What impact will big data have on the supply chain?
What impact will big data have on the supply chain? The arrival of the big data era provides a rare opportunity for supply chain management, but at the same time It will also be accompanied by some bad effects, both pros and cons. Being able to adapt to changes with the times is the right direction. The following is about the impact of big data on the supply chain. What impact will big data have on the supply chain1
Challenges faced by the traditional supply chain management model
The advent of the big data era not only provides us with great development opportunities , the important thing is that the challenges faced by the traditional supply chain model have greatly intensified the competition between enterprises under the new productivity conditions. It is precisely because the productivity characteristics of the big data era are different from the traditional productivity characteristics of the supply chain management model. Therefore, the challenges faced by the traditional supply chain management model are also very serious. The replacement of old things by new things must be the transformation and upgrading of the old things themselves to adapt to the development of new things. The supply chain management model No exception.
1. The response speed is slow
While the technical level of traditional supply chain management continues to improve, it has experienced from the most basic MIS to ERP, and then from ERP to the current supply chain integration. However, from an overall level, traditional supply chain management still has inventory management driven by order orders. The management of rotating inventory is essentially a business model that responds to traditional supply chain management. Under the management level of the business model, turnover inventory constitutes Jingying's basic guarantee
Safety inventory has become the bottom line of service level for order management. On the other hand, the emergence of this model also illustrates to a certain extent that the response speed of the product life cycle theory relies on turnover inventory and safety inventory to ensure customer service levels, so the response speed to customer needs is relatively slow in this model. .
2. Terminal consumer demand cannot be effectively met
The traditional supply chain model’s contribution to business operations mainly lies in the design of products by enterprises to meet part of the market demand in a permanent form. , in this case, the basic needs of end consumers can be met, but existing products cannot meet the potential deep-seated needs of end consumers
The design and ecology of this kind of product operation are destined to end users The business logic is disconnected between consumer demand and the source of production and manufacturing. The supply-side manufacturing cannot carry out personalized design for the end-user experience and can only improve its production efficiency in a batch mode in the short term.
For example, before the emergence of the Internet era, most of the clothes on the market were designed based on designers’ evaluation of end-user experience, but were not customized to the personalized needs of more users, especially general users. , and the cost of customizing clothes is very high and takes a long time, which fundamentally restricts the universal satisfaction of terminal consumer needs.
3. The inventory cycle is long
The traditional supply chain management model uses inventory management as the basic condition to support business operations. Inventory becomes the current asset for operation, and the inventory of most industries Inventory is calculated on a monthly basis. Due to different product attributes, inventory management inventory is different
From an overall level, most of the inventory cycle is calculated in warehousing, packaging, handling, transportation, etc. Under the conditions, basically the inventory in transit and turnover inventory cycles are more than two months. From the perspective of capital utilization, this restricts the utilization of working capital to a great extent.
4. Poor synergy effect
The poor synergy effect of the supply chain management model is mainly reflected in the fact that manufacturing enterprises cannot quickly establish channels, and sales channels fail to realize and Effective interaction between end consumers and feedback from end consumers cannot actually become the basis for manufacturing companies to upgrade their products
From the management level of the entire supply chain, it can be seen that each link is realizing its own goals Maximizing interests, but failing to maximize overall benefits, there is mutual squeeze in the face of market competition, and it is not uncommon to sacrifice the overall benefits of the entire supply chain in order to safeguard the interests of one's own links.
5. The management cost is very high
Due to the low level of informatization, the management cost of the traditional supply chain model cannot effectively transmit information to the enterprises designed in each link, which ultimately results in The amortization cost of the fixed costs paid by each company is very high, and labor costs are particularly prominent. Management costs have become one of the higher parts of supply chain management because of the management chaos caused by severe fragmentation.
Supply chain management must conform to the historical trend of development in 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 from the perspective of productivity and production Starting from the contradictions of relationships, time can analyze the characteristics of productivity factors to carry out targeted reforms in all aspects of production relations. This is a concentrated expression of the determination of production relations by productivity, and it is also the inevitable requirement that production relations must comply with the development of productivity.
