service industry, increase the net profit of the us retail industry by 60% and reduce the cost of manufacturing product development and assembly by 50%. The new demand brought by big data will drive the innovative development of the entire information industry. Big data will add 216 billion pounds ($346.7 billion) or more to potential gains for the UK economy, according to new research from the centre for economic and business research.
2、Challenges and opportunities brought by the era of big data to procurement and supply chain
First, the business environment and business model are becoming more complex and more volatile, diverse and personalized. Second, the rapid development of e-commerce business model breaks the national boundary, making cross-border business increase rapidly and commercial activities frequent, accompanied by the sharp increase in data volume. Thirdly, big data application processing has become an important focus of enterprise and social competitive development. Fourthly, effective mining of big data has become an important issue facing The Times. Finally, many enterprises do not fully understand the importance of big data and its value.
The following is the research, cognition and application of big data by some institutions, showing its development status from different aspects.
1) Gartner’s 2013 report “hype behind the popularization of big data in 2013” (see figure 1) pointed out that 64% of the surveyed enterprises were working on big data or were about to work, but the actual situation was not satisfactory. Many of them did not know what they could do with big data. In 2012, 27% of companies started working on big data, and 31% planned to start big data projects within two years. In 2013, 30 per cent of companies had introduced big data and 34 per cent planned to participate. The reason for this phenomenon is that many enterprises believe that big data can help them improve user experience, improve enterprise efficiency or discover new business models or products. 56% do not know how to extract value from data; 41% of businesses fail to integrate this technology into their corporate strategy; 34% of companies lack the ability to handle big data. 33% of enterprises have difficulty integrating diverse data resources; 29% of enterprise infrastructure is challenged; 27% of businesses face privacy and data security issues; 26% of enterprises have doubts about the investment in big data projects; Even 23% don’t know what big data is.
2）Supply Chain Insights Research Firm
According to a 2012 study on big data and Supply Chain management by Supply Chain Insights Research Firm, enterprises have realized a lack of understanding of big data and its technology. Findings:
In the big data project currently under construction, 36% of the organizations currently have a cross-functional team to evaluate the potential value of big data for their supply chain. The involvement of the CIO is usually the responsibility of the CIO. The team leader who evaluates the big data use and analysis technology of the supply chain is the CIO with 47%, is the business division leader with 21%, and has a cross-business functional management team with 21%. Because of the high complexity of the information management system in focus enterprises, there are usually multiple systems supporting their supply chain, so the amount of data is huge and the integration is difficult. Data grows fast. Eight percent of respondents have petabytes of data in a single database, and 47 percent expect petabytes of data in their database over the next five years. And 68% of companies that are working on big data projects are expected to have petabyte-level data in their database within five years. (5) in the enterprise self-assessment ability to use different data types, application of the best data derived from the traditional supply chain of the transaction data (58% of respondents use this type of data that the data is still the most familiar enterprise), the new geography and map data (47% of respondents use this type of data) and traceability of product data (42% of respondents use this type of data). This was followed by various device data on the Internet of things (28 percent of respondents used this type of data) and sports application data (26 percent used this type of data). Surveys show that respondents have a higher level of mastery of structured data types.
The focus of the plan is currently on visibility of the supply chain, but in the future it is on demand data. The higher the expected earnings, the lower the current performance rating, which is reflected in the area of demand data. With greater familiarity with transaction data and supply systems, retailers with longer supply chains and across multiple borders say that focusing on visibility of supply chains is considered most important.
In July 2013, Supply Chain Insights Research Firm conducted further quantitative Research to understand and study the ability to harness big data that Supply Chain leaders are building. The study was based on an online survey of 123 manufacturers (59%), retailers (26%), wholesalers/distributors/partners (12%) and third-party logistics providers (2%).
Of the respondents, 31% were supply chain team (15% were team members, 12% were in charge, 2% were in other positions, and 1% were support staff). 25% are IT teams (15% are IT directors, 3% are cios, 3% are directors, 2% are managers, 1% are system administrators); Forty-four percent were other teams (16 percent were sales teams, 10 percent were cross-functional business leaders, 7 percent were finance, 3 percent were BI analysts, 2 percent were marketing people, and 6 percent were others).
