What impact will big data have on the supply chain?

What impact will big data have on the supply chain?

What impact will big data have on the supply chain? The arrival of the era of big data provides a rare opportunity for supply chain management, but it will also be accompanied by some bad effects, both advantages and disadvantages. Keeping pace with the times is the right direction. The following is about how big data will affect the supply chain.

What impact will big data have on the supply chain? 1 Challenges faced by traditional supply chain management model

The arrival of the era of big data not only provides us with great development opportunities, but also the challenges faced by the traditional supply chain model greatly intensify the competition among enterprises under the new productivity conditions, precisely because of the contradiction between the productivity characteristics of the era of big data and the traditional productivity characteristics supply chain management model.

Therefore, the challenges faced by the traditional supply chain management model are also very severe. The substitution of new things for old things is bound to be the transformation and upgrading of old things themselves, and the supply chain management model is no exception to adapt to the development of new things.

1, slow response.

While the technical level of traditional supply chain management is constantly improving, it has experienced the evolution from the most basic MIS to ERP, and then from ERP to the current supply chain integration. However, on the whole, the traditional supply chain management still has order-driven inventory management, and the management of turnover inventory is essentially a business model to deal with the traditional supply chain management. Under the management level of the second business model, turnover inventory constitutes the basic guarantee of crystal clear.

Safety stock has become the bottom line of order management service level. On the other hand, the appearance of this model also shows that the response speed of product life cycle theory depends on turnover inventory and safety inventory to ensure the service level of customers, so the response speed of customer demand is relatively slow under this model.

2. Terminal consumption demand cannot be effectively met.

The contribution of traditional supply chain model to enterprise management mainly lies in that enterprises are a permanent form to the market, and designed products meet some needs. In this case, the basic needs of end consumers can be met, but the existing products can not meet the potential deep-seated needs of end consumers.

The design and ecology of this product management are doomed to the commercial logic that the terminal consumption demand is out of touch with the source manufacturing. Supply-side manufacturing can not be personalized for the end user's experience, but can only improve its production efficiency in batch mode in a short time.

For example, before the advent of the Internet era, most of the clothes on the market were designed according to the designer's evaluation of the end-user experience, rather than customized for the individual needs of more users, especially ordinary users. Moreover, the cost of clothing customization is very high and the time is relatively long, which fundamentally restricts the universal satisfaction of end users' consumption needs.

3. Long inventory cycle

Under the traditional supply chain management mode, inventory management is the basic condition to support the operation of enterprises, and inventory becomes the current asset to realize the operation. Inventory count in most industries is calculated on a monthly basis. Due to different product attributes, the inventory under inventory management is also different.

On the whole, the inventory cycle is mostly based on the calculation of storage, packaging, handling, loading and unloading, transportation and other conditions. Basically, the cycle of in-transit inventory and turnover inventory is more than two months. From the perspective of capital utilization, it greatly restricts the utilization rate of working capital.

4. Poor synergy.

The poor coordination of supply chain management mode is mainly reflected in the fact that manufacturing enterprises can not establish channels quickly, sales channels can not interact effectively with end consumers, and the feedback from end consumers can not actually be the basis for manufacturing enterprises to upgrade their products.

From the management level of the whole supply chain, each link is maximizing its own interests, but it fails to maximize the overall interests. In the face of market competition, it is not uncommon that there is mutual extrusion and the interests of the whole supply chain are sacrificed to safeguard their own links.

5. The management cost is high

Due to the low level of informatization, the management cost of the traditional supply chain model can not effectively transmit the enterprise information designed in each link, which eventually leads to higher amortization costs, especially labor costs, among the fixed costs paid by their respective enterprises. The management cost caused by serious fragmentation has become one of the relatively high parts of supply chain management.

Supply chain management should conform to the historical development trend of the era of big data.

From the perspective of Marxist in-depth research theory on economics, the correct research method in the era of change should start with the contradiction between productivity and production relations. Only by analyzing the characteristics of productivity elements in time can we carry out targeted reforms in all aspects of production relations, which is the concentrated embodiment of productivity determining production relations and the inevitable requirement that production relations must conform to the development of productivity.