(1) Analysis of the dominant factors of productivity in the era of big data
The three elements of productivity are workers, production tools and labor objects. The era of big data has changed the three elements of traditional productivity. The element characteristics make science and technology, especially the technology of data acquisition, processing, analysis and application represented by artificial intelligence with the Internet as the core, become the core characteristics of productivity. These core features fundamentally change the living environment of traditional supply chain management, that is, they change the ecological characteristics of supply chain management.
1. The productivity changes in the big data era determine the changes in supply chain management
The productivity of each era determines the management characteristics and management models of production concerns in that era. This It is determined based on the development of human civilization, and the era of big data is no exception. Therefore, when the three elements of productivity in the big data era have undergone fundamental changes, the subsequent supply chain management must also be transformed according to the actual situation and conform to the characteristics of productivity development in order to enhance competitiveness and achieve efficiency improvement and development.
2. Workers have undergone decisive changes
Before the emergence of the big data era, traditional workers relied on physical labor and basic mental labor to perform tasks. Supply chain management, this kind of mental work mainly includes basic information processing, some specifications of business knowledge, business-related data processing, etc. However, after the emergence of the big data era, workers need more participation and big data related Mental work, such as data acquisition, analysis of supply chain data, consumer-related data research and prediction
Monitoring and analysis of product performance related to product design, etc., this fundamentally You have changed the level of workers' demand for knowledge, and you have changed workers' thinking patterns and concepts about supply chain management. This includes personnel administration, recruitment, performance appraisal and other aspects, which have changed the original requirements for supply chain managers.
Supply chain management is close to the front-end of consumers and requires more mathematical collection and description of consumer behavior. Such information processing has greatly changed the original management model that relied on research and prediction, thus It has also changed the requirements for workers on the consumer side
These requirements essentially require changes in the original management model, and are also an effective improvement in the value created by workers, but the subject of this creation must be the workers themselves. changes.
Therefore, from an overall perspective, the demand for human resources is the first priority for productivity changes in the big data era.
3. The production tools in the means of production have undergone great changes
Traditional supply chain management is basically the setting of the traditional Internet computer network based on the transmission of information. In this mode, the Internet is only used as a tool for information transmission. The computer is also an input port for information collection. Most computer users use it to enter relevant information or use the computer network to transmit relevant information. business data. In the era of big data, computers are more inclined to collect, analyze and process relevant data, with more emphasis on the combination of software and intelligent hardware
The ultimate goal may be to achieve human-machine integration, and enter and transmit relevant data It has become the most basic function, so from the perspective of the purpose of computer network, the function has completely changed the original goal.
4. The labor objects have undergone great changes
The labor objects of supply chain management in the big data era have gradually transformed from product manufacturing, circulation and sales based on traditional inventory management. In order to design the characteristics of product manufacturing, that is, the characteristics that meet the in-depth needs of consumers
The use of data has gradually transformed from the original post-hoc analysis and explanation to the correlation application of big data. This is almost reflected in Statistical analysis of large-scale payment information every year, such as statistics on the number of red envelopes sent by WeChat in the past two years
Alipay’s statistical analysis of monthly bills pointed out by users, and e-commerce statistics on consumer purchasing behavior Analysis, such data analysis finally forms the judgment of supply in supply chain management, and also forms the judgment and evaluation of consumers' future in-depth needs. The original analysis and prediction gradually transformed into the application of big data correlation. What impact will big data have on the supply chain 2
Characteristics of productivity in the era of big data
Productivity in the era of big data is different from the changes in productivity factors brought about by previous technological changes, which can be summarized The summary is as follows.
Judging from the overall characteristics of various changes from the entire agricultural civilization to the industrial civilization era, the agricultural civilization era is mainly characterized by changes in production tools. Typical changes include the emergence and application of bronzes, iron tools The emergence and large-scale popularization and application are the main features, which greatly promoted the improvement of production efficiency, thereby promoting the improvement of the efficiency of the entire society and the substantial accumulation of material wealth, bringing the feudal civilization to an unprecedented heyday.
