Marx once said that “the society of the feudal masters was produced by the hand mill, and the society of the industrial capitalists was produced by the steam mill.” “The difference between various economic times is not what is produced, but how to produce and what means of labor are used for production.”
From agricultural society, industrial society to information society, human production tools have evolved from hand tools and energy conversion tools to today’s intelligent tools, such as CNC machine tools, robots and other tools that replace manual workers, as well as CAD, CAE and other services for mental workers. Tools, this is a tool revolution. Today we will focus on the decision-making revolution.
Management is decision-making: entrepreneurial decision-making and manager decision-making
Simon, known as one of the “Fathers of Artificial Intelligence”, won the Turing Prize and the Nobel Prize in Economics in 1975 and 1978. Simon believes that management is decision-making, and decision-making is divided into two categories: procedural decision-making and non-procedural decision-making. The so-called procedural decision-making is a regular, regular and followable decision-making. A set of rules and processes can be formulated. Decisions that can be described by data + algorithms are decisions with deterministic answers. An important direction of digitalization today is that every decision-making behavior in the process of enterprise R&D, design, production, operation, and management, whether it is a human decision or a machine decision, is trying to replace it through data + algorithms. This is a regular and procedural decision based on historical experience. This decision can be called a manager’s decision. Of course, not all decisions can be replaced by data + algorithms, such as entrepreneurial decisions. Entrepreneurs (entrepreneur) are people who dare to take all risks and responsibilities to create and lead a business. Entrepreneurs’ decisions are decisions based on future insights, which cannot be described by data + algorithms, and there is no standard answer beforehand. Decision-making fields that may not have happened in the past or whose nature and laws have not yet been discovered, mainly rely on entrepreneurs to make decisions.
Data-Based Decisions: Redefining the US Department of Defense
In 2020, the U.S. Department of Defense released the “Data Strategy” report, which was summarized in one sentence to redefine the U.S. Department of Defense based on data decision-making. In the report, the US Department of Defense’s vision is to become a data-centric organization that uses data on a rapid scale to gain operational advantage and improve efficiency. In the view of the US Department of Defense, the value of data lies in the formation of data advantages on the battlefield in joint global operations; in senior leadership decision support, the use of data to improve the management of the Department of Defense; in specific business analysis, the use of data to promote all levels Wise decisions. After all, the core is to use data to promote scientific decision-making at all levels of the U.S. Department of Defense.
Digitalization has brought about the reconstruction of consumer decision-making systems
Digitization has not only brought about the decision-making of enterprises, but also reconstructed the decision-making system of consumers. How do consumers and customers change in the digital age? First, the emergence of new consumer groups, such as post-00s and post-95s, means the rise of digital natives. The new generation of digital natives do not live without the Internet, and the digital space is the living space. Second, the decision-making chain of this generation has changed. Whether it is to buy a car, buy clothes, or go to dinner, all decisions are made through online discovery, online experience, community discussions, order purchases, and sharing of experiences. Third, the decision logic behind consumer behavior has changed. Digitalization has brought an era of non-essential consumption. In the past, consumers only considered the cost-effectiveness of the product. Now the cost-effectiveness is still very important. However, on this basis, consumers pay more attention to whether their rights to participate, express, and share in the consumption process are fully satisfied and respected. Fourth, when the consumer group changes, the consumption link changes, and the consumption decision-making model changes, the final product to be selected must also change. Consumers have more rights to express, speak, choose, and participate, which can be defined as the rise of customer and consumer sovereignty. When consumers’ needs become more personalized, real-time, scenario-based, content-based, and interactive, are the company’s supply capabilities and supply systems fully prepared for changes in demand? The answer is no.
Examining what is an enterprise from a digital perspective
From a digital perspective, what is a business? What is the core competitiveness of an enterprise? Coase, who won the Nobel Prize in Economics in 1991, once said: “The essence of an enterprise is a mechanism for resource allocation, an organization that replaces the market for resource allocation (market, government, enterprise).” The boundary of an enterprise depends on management costs and The size of the transaction cost. The government can allocate resources, enterprises can allocate resources at their disposal, and the market can allocate resources through price signals. Companies use less technology, land, capital, and talent to meet the needs of customers and consumers with higher efficiency, lower cost, and higher quality.
