5.3.1 Enterprise Resource Planning
Enterprise Resource Planning (ERP) is a highly integrated computerized management system for the comprehensive management of resources (people, money, materials, information, etc.) owned by an enterprise. And the corresponding computer management system has experienced the basic MRP stage, closed-loop MRP stage, MRP-II stage and ERP stage.
5.3.1.1 Material Requirement Planning (Material Requirement Planning, basic MRP)
Material Requirement Planning, with the help of the computer's arithmetic ability and the system's ability to manage the customer's order, in-stock materials, and the composition of the product, realizes the development and calculation of Material Requirement Planning based on the customer's order and in accordance with the list of the structure of the product. The material requirements plan is based on customer orders. To realize the reduction of inventory, in order to achieve "both to reduce inventory, but not material shortages," the purpose.
MRP is mainly used in the manufacturing industry, with the supply side to buy raw materials, after processing or assembly, manufacturing products, sales to the demand side of the management function. Any manufacturing business production activities are carried out around its products, manufacturing information systems also reflect this feature. MRP is from the structure of the product or bill of materials to achieve the integration of material information.
Material demand information, product structure, procurement and supply lead time, inventory information is the four main data to run MRP. The accuracy of these data determines the effectiveness of MRP.
MRP generally contains the following modules: Master Production Schedule (Master Production Schedule, MPS) module, is the production plan outline of the product series or categories into a specific product or a specific part of the plan, according to which you can develop a material requirements plan, production schedule and capacity demand plan; Material Requirements Program (MRP) The Material Requirements Planning (MRP) module is used to calculate the time and quantity of material requirements, especially the quantity and time of relevant demand materials; Bill of Material (BOM) module is used to calculate the product structure of each product and all the materials to be used; Inventory Control (IC) module is used to calculate the quantity and time of all products, parts, work-in-progress, inventory and other materials in accordance with the storage theory of enterprises. All products, parts, work in progress, raw materials and other changes in the module; Purchase Order (Purchasing Order, PO) module, is the module to the supplier order; processing orders (Manufacturing Order, MO) module, used to generate orders for processing products.
5.3.1.2 Closed-loop MRP
Because the basic MRP is based on the following two assumptions: first, the production plan is feasible, i.e., it is assumed that there is enough equipment, manpower and capital to ensure the realization of the production plan; second, it is assumed that the purchasing plan is feasible, i.e., there is enough supply capacity and transportation capacity to ensure the completion of the material supply. However, in the actual production, capacity and material resources are always limited, and thus there is often a production plan can not be completed. MRP system in the 1970s developed into a closed-loop MRP system. Closed-loop MRP system in addition to material requirements planning, but also the production capacity demand plan, shop floor operation plan and procurement operation plan are all included in MRP, forming a closed system.
Simply put, the formation of closed-loop MRP is in the basic MRP based on the addition of capacity requirements plan, the formation of a "plan - implementation - feedback - plan" closed-loop system. The normal operation of MRP system requires a realistic and feasible master production plan. In addition to reflecting market demand and contract orders, it must also meet the enterprise's production capacity constraints. Therefore, in addition to the preparation of the resource requirement plan, we must also formulate the capacity requirement plan to balance with the capacity of each work center. Implementation of the plan can only begin when measures have been taken to ensure that both capacity and resources meet the load requirements. To ensure the realization of the plan, we need to control the plan, and to execute MRP, we need to control the priority of processing with work orders, and control the priority of purchasing with purchase orders. In this way, the basic MRP system is further developed to include the functions of capacity requirement planning and execution and control planning, forming a circular loop called closed-loop MRP.
5.3.1.3 Manufacturing Resource Planning (Manufacture Resource Planning, MRP II)
The emergence of the closed-loop MRP system has made the harmonization of various subsystems in terms of production activities. But this is not enough, because in the management of enterprises, production management is only one aspect, it involves only logistics, and logistics is closely related to the flow of funds. This in many enterprises is managed separately by the accounting staff, which results in the duplication of data entry and storage, and even cause data inconsistency. Thus, in the 1980s, the people of production, finance, sales, engineering, procurement and other subsystems into an integrated system, and is called Manufacturing Resource Planning (Manufacturing Resource Planning) system, the acronym or MRP, in order to distinguish between the logistics demand planning (also known as MRP) and recorded as MRP Ⅱ.
The main difference between MRPⅡ and MRP is that it uses management accounting to realize the integration of material information and capital information, and manages the economic benefits brought about by the implementation of enterprise "material planning" in monetary form.
In the MRPⅡ system, based on MRP's product structure, starting from the material cost of the lowest purchased part, the material cost, labor cost and manufacturing cost (indirect cost) of each piece of material will be accumulated layer by layer upward to arrive at the cost of each layer of parts and components up to the final product. The profitability of each type of product is further analyzed in the context of marketing.