(A) Analysis of the leading factors of productivity in the era of big data

The three elements of productivity are laborers, production tools and labor objects. The era of big data has changed the three elements of traditional productivity, making the technology of data acquisition, processing, analysis and application represented by science and technology, especially artificial intelligence with the Internet as the core, become the core feature of productivity. These core characteristics fundamentally changed the living environment of traditional supply chain management, that is, changed the ecological characteristics of supply chain management.

1, the productivity change in the era of big data determines the change of supply chain management.

The productivity of each era determines the management characteristics and management mode of production concern in this era, which is based on the development of human civilization, and the era of big data is no exception. Therefore, when the three elements of productivity have undergone fundamental changes in the era of big data, the following supply chain management must also be changed according to the actual situation and conform to the development characteristics of productivity in order to enhance competitiveness and achieve efficiency improvement and development.

The workers have experienced decisive changes.

Before the era of big data, traditional workers managed the supply chain with manual labor and basic mental labor, mainly including basic information processing, some specifications of business knowledge, business-related data processing and so on. However, after the era of big data, workers need to participate in more mental work related to big data, such as data collection, supply chain data analysis, consumer-related data research and prediction.

Monitoring and analysis of product performance related to product design, etc. , fundamentally changed the level of workers' demand for knowledge mastery, and you changed workers' cognitive changes and conceptual changes in supply chain management. Then it includes personnel administration management, which changes the requirements of supply chain managers from all aspects such as recruitment performance appraisal.

Supply chain management is close to the front end of consumers and needs to collect more mathematics to describe consumers' behavior. This kind of information processing has greatly changed the original management mode that relied on research and prediction, thus changing the requirements for consumer workers.

These requirements essentially need to change the original management mode, but also effectively enhance the value created by workers, but the main body of this creation must be the changes of workers themselves. Therefore, on the whole, the demand for human resources is the top priority of productivity change in the era of big data.

3. The mode of production has changed greatly.

Traditional supply chain management is basically based on the transmission of information and the setting of traditional internet computer networks. In this mode, the Internet is only a tool for information transmission, and the computer is also an input port for information collection.

Most computer users are used to inputting relevant information or using computer networks to transmit relevant business data. In the era of big data, computers tend to collect, analyze and process relevant data, emphasizing the combination of software and intelligent hardware.

The ultimate goal may be to realize man-machine integration, and the input and transmission of relevant data become the most basic function, so from the use of computer networks, the function has completely changed the original goal.

4. Great changes have taken place in the object of labor.

In the era of big data, the labor object of supply chain management has gradually changed from product manufacturing, circulation and sales based on traditional inventory management to designing the characteristics of product manufacturing, that is, meeting the deep needs of consumers.

The use of data has gradually changed from the original post-event analysis and interpretation to the related application of big data, which is almost reflected in the statistical analysis of large-scale payment information every year, such as the statistics of the number of WeChat red envelopes in the past two years.

Alipay pointed out the statistical analysis of monthly bills to users and stepped into the statistical analysis of e-commerce on consumer purchase behavior. Such data analysis finally forms the judgment of supply in supply chain management, and also forms the judgment and evaluation of consumers' deep demand in the future. The original analysis and prediction has gradually become the application of big data correlation.

What impact will big data have on the supply chain? 2 productivity characteristics in the era of big data

The productivity in the era of big data is different from the changes in productivity factors brought about by technological changes in the past, which can be summarized as follows.

From the overall characteristics of all kinds of changes from the whole agricultural civilization to the industrial civilization era, the agricultural civilization era is characterized by changes in production tools, among which the appearance and application of bronzes and ironware are typical changes, which greatly promoted the improvement of production efficiency, thus promoting the improvement of the whole social efficiency and the massive accumulation of material wealth, and made feudal civilization have an unprecedented heyday.