The chairman and general manager of the company will convene a regular weekly meeting every week, and the company’s R&D team, logistics center, production workshop, and retail team will also hold their own weekly meetings. We think about what problems these meetings will solve, and the core is to collect all kinds of information and make decisions. The R&D team thinks about how to shorten the development cycle of an airplane, a car, a cosmetic, a piece of clothing, and a CNC machine tool; the director of the production workshop thinks about how to improve the accuracy of the use of lathes, increase the output of a team, and increase the efficiency of a group of equipment , Increase the number of warehousing turnover, how to reduce the inventory quantity. All these problems are essentially transformed into a problem: how to optimize the efficiency of resource allocation you are facing.
Whether it is new product development, customer positioning, marketing strategy, R&D organization, delivery cycle, inventory management, new market entry, business model, etc., it is essentially a decision, and each decision to optimize the efficiency of resource allocation also determines The core competitiveness of an enterprise. Therefore, behind the enterprise competition is the competition for the efficiency of resource optimization. The question is, how do companies optimize the efficiency of resource allocation in today’s digital age, and what is the difference from before. Today, companies face various decisions in every link of R&D, design, production and operation, logistics management, and customer service. The value of digitization is whether the right data can be passed to the right time at the right time and in the right way in the face of these decisions. The right people and machines are the automatic flow of data. The automatic flow of data is essentially a more accurate, efficient, and low-cost decision. Based on this decision, the company’s resource allocation efficiency is optimized and the company’s core competitiveness is improved.
Before the 2008 international financial crisis, the price of iron ore fluctuated violently, as did the price of steel products. An order came at this time, accept it or not? Can the enterprise’s information system and digital system tell executives whether the order should be accepted, whether there is a profit, and can it be delivered on time? This is a decision issue. The automotive industry develops a new car, how to define consumers, how to insight into the real needs of consumers, how to define product features and colors, how to find suppliers, and how to stock up? How does the cosmetics industry lock in functions, find a suitable spokesperson, when to go public, and through what channels? For an enterprise, all these are how to make decisions in a highly uncertain world.
Two revolutions brought about by digitalization: tool revolution and decision-making revolution
The so-called tool revolution is to put the machine tools, robots, and equipment of the industrial age with brains and sensors, from traditional energy conversion tools to tangible things like 3D printing, CNC machine tools, AGV carts, automatic harvesting, and automatic sorting. Smart tools. In addition, some tools are CAD, CAE, and CAM software tools. Tools determine the efficiency of doing one thing, but it is far from enough. Decisions determine the direction of doing one thing. Only when the two are combined can the goal be reached faster.
Today companies have installed a variety of business systems, such as customer portraits in the demand field, PLM in the R&D field, ERP in the management field, MES in the manufacturing field, and MRO in the service field. The ultimate goal of these software is to help companies improve efficiency in every aspect of the R&D design, production and supply chain. Of course, the actual operating status is an organic whole, integrating IT technology, OT technology, and AT technology to enable the company to evolve from all optimization to global optimization. The core of digitalization in the future and today is a tool revolution and decision-making revolution for enterprises.
The essence of decision automation: automatic flow of data
Let’s think about how data flows within the enterprise. When an enterprise obtains customer demand information, the information is transformed into a set of data expressed in models, which flows in every link of operation management, product design, industrial design, manufacturing, process control, product testing, and product maintenance. In the past, the flow of this information was based on the flow of paper and the flow of Excel sheets. It required phone calls, meetings, or in a noisy workshop, two workers yelled at their necks. Whether in the past or today, companies are doing one thing, that is, how to collect, process, process, and execute data in production and operation management. The core of digitization is how to convert every paper-based information flow that requires personnel to participate in a data system based on performance models and process models that can automatically flow without personnel participation.
Many factories have various equipment such as robots, CNC airports, AGV carts, and three-dimensional warehouses, which are considered to be intelligent manufacturing. Indeed, equipment is part of intelligent manufacturing, but more importantly, there is a brain system and a nervous system. This nervous system is just invisible data, and there is less and less need for human participation in every link. Today there are two kinds of automation, one called visible automation, CNC machine tools, robots, AGV carts, and three-dimensional warehouses; the other kind of automation is invisible data. The future direction of digital transformation is how to automatically generate product design and processing technology without human involvement, and automatically transport it to the machine tool, without the need to develop new codes, and each link less and less does not require human participation. Behind each link is essentially a decision, which is a set of decisions that assist people, support people, substitute people, and support, assist, and replace machines.