The basic idea of MRP Ⅱ is to take the enterprise as an organic whole, from the perspective of overall optimization, through the use of scientific methods of various manufacturing resources and production, supply, marketing, financial links for effective planning, organization and control, so that they can be coordinated and fully functional. MRP Ⅱ of the traditional accounts with the occurrence of the accounts of the affairs of the combination, not only Manage the current status of funds in accounts, but also trace the ins and outs of funds. It generally includes the following modules: Product Data Management Module, Master Production Planning Module, Material Requirement Planning Module, Inventory Management Module, Capacity Requirement Module, Sales Management Module, Purchasing Module, Workshop Operation Management Module, Financial Management Module, and Quality Management Module. These modules are structurally independent of each other, but functionally interdependent.
5.3.1.4 ERP
The concept of ERP was put forward by Gartner Group in 1990, and its exact definition is: MRPⅡ (Enterprise Manufacturing Resource Planning), the next generation of manufacturing systems and resource planning software. MRPⅡ focuses mainly on the management of human, financial and material resources within the enterprise. Ⅱ based on the expansion of the scope of management, it will be customer demand and internal manufacturing activities, as well as suppliers of manufacturing resources integrated together to form a complete supply chain, and all links in the supply chain such as orders, purchasing, inventory, planning, manufacturing, quality control, transportation, distribution, service and maintenance, financial management, personnel management, laboratory management, project management, recipe management and so on. Effective management. With the rapid development of IT technology and the application of network communication technology, ERP system adopts client/server (C/S) architecture and distributed data processing technology, supporting Internet/Intranet/Extranet, e-business, E-commerce, and electronic data interchange (EDI). In addition, it can realize interoperability on different platforms.
ERP integrates customer demand and manufacturing activities within the enterprise as well as the manufacturing resources of suppliers to form a complete supply chain, and its core management ideas are mainly reflected in the following three aspects: ① embodies the idea of the management of the entire supply chain of resources; ② embodies the idea of lean production, agile manufacturing and synchronous engineering; ③ embodies the idea of prior planning and prior control.
Soon after the emergence of ERP, computer technology has encountered the Internet/Intranet and network computing boom, the tendency of internationalization of the manufacturing industry and the deepening of manufacturing information technology. As the future Intranet will become the choice of many large companies network construction, the use of Web clients have the advantages of low cost, easy installation and maintenance, cross-platform operation and has a unified, friendly user interface, coupled with all the database vendors support for Web technology, so that at present, almost all the client / server application development vendors are planning to Web browser front-end installed on their products. Several of the major manufacturing software companies, Oracle, SALP, and BAAN, are scrambling to "Web-enable" the clients of their MRP II/ERP client/server applications.
5.3.2 Decision Support System
Decision Support System (DSS) is a human-computer interactive system based on computers and applying decision science and its related theories and methods, which is mainly oriented to semi-structured and unstructured decision-making problems in the strategic planning of organizational management, providing users with the convenience of obtaining data and constructing models. The concept of DSS was introduced in the 1970s and developed in the 1980s. It is based on the following reasons: the traditional MIS did not bring great benefits to the enterprise, the positive role of people in management to be played; people's understanding of the laws of information processing to improve, in the face of changing environmental needs, require a higher level of the system to directly support decision-making; the development of computer application technology to provide a material basis for DSS.
5.3.2.1 Characteristics of DSS
Based on the definition, the characteristics of decision support systems can be summarized as follows:
(1) DSS is interactive. It is manifested in the fact that decisions are made through multiple dialogues between the manager and the system, and that human factors such as preferences, subjective judgments, abilities, experiences, values, etc. have an important influence on the decision-making results of the system.
(2)The problem solved by the DSS system is for semi-structured decision-making problems, and the use of models and methods is determined, but there are differences in the decision-maker's understanding of the problem, the use of the system has a specific environment, and the conditions of the problem are not certain and unique, which makes the decision-making results uncertain.
(3) The system has a specialized structure for storing and researching alternate models and methods, providing the ability to compare, link and synthesize models. The driving force of the system comes from the model and the user, the person is the initiator of the system operation, the model is the core of the system to complete the conversion of each link.
(4) DSS only plays the role of assisting decision-making, DSS should not replace the judgment of the manager, but should allow the manager to be in an active position to improve the decision-maker's ability to make scientific decisions.
(5) DSS should be easy to learn, use and modify, and thus to do a dynamic analysis of the user's needs, to achieve timely improvement of the various functions of the DSS.
5.3.2.2 Model library, method library and database of DSS
(1) Model library.