Industrial civilization mainly focuses on the dynamic transformation of production tools, including the long-term accumulation of experience, the invention and application of age of steam steam engine in the18th century, and the application of electricity and machines powered by electricity in the industrial age, which greatly promoted the transformation of social productive forces and promoted the transformation of human civilization from feudal civilization to capitalist civilization and socialist civilization, and continued to develop in the political system to this day.

With the passage of time, at the beginning of the 20th century, some scholars proposed the arrival of productivity changes represented by new technologies, including new energy, new materials and computer technology. After half a century's development, the application of these technologies has also greatly promoted the improvement of production efficiency and changed the specific characteristics of production methods.

Mainly manifested in the rise of new economics and the refinement of management school. New business models and enterprise organizations emerge one after another, and the capital market represented by the securities market has become a barometer of economic development. These productivity development phenomena have become people's knowledge.

The application of network information in the new technology era. Today, the era of big data can be summarized as the productivity revolution based on the information age, characterized by the productivity revolution of production tools, workers, human resources and production methods brought about by intelligent data information processing and application.

Compared with the above-mentioned changes in other productive forces in human history, the changes in the era of big data are more sudden in time, have a greater impact on social production and lifestyle, spread faster, and bring the production links and consumption terminals of the supply chain closer. Relying on the combination of modern intelligent hardware and software, the information acquisition ability of both ends is greatly improved, supply and demand are fully integrated, and the turnover speed of product life cycle is accelerated.

What impact will big data have on the supply chain? 3 opportunities brought about by changes in the era of big data

With the change of productivity in the era of big data, enterprise organizations have a rare opportunity in supply chain management, which is mainly reflected in the following aspects:

1, the concept of supply chain management is accurate.

With the progress of production and the development of technology, management concept has increasingly become the core and essence of advanced production management methods. The change in the era of big data has enabled the concept of supply chain management to achieve deep and precise development, including the collection of demand information of supply chain consumption terminals and the feedback of user experience to production terminals, as well as the redesign, manufacturing and production of products to meet the deeper and more accurate needs of end consumers.

In terms of supply channels, the accurate transmission of information through the network is conducive to the diversification of channels, and the rapid sales ability of channels can be realized through accurate marketing advertisements.

In terms of inventory, the main significance is the inventory management driven by consumer demand. The concept of time inventory ordering batch and safety inventory greatly reducing zero inventory has been able to fully realize turnover inventory. The level is greatly reduced, so from the perspective of inventory cost, we can see the accuracy of supply chain management.

Ultimately, as a whole. It not only meets the deep-seated needs of consumers' terminal needs, but also meets the high-level goals of producers to reduce costs, order citizens and improve user experience in time.

2. Increased synergy.

Through the data processing of intelligent software and hardware technology, the information processing, collection, analysis and application of all links in the supply chain are optimized in time and effectively, which not only realizes the academic and agility of all links, but also realizes the synergy of all links as a whole. For example, the supply chain management of contemporary e-commerce, the most typical is the collaborative combination of self-operated logistics system and platform represented by JD.COM Mall.

Not only can orders be processed quickly, but JD.COM Mall's self-operated logistics system can also optimize inventory management, so that mall sellers can choose products based on big data, formulate marketing strategies and optimize procurement channels, and finally achieve the maximum synergy of supply chain integration.

In addition to the typical representatives of e-commerce companies, in the automotive aftermarket of China, especially for the realization of big data in the automotive parts supply chain, logistics services such as accurate classification, packaging and selection have been carried out, effectively realizing the supply chain management of multi-category products and complex product characteristics of the same product with multiple parameters.

It has laid a solid foundation for the successful user experience of small and medium-sized enterprises in China automobile aftermarket, especially the terminal enterprises of consumers. Compared with the traditional auto repair shop, this small and medium-sized enterprise that uses data for supply chain management has obvious advantages in competitiveness, especially in user experience.

3. Customer demand customization drive

The application of big data can effectively meet the precise needs of consumers in supply chain management. It can not only analyze transactions, consumers' purchasing behavior and consumers' expectations for the future, but also realize production customization according to this analysis, and turn large-scale production with supply-side problems into customized production characterized by personalized demand.