Now we re-understand what digitalization, networking, and intelligence are. The so-called digitization is to express the world seen by the physical world through 01 codes in cyberspace and digital space. The so-called networking is to realize data connection between multiple equipment business systems. The so-called intelligence refers to less and less people need to participate in the process of connecting each link of data.
New decision-making model: data + algorithm = service
Today’s intelligence relies on data + algorithms, that is, algorithms that collect physical data and precipitate the laws of the physical world. It has to solve four basic problems: one is to describe what happened in physics, the other is to insight into why it happened and what is wrong, the third is to predict what will happen in the future, and the fourth is to help people or replace people and support people’s decision-making. In this process, the role played by the digital system is becoming more and more important. At the beginning, it was just a description. Later, insight, prediction, and decision-making are needed. When the data can complete these four actions, unmanned driving can be realized, that is, artificial intelligence algorithms are used to make decisions. The goal of implementing this decision is to optimize the allocation of resources. Decisions in the digital age are based on data + algorithms. Among them, the data should be more comprehensive, real-time, and accurate; the software model behind the algorithm is a set of codes for the laws of the physical world, which should be more accurate and objective.
Change in decision-making mode: building a decision-making system based on customer insights
Looking to the future, the manufacturing industry or the entire business system will become more and more complex. This complexity comes from the intelligence of products, the complexity of the supply chain, and the personalized, real-time, and scenario-based requirements. However, in the past ten or twenty years, it has become more and more difficult to adapt to the changes in today’s manufacturing and business systems in the information age to build a variety of business systems lined with chimneys. Today, only new architecture systems based on mid-stage, big data, artificial intelligence, 4G, 5G, and cloud SAAS can respond to the complexity of business systems in real time.
To achieve such a transformation, on the supply side, it is necessary to upgrade from various installations of enterprise ERP, manufacturing execution systems, machinery and equipment in the past to cloudification, mid-stage, IOT, and mobile; on the client side, whether through industrial The Internet, through construction machinery, through airplanes, trains, or through retail, must be accessible, insightful, analytical, and serviceable to customers and consumers. More importantly, digital transformation requires the establishment of an accurate decision-making system based on customer needs. In the past, companies made decisions based on experience, but today companies need to build information based on customers, needs, and real-time product operation to do product innovation, intelligent manufacturing, channel management, distribution, and digital marketing. The decision-making system and link have changed.
Industry leaders have built their own next-generation digital solutions
Leading companies in the digital transformation industry have a very important trend to build their own digital solutions around real-time decision-making based on customer needs.
Wu Mart: In January 2018, the retail company Wu Mart launched ERP, which was a beacon project in China’s retail field at that time; but in August 2020, Wu Mart dismantled the ERP system, leaving only the financial and human resources, and the entire business system Switched to a self-developed multipoint system. Of course, the “multipoint” system not only serves Wumart, but also other companies.
Tesla: Tesla did not install general-purpose ERP software for the automotive industry. Instead, it used a self-developed business system to cover finance, production and sales, sales, and procurement, and at the same time expanded to online marketing. Why did Tesla build a set of business systems independently? Because it needs to directly interact with consumers, it needs to be downloaded to the car operating system through OTA, it needs to be served in real time, and it needs to build a complete closed loop of consumer research and development, production, procurement, and service. When Tesla was doing digital transformation around this goal, it found that the existing business system was far from meeting the needs.
Midea: Midea has undergone digital transformation of 1.0, 2.0, and 3.0. In 2016, when Midea was working on Digital 2.0, it wanted to build a data-driven C2M customer customization system, build a new order-driven business model, restructure the channel system, and realize the traditional transformation from a recording system to a real-time decision-making system. Midea made a painful and difficult choice and decided to independently develop cloud-based software systems such as supply chain PLM and MES.
