System simulation research on production performance of logistics enterprises

System simulation research on production performance of logistics enterprises | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article System simulation research on production performance of logistics enterprises JIE WANG, ZENGHAI WU, HAN XIE, SHENG LUO, MIN FAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6200753/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract At present, under the background of global economic integration, the rapid development of science and technology, and other times, the logistics industry has become the mainstream trend of the times by shifting from the traditional development stage to the intelligent and intelligent development stage. It has gradually become the main development goal of China's logistics industry in the future. Based on the actual situation of logistics enterprises in the era of intelligent logistics, this study constructs a system dynamics simulation model based on the system dynamics theory and efficiency theory. It assigns values to the parameters of all kinds of variables required by the model. On this basis, the model is simulated by computer, and the simulation results are analyzed. At the same time, the parameter settings in the system are adjusted to explore the changing dynamics of the input subsystem, output subsystem, and environmental subsystem under different parameter states. Business and commerce/Business and management Humanities/Complex networks Social science/Business and management Social science/Science technology and society Logistics companies Production performance System dynamics Simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 Figure 23 I. INTRODUCTION A. RESEARCH BACKGROUND As we all know, ‘logistics’ refers to the process of transporting materials or commodities needed by customers from the supplier's location to the customer's location, involving transport, loading and unloading, distribution and processing, as well as information transfer and other logistics activities. On the one hand, the transformation and upgrading of the logistics industry, also makes the shortcomings of the previous rough logistics revealed, such as the logistics supply chain produces more resource waste. At the same time, in the development trend of intelligent logistics, the logistics industry for research and development personnel, high-end technical personnel demand gradually increased, the level of technological innovation has become a measure of the current logistics enterprises, one of the main indicators of the strength of the competitiveness of the weak. On the other hand, good or bad production performance often determines whether an enterprise in the industry can survive for a long time, logistics enterprises as an important body of economic development, especially so. In today's society, logistics enterprises in the pursuit of low-cost production, at the same time also affect the quality of the product, which is affected by the decline in customer satisfaction and other issues. In the market economy, logistics enterprises, as an important part of today's social and economic development, all aspects of its objectives are aimed at creating greater economic profitability for the enterprise itself, to achieve greater profitability. To enhance the effective competitiveness of logistics enterprises in today's fierce economic environment, but also to effectively achieve the development goals of logistics enterprises' upgrading and transformation, and to promote the long-term sustainable development of logistics enterprises, the study of the production performance of logistics enterprises seems to be very necessary. Therefore, it can be seen from the above, that logistics enterprises play a crucial role in the development of China's economy, and become a major growth point for economic development. However, the traditional logistics enterprises are subject to their scale, capital scale, and business management style, the production performance level is relatively low. At the same time, in the management of production performance, managers have not paid sufficient attention and unique insights, the lack of systematic, effective production performance management methods, and specific practical operation processes have not been perfected [ 1 ] . Therefore, for the logistics enterprise production performance research has great significance in time, this paper is based on the theory of system dynamics, the construction of input subsystems, output subsystems, and environmental subsystems, the use of VENSIM PLE software simulation model analysis and research, comprehensive measurement of logistics enterprise production performance level in the era of intelligent logistics, to provide practical and feasible development direction for the development of the future logistics enterprise upgrading and transformation of the production performance, while at the same time, the specific operation process has not been perfected [ 1 ] . To provide a practical development direction for the future development of logistics enterprises in production performance upgrading and transformation, and at the same time, to provide a certain reference for the improvement of production performance management. B. RESEARCH SIGNIFICANCE 1) THEORETICAL SIGNIFICANCE First of all, the logistics industry, as a pillar industry of China's national economic development, has been used by many scholars for related efficiency research, such as operational efficiency, innovation efficiency, etc., and most of the research is carried out from the perspective of static efficiency, and few scholars pay attention to the production efficiency of logistics enterprises. From the perspective of system dynamics, there are even fewer studies that construct simulation models to comprehensively analyze the production performance level of logistics enterprises [ 2 ] . Secondly, most of the studies on the production performance level of logistics enterprises either construct empirical models from the perspective of inputs or outputs, and most of the scholars include the environmental impact factors as control variables in the research models. When constructing the research model, fewer scholars include environmental influences as the main influence variables in the complete research model. Therefore, based on system dynamics theory, this paper constructs input subsystems, output subsystems, and environmental subsystems from an integrated perspective, and conducts simulation research using simulation software to measure the production performance level of logistics enterprises. 2)PRACTICAL SIGNIFICANCE First of all, this paper analyses and applies the knowledge related to system dynamics theory to construct the causality diagrams of various subsystems of production performance, including input subsystems, output subsystems, and environmental subsystems, as well as the overall system flow diagrams, and carries out the system dynamics simulation analyses on the production performance of logistics enterprises [ 3 ] . As a result, the managers of logistics enterprises can have a clearer understanding of the intrinsic connection between the various parts involved in the production performance process, and can quickly and effectively identify the bottlenecks in the process of achieving the production performance goals, enhance the management efficiency of the managers, and at the same time, provide a certain basis for the improvement of the production performance of logistics enterprises [ 4 ] . Secondly, the power model of the production performance simulation system of logistics enterprises constructed in this paper is not only applicable to logistics enterprises but also applicable to other industries of the same type under the premise of considering different influencing indexes, which can provide certain reference and guidance for other enterprises of the same type in the study of production performance level [ 5 ] . II. THEORETICAL FOUNDATIONS AND RELATED CONCEPTS A. THEORETICAL FOUNDATIONS 1) SYSTEM DYNAMICS THEORY System dynamics, as a branch of system science, is a branch of a hundred schools of thought, reflecting the characteristics of the