Research shows that the application of big data is more of an opportunity than a problem. 76 per cent thought there was opportunity, 11 per cent had no concept, and 12 per cent had problems with big data. While databases are growing, they can be managed, however, the largest database is not an enterprise resource planning (ERP) database, but in the area of product traceability data.
28 percent of the respondents have launched a big data application project, another 37 percent have planned big data projects, and the remaining 20 percent have no plans to carry out big data activities. Those respondents who believed that they had the opportunity to apply big data believed that the greatest opportunity for big data application lies in the management of relevant new data, rather than the management of data volume or speed.
Among those who plan to carry out big data projects, 9% are ready to carry out in that year, 53% in 1-2 years, 31% in 3-5 years and 7% without knowing when.
Among the key elements of supply chain, the top three are: the volatility of demand and supply (51%), the ability to apply big data (43%), the talent problem (34%) and the speed of business growth (34%). The top three trends driving supply chain excellence by 2020 are: data visualization (46%), increased visibility of supply chain (39%) and big data (37%).
At present, the application of big data is still in its infancy. In the future, more opportunities and applications will appear in the field of “demand”. Demand planning, order management and price management are the top three areas that benefit the most from big data.
At present, the application of big data in the supply chain focuses more on the demand side and marketing field close to the market. Compared with the field of procurement and supply, the field of market demand develops the application of big data in the first place, and many enterprises have gained a lot. Therefore, in the field of procurement and supply, efforts should be made to catch up with the pace of The Times, and use big data to make greater contributions to the supply of enterprises and supply chains. With enough data, good methods and sophisticated tools are needed to turn it into value. In the supply chain, the most prominent and most valuable application of big data is realized with the help of business intelligence BI software system and supply chain management SCM software system.
二、Application of business intelligence technology in procurement and supply chain management in big data environment
Today, customers demand more and more personalized features, e-commerce and Internet marketing has universal access, a variety of marketing methods, then the mobile Internet and social networking has been gradually into the different level of social life and work, and the traditional management mode and means is difficult to grasp and control the change of demand.
In the era of big data, consumers can choose to buy fully customized products or customize products by themselves in an alternative environment. For example, when buying computer products online, consumers can customize products according to their own needs and preferences. For merchants, in order to expand sales scope and increase market share, they usually adopt special promotion strategies, and conduct deep binding and associated sales of various related products. Personalization drives the product’s life cycle to be shorter and shorter, and the elimination rate to be larger and larger, which forces new products to be launched faster and faster. At certain points in time, e-commerce companies will adopt large scale reduction sales methods, such as double eleven, Christmas, etc., which will trigger large-scale purchase behaviors of consumers.
In general, new business forms, patterns and behaviors will emerge in the new environment and form of society and market, which will bring new problems for the matching of demand and supply balance in the supply chain, making it more difficult for enterprises to master the integration of market demand and resources, leading to imbalance between demand and supply and inaccurate prediction. When the lag of demand signal transmission makes the purchase and supply plan can not catch up with the change of demand, it will cause a large amount of inventory overstocking and often lead to the phenomenon of inventory shortage. As a result, rising costs eat into profits.
For these problems, the enterprise can make full use of big data technology, based on the existing business data, using BI business intelligence and information technology such as supply chain management (SCM, the depth of excavation and analysis of the key business, to master its features and characteristics, find opportunities to improve and optimize, thus make the transition from extensive management to fine management. For improving the business can be implement in all the work and all aspects of procurement and supply business, the application more harvest or larger link in demand forecasting, purchasing strategy and business rule making, the analysis and improvement, supplier sourcing of daily business management, reduce the number of inventory, visual monitoring and early warning, etc.
Case study: big data drives unilever’s supply chain
For unilever (China), the fact that consumers remove a bottle of unilever’s shampoo from supermarket shelves means that its 1,500 suppliers, 253,000 square metres of production base, nine regional warehouses and 300 hypermarkets and distributors are all affected.
