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[ China Agricultural Machinery Industry News ] Internet, big data, and cloud computing are constantly subverting the traditional industrial structure, and it is likely that new development opportunities will be eliminated by the industry. Industry big data provides space and potential for agricultural machinery companies to gain deeper and more comprehensive insights. With big data and related technologies, they can avoid information silos, data chimneys, develop targeted development and competition strategies, and implement personalized marketing. .
At present, the agricultural machinery industry is in the critical stage of market deep adjustment, industrial innovation transformation, product upgrading, and the pace of revitalizing the stock and optimizing the increment. As a barometer of industry development, industry big data has become an important part of information technology. Based on this, the industry will formulate, plan, and make decisions. Agricultural machinery companies will formulate strategies, optimize resources, and enhance their capabilities. Users will arrange purchase time, purchase targets, and job production accordingly. The decision support of big data is even more important. It can be said that the industry has basically reached a consensus on the importance of big data. At the same time, due to the existence of information barriers and data inconsistency between enterprises and units in the industry big data, data blocking is common in different units and different enterprises. Most of the data is difficult to achieve orderly integration and coordinated advancement, which increases data collection. The difficulty of integration.
Recently, at an industry exchange meeting, many experts hoped that all parties should pay attention to the construction of big data, call for standardization, strengthen the production and sales of agricultural machinery industry, agronomy and other data, and get corresponding response and support. There has been a lack of big data platform construction in the agricultural machinery industry. Relatively good production and sales data has a variety of calibers, and there are large differences in data sources such as backbone enterprises, some enterprises, and pre-judgment. Take large and medium-sized tractors as an example. Some experts judge that the industry sales volume is close to 500,000 units, and some industry data shows about 300,000 units. Lack of comprehensive and data support, it is difficult for agricultural machinery enterprises to specifically grasp the real situation of the market, and it is easy to cause misjudgment of “market prosperity†or “market downturnâ€. In strategic decision-making, some agricultural machinery enterprises believe that the downturn in the industry is only a short-lived phenomenon. They firmly believe that “the past is spring†and did not choose to shrink the front line to reduce operational risks. Instead, they insisted on the original extensive growth strategy and pursued in an all-round way. Performance waterfall." Some enterprises believe that the industry has reached the critical stage of industrial transformation, entering the new normal of improving quality and efficiency, slowing down the growth rate, actively optimizing the corresponding resource input, reducing business objectives, controlling stocks, optimizing increments, and accelerating industrial transformation. Find and grasp new development opportunities.
Objectively speaking, industry big data is in the initial stage of construction and has begun to have a certain reference value. Looking at the development of big data in the industry, there are still some data source problems encountered in development. Big data such as agricultural machinery, agronomy, agricultural resources and customer relationships are lacking in content. It is difficult to give specific decision-making support to industry units, and it is far from scientific, comprehensive, accurate and timely information transmission requirements.
First, industry big data is waiting for the system. The basic requirement of the industry big data is to be able to show the whole picture of the industry, and there can be no "only reference, no harm, no harm". The statistics of big data in the industry show more than six or six phenomena, with more agricultural machinery data, less agronomic data, more power machinery, fewer supporting agricultural tools, fewer traditional enterprises, fewer emerging enterprises, more low-end products and fewer products; More, less subdivided data, more model data, less regional data. It is worth mentioning that the missing data is more representative of the systematic nature of the data. The lack of relevant systems and standards has led some departments to be reluctant to open, and some companies are reluctant to share their own data.
Second, industry big data needs to be comprehensive. The regional agricultural machinery agronomy and agricultural resources integration data are mostly in a weak stage. It is difficult to effectively support the improvement of traditional products and the research and development of new products, restricting the development of mechanized products throughout the whole process, and it is difficult to continuously monitor the working conditions of equipment throughout the life cycle of crops. National Supplementary Data Due to the partial regional policy, the relevant low-end, saturated and other products are not subsidized, and the overall industry information is difficult to fully reflect. Industry association data mainly lacks data support for emerging enterprise data and regional sales. In recent years, the number of agricultural machinery emerging enterprises has increased, especially the sales of leading products of tractors' third- and fourth-line brands has become an important part of the power segment above 100 horsepower. It is difficult to find hidden young tigers in the market jungle without seeing the changes in the products of emerging companies. Faced with industrial upgrading and accelerated pace of industry reshuffle, emerging companies have already supported the half-wall market in quantity and are expected to gain new industrial advantages. Most of the customer relationship construction companies lack corresponding construction, and the data lacks coherence and system. Among them, the number of regional demand and models can show the law of market development and user demand, which is the data foundation with research value.