cross-fertilization of different disciplines. At the same time, it reflects the characteristics of information system theory and control theory, can find system problems and solve system problems in time [ 6 ][ 7 ] , used to explore the information feedback within the system as well as the system itself. In 1956, Professor Forrest of the Massachusetts Institute of Technology put forward the term ‘system dynamics’, which formally gave birth to the theory of system dynamics, which reflects the intricate relationship between the system itself, and the different variables within the system, these variables are often between the birth of system dynamics, and the system dynamics theory [ 7 ] . Dynamics theory, which embodies the system itself, the intricate relationship between different variables within the system, these variables often show the characteristics of high-order non-linear, and with time in the dynamic change, it can be subjective qualitative analysis and objective quantitative analysis of the organic fusion, to provide a comprehensive and comprehensive analysis of the system itself and the relationship between different variables within the system. On the one hand, the scope of application of system dynamics is very wide, initially mainly used in industrial enterprises in the field of production management, with the development and progress of the economy, so far all kinds of systems, involving various fields: social, economic, ecological, agricultural, environmental protection and so on [ 8 ] . On the other hand, the role of system dynamics is also very powerful, mainly by a large system of various subsystems or various factors affecting the causal feedback relationship between the establishment of the system dynamics model, the model can be used to carry out the management of prediction, control optimization, etc., not only to explore the system itself, the system within the dynamics of the relationship between the different variables over time but also to coordinate the relationship between a variety of variables, so the system dynamics compared to other simple system dynamics [ 9 ] . System dynamics has its unique advantages compared with other pure linear regression and linear programming methods. Based on previous research on system dynamics, this paper summarizes the connotation of system dynamics as follows: First, the system involved in system dynamics has the characteristic of information feedback. Information feedback means that there is a meaningful connection between the system itself and different variables within the system, so the theory of system dynamics can be gradually improved based on cybernetics and can achieve real-time dynamic prediction research on the system dynamics simulation model. Second, the research object of system dynamics has a certain degree of complexity. The integrated system constructed based on system dynamics theory includes several subsystems, and there is a causal relationship between the subsystems and different variables within the subsystems, which can produce the corresponding logical relationship, and explore the relationship between the integrated system and subsystems and the relationship between different variables within the subsystems under the macro perspective. Thirdly, the development of system dynamics research relies on the analysis of computer simulation models [ 10 ] . Firstly, the influencing factors within the system are analyzed through theory, the stock flow diagram is constructed based on the causal logical relationship between the integrated system and the subsystems and the different variables within the subsystems, and formulas and model equations are assigned to each variable to construct the computer simulation tests and analyses through the basic data. At the same time, to ensure the reasonableness of the model and the reliability of the data, it is also necessary to carry out different validations of the model, such as validity tests, etc. Finally, analyzing the results of the simulation data and carrying out sensitivity analyses, etc., will provide a certain basis for strategic decision-making for the improvement of the system and the improvement of its efficiency. 2)efficiency theory ‘Efficiency’ was originally a concept in the field of physics, used to express the ratio of output energy to input energy when a machine does work. With the development of the economy and technological progress, in the field of management, efficiency refers to the amount of work done per unit of time and the most effective use of social resources to meet human needs; while in the economic field, efficiency refers to the pursuit of maximizing economic benefits under the conditions of given inputs, not wasting or maximizing the use of economic resources, and achieving the optimal allocation of economic resources [ 11 ] . Economists are committed to improving the efficiency of the use of scarce resources and exploring how to maximize the satisfaction of production and living needs under limited conditions [ 2 ] . At present, academic research on ‘efficiency theory’ mainly has two mainstream directions, one is the classical economic efficiency theory, and the second is the neoclassical economic efficiency theory; there is also some research on allocation efficiency and technical efficiency [ 12 ] . The first is the classical theory of economic efficiency. The main representative, Adam Smith, first put forward the idea of division of labor in his monograph The Wealth of Nations and systematically elaborated on the important role of division of labor in enhancing labor efficiency and increasing national wealth. On this basis, Adam Smith pointed out three advantages of the division of labor: firstly, it enhances workers' proficiency in their work; secondly, the division of labor can greatly reduce the time lost due to the transfer of work; and thirdly, the division of labor will promote the advancement of machinery and equipment, and at the same time improve the efficiency of machinery and equipment [ 13 ] . Next is the neoclassical economic efficiency theory. The neoclassical theory of economic efficiency was developed based on the theory of free market competition initiated by Adam Smith, and gradually formed by the continuous reasoning and improvement of many Western economists [ 14 ] . One of the most representative is the proposal of Pareto efficiency, Pareto efficiency is also the main standard to measure whether the allocation of resources is efficient. The theoretical study of allocation efficiency in neoclassical economics can be divided into two factions: one is the theory of allocation efficiency proposed by economist Marshall using the partial equilibrium of supply and demand. In Marshall's view, he takes a perfectly competitive producer as a case study enterprise, and the marginal cost function of this case study enterprise is the supply function, and the supply function of the whole market is the algebraic sum of the supply functions of all perfectly competitive enterprises [ 15 ] . The marginal utility function of the customer is the familiar demand function, and the equilibrium price and the corresponding equilibrium output, which represent the equilibrium state of the market, are calculated by linking the supply function with the demand function. Since a perfectly competitive market conforms to the premise that equilibrium price and marginal cost are equal, it is possible to optimize the efficiency of the current allocation [ 16 ] . The second is the Pareto theory of efficiency, which was developed by the economist Pareto based on general equilibrium analysis. Pareto then in the general equilibrium theory of Vallas based on the efficiency of the market for the rationalization of the allocation of resources, in the absence of an optimal allocation of resources, it is necessary to make everyone enjoy the benefits of the allocation of resources at least the same as they are in the initial, there can not be a reduction in the resources of the man-made mastery of the situation, and at least one of the people to get the better than the initial, if this is the case, then there must be a man-made mastery of resources, and at least one person to get better than the initial. If this