These are some of the basic nodes that make up the company’s supply chain system. At one end, it connects 1,500 suppliers from around the world, and at the other, more than 80,000 sales terminals from about 300 retailers and dealers, including wal-mart, tesco, watsons and metro. In addition: products of nearly 3,000 specifications (SKU) from 16 brands, including qingyang shampoo, lux soap, zhonghua toothpaste and secret laundry detergent, as well as annual sales of more than 10 billion yuan in China. Every time a consumer buys a product, the organization of unilever’s entire supply chain is affected.
Deep data mining and demand analysis
Different from consumer durables such as home appliances and automobiles, it is easier to predict the consumption trend and cycle. Due to the higher purchase frequency of consumers, more complicated consumption structure and a lot of uncertainties in the sales process, it is difficult for enterprises to make demand forecast. The biggest headache is big-account purchases, which can quickly run out of stock in supermarkets. To avoid a similar rush, without adding inventory and costs, and without losing customers, unilever needs to forecast sales accurately for the future. Each day, business personnel scattered across the country visit the store, input sales data to a handheld terminal, and constantly summarize sales information to the company’s central database. At the same time, direct docking with the company headquarters database such as wal-mart and dealer’s inventory system, POS system will store sales and inventory data timely reflect to the center of the company database, whether China headquarters in Shanghai or London to global headquarters management personnel, can know China more than 1 m retail stores in any one day sales and business data. There are more than 70,000 sales terminals, and data updates are made on a weekly basis. The data sources of these large samples can ensure that the fluctuations of sales forecasts (such as troublesome and unpredictable group-buying situations) can be kept within a reasonable range.
However, it is not enough to predict the demand in the future for a period of time simply by summarizing the data of purchase behavior. The analysis curves representing the predicted sales volume and actual sales volume only complete the theoretical work by relying on mathematical models and complex calculations, and further analysis is needed. This needs other business data, such as for a certain product promotion scheme is formulated by the price cut or to buy a gift, in a certain period of time spent much publicity, covers many areas or channels, etc., will affect the increase sales of the products finally, at the same time also with other business departments such as production, procurement, finance, market team collaboration, the common use of these data, prediction and analysis results.
Unilever classifies four business categories according to the product forms of 16 brands, and there is a team in each category to predict the sales situation of products, and analyze the actual operation of further influencing the procurement and production. When the shampoo is sold by bottle, the purchasing department will get the information that raw material A and packaging material B will have new demands. In the system, A bottle of shampoo will be decomposed into more than 40 raw materials, and the data will be put on its BOM.
Global collaborative purchasing
According to the global procurement and production system implemented by the company, the impact of consumer purchasing behavior on procurement and production is global. At present, the products of more than 400 brands of the company are produced in 270 production bases in six continents. All procurement issues related to raw materials and packaging materials, including the choice of place and supplier, and the arrangement of purchase scale and frequency, are uniformly distributed globally. This global operation will show scale effect on cost intensity, but it also challenges the company’s supplier management level.
In 2002, the company set up a global sourcing center in Shanghai to export raw materials and finished products from China to the world. The toothpaste produced here goes as far as Chile. Using big data and business analysis, some can improve the efficiency of partners of cooperation at the same time here: some internally is rated a-class supplier is seen as strategic partners, they will provide customized materials for production, and its own design and r&d staff will be the supplier of equipment, process is very familiar with, such as the two sides will be for A new product in A very early start cooperation, unilever will provide guidance to suppliers from the aspects of technology.
Unilever USES big data to manage suppliers and has a set of globally implemented standards. An interagency management team every year to review supplier rating, the a-class supplier to audit twice more, is not only A technical level, product quality, funds and other conventional indexes, including green, environmental protection, labor conditions, such as social responsibility, if failed to meet the requirements in respect of which, will face the risk of disappearing from the purchase list.
Efficient cooperative production
Whenever a product is sold, the production department and the planning department respond to data about the product sold. According to the relevant data of the sold products, the production planning manager will analyze and make decisions. In addition to the demand planning manager to get the demand forecast, he must also obtain other business information, such as the ability of all suppliers to deliver through the procurement team, the knowledge of actual production capacity on the current production line through the plant manager, and so on. The information is then aggregated and analyzed to produce the level of capacity supply for the next period.