Industrial civilization mainly focuses on the changes in the power of production tools resulting from changes in the energy of production tools. It mainly includes the accumulation of long-term experience, the invention and application of steam engines in the steam age in the 18th century, electricity and electricity in the industrialization era. The application of machines as power energy has greatly enhanced the transformation of social productivity, promoted human civilization from feudal civilization to capitalist civilization and socialist civilization, and the development of political systems continues to this day.
As time goes by, some scholars in the early 20th century proposed the coming of productivity changes represented by new technologies, including new energy, new materials and computer technology. After half a century of 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 by the rise of new economics and the refinement of management schools. New business models and corporate organizational methods are emerging one after another. The capital market, represented by the securities market, has become a barometer of economic development. These productivity development phenomena have become people's common knowledge.
The application of network information in the new technological era. The emergence of the big data era today can be summarized as a revolution based on the information age and the productivity brought about by intelligent data information processing and application in terms of production tools, workers, i.e. changes in human resources, and production methods. Characteristic changes in productivity.
Compared with the above-mentioned other changes in productivity in human history, the changes in the big data era are more sudden from the perspective of time, have a greater impact on social production and lifestyle, spread faster, and Close to the production segment and consumer terminal of the supply chain, relying on the combination of modern intelligent hardware and software has greatly improved the ability to obtain information at both ends. Supply and demand are fully integrated and highly unified, and accelerated the turnover speed of the product life cycle. What impact will big data have on the supply chain 3
Opportunities brought by changes in the big data era
With the changes in productivity in the big data era, business organizations have opportunities in supply chain management It is rare, mainly reflected in the following aspects:
1. Accurate supply chain management concept
With the advancement of production and the development of technology, management concepts are increasingly becoming advanced production management methods. the core and essence of. The changes in the big data era have enabled the supply chain management concept to achieve in-depth and precise development, including the collection of supply chain consumer terminal demand information and user experience feedback to the production end, and the redesign, manufacturing and production of products to meet the needs of end consumers. Deeper and more precise needs.
In terms of supply channels, the precise transmission of information through the Internet is conducive to the diversification of channels, and the rapid sales capabilities of channels can be achieved through the placement of precise marketing advertisements.
In terms of inventory, the main significance is inventory management driven by consumer demand. The concept of time inventory ordering batches 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 look at the precision of supply chain management.
Finally overall. It not only meets the deep-seated needs of consumers' terminal needs, but also satisfies the high-level goals of producers to reduce costs and order citizens in a timely manner with perfect user experience.
2. Increased synergy effects
Through data processing of intelligent hardware and software technology, information processing, collection, analysis and application in all aspects of the supply chain can be timely and effective To achieve optimization, it not only achieves the academic and agility of the execution level of each link, but also achieves the synergy of all links as a whole. For example, in the supply chain management of contemporary e-commerce, the most typical one is the self-operated one represented by JD.com. The synergistic combination of logistics system and platform
Not only achieves rapid processing of orders, but also JD.com’s self-operated logistics system optimizes inventory management, and enables sellers in the mall to use a large amount of data as a basis. Select products, formulate marketing strategies, and optimize procurement channels, thereby ultimately achieving the maximum synergy effect of supply chain integration.
In addition to typical representatives of the industry such as e-commerce companies, in China's automotive aftermarket, especially for the realization of big data in the auto parts supply chain, accurate classification, packaging, selection and other logistics services can be effectively realized. The supply chain management of complex product characteristics with multiple product categories and multiple parameters of the same product
has laid a solid foundation for small and medium-sized enterprises in China’s automotive aftermarket, especially recent consumer terminal enterprises, to implement successful user experience. Compared with traditional auto repair shop stores, this kind of small and medium-sized enterprises that use data for supply chain management have huge and obvious advantages in terms of competitiveness, especially in terms of user experience.
3. Driven by customization of consumer demand
The application of big data can effectively meet the precise needs of consumers in supply chain management. It can not only analyze transactions and consumer purchases. The analysis of behavior and consumers' future expectations can realize production customization based on this analysis, transforming mass production with supply-side problems into customized production characterized by personalized needs.
