How far are agrochemical companies from achieving smart manufacturing?
Category:
Industry Information
Author:
Yellow-flowered Tree
Source:
CCPIA China Pesticide Industry Association
Release date:
2021-10-26
How far are agrochemical companies from achieving smart manufacturing?
As science and technology continue to advance, global manufacturing is accelerating its transition into the intelligent era. Smart manufacturing—characterized by the deep integration of next-generation information technologies with advanced manufacturing techniques—has become a core driving force. The convergence of big data, cloud computing, and artificial intelligence with manufacturing not only empowers traditional production factors but also breaks the constraints imposed by the limited supply of labor, capital, and land on economic growth, laying the groundwork and opening up new possibilities for sustained industrial upgrading and transformative development. Smart manufacturing is continually transcending the limitations of conventional manufacturing, giving rise to new business models and propelling the sector to a new stage of high-quality development.
Smart manufacturing is an information‑driven approach to production that spans the entire product lifecycle, enabled by ubiquitous sensing. Built upon advanced technologies such as modern sensor systems, networking, automation, and human‑like intelligent systems, smart manufacturing leverages intelligent sensing, human–machine interaction, decision‑making, and execution capabilities to achieve intelligence across design, manufacturing processes, and manufacturing equipment. It represents a deep integration and convergence of information technology, artificial intelligence, and manufacturing‑equipment technologies. By redefining the concept of manufacturing automation, it extends toward flexibility, intelligence, and high levels of integration. Characterized by smart factories as its platform, intelligent key manufacturing stages at its core, end‑to‑end data flows as its foundation, and network connectivity as its backbone, smart manufacturing can shorten product development cycles, reduce resource and energy consumption, lower operating costs, boost production efficiency, and enhance product quality.
The development of intelligent manufacturing is, in essence, a gradual, step-by-step process. It requires carefully aligning the roles and contributions of relevant technologies with the characteristics of each stage of development, while also defining their position within the broader production system. The ultimate goal is to integrate these technologies with the enterprise’s existing production management units, creating a cohesive whole that effectively supports the organization’s strategic objectives.
The development of intelligent manufacturing should, in general, follow a gradual, step-by-step process. It requires, based on production needs and the characteristics of relevant technologies at different stages of development, appropriately defining the roles these technologies can play and their position within the overall production system. The ultimate goal is to integrate various technologies with existing industrial automation systems, creating an organic whole that effectively supports the enterprise’s strategic objectives. In light of the challenges intelligent manufacturing seeks to address and its role within the broader production ecosystem, the evolution of intelligent manufacturing can be broadly divided into three phases.
Phase One—The Beginner Stage. Building on the foundation of traditional industrial automation systems, enterprises leverage advanced technologies such as industrial big data analytics and artificial intelligence to enable capabilities like image recognition and predictive maintenance. These technologies reveal both visible and latent conditions within the production process, helping operators make informed decisions and optimize the performance of industrial automation systems.
At this stage, intelligent manufacturing primarily operates within individual production units—such as production lines, workshops, or warehouses—by establishing localized, highly automated and digitally integrated facilities. This enables the transformation of these units toward reduced‑labor or even fully unmanned operations. Intelligent manufacturing systems collect a wide range of data, enabling digital sensing and analysis of production conditions, thereby providing decision‑making support and valuable insights for enterprises.
Phase Two—Intermediate Level. Devices such as sensors and controllers, biometric technologies including video and audio recognition, the Industrial Internet, blockchain, and other foundational and integrated technologies are steadily maturing and being widely adopted. As a result, enterprise‑level smart manufacturing systems can access more comprehensive and precise information, while issues related to system determinism, availability, and cost-effectiveness, as well as industrial data security and trust, are being addressed in a balanced manner. Within enterprises, smart manufacturing systems have evolved into relatively self‑sufficient, localized ecosystems—covering specific units or functions within the production process. By establishing a data‑driven closed loop encompassing “state sensing–real‑time analysis–autonomous decision‑making–precise execution–learning and improvement,” these systems leverage automated data flows enabled by software to mitigate the uncertainties inherent in complex systems, thereby optimizing production processes under specified timeframes and target scenarios.