According to these data, the factory finally work out production schedule and command an annual output value of 14 billion yuan of production system in every week, every day how to schedule it every factory, every line, depending on the speed and expertise to arrange production (shampoo production line has more than ten), completed the shampoo production of more than 300 specifications (SKU), to maximize the capacity of as much as possible, in order to meet those scattered all over the country and even the rest of the world’s growing demand. The question of when and where consumers plan to buy the shampoo will pose a complex logistical problem for analysts at unilever.
Channel supply chain management, winning on the shelf
Unilever has 9 sales regions in China. First, the finished products will be sent from the total warehouse of hefei production base to the regional warehouses of 9 cities including Shanghai, guangzhou, Beijing, shenyang and chengdu. To ensure that the shampoo reaches the final shelves on time, the distribution resource planner must plan the route as well as take into account inventory costs and fluctuating transport capacity along each route. For example, the Spring Festival will be the peak season for unilever’s products sales, while the railway line to the west will be very crowded and the road transport will be busy near the Spring Festival, which also takes into account many temporary emergencies on the road. Therefore, sufficient data must be available for detailed analysis and consultation with other business units to make decisions such as “planning how to build inventories in advance in the western district”.
Unilever USES live data, from the changes of each product on the supermarket shelves to its own suppliers, which is a data link that can generate high value, and it USES the data of each node on the link to optimize and improve the business, which makes the business operation achieve a remarkable performance. Through the analysis of the shortages, for example, find out the real cause of the shortage of a bottle of shampoo on the shelf, is not in time order in store, or virtual inventory system, or because the inventory piled up problems, find the real reason for the shortage rate is improved and satisfy its focus on store shelves rate increased to 98%, up 8% (shelves every 3% increase in rate, will drive product sales increased by 1%); And as with a convenience store launched bus project optimization, the unilever hefei general warehouse, tesco jiashan warehouse, tesco store between hefei and the both sides of the pickup, delivery and transportation lines together carries on the analysis and optimization design, reduced the empty rate of return, save about 10% of the logistics cost, but also completed the company on carbon emission reduction requirements; For another example, through analysis and optimization, service efficiency and customer return on investment are improved. In 2011, unilever’s ranking rose from 20 in 2004 to the second place, achieving its goal of “winning on customers”. It has greatly improved its sales, procurement, inventory, production and logistics businesses.
Optimization and decision-making of procurement and supply chain management in big data environment
The increasingly complex business environment makes the rationality of supply chain network structure an important problem of supply chain management and a new challenge for enterprise supply chain management. Enterprise and supply chain management is faced with continuously improving customer satisfaction, to meet the challenge of the globalization management, they hope to continuously expand and occupy more market, to develop and produce more and better products, in the most appropriate time and place, with the most low cost, the most preferential price, the best state and quality as the most suitable customers provide the best goods and services, can effectively identify and determine the supply chain strategy to achieve the balance of cost and service, and to maximize their own interests.
For a long time, enterprise and supply chain managers have been suffering from the lack of effective management methods and technical means, unable to achieve scientific and correct decision-making and optimization to guide the business to achieve the best operation, such as:
Where should raw material/parts be obtained at the lowest cost on the basis of the original service level?
How to improve the service level while keeping the cost basically unchanged?
What procurement strategies should be adopted to balance established costs and services? Is the warehouse set up by itself or by the supplier? Where is the best place to locate? Which points of production or business should the goods in the warehouse be supplied? How much and in what way is the best supply? How to integrate existing/new supplier capabilities to support targeted production capacity if new products or new markets are introduced? How much stock should be held in advance when seasonal demand will increase? When the supply capacity is insufficient, should we expand the supply capacity of existing suppliers or seek new suppliers? Which factories (DC, warehouse, etc.) should produce (deliver, store, etc.) which products? How much of each production (delivery, storage) can maximize value? Should we add (or reduce) operating facilities (factories, warehouses, DC, service centers, stores, etc.)? How does the inventory strategy for various items in the warehouse (DC) minimize inventory and reduce shortages? Which downstream nodes should a supplier (factory, DC, warehouse) supply? For what? How much profit do they each provide? What level of inventory should be set up throughout the distribution and distribution network? What are the inventory Settings needed to meet market demand and minimize network inventory? How should the pricing and promotion be used to increase sales at the lowest cost?