Third, the release of big data in the industry is waiting for agility. Most industry information releases are not yet available on a monthly basis, and the demand unit is unable to continuously obtain the required, real-time data. Subsidy data Due to the different time of opening, closing and subsidizing information in different regions, it is difficult to display the data information of the same caliber industry in time. At the same time, the information on subsidized products caused by market overdraft factors lags behind, and the real competition in dynamic markets is difficult to be embodied. Agricultural machinery products lack corresponding industry statistical resources and platforms. Although industry associations can publish individual product information, most of them are still in the “to be sorted†stage, and the overall industry data is difficult to be released in time. Agronomy and agricultural resources are subject to many aspects such as planting patterns, seasonal needs and statistical resources, comprehensive quality of statisticians, and heavy workload. It is also difficult to organize and release in a timely manner.
Under the background of in-depth industrial transformation and deep market adjustment, the cyclical, structural and phased factors of agricultural machinery market are superimposed and promoted, reminding the industry to strengthen the construction of big data, and to identify potential risks and opportunities. Early warning, early detection, early disposal, to make precautions, prevent problems. The construction of big data in the industry must be continuously promoted and strengthened. It cannot be like a cloud like a fog.
The first is to optimize big data resource management. Integrate and optimize relevant resources to guide the healthy development of big data. At present, agricultural machinery enterprises generally do not have the resources to establish relevant big data. To break the "information island" and "data chimney" and change the fragmentation of data requires the support and guidance of relevant government departments and industry associations. Formulate or improve corresponding laws, regulations, rules and regulations, use information technology to improve market supervision, timely discover and solve problems in the development of big data, and form an integrated online and offline regulatory structure. Through the use of administrative collection, online search, voluntary provision, and paid purchase, enterprises, industry associations, scientific research institutions, and social organizations will be actively involved to study and formulate data implementation methods, and regulate the content and use of data through laws and regulations. way.
The second is to comprehensively improve the quality of data personnel. As big data has just emerged, it is in its infancy, and the basic work of big data in the agricultural machinery industry is correspondingly weak. It is necessary to give more resources to promote personnel training and talent introduction. Integrate social resources such as enterprises and institutions, promote strategic cooperation, build mutually beneficial relationships, and build a long-term mechanism for education and training of professionals in the big data industry. We will improve the incentive package for talents, attract high-level talents, establish a flexible talent-inducing mechanism, and build a support system for entrepreneurial innovation in big data and a highland for talents.
The third is to innovate big data construction methods. Focus on promoting data processing services in agricultural machinery, agronomy, agricultural materials, e-commerce and other industries to create a "big data +" industry model. Promote the deep integration of big data with agriculture, economic operation, customer management, etc., so that data can be flowed to the required customers in an orderly, safe and controllable manner. Industry units should use the big data analysis, access to the full-cycle market research, product development, production and marketing, machine integration, marketing, after-sales service and other product lifecycle management of cloud services and cloud applications, improve decision support, operational efficiency, Promote the transformation and development of industrial manufacturing to industrial intellectual creation and industrial creation.
Although the pearl is beautiful, it needs a link. The construction of big data in the industry is a systematic project, which meets the requirements of smooth information, and still faces many difficulties in standard development. It requires all parties to work together, promote synergies, and establish a long-term development mechanism. The industry should be customer-oriented, strengthen the construction and use of big data, actively study market changes, accumulate more energy for industrial development, and strive to create a "new business card" for the development of big data in the industry. (Original title: Agricultural machinery industry development, need big data support)