At this stage, for specific units or functions within the production process, intelligent manufacturing systems not only serve as “think tanks” but also begin to assume the role of “decision-makers,” taking a leading position in the production management of particular areas or units within the enterprise. Based on production‑process data, they assess the current production status, formulate control decisions, and issue execution commands, while simultaneously leveraging post‑execution feedback to optimize and adapt the system.
Phase Three—Advanced Stage. In an increasing number of production units across enterprises, intelligent manufacturing systems are transitioning from a supporting role to a commanding one, giving rise to multiple locally autonomous intelligent manufacturing ecosystems. Enterprises are establishing smart decision‑making and integrated control platforms powered by next‑generation information technologies such as cyber‑physical systems, big data, artificial intelligence, and edge computing, endowing them with an intelligent architecture capable of self‑sensing, self‑organization, and self‑decision‑making. These systems enable real‑time, data‑driven issuance of operational directives and the efficient, optimized allocation of resources, achieving precision, efficiency, high quality, low resource consumption, safety, and environmental sustainability—thereby comprehensively elevating development standards and realizing high‑quality growth in the steel industry.
At this stage, driven by the deep integration of next-generation information and communication technologies with advanced manufacturing technologies, intelligent manufacturing systems permeate the entire production and business process—spanning R&D, process planning, production, safety, finance, human resources, procurement, warehousing, marketing, and service—and assume the role of decision-maker at every level of enterprise operations, thereby assuming a comprehensive, overarching leadership position. These systems have evolved into a fully autonomous ecosystem, giving rise to a new mode of production endowed with capabilities such as self‑sensing, self‑learning, self‑decision‑making, self‑execution, and self‑adaptation.
Regardless of the stage of development, the goal of intelligent manufacturing is not to replace humans with machines or to automate human tasks, but rather to leverage more efficient and reliable technologies and systems to enhance human–machine collaboration and achieve high-quality growth.
In recent years, China’s agrochemical industry has witnessed rapid advances in engineering and technological capabilities. With the continuous upgrading and widespread adoption of control devices—such as electronic and digital instruments, level‑measurement systems, and safety‑monitoring equipment—as well as advanced automation and management systems—including DCS (distributed control systems), PLCs (programmable logic controllers), SCADA (supervisory control and data acquisition) systems, MES (manufacturing execution systems), and ERP (enterprise resource planning) solutions—the major production processes in the agrochemical sector have achieved automated control and integrated production information at varying levels. Consequently, optimization and precise control of operational parameters, enhanced safety management, and improved production scheduling and overall operational efficiency have all been significantly strengthened. Pilot projects for smart manufacturing—such as continuous synthesis lines, multifunctional synthesis workshops, “unmanned workshops,” “dark factories,” and intelligent warehouses—are proliferating. The agrochemical industry is steadily transitioning from a workshop‑style production model to process‑oriented, automated manufacturing, and is gradually advancing toward an intelligent phase supported by big data, cloud computing, and artificial intelligence. Leading companies in the sector are actively pursuing intelligent transformation and upgrading across R&D, production, warehousing, marketing, and resource management, successfully enhancing intrinsic safety, improving product quality and production efficiency, reducing costs, and lowering resource consumption, while reaping substantial economic benefits.
However, intelligent manufacturing in the agrochemical industry is still in its infancy. Technical challenges urgently require resolution. For instance, enabling technologies such as big data, cloud computing, and artificial intelligence have yet to be effectively leveraged, and traditional application architectures remain unbroken; consequently, smart‑factory development is still in the exploratory phase, with only limited pilot implementations. In most enterprises, production‑parameter data is highly complex, and basic data‑collection systems are insufficient to support the deployment of intelligent applications. Moreover, individual production units operate relatively independently, lacking unified standards, interfaces, and coding schemes, which hinders interoperability and data sharing, making it difficult to close information loops. As a result, both internal and external information silos persist, preventing the full realization of the value inherent in vast amounts of data. Even when data is collected, it often fails to be effectively integrated, processed, and utilized, thus falling short of serving as a reliable basis for decision‑making. Furthermore, the pesticide industry comprises numerous small and medium‑sized enterprises, whose weak IT infrastructure, shortage of specialized talent, and limited financial resources collectively constrain the overall advancement of intelligent manufacturing within the sector.
At present, the vast majority of companies in the agrochemical industry remain traditional manufacturers. Although many corporate executives recognize that smart manufacturing is key to future transformation and upgrading, businesses still need practical, tailored solutions that align with their specific circumstances. As a result, many firms are adopting a wait-and-see approach, often preferring to follow well-trodden, off-the-shelf paths in order to minimize trial-and-error costs and uncontrollable risks.