The complexity of these problems lies in the balance between so many factors involved that it is impossible to solve them by means of artificial means or simple calculations. To make the optimal decision on these problems, it is necessary to use big data as the basis, extract data with BI analysis, and simulate and optimize the whole supply chain network or some local links with the simulation and optimization function of supply chain management system. The objects for simulation optimization can be events, facilities, paths, processes, products, transportation, nodes, etc., or the network composed of these elements and related businesses, which can be single-objective optimization or multi-objective optimization. In general, the working principle and flow chart of supply chain optimization decision are shown in FIG. 11.
Case: supply chain optimization case — the optimization of ford’s supply chain business
To enhance its competitiveness, ford has successfully implemented close business connectivity with its thousands of suppliers and service providers using business data and optimization tools.
Ford has more than 4,000 suppliers worldwide and supplies more than 100 of its manufacturing plants around the world. Ford’s goal is to have a good way to optimize its complex, global supply and production network, using the Internet to connect its automotive supply operations, suppliers and service providers directly with suppliers and logistics providers to exchange and share information on the delivery of materials and production plans. It adopts the method of supply chain optimization modeling, and it can filter and evaluate a data class group of nearly infinite elements at the same time. On this basis, it can be optimized to provide ford with an optional sequence-based decision-making process.
After obtaining data related to parts and production, the optimization system produces an optimized supply process consistent with ford’s intended supply business priorities. These priority factors can easily be introduced into every simulation run, enabling ford to balance elements and subtle differences in the supply chain network, thus providing advanced analysis with “what if” capabilities.
For example, some production planning managers may intend to implement special policies and processes in their factories to receive and manage inventory and parts, such as keeping less than two hours of inventory in their own warehouses. Before implementing these business changes, constraints can be inserted into the optimization system, and then the impact of constraints on supply cost and other factors in the network can be observed through the system. With this optimization method, ford can make optimal decisions. For example, “if you spend X doing something, will you get more value than X?” “Is this the right business decision in the supply network?” .
Ford’s network is complex, with the global supply division employing some 300 logistics professionals to transport incoming materials to assembly points and deliver cars from factories to dealers and customers around the world. The average car in a final assembly plant needs about 2,500 parts. Close to 4,000 global suppliers ship parts and components to 31 factories that produce engines and tachometers, 13 stamping plants and 54 assembly plants. From those assembly plants, the vehicle is shipped to more than 20,000 dealers in more than 200 countries. Ford’s $6.5 billion a year in transportation costs actually encompasses all existing modern modes of transportation.
In order to get closer to its customers, ford turned to order oriented production, a consumer-driven business environment and a lean manufacturing approach, in the hope that the logistics department would provide a “complete, timely and repeatable logistics cost assessment scheme” based on potential resources. Another expectation is to implement the goals and strategies for integrating incoming logistics with the synchronized material flow in the plan. The challenge is the lack of service in the U.S. rail system, which requires a shift to road transportation.
However, factory-level obstacles often disrupt the execution of plans, such as the inability of incoming trucks to unload at predetermined locations, the inability to deliver components and components directly to individual assembly lines, and so on. Ford clearly understands that it must closely integrate its logistics processes with those of its logistics partners to achieve this goal. Ford, whose Logistics service providers include Penske, Worldwide Logistics, FedEx and Autogistics, a division of UPS, believes that seamless integration of business with these service providers is based on the integration and sharing of information.
In three stages, ford optimized the supply and purchase cycle of the new model project, namely, the strategic stage, the tactical stage and the operational stage. The strategic stage includes resource decision-making, for example, multiple resource schemes arising from a factory change, monetary and trade issues, market issues, etc. The information is then fed into a strategy simulation scenario, where the supply and logistics costs are assessed and then fed back into the strategy optimization process. The operational planning process begins when the resource decision scheme is established. Ford provides its logistics needs to leading logistics providers who support the plan by designing logistics networks. This system not only enables ford to adapt quickly and flexibly to changing conditions, but also increases the predictability of supply strategies.