Common in the field of management are information processing models, which are expressed as mathematical expressions, computer programs, etc. Through the establishment and use of models, decision makers can obtain useful information to assist decision making. Establishment of the model is related to the decision-making field of experts and scholars in the exploration of the changing law of things in the abstraction of their mathematical models, this work is creative labor, need to spend a lot of energy to get the regularity or similar mathematical models.
After the establishment of the mathematical model is an important issue is the model solution algorithm, it can be an accurate solution, can also be an approximate solution, this algorithm is proposed by the computer numerical computation scholars to complete. With the model algorithm, it can be developed into a program in a computer language. The actual decision maker can then use the model program to execute it on the computer, calculate the results, and get the information to assist decision making. Model is an important means of assisting decision-making, model library is a collection of models, it is in accordance with certain organizational methods, the model organically brought together by the model library management system unified management. Model library and model library management system constitutes the model library system.
(2) Method library.
The method library system consists of method library and method library management system. Its basic function is to provide the necessary algorithms for the solution analysis of various models and the required methods for the decision-making activities of users. The methods in the method library can usually include various optimization methods, prediction methods, statistical methods, countermeasure methods, risk methods, matrix equation solving and so on.
The method library management system is responsible for describing, entering, storing, adding, modifying, deleting and other processing of methods. The usual method is to choose the appropriate computer programming language, the relevant algorithms into a set of executable programs stored in the computer. These programs can be expressed as functions or procedures with descriptions, and then call the corresponding program model according to the needs of solving the problem, so as to achieve the purpose of solving the problem. In addition, the method library management system should also have the ability to interact with the database, model library, and for the user to select the algorithm to provide flexible and convenient interactive reveal function.
(3) database.
The database is a software system for collecting, processing, storing and outputting information, therefore, the development and application of model library and method library should be based on database. Only with a perfect database system, the model library and method library can play its role under the premise that the information has a fundamental guarantee. In turn, the development of the model library and method library and to the database research and application of new topics, promote its research on how to provide more suitable models and methods for the operation of the data model.
The model library and the method library are inseparable, whether it is the parameter estimation of the model, the solution of the model or the validation of the model are specifically realized through various methods. The richness of the methods in the method library and the performance of the methods determine the effect of model use. In short, from the point of view of assisted decision-making, the "three libraries" is an important aspect of the support for problem solving, a strong assisted decision-making system should have the "three libraries", and take it as the core.
5.3.3 Expert System
In the past 30 years, Artificial Intelligence (AI) has gained rapid development, and has been widely used in many disciplines and achieved fruitful results. As an important branch of Artificial Intelligence, Expert System (Expert System,ES) is an emerging applied science produced and developed in the early 1960s, and is being perfected and matured with the continuous development of computer technology.In 1982, Feigenbaum, a professor at Stanford University, gave the definition of Expert System:" An expert system is an intelligent computer program that uses knowledge and reasoning processes to solve complex problems that require the expertise of an outstanding person to solve."
An expert system is a system of intelligent computer programs that contains within it a large amount of knowledge and experience at the level of an expert in a particular field, and is able to use the knowledge and problem-solving methods of human experts to deal with problems in that field. In other words, an expert system is a program system with a large amount of specialized knowledge and experience, which applies artificial intelligence technology and computer technology to reason and make judgments based on the knowledge and experience provided by one or more experts in a certain field, simulating the decision-making process of human experts in order to solve those complex problems that need to be handled by human experts. In short, an expert system is a computer program system that simulates human experts to solve problems in a domain.
5.3.3.1 General Characteristics of Expert Systems
Overall, expert systems have a number of ****same characteristics and advantages.
(1) Enlightenment. An expert system can use the expert's knowledge and experience to reason, judge and make decisions. Most of the world's work and knowledge is non-mathematical, and only a small fraction of human activity is centered on mathematical formulas (about 8%). Even the disciplines of chemistry and physics rely mostly on reasoning for their thinking; the same is true for biology, most medicine, and all of law. Business management thinks almost entirely by symbolic reasoning, not numerical computation.
(2) Transparency. An expert system is able to explain its own reasoning process and answer questions posed by the user so that the user can understand the reasoning process and increase trust in the expert system. For example, a medical diagnostic expert system to diagnose a patient with a viral cold, and must use a certain treatment program, the expert system will explain to the patient why he has a viral cold, and why to take this treatment program.
(3) Flexibility. Expert systems can constantly grow knowledge, modify the original knowledge, constantly updated. Because of this feature, makes the expert system has a very wide range of applications.
5.3.3.2 Structure and Types of Expert Systems
(1) Structure of an expert system.
The expert system usually consists of six parts: human-computer interaction interface, knowledge base, reasoning machine, interpreter, comprehensive database, and knowledge acquisition.