Rhino and Hema: In the past three or four years, Alibaba has an enterprise called Rhino Intelligent Manufacturing, which is a comprehensive cloud-based manufacturing of core elements and an online factory defined by cloud algorithms. Rhino Intelligent Manufacturing has rewritten all software in the apparel industry, except for CAD and other development tools, based on the cloud side-end architecture. The core system of Hema Xiansheng is to decouple the retail ERP, WMS, CRM, and marketing systems in the past, and re-develope a new retail operating system based on the cloud side. Only with this set of systems can online and offline integration, real-time optimization of full-link logistics, and high serviceability and high scalability of the business system be realized.
Why do industry leaders build their own next-generation digital solutions? The conclusion is that although there are various new technologies such as the Internet of Things, big data, cloud computing, artificial intelligence, etc., relative to the rapid changes in enterprise needs, the supply capacity of digital technology solutions is far from satisfying the digitalization of enterprises. need. There is not a large number of mature solutions waiting for the digital transformation of enterprises, and the commercial market supply capacity of digital solutions is not enough.
Rethinking the value of cloud computing: Promote companies into the era of high-frequency competition
Another perspective for examining corporate decisions is frequency. Today’s corporate competition is entering the era of high-frequency competition. Behind the high-frequency competition is the high frequency of decision-making. Today’s digitalization has started a time-reduction revolution, and companies must quickly respond to changes in market demand. To this end, companies need to rethink the value of cloud computing. Cloud computing can help companies move more calmly to the era of high-frequency competition. For example, Alibaba Cloud has helped companies build a high-frequency decision-making mechanism. Yang Xueshan, the former deputy minister of the Ministry of Industry and Information Technology, mentioned in his book “Principles of Intelligence” that the ability of a subject to respond to changes in the external environment is called intelligence. This main body can be a robot, CNC machine tool, AGV trolley, three-dimensional warehouse, a research and development team, an enterprise, or a person. Whether this subject is very intelligent depends on how to respond when external demands change.
Alibaba Cloud has built the ability of enterprises to respond in real time. The ability to respond to demands in real time is a magic weapon for companies to win high-frequency competition, the only way to deal with uncertainty, and a yardstick for evaluating the effectiveness of corporate digitalization. Today, the cycle of brand and distribution finance has gone from one month to one day, the launch cycle of new products has been shortened from 16 months to 3 days, and the precise forecast based on consumer demand has gone from 45 days to 20 days. Digitization not only allows companies to make accurate decisions, but also real-time and high-frequency. Whether it is the launch cycle of new products, operational decision-making, production scheduling, supply chain turnover, and service response time have become shorter and shorter. High frequency means intelligence, which means that it can respond quickly to changes in the environment.
Digital Ultimate Territory: Building a real-time feedback and decision-making optimization system between the physical world and the digital world
Looking to the future, we are building a digital twin world. The Internet of Things, big data, cloud computing, and artificial intelligence are all about presenting, predicting, and optimizing decisions in the digital world of what is seen and heard in the physical world. Five or ten years later, architecture, medical care, and cities will be built in the digital twin world. From atoms to genes, from devices, the earth, and the universe, from a human cell, to an organ, and to the human body will all be in the digital twin world.里 presented. This process is to continuously transfer the data of the physical world to a digital twin world in real time. After the optimization and decision-making in the digital twin world are completed, the digital decision instructions are then transferred to the physical world for optimization. Constructing the real-time feedback optimization of the physical world and the digital world is the trend of the digital world in the next five, ten years or longer.
The evolution of organizational decision-making mechanisms in the digital age: from relatively deterministic low-frequency decision-making to uncertain high-frequency decision-making
Today, when the structure and logic of information transmission change, the mode of organization management must also change. The book “The Heat Wave” written by an American scholar describes that in 1987, the highly developed Chicago, the United States, killed many people. This disaster scenario raises a question worth thinking about. In an era of high-frequency competition, can traditional organizations that operate with strong regulations and accustomed to handling deterministic events cope with a sudden and constantly changing event? Deterministic organization and behavioral inertia are the culprits that cause errors in response to emergencies. Grey rhinos may arrive every day. In a highly uncertain environment, the normal low-frequency decision-making mechanism cannot meet the high-frequency decision-making needs of emergencies. In the face of a highly competitive system, how to make a decision is not only a technical issue, but also requires a “genetically modified project” in the most basic unit of corporate organization and management to build a high-frequency, multi-center, and short-link decision-making process. mechanism. This is the only way for an organization from the industrial age to the digital age.