The most significant impact is to optimize the quantity and ideal location of cross-space in north American assembly plants with the minimum total cost. Ford has modeled and analyzed the impact and constraints of related cost factors and supply chain networks. Optimization systems are simulated under specific assumptions for 21 assembly plants, 1,500 suppliers and 46,000 different incoming parts and components. After checking the supplier’s location and demand points according to the demand, ford was prepared to set up 45 distribution centers on the supply chain network as the place for warehouse operation. After nearly 2 months of modeling and simulation analysis, the optimization plan only required 15 warehouse operation sites, which greatly saved the cost.
It then moves into the “what if” phase of analysis, where other resources, such as those from elsewhere within the ford enterprise and the outsourcers, need to be considered. The global supply chain technology department has run more than 40 simulation schemes and solved them when changing cost, quantity, frequency and other factors. For each scenario, it takes about an hour to simulate changes to produce results that fit into the business environment.
At the same time, data collection and optimization is also a key work, and the direct smooth and smooth data flow between points and points in the supply chain network is very important for the optimization process. Initially, due to the lack of tools and ability to collect, store and analyze big data, the proportion of time spent in the traditional optimization work was: 90% of the efforts were used to collect and input data, 5% for process analysis and the other 5% for output results. However, after the optimization system was adopted, the proportion changed significantly: 5% of the optimization work was used for data processing and input, 5% for process analysis, 5% for output results, and 75% for analysis of results. The rest of the time is spent reviewing the optimization process and selecting the solution. As Koenigbauer remarks: “right now, we spend 75 percent of our time analyzing the output from the optimized system and thinking about what the next solution really means for our business. We also have the ability to integrate with other parts of the business and coordinate with our internal colleagues to solve the problem of material delivery to the factory. “” if you have a pretty good supply chain network and simply take advantage of optimized systems, you can increase efficiency by 20-30%. In the material purchase project, we not only saved the transportation cost, but also kept the transportation cost unchanged when the delivery frequency increased obviously. We went from an average 22 per cent per day for parts to a 97 per cent per day rate. That’s a huge benefit for ford.”
China is a natural market for big data. A study by EnfoDesk found that 2014 will be a year of rapid development of big data in China’s supply chain, with a market size of 2.9 billion yuan and a growth rate of 42%. Supply chain big data application enterprises must take a strong position in advance. By 2016, China’s supply chain big data market will reach 5.96 billion yuan.
In view of the development process of domestic data centers, domestic large data centers start from the banking industry and gradually develop into large state-owned enterprises such as insurance, telecom and power. Up to now, domestic industries that realize big data centralization include public utilities, finance and power. In the future, industries realizing big data centralization include medical treatment, automobile, retail, manufacturing, etc. Industries that are able to achieve big data value-added mainly include e-commerce, logistics, etc
At present, China’s supply chain big data industry is in its infancy and will develop rapidly in the next few years. The supply chain collaborative data platform with deep industry accumulation will be the field where capital mainly enters in the coming years. The entry space of the collaborative application market of the tertiary industry supply chain is relatively large, especially in the segments such as medical, finance and e-commerce. The collaborative market maturity of the second industry supply chain is gradually improved, especially in the fields of logistics, automobile, retail and public utilities. The collaborative data of supply chain will play a core driving role in market upgrading.
IDC expects the market for big data technology and services to grow by 27% in the same period last year, to reach 32.4 billion in 2017. All these indicate that the application of big data has a very broad prospect. Facing a tidal wave of big data, China’s enterprises should quickly meet the challenge, seize the opportunity, purchasing and supply chain management, in particular, must be fully aware of the important value of data, the active use of big data and other related information management tool, to carry out the application in the business of purchasing supply, grasps the business rules, find business opportunities, the optimization of various business make rapid scientific correct decision-making, and direct the implementation of procurement, the escort for the enterprise and supply chain management, make full use of big data application value to realize the maximization of the profit of enterprise and supply chain management and value maximization.
This article is from: China logistics and purchasing federation