is the case, then it proves that society has the most rational allocation of the resources it possesses. Later scholars called this kind of allocation efficiency ‘Pareto efficiency’, and a perfectly competitive market is a sufficient and necessary condition for realizing Pareto efficiency, neo-classical economics has proved that a perfectly competitive market can produce Pareto efficiency, thus making the concept of ‘invisible hand’ has become more and more important [ 17 ] . The concept of the ‘invisible hand’ has become clearer. Finally, several efficiency theories have been studied outside the mainstream. For example, Fayol constructed the marginal production function and then elaborated the theory of marginal efficiency. Zhou Yong [ 3 ] suggests that Marx's labor theory of value can be used to measure not only the value of commodities but also the economic efficiency of products. Yao Yu and Yue per Yu [ 4 ] argued that the main focus of Western research on equity and efficiency economics is on equity of opportunity and individual efficiency and that there are inherent flaws in these two theories. Marxism, in a way, fills the original Western countries based on the historical view of the materialism of individual private ownership, and this is the change of Marxism to the theory of fairness and efficiency proposed by Western scholars [ 17 ] . While scholars in the West regarded individual private ownership as unchanging, scholars with Marxism as their guiding philosophy regarded it as historical, and thus the concept of efficiency, recognized by the secular world, slowly began to emerge. B. DEFINITION OF RELEVANT CONCEPTS 1) logistics The earliest origin of logistics is in the military field, the United States Army, to ensure the timeliness and safety of material supply and research on how to efficiently and cost-effectively transport materials. With the development of the economy and technological progress, logistics began to be used in the field of enterprise production. In 1985, the American Society of Logistics Management defined the concept of ‘logistics’: to meet the needs of customers for planning, implementation, and control steps to achieve goods, services, and related information from the place of supply to the place of consumption, efficient and low-cost flow and storage [ 18 ] . The concept of logistics is defined as the efficient and inexpensive flow and storage of goods, services, and related information from the place of supply to the place of consumption through planning, implementation, and control. The concept of logistics was first introduced in China in the 1990s. China's national standard ‘Logistics Terminology’ (GB/T18354-2021) defines ‘logistics’ as follows: according to the actual demand, the basic functions of transport, storage, loading and unloading, handling, packaging, distribution processing, distribution, information processing, etc. are organically combined, to make the goods physically flow from the place of supply to the place of receipt. 2) logistics company Logistics enterprises are not limited to transport, distribution, etc. In today's society, logistics enterprises refer to professional organizations engaged in a series of logistics activities such as transport, warehousing, distribution, loading and unloading, distribution and processing, etc. [ 5 ] . Since the 21st century, the name ‘logistics’ has only begun to appear in China's economy and society, and the services provided are still limited to the traditional logistics model [ 6 ] . In 2005, China formally defined the concept of ‘logistics enterprise’: based on the goal of obtaining the maximum benefit under the premise of ensuring the lowest cost, the core business focuses on the basic functions of logistics, such as transport, warehousing in Germany, and business management through the logistics information system, providing specialized and standardized logistics services [ 19 ] . Under the new era, with the rapid development of the economy and the improvement of technological innovation level, modern logistics enterprises began to intelligent, intelligent transformation and upgrade, the use of modern science and technology to the whole process of logistics activities, to provide customers with more high-quality, professional services [ 20 ][ 21 ] . From the point of view of the secondary market segment of logistics, the business scope of logistics enterprises includes but is not limited to air transport, road transport, pipeline transport, warehousing, loading and unloading, and handling, as well as agency services [ 22 ] . 3) Production performance Production performance refers to the performance of the personnel in the production department of an enterprise in fulfilling the set objectives under specific resources and conditions and is an assessment and feedback on the degree of fulfillment of the production objectives as well as the efficiency of their fulfillment [ 24 ] . Generally speaking, production performance is measured by production effectiveness, which focuses on evaluating the extent to which the enterprise has achieved the set objectives within a certain period [ 25 ] . Production performance is closely linked to production management activities, which are carried out through the basic management functions of planning, organizing, coordinating, and controlling. In detail, the production department of the enterprise, according to the enterprise's established goals and plans, makes full use of planning, organization, command, coordination and control and other management functions, reasonable and effective allocation of human, financial, and material resources, to achieve the external market on the quality of the output, the cost of inputs, the product delivery time and other aspects of the requirements of the product, to produce products in line with market expectations [ 26 ] . TABLE I Production performance measurement indicators Metrics Explain Cost investment Cost is a collection of various expenditures incurred by an enterprise in the activities of manufacturing products. The cost will greatly affect the total efficiency of the entire enterprise [ 33 ][ 33 ] . The relatively high cost will inevitably decline the net profit of the enterprise. Therefore, cost performance management has become an important aspect of the production performance management of logistics enterprises [ 35 ] . Quality output Quality is the foundation of a company's survival and an important indicator to measure the production performance of logistics companies [ 35 ] . Product delivery time In the fierce modern market environment, efficiency and speed are an important indicator to determine whether you can effectively grasp business opportunities [ 37 ] . Delayed delivery will not only lead to the loss of corporate reputation but also have a largely direct negative impact on customers' business activities, leading to customer loss. On-time delivery can win a large number of customers for logistics companies and better meet customer needs [ 38 ][ 39 ] . Therefore, product delivery has also become an important indicator to measure the production performance of logistics companies. For logistics enterprises, production performance includes transport, storage, loading and unloading handling, packaging, consolidation, and other functions, which are commonly used to measure indicators such as the volume of goods transported, the rate of transport, the rate of cargo damage, etc. This paper refers to the research of scholars such as Wang Haiyan [ 7 ] on the production performance of the manufacturing industry and attributes the indicators of the production performance of logistics enterprises to the cost of inputs, quality outputs, and product delivery time [ 40 ] . III. METHOD A. CAUSAL ANALYSIS AND MODELLING A causal feedback diagram is the basis for constructing the system stock-flow diagram, but also an important tool for system dynamics modeling, which can accurately sort out the logical relationship between the systems, the complete causal feedback diagram contains three important elements: variables, causal chain, and polarity presented by the causal chain, as shown in Fig. 1, each causal chain has polarity, which is represented by ‘+’ and ‘-’ indicates that the causal chain on the left has a positive polarity and becomes a positive feedback loop; the same is true for the right side [26] [27] . 