For example, in the production of clothing, in the traditional mode, almost all designers design to guide consumers to buy, and the customized demand is in a weak position in the market competition, which cannot meet the individual needs of consumers. Moreover, the customization cost of clothes is very high, and consumers can't bear the customization cost, which leads to the slow development of customization.

In recent years, an infrared technology combines software and hardware to describe the human body, which can not only describe the physical characteristics of consumers, but also design according to different consumers' preferences for clothes, so that consumers can quickly design according to their own wishes, and they can also choose existing clothes through smart fitting glasses at the stage of purchase and transaction.

In this process, data analysis and processing are realized through data collection and interaction between consumers, and the future consumption trend of clothing is described. In addition, consumers can provide consumers with in-depth and long-term services, so that they can only profit from transactions, and improve the stickiness of consumers from the long-term services of individual consumers, which is conducive to the use of data by small and medium-sized enterprises to achieve lean management.

4. Optimization of supply-side structure management

Supply-side reform is the dominant policy in China's "Thirteenth Five-Year Plan" period, and the era of big data provides favorable conditions for supply-side reform. At present, under the traditional mode and main development mode driven by investment demand and foreign trade, overcapacity generally appears in most industries in China. To solve the problem of overcapacity, we mainly start from two aspects. On the one hand, it can improve the quality of manufactured attack products.

Realizing sustainable development strategies such as industrial transformation and upgrading, optimizing structure and improving manufacturing efficiency, with special emphasis on environmental protection; On the other hand, it is necessary to aim at the consumption demand of end consumers and realize competitive products that are marketable and truly meet the needs of consumers. The era of big data provides a rare opportunity for supply-side reform.

Optimal management of supply-side structure is a typical example of energy utilization. With the increasingly serious environmental problems, China must take very effective management measures to replace traditional fossil energy with new energy, which is mainly reflected in the data-centered management and treatment of new energy gradually replacing traditional fossil energy, thus improving the environment and improving energy utilization. In 20 10, the government issued efforts to close nearly 100 thermal power plants, and plans to increase 100 nuclear power plants during the 13th Five-Year Plan period.

To realize the clean energy substitution project and energy utilization in the eastern coastal areas, we must use big data to control the effective use of energy, gradually improve the traditional fossil energy that pollutes the environment, and finally realize the sustainable development of China's economy.

5. The application of big data in SMEs has enhanced the competitiveness of SMEs.

Under the condition of traditional productive forces, small and medium-sized enterprises are facing fierce market competition, lack of creativity in resources and low efficiency in underground utilization, which have all caused large enterprises to squeeze the living space of small and medium-sized enterprises. After the emergence of big data, small and medium-sized enterprises are not as strong as large enterprises in terms of resources and innovation ability, but they use strategic flexibility to give full play to their agility in the immediate market.

Use big data to subdivide the market again, lock in the target market segment, dig deep into customers and innovate products twice, so as to realize the asymmetry in market competition, continuously meet the micro-innovation needs of consumers and improve the competitiveness of their products and services.

Effectively improve their own shortcomings, and ultimately enhance their competitiveness. In the macro environment where the country strongly advocates mass innovation and mass entrepreneurship, small and medium-sized enterprises use big data technology to firmly grasp the effective needs of target customers in market segments in terms of information communication, marketing competition and strategic reinvestment, which not only meets the targeted in-depth needs, but also improves the tools and methods for controlling user experience and meets the potential needs of target customers in market segments. While creating and realizing customer value, it also creates a large number of jobs. Since then, brand competition has been deeply rooted in people's hearts.

Judging from the number of patent applications in China, in addition to the large-scale passenger aircraft enterprises that dominate the market competition, which have invested a large proportion in R&D and produced a large number of patents, the majority of small and medium-sized enterprises have greatly increased the number of patent re-applications by using their own conditions, enhanced their competitiveness, and achieved value remodeling and brand building at the same time.