1) Knowledge Base. Knowledge base is used to store the knowledge provided by experts. The problem solving process of the expert system is to simulate the expert's way of thinking through the knowledge in the knowledge base. Therefore, the Knowledge Base is the key to the superior quality of the expert system, i.e., the quality and quantity of knowledge in the Knowledge Base determines the quality level of the expert system.
2) Comprehensive database (Global Database). Comprehensive database, also known as the global database or total database, which is used to store the initial data of the domain or problem and the intermediate data (information) obtained in the reasoning process, i.e., some of the current facts of the object being processed.
3) Reasoning Machine (Reasoning Machine). The reasoning machine repeatedly matches the rules in the knowledge base for the conditions or known information of the current problem to obtain new conclusions to get the problem solving results. Here, there can be two types of reasoning: forward and reverse reasoning. Forward reasoning is to match from the antecedent to the conclusion, while reverse reasoning assumes that a conclusion is valid first to see if its conditions have been satisfied. It can be seen that the reasoning machine is like an expert's way of thinking about problem solving, and the knowledge base realizes its value through the reasoning machine.
4) Explainer (explorer). Explainer can explain to the user the behavior of the expert system, including explaining the correctness of the conclusion of the reasoning and the system to output other candidate solutions. The interpreter is also able to explain the conclusions and the solution process based on the user's questions, thus making the expert system more humane.
5) human-computer interaction interface (Interface). Interface, also known as the interface, it can make the system and the user to carry on a dialogue, so that the user can enter the necessary data, ask questions and understand the reasoning process and reasoning results. The system, in turn, through the interface, requires the user to answer questions, and answers the questions posed by the user and provides the necessary explanations.
6) Knowledge Acquiring. Knowledge Acquiring is the expert system knowledge base is the key to the superiority of the expert system design is also the "bottleneck" problem, through the knowledge acquisition, you can expand and modify the content of the knowledge base, but also to achieve the automatic learning function.
The working process of the expert system: the knowledge is stored in the knowledge base in advance (some expert systems can also acquire knowledge through learning), the user inputs information through the human-computer interface, the expert system in the original knowledge base in the knowledge base and the information obtained on the basis of the use of inference machine and the coordinated work of the integrated database, to complete the inference process, to reach a conclusion, and finally in the form of multimedia will be presented to the user. The conclusion is finally presented to the user in the form of multimedia.
(2) Types of expert systems.
1) Interpretation expert system. The task of an interpretation expert system is to determine the meaning of known information and data by analyzing and interpreting them. Examples of such practical systems are satellite image (cloud maps, etc.) analysis, integrated circuit analysis, DENDRAL chemical structure analysis, ELAS petroleum logging data analysis, chromosome classification, PROSPECTOR geological exploration data interpretation, and hilly water finding.
2) Predictive Expert System. The task of the prediction expert system is to infer what may happen in the future by analyzing the known conditions in the past and present. For example, severe weather (including heavy rain, hurricanes, hail, etc.) forecasting, battlefield outlook prediction, crop pest forecasting and other expert systems.
3) Diagnostic expert system. The task of diagnostic expert systems is to deduce the cause of malfunction (i.e., failure) of an object on the basis of observations (data). Examples of diagnostic expert systems are particularly numerous, such as medical diagnosis, diagnosis of electro-mechanical and software malfunctions, and diagnosis of material failures.
4) Design expert system. The task of designing an expert system is to derive a target configuration that satisfies the constraints of the design problem based on the design requirements. Design expert system involves circuit (such as digital circuits and integrated circuits) design, civil and architectural engineering design, computer structure design, mechanical product design and production process design.
5) Planning expert systems. The task of the planning expert system is to find a certain sequence of actions or steps that can achieve a given goal. Planning expert systems can be used for robotics planning, transportation scheduling, engineering project justification, communications and military command, crop fertilization program planning.
6) monitoring expert system. Surveillance expert system's task is to the system, object or process behavior for continuous observation, and the observed behavior and its behavior should be compared to find anomalies, issued an alarm. Surveillance expert systems can be used for safety surveillance of nuclear power plants, air defense surveillance and alerts, monitoring of state finances, surveillance of infectious disease outbreaks, and surveillance and alerts of crop pests and diseases.
7) control expert system. Control expert system's task is to adaptively manage the comprehensive behavior of a controlled object or object to meet the expected requirements. Air traffic control, business management, autonomous robot control, combat management, production process control and production quality control are all potential application aspects of control expert systems.
8) Debugging expert systems. The task of debugging expert systems is to give advice and methods for dealing with malfunctioning objects. Debugging expert system is characterized by the simultaneous planning, design, forecasting and diagnostic expert system functions. Commissioning expert systems can be used for the commissioning of new products or systems, as well as for adjustments, measurements and tests of maintenance equipment at repair stations. Examples in this regard are still rare.
In addition, there are decision-making expert systems and consulting expert systems.