1)Input subsystems From the above figure, it can be seen that the following negative feedback loops are included in the model: 01) Promotion time → new employee promotion rate; 02) Separation rate of mature workers → number of mature workers; 03) Separation rate of new employees → promotion rate of new employees; 04) Material wastage → quantity of raw materials; 05) Depletion of raw materials → quantity of raw materials; 06) Depreciable life → depreciation rate → number of equipment. (The depreciation rate follows the double-declining balance method. Note: Annual depreciation rate = 2 ÷ Estimated depreciable life × 100%,) From the figure above, the following positive feedback loops are included in the model: 01) Base rate → Recruitment rate → Number of new employees → Number of mature workers → Direct labor → Cost input → Input subsystem → Production performance level of the logistics company; 02) Base rate → recruitment rate → number of new employees → direct labor → cost input → input subsystem → production performance level of the logistics firm; 03) Unit labor cost → direct labor → cost input → input subsystem → logistics firm production performance level; 04) Base increase → purchase increase → raw material quantity → direct material → cost input → input subsystem → production performance level of logistics enterprises; 05) Basic growth rate → equipment growth rate → number of equipment → equipment maintenance costs → manufacturing costs → cost input → input subsystem → production performance level of logistics enterprises; 06) Industrial value-added rate per unit of time → equipment growth rate → number of equipment → equipment maintenance costs → manufacturing costs → cost inputs → input subsystem → production performance level of logistics enterprises; 07) Unit equipment maintenance cost → equipment maintenance cost → manufacturing cost → cost input → input subsystem → production performance level of logistics enterprises; 08) Equipment failure rate → equipment maintenance cost → manufacturing cost → cost input → input subsystem → logistics enterprise production performance level; 09) Depreciation cost per unit of equipment → equipment depreciation cost → manufacturing cost → cost input → input subsystem → logistics enterprise production performance level; 10) Training cost → manufacturing cost → cost input → input subsystem → logistics enterprise production performance level; 11) Sales advertising costs → cost input → input subsystem → production performance level of logistics enterprises; 12) Design and R&D costs → cost input → input subsystem → production performance level of logistics enterprises. 2) Output subsystems From the above figure, it can be seen that the following negative feedback loops are included in the model: 01) Operating time → delivery output; 02) Equipment failure rate → delivery output. From the above figure, it can be seen that the model contains the following positive feedback loops: 01) new employee skill level → employee skill level → delivery output → output subsystem → logistics enterprise production performance level; 02) Mature worker skill level → employee skill level → delivery output → output subsystem → logistics enterprise production performance level; 03) Employee skill level → product qualified output → delivery output → output subsystem → logistics enterprise production performance level; 04) Product qualification rate → product qualification output → delivery output → output subsystem → logistics enterprise production performance level; 05) Raw material qualification rate → product qualification output → delivery output → output subsystem → logistics enterprise production performance level; 06) Maximum output of new employees → maximum output of employees → delivery output → output subsystem → production performance level of logistics enterprises; 07) Maximum output of mature workers → maximum output of employees → delivery output → output subsystem → production performance level of logistics enterprises. 3）environmental subsystem From the above figure, it can be seen that the following positive feedback loops are included in the model: 01) Government subsidy → environmental subsystem → production performance level of logistics enterprises; 02) Talent policy → environmental subsystem → production performance level of logistics enterprises; 03) Government preferences→environmental subsystem→production performance level of logistics enterprises; 04) Laws and regulations→environmental subsystem→production performance level of logistics enterprises; 05) Platform construction→environmental subsystem→production performance level of logistics enterprises; 06) Corporate culture→environmental subsystem→level of production performance of logistics enterprises. B. RESEARCH HYPOTHESIS AND INITIAL DATA Based on the causal relationship between the variables in the previous section, this paper constructs a comprehensive system dynamics model of the production performance of logistics enterprises based on input subsystems, output subsystems, and environmental subsystems, and the following figure shows the system flow diagram of the model. 1) RESEARCH HYPOTHESIS Based on the literature base and real conditions, the following basic assumptions are made about the research model: The system dynamics model of production performance of logistics enterprises constructed in this paper is a continuous and progressive value-added cyclic process; To reflect the operation of the system more intuitively, the system dynamics model of the production performance of logistics enterprises constructed in this paper does not take into account the problem of time delay: 01) The logistics enterprise will not have problems such as bankruptcy in the short term; 02) The assets of the logistics enterprise will not be transferred in large quantities; 03) Logistics companies do not take into account competitive barriers in the market; 04) The relevant coefficients in the model are set at the beginning of the period according to the general regulations of the market industry; 05) Significant external policy changes and other unusual systemic environmental changes faced by the logistics company are not taken into account. To better understand the stock-flow diagram in the system dynamics model, the following will explain the various types of variables within the model. There are four main types of variables in a system dynamics model: state variables (stocks), rate variables (flows), auxiliary variables, and constants. State variables (stocks) are variables that accumulate over time. Rate variables (flows) can directly cause changes in the state variable and can represent the rate of change of the stock. The intermediate variable between the state variable (stock) and the rate variable (flow) is called an auxiliary variable, which can assist in influencing the rate of change of the state variable (stock). Generally, by setting fixed parameter values, these variables do not change with time and other variables and are called constants. Also to be emphasized is the table function (with lookup), a custom function in the VENSIM PLE software, defined in a way that is usually graphical. It is mainly used when there is a special non-linear relationship between two variables that cannot be expressed by conventional functions. In addition, VENSIM PLE software will run with the time parameter as a variable in the model, which can also be set as a shadow variable. Based on the system dynamics model of production performance of logistics companies set up in this paper, it involves 6 state variables, 11 rate variables, 13 auxiliary variables, 24 constants, and 4 table functions, in addition to 2 shadow variables, totaling 60 variables. 2) initial data When the system dynamics model is created, the VENSIM PLE software will ask the user to determine the initial time, end time, time step, and units by defining the INITIAL TIME, FINAL TIME, TIME STEP, and UNITS FOR TIME so that the model can be computed in chronological order. 