For example, in the traditional production of clothes, designers are almost responsible for designing and guiding consumers to purchase. The demand for customization is in a weak position in market competition, and there is no way to meet the individual needs of consumers. Moreover, the cost of customizing clothes is very high, and the majority of consumers cannot bear the cost of such customization, which has resulted in the slow development of customization.
In recent years, infrared technology has combined software and hardware to describe the human body, which not only enables the description of consumers’ physical characteristics, but also enables design according to different consumers’ preferences for clothes, which can quickly It allows consumers to design according to their own wishes, and can also select existing clothes through smart fitting mirrors during the purchase and transaction stages.
In this process, data collection is used to interact with consumers. The interaction and other links realize the analysis and processing of data, describe the future consumption trend of clothes, and provide in-depth and long-term services to the final consumers. In this way, profits can only be obtained from transactions and from a single In the long-term service of consumers, the improvement of consumer stickiness will be beneficial to the majority of small and medium-sized enterprises to use data to achieve lean operations.
4. Optimization of supply-side structural management
Supply-side reform is the leading policy during my country’s 13th Five-Year Plan period, and the big data era provides opportunities for supply-side reform. favorable conditions. At present, most industries in our country generally have overcapacity under the traditional model, the main development model driven by investment demand and foreign trade. Solving the problem of overcapacity mainly starts from two aspects. On the one hand, it improves the production and manufacturing of attack test products. Quality
Realize the transformation and upgrading of the industry, optimize the structure, improve the efficiency of production and manufacturing, and pay special attention to environmental protection and other sustainable development strategies; on the other hand, we must target the consumption needs of end consumers to achieve marketable and Competitive products that truly meet consumer needs. The era of big data provides a rare opportunity for supply-side reform.
The optimal management of the supply-side structure is exemplified by the use of energy. As environmental problems become increasingly serious, our country must take very effective management measures to replace traditional fossil energy with new energy. This is mainly reflected in the Data-centered management processes new energy to gradually replace traditional fossil energy to improve the environment and increase energy utilization. In 2010, the government issued an effort to close nearly a hundred thermal power plants and planned to increase the development of 100 nuclear power plants during the 13th Five-Year Plan period.
To realize the clean energy replacement project in the eastern coastal areas and the energy-utilizing Jiaotong University area, we must use big data to effectively control the effective use of energy, gradually improve the traditional fossil energy that pollutes the environment, and ultimately realize the realization of our country's economy. sustainable development.
5. Big data application of small and medium-sized enterprises improves competitiveness
Under traditional productivity conditions, small and medium-sized enterprises face fierce competition in the market, insufficient resources, insufficient creativity, and insufficient efficient utilization of underground resources. Various aspects such as large enterprises have squeezed the living space of small and medium-sized enterprises. After the emergence of big data, although small and medium-sized enterprises are not as strong as large enterprises in terms of resources and innovation capabilities, they can take advantage of strategic flexibility and give full play to their targeting. The agility to launch efforts in the immediate market.
Use big data to subdivide the market again, lock in target market segments, conduct in-depth exploration of customers, and carry out secondary innovations in products to achieve asymmetry in market competition. In micro-innovation In terms of continuously meeting the needs of consumers and improving the competitiveness of its own products and services.
It effectively improved its own shortcomings and ultimately improved its competitiveness for survival. In the macro environment where the country vigorously advocates mass innovation and entrepreneurship, small and medium-sized enterprises use big data technology to improve their performance in information communication, marketing competition, and strategic re-engineering. Investment and other aspects have firmly grasped the effective needs of target customers in market segments, which not only meet the targeted in-depth needs but also improved the tools and methods to control user experience and meet the potential needs of target customers in market segments. In creating and realizing While adding value to customers, it also created a large number of jobs. From then on, brand competition became deeply rooted in the hearts of the people.
Judging from the number of national patent applications, in addition to the large passenger aircraft companies that dominate the market competition, they invest a large proportion in R&D and produce a large number of patents. While meeting the market target demand, the number of re-applications for patents using their own conditions has increased significantly. While competitiveness has been improved, value reshaping and brand building have been achieved.