Smart manufacturing is a long-term, incremental, and continuously improving systems engineering endeavor. The agrochemical industry exhibits both batch‑production and continuous‑process characteristics, making its digital transformation an exceptionally complex systems‑level undertaking with no shortcuts. Given the diversity of enterprises, product processes, and management foundations, implementation approaches will vary from one case to another, necessitating tailored solutions that are closely aligned with specific operational contexts. To date, there remains no one‑size‑fits‑all, universally applicable solution. So, how can agrochemical companies achieve smart manufacturing?
First, optimize the enterprise’s top-level design to transform the present and shape the future. Senior management should adopt a highly forward-looking, strategic mindset, thoroughly analyze the company’s current stage of development and its evolving needs, and align these insights with its key pain points and strategic objectives. By gaining a clear understanding of the characteristics of the early stages of smart manufacturing, setting realistic expectations for the investment‑return cycle of digital transformation, and developing practical, actionable implementation plans and roadmaps, enterprises can advance steadily and pursue continuous improvement.
Secondly, prioritize the intelligent transformation of specific areas or production units. Building on the completion of automation and digitalization upgrades, enterprises should establish standards for smart‑factory development, select strategic partners to implement their smart‑manufacturing initiatives, actively adopt cutting‑edge technologies and equipment, and advance the intelligent retrofitting of existing machinery—focusing particularly on localized or unit‑level upgrades. At the same time, they should develop and refine intelligent production management systems, deploy networked, distributed manufacturing facilities, and ensure robust, comprehensive information‑technology and digital infrastructure, while maintaining its integrity to leave room for future technological innovation and iterative enhancements.
Third, it is essential to prioritize the recruitment and development of talent supporting intelligent manufacturing. Intelligent manufacturing emphasizes human–machine collaboration rather than replacing humans with machines; the implementation and operation of enterprise‑level smart technologies rely on a skilled workforce that can promptly translate cutting‑edge technological advances into tangible economic benefits. Technicians remain the “core productive force” of enterprises. In September 2021, the Ministry of Human Resources and Social Security and the Ministry of Industry and Information Technology jointly issued technical skill standards for a range of professions supporting intelligent manufacturing, including intelligent manufacturing engineering technicians, artificial intelligence engineering technicians, Internet of Things engineering technicians, integrated circuit engineering technicians, cloud computing engineering technicians, industrial internet engineering technicians, and digital management specialists. Over the next few years, through higher‑education institutions and vocational training programs, the current shortage of specialized talent in intelligent manufacturing will be alleviated to some extent. Pesticide companies, too, need to plan ahead and systematically develop strategies for recruiting and cultivating the talent required to support their intelligent manufacturing initiatives.
Fourth, actively expand financing channels and secure effective support policies. The implementation of intelligent manufacturing and the adoption of smart upgrades both require substantial capital investment, which to some extent constrains enterprises’ demand for such transformations. According to statistics, among companies with annual revenues below RMB 500 million, 50% rely on internal funds for smart‑upgrade projects, 25% receive government subsidies, while bank loans and capital‑market financing each account for roughly 11%. In recent years, the agrochemical industry has enjoyed robust growth, attracting increasing attention and favor from the capital markets, making this an opportune moment to embark on intelligent transformation. Moreover, governments at all levels have introduced a series of complementary guidance and incentive policies aligned with the “Made in China 2025” strategy, involving numerous issuing authorities. Enterprises should proactively seek to access more targeted and effective support measures to facilitate the successful implementation of smart‑upgrade initiatives.
During the 14th Five-Year Plan period, intelligent manufacturing will be the core strategic direction for engineering and technological advancement in the agrochemical industry. Intelligent upgrades will enable agrochemical companies to achieve real-time monitoring of on-site production conditions and optimized scheduling, thereby further enhancing product quality, boosting production efficiency, strengthening intrinsic safety, reducing costs, and lowering resource consumption. This will ensure that enterprises operate safely, stably, continuously, at full capacity, and with superior performance, maximizing their economic returns. Moreover, intelligent manufacturing will drive the agrochemical value chain toward higher-end, smarter, and greener operations, paving the way for high-quality, sustainable development.
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