01) Initial time = 2018; 02) End time = 2027; 03) Time step = 1; 04) Time unit: year. C. EQUATION SETTING 01) Production performance level of logistics companies = INTEG [(input subsystem - output subsystem) * environmental subsystem, 1000], in million yuan; 02) Input subsystem = cost input * 0.9, in million yuan; 03) Output subsystem = delivery output * 0.9, in million yuan; 04) Environment subsystem = Government subsidy * Talent subsidy * Government preference * Laws and regulations supervision * Platform construction * Corporate culture, unit: ten thousand yuan; 05) Number of new employees = INTEG (Recruitment rate - Promotion rate of new employees - Separation rate of new employees, 2000), unit: person; 06) Number of Mature Workers = INTEG (Promotion Rate of New Employees - Separation Rate of Mature Workers, 5000), unit: person; 07) New Employee Separation Rate = Number of New Employees * New Employee Separation Rate, unit: none; 08) New Employee Promotion Rate = New Employee Separation Rate * Promotion Time, Unit: None; 09) Mature Worker Separation Rate = Number of Mature Workers * Mature Worker Separation Rate, Unit: None; 10) Recruitment rate = base rate * 0.8, unit: none; 11) Base rate = 100*Time, unit: none; 12) Direct Labor = Unit Labor Cost * (Number of Mature Workers + Number of New Hires), in; 13) Quantity of Raw Materials = INTEG (Increase in Purchases - Depletion of Raw Materials - Depletion of Materials, 5000), in; 14) Purchase Increase = Quantity of Raw Materials - Base Increase, in million; 15) Material Loss = Raw Material Quantity * Raw Material Depletion Ratio, in million; 16) Raw Material Loss = Raw Material Quantity * Raw Material Consumption Ratio, in ten thousand yuan; 17) Direct materials = raw materials * 1.5, unit: ten thousand yuan; 18) Equipment growth rate = Industrial value-added rate per unit of time + base growth rate, in nil; 19) Number of equipment = INTEG (equipment growth rate - equipment depreciation rate, 5000), unit: ten thousand yuan; 20) Equipment depreciation rate = 2 / depreciable life, unit: none; 21) Equipment maintenance costs = unit equipment maintenance costs * number of equipment * equipment failure rate, unit: million; 22) Manufacturing cost = training cost * equipment depreciation cost * equipment maintenance cost, in; 23) Equipment depreciation cost = number of equipment * unit equipment depreciation cost, unit: ten thousand yuan; 24) Employee skill level = mature worker skill level + new employee skill level, unit: none; 25) Qualified product output = Employee skill level * Qualified rate of raw materials * Qualified rate of output, unit: ten thousand yuan; 26) Employee Maximum Output = Mature Worker Maximum Output + New Employee Maximum Output, unit: million; 27) Delivery Output = (Product Qualified Output + Employee Maximum Output)*Operation Time, unit: ten thousand yuan; 28) Cost inputs = Manufacturing costs * Direct labour * Direct materials * Design and R&D costs * Sales and advertising costs, in million yuan; 29) Industrial value-added rate per unit of time = WITH LOOKUP ([(2018,0)-(2027,0.5)], (2018,0.22), (2019,0.24),(2020,0.19),(2021,0.16),(2022,0.17),(2023,0.2),(2024,0.25 ),(2025,0.28),(2026,0.29),(2027,0.3) ) in n/a; 30) Talent Allowance = WITH LOOKUP ([(2018,0) - (2027,5000)], (2018,787), (2019,899), (2020,921), (2021,1123), (2022,1326), (2023,1468), (2024,2548), (2025. 3296), (2026,3649), (2027,4205) ) in $ million; 31) Basis Increase = WITH LOOKUP ([(2018,0)-(2027,7000)], (2018,2441), (2019,2266), (2020,2280), (2021,2436), (2022,3628), (2023,4021), (2024,5421), ( 2025,6311),(2026,5675),(2027,6840) ) in $ million; 32) DESIGN RESEARCH COSTS = WITH LOOKUP ([(2018,0)-(2027,4000)], (2018,560), (2019,600), (2020,1020), (2021,940), (2022,640), (2023,860), (2024,1267), (2025. 1783), (2026,2230), (2027,3100) ), in $ million; 33) Turnover rate of new employees = 0.3, in nil; 34) Promotion time = 0.6, unit: year; 35) Separation rate of mature workers = 0.2, Unit: None; 36) Yield pass rate = 0.8* raw material pass rate, in nil; 37) Semi-finished product rate = 0.2, Unit: None; 38) Corporate culture = 0.6, Unit: none; 39) Operating time = 3, unit: hour; 40) Unit labor cost = 500, in; 41) Depreciation cost per unit of equipment = 600, in: million; 42) Maintenance cost per unit of equipment = 500, in million yuan; 43) Raw material pass rate = 0.8, in nil; 44) Raw material consumption ratio = 0.4, in nil; 45) Raw material consumption ratio = 0.2, in nil; 46) Training cost = 860, in; 47) Platform construction = 0.8, Unit: Nil; 48) Skill level of mature workers = 800, Unit: million; 49) Mature worker turnover ratio = 0.2; 50) Depreciation life = 5, in years; 51) Government concessions = 500, in million; 52) Government subsidy = 0.5, in nil; 53) Skill level of new employees = 400, in million; 54) Maximum output of new employees = 500, in million; 55) Turnover rate of new employees = 0.3, Unit: None; 56) Regulation of laws and regulations = 0.8, Unit: None; 57) Equipment failure rate = 0.3, Unit: None; 58) Sales and advertising costs = 1,200, in millions of yuan; 59) Semi-finished goods ratio = 0.2, unit: n/a； 60) Maximum output of mature workers = 900, in million. IV. MEASUREMENTS Once the parameters and equations of the model are perfected, they need to be tested, which is a necessary part of constructing the system dynamics model. The running simulation of the model is a simulation of the real world, then the model must first have usability, so it is necessary to build a series of tests on the model, the most basic is the model running test, as shown in Figure 8, which shows that the model runs successfully. Given the complexity of the system, three common important tests are made, including gauge consistency test, model validity test, and sensitivity analysis. A. GAUGE CONSISTENCY TEST The gauge consistency test is the most basic in the SD model, which mainly ensures that the equations are constructed with a uniform scale and there is no logical error. In this paper, the ‘Model Check’ function of the software is used to carry out the test, and the results show that there is no problem with the scale. B. VALIDITY TEST The current academic use of system simulation methods is more recognized as running simulations of the model, which is done to assess the accuracy of the results. The most commonly used method is validity testing, which aims to verify that the results obtained from the model reflect the actual system model characteristics and the actual effects of change and that the analytical investigations of the model lead to an adequate investigation and understanding of the problem [26] [27] . 1) Input subsystems 01) direct labor From the above chart, we can see the trend of the number of new employees, the number of new employees shows an increasing trend year by year. From 2018 to 2019, the number of new employees rose sharply, this is due to the largest number of recruiters during this period, resulting in a sharp rise in the number of new employees. Afterward, due to the stabilization of production, the number of mature workers increases and the need for new hires gradually decreases. Since the number of hires is approximately equal to the sum of the number of promotions and the number of departures, the number of new hires gradually shows a stable trend. Similarly, the trend of the number of mature workers is also increasing year by year. The number of mature workers is stable between 2018 and 2019 since the departure rate of mature workers is similar to the promotion rate, which keeps the number of mature workers stable. The fastest growth rate of mature workers in 2019-2020 is since the number of new hires is the largest in 2018-2019, which is the result of a one-year growth and training period, which transforms new hires into new employees. Training period, resulting in the transformation of new hires into mature workers, which leads to a significant increase in the promotion rate of new hires in 2019-2020, thus advancing the growth of mature workers. In the following years, the promotion rate of new employees decreases year by year, but it is still higher than the separation rate of mature workers, resulting in the number of mature workers showing a rising trend year by year, but the growth trend is slowing down. In this simulation model, it is assumed that the unit labor cost remains unchanged, thus, the direct labor input of logistics enterprises increases year by year at a slower rate. In the current era of intelligent logistics, although the number of front-line logistics personnel or grass-roots logistics personnel is reduced, with the investment in high-tech logistics, logistics information system development, research and development personnel, the number of high-end technicians is also higher, so in the long run, the direct labor input of logistics enterprises still shows a year-on-year trend of increasing. 02) direct material As can be seen from the above chart: the quantity of raw materials for logistics companies shows a year-on-year decreasing trend, and similarly, the direct materials for logistics companies also show a year-on-year decreasing trend. 03) Manufacturing cost As can be seen from the above chart, in the traditional product cost structure: the largest proportion of raw material costs is one of the important factors leading to high costs, which is due to the existence of a large number of logistics enterprises' inventory, resulting in a large increase in inventory costs due to the search for high-quality and inexpensive suppliers can reduce the cost of production of products to a large extent, and more importantly, to increase the turnover of raw materials to reduce inventories. Although the labor costs of production workers are also increasing year by year, the relative stability of labor costs on the finished product is relatively minimal. Manufacturing costs are increasing at a faster rate, so the control of manufacturing costs is particularly necessary. In the selected composition of several manufacturing costs, equipment maintenance costs, and equipment depreciation costs accounted for the vast majority of inputs, so strict control of product equipment damage rate can reduce manufacturing costs to a large extent. Logistics enterprises are different from general manufacturing enterprises, logistics enterprises in the daily operation of the facilities and equipment used in the process, with a high degree of knowledge-intensive, such as large cranes, various types of conveyor devices, and the logistics information platform. Logistics facilities and equipment in the daily operation of the process, once these facilities and facilities are damaged, the maintenance is difficult, and maintenance costs are high, so the logistics enterprise manufacturing costs appear a year-by-year increasing trend. In summary, reducing inventory, and reducing the damage rate of facilities and equipment can significantly control the production cost of the product. 04) Cost of sales advertising As can be seen from the above figure, in this model, it is assumed that the sales and advertising costs show a stable trend and do not change significantly. As we all know, in the composition of the product cost, sales advertising cost is a proportion that is also a larger part, so the level of sales advertising cost will also determine the level of the final cost input. Under the influence of the current social media, logistics enterprises must pay attention to the investment of sales advertising costs to obtain a better reputation, more stable customer resources, and more diverse types of business. Good sales advertising can play a positive role in promoting the daily operation of logistics enterprises, and the benefits of the economic output of the enterprise outweigh the disadvantages. 05) Design development costs As can be seen from the above figure, in this model, it is assumed that the design and development costs show a stable trend and do not undergo large changes. In the composition of product cost, design R&D cost is a relatively large part, especially in the current era of intelligent logistics, each logistics enterprise is focusing on technology research and development innovation, intelligent logistics platform construction, and other businesses, so the input of design R&D cost will directly determine the daily operating costs of logistics enterprises, for logistics enterprises, better management of design R&D cost is a new direction for future development. 06）Short As can be seen from the above figure: cost inputs show a stable trend in 2018-2024 and a decreasing trend after 2024, and the curve of the input subsystem follows a similar trend. Cost inputs are composed of direct labor, direct materials, manufacturing costs, sales and advertising costs, and design and development costs, the direct labor in the previous section shows a year-on-year increasing trend, direct materials show a year-on-year decreasing trend, manufacturing costs also show a year-on-year increasing trend, while sales and advertising costs and design and development costs are assumed to remain unchanged, the above factors together, cost inputs in the late stage shows a year-on-year decreasing trend. This shows that the inputs of direct labor, direct materials, and manufacturing costs will be reduced in the later stage of the cost of logistics enterprises, and the level of daily operational efficiency will be improved. 2) Output subsystems 01) Qualified Product Yield From the above figure, it can be seen that the qualified production volume of products shows a trend of growth year by year. In this model, it is assumed that the product qualification rate, semi-finished product rate, and the skill level of employees will not undergo large changes in the future, and tend to be stable values 2018-2020 the fastest growth in the volume of qualified products, 2018-2019 the high speed of qualified production growth due to the sharp rise in the number of new employees recruited during this period, the rising level of the number of new employees to make up for the lack of their skill level makes qualified. The production of qualified products grows rapidly in absolute terms. 2019 - 2020 qualified production grows at a high rate, during this period, although the growth rate of the number of new employees decreases, the number of mature workers grows the fastest during this period, so the production of qualified products also maintains a high rate of growth. 2020 and beyond, the production of qualified products continues to grow, but the rate of growth each year is gradually slowing down during this period. During this period, the number of new employees decreases year by year, while the number of mature workers shows a slow growth trend, and this trend of qualified production is very similar to the growth trend of mature workers. It can be concluded that mature workers are the backbone of the company's production and largely influence the production of qualified products. 02) Delivery schedule outputs From the above figure, it can be seen that the delivery output is increasing year by year between 2018-2027 and the rising trend is slowly decreasing, from the above figure, it can be seen that the qualified output of the product is increasing year by year and the rising trend is also faster, the operation time and the maximum output of the employees are assumed not to be changed in this model, and they tend to be stable. From the above figure, it can be seen that the delivery output is increasing year by year between 2018-2027 and the rising trend is slowly decreasing, from the above figure, it can be seen that the qualified output of the product is increasing year by year and the rising trend is also faster, the operation time and the maximum output of the employees are assumed not to be changed in this model, and they tend to be stable. The stability of product quality and product quality can be well reflected in the delivery output. Delivery output is low at the beginning of the period due to a sharp rise in the recruitment of new employees with low skill levels and high losses of facilities and equipment. 2019-2020 has the highest growth in delivery output, thanks to a sharp rise in the number of mature workers and a large reduction in new employees due to the large promotion of new employees in that period. Delivery output also rises gradually after 2020, but the growth gradually slows down, which coincides with the growth rate of mature workers. From the above analysis, it can be concluded that logistics enterprises can effectively improve productivity through the rational and effective use of equipment, reduce the proportion of maintenance time and downtime of facilities and equipment, making it easier for logistics enterprises to meet deadlines and achieve greater delivery output. 03) Short From the above figure, it can be seen that the output of the delivery period shows an increasing trend year by year, and similarly, the output subsystem also shows an increasing trend year by year. Factors affecting the output of the delivery period include the qualified output of the product, the skill level of the staff, and the operation time. Product-qualified output and production-qualified rate, with technological progress and advanced facilities and equipment input, the logistics enterprise's production-qualified rate increases year by year, so the product-qualified output increases. In terms of staff skill level, with staff education and training, staff technology upgrading, whether it is a new employee or mature worker, their skill level is showing a higher trend. With the industrial output value of the logistics industry as a whole and the technical upgrading of the logistics enterprises, the actual operating time of the logistics enterprises shows a lower trend. To sum up, the delivery output of logistics enterprises is increasing year by year, so the level of output subsystem is also getting higher and higher. 3）environmental subsystem From the previous system flow diagram of the production performance level of logistics enterprises, the environmental subsystem is jointly influenced by six factors: government preferences, government subsidies, talent subsidies, enterprise culture, laws and regulations, and platform construction. Government preferences focus on government organizations directly for certain logistics enterprises, preferential exemptions or capital investment, which is a direct investment for logistics enterprises, can reduce the economic difficulties of daily operations. Government subsidies refer to certain discounts and subsidies given by the government to certain logistics enterprises for certain business investments. Talent subsidy refers to the high-end technical personnel working in the logistics enterprise, in addition to the logistics enterprise itself giving a certain salary, the government will also give this part of the person a certain amount of subsidies and concessions. Laws and regulations refer to certain laws and regulations, such as the Customs Law and the Transportation Law, which are observed by the logistics enterprises in their daily operation. Corporate culture refers to the prevailing cultural atmosphere within the logistics enterprise, a radical or conservative corporate culture, that will lead to different corporate decisions and development direction. Platform construction focuses on the logistics information platform and logistics information system developed and designed by the logistics enterprises themselves, such as RFID and NFC technologies. In this model, the above factors present a stable trend, only affecting the production performance of logistics enterprises in the daily operation process, and will not have a direct impact on the input and output level of logistics enterprises, so the environment subsystem presents a stable linear trend. 4）Production performance level of logistics companies As can be seen from the above figure: the input subsystem shows a decreasing trend, the output subsystem shows an increasing trend year by year, and the environmental subsystem shows a stable trend. Under the joint effect of the above three subsystems, the level of production performance of logistics enterprises shows a stable trend in 2018-2019 without large changes, while in 2019-2021, it shows a substantial increase. The peak appears near 2021, and after 2021, it shows a small decline until 2027. Logistics enterprises are different from general traditional enterprises, logistics enterprises emphasize high technology, high efficiency, high yield, and technological innovation and scientific and technological progress require greater capital, talent, material resources investment support, although the external environment of government subsidies, government concessions, talent subsidies and so on to the rich, but the cost of inputs is still high. Therefore, for logistics enterprises, logistics enterprises should pay more attention to the research and development of knowledge-intensive technology, high-end logistics information platforms, and high-efficiency logistics and distribution technology, which can to a large extent enhance the level of production performance of logistics enterprises. High technology also corresponds to high output and high returns. C. SENSITIVITY TEST Sensitivity analysis is a key step in the testing of SD models, where the parameters of the main variables in the model are adjusted, the model is run, and the change in the value of that variable before and after the adjustment is compared to determine the extent of its impact. If the model is verified to be insensitive to changes in most of these parameters, then the model is stable and robust. The method of model sensitivity analysis focuses on changing one parameter and observing the degree of change in other variables of the model, i.e. sensitivity. This study refers to the method of Gu Chaolin et al. [8] . 1) Input subsystems 01) direct labor The green line (the line has the number 2) is the CURRENT model, representing the simulation results under the initial data settings. The blue line (the line has the number 1) is the PLAN 1 model, which represents the positive change of the influencing factors involved in direct labor, i.e. the turnover rate of new employees is adjusted from 0.3 to 0.8 in the original model, the promotion time is changed from 0.6 to 0.8 in the original model, the turnover rate of mature workers is adjusted from 0.2 to 0.6 in the original model, and the unit cost of labor is changed from 500 to 1,000 in the original model, and the red line (the line has the number 3) is the PLAN 2, which represents the negative change of the influencing factors involved in direct labor. Negative change in the impact factors involved in direct labor, i.e. new hire separation rate changes to -0.3, time to promotion changes to 0.1, mature worker separation rate changes to -0.2, and unit labor cost changes to 100. From the dynamic simulation results of the above figure, it can be seen that: under PLAN 1, the growth rate of direct labor is faster and larger. This indicates that when the employee separation ratio becomes larger, the employee promotion time becomes longer, and the unit labor cost becomes larger, the direct labor cost of the logistics enterprise increases, and the cost input is larger, and in the case of constant delivery output, the cost input of the logistics enterprise is higher, and therefore the production performance level of the logistics enterprise is poorer. Under PLAN 2, the increase in direct labor at the beginning of the period is greater than in the CURRENT model, but at the later stage, the direct labor cost under PLAN 2 will be lower than in the CURRENT model. This indicates that when the employee turnover rate decreases, the promotion time of employees becomes shorter, and the unit labor cost decreases, the direct labor input of the logistics enterprise will be reduced, and the production performance level of the logistics enterprise can be maintained at a high level under the premise of constant delivery output. 02) direct material The green line (the line has the number 3) represents the CURRENT model, i.e. the simulation results at the initial value setting. The red line (the line has the number 2) is the PLAN 3 model, which represents the positive change of the influencing factor involved in the direct material variable, i.e., the raw material consumption ratio is changed from 0.4 in the original model to 0.8, and the raw material loss ratio is adjusted from 0.2 in the original model to 0.8. The blue line (the line has the number 1) is the PLAN 4 model, which represents the negative change of the influencing factor involved in the direct material variable, i.e., the raw material consumption ratio is changed to -0.2, and the raw material loss ratio is adjusted to -0.2. Adjusted to -0.2. From the dynamic simulation results of the figure above, we can see that the trend of the curve in the original model is similar to that in the PLAN 4 model, but the slope of the curve in the PLAN 4 model is larger, indicating that under the PLAN 4 model, the input of direct materials decreases faster. This shows that when the raw material consumption ratio and the raw material consumption ratio remain low, the investment in raw materials and various logistics facilities and equipment is lower, resulting in a lower direct material investment. When the output remains unchanged during delivery, the production performance level of logistics companies is better. However, under the PLAN 3 model, the curve gradually stabilized in the later stage after one to two years of rapid decline in the early stage and did not change significantly. This shows that when the raw material consumption ratio is higher than that of raw material consumption, the material loss and waste rate are higher, and the investment in logistics facilities and equipment is also higher, which ultimately leads to high investment in direct materials. When the output remains unchanged during delivery, the logistics company's cost investment is higher, so the production performance level of logistics companies is poor. 03) manufacturing cost The red line (the line has the number 2) represents the CURRENT model, i.e. the simulation results with the initial values set. The blue line (the line has the number 1) is the PLAN 5 model and represents the positive change in the influencing factors involved in the manufacturing cost variable, i.e., the training cost was adjusted from 860 to 2000 in the original model, the depreciation cost per unit of equipment was changed from 600 to 2000 in the original model, the depreciation life was changed from 5 years to 10 years in the original model, the maintenance cost per unit of equipment was adjusted from 500 to 2000 in the original model, and the equipment failure rate was changed from 0.3 to 0.8. The green line (the line has the number 3) is the PLAN 6 model and represents a negative change in the influencing factors involved in the manufacturing cost variable, i.e. training costs change to 100, depreciation cost per unit of equipment changes to 100, depreciation life changes to 1 year, maintenance cost per unit of equipment changes to 100, and equipment failure rate changes to 0.1. From the dynamic simulation results in the above figure, it can be seen that: under the PLAN 5 model, the manufacturing costs show a year-on-year incremental change trend, but the magnitude of the change is slower and the increase is smaller, which indicates that under this simulation setup, the inputs of the manufacturing costs are lower, and the production performance level of the logistic enterprise can be maintained at a higher level in the case that the output of the delivery period remains unchanged. The training cost and equipment maintenance cost are kept low, however, the equipment depreciation cost is higher than under PLAN 6, which may be related to the depreciation life, the longer the depreciation life, the lower the depreciation cost, and the shorter the depreciation life, the higher the depreciation cost. Under the PLAN 6 model, manufacturing costs show an increasing trend of change from year to year, but the change is slightly larger than PLAN 5. This indicates that when the training costs and equipment maintenance costs are low, the input of manufacturing costs of logistics enterprises is correspondingly low, and under the premise of relatively stable delivery output, the production performance level of logistics enterprises can reach a high level. 2) Output subsystems 01) Qualified Product Yield The green line (the line has the number 3) represents the CURRENT model, i.e., the simulation results at the initial value setting. The red line (the line has the number 2)is the PLAN 7 model, which represents the positive change of the influencing factors involved in the product qualified yield variable, i.e. the skill level of mature workers is adjusted to 2000 from 800 in the original model, the skill level of new employees is changed to 1000 from 400 in the original model, the raw material qualification rate is changed to 0.9 from 0.8 in the original model, and the semi-finished product rate is changed to 0.5 from 0.2 in the original model. The blue line (the line has the number 1) is the PLAN 8 model, which represents the negative charge of the influencing factors involved in the product-qualified yield variable. PLAN 8 model, which represents a negative change in the influencing factors involved in the product qualification yield variable, i.e., a change in the skill level of mature workers to -1000, a change in the skill level of new employees to -2000, a change in the raw material qualification rate to 0.1, and a change in the semi-finished product rate to 0.1. From the dynamic simulation results of the above figure, it can be seen that: under the PLAN 7 model, the curve of product-qualified output shows a rising trend year by year, and the level of growth is the fastest under the three models. Among them, the output qualification rate, semi-finished product rate, and staff skill level are higher, which leads to higher qualified output, so in the case of stable cost inputs of logistics enterprises, the output subsystem of logistics enterprises maintains a higher level, the production performance level of logistics enterprises is higher, and the daily operation is in good condition. Under the PLAN 8 model, the product-qualified yield shows a slowly rising trend and the rise is very small. Compared with the PLAN 7 model, the output qualification rate, semi-finished product rate, and employee skill level are lower under this model, which leads to lower qualified product output, which indicates that the output effect of the logistics enterprise is poorer, which is mainly manifested in the lower efficiency of logistics and distribution, higher error of logistics and distribution, etc. 02) Delivery schedule outputs The green line (the line has the number 3) represents the CURRENT model, i.e., the simulation results at the initial value setting. The red line (the line has the number 2) is the PLAN 9 model, which represents the positive change of the influencing factors involved in the delivery output variable, i.e., the maximum output of new employees is changed from 500 to 1000 in the original model, the maximum output of mature workers is adjusted from 900 to 2000 in the original model, and the operating time is changed from 3 to 6 in the original model. The blue line (the line has the number 1) is the PLAN 10 model, which represents the negative change of the influencing factors involved in the delivery output variable, i.e. The maximum output of new employees changes to -1000, the maximum output of mature workers changes to -500, and the operating time changes to -3. From the dynamic simulation results in the above figure, it can be seen that under the PLAN 9 model, the turnaround output curve shows a rising trend year by year and the growth is also maximum. This indicates that higher product-qualified output and maximum employee output can lead to better delivery output. The good delivery output indicates that the production performance of logistics enterprises can be maintained at a high level under the premise of stable and unchanged inputs, which is more beneficial than detrimental to the long-term development of logistics enterprises. Under the PLAN 10 model, the output curve of the delivery period shows a slow upward trend with a small increase and a negative value of output. Compared with the PLAN 9 model, the maximum output of the employees under this model is lower, which leads to lower delivery output indicators, which indicates that the production performance level of the logistics enterprise is poor, which is mainly manifested in the poor customer satisfaction of the logistics enterprise, the logistics and distribution time is longer, and so on. 3) environmental subsystem The blue line (the line has the number 1) represents the CURRENT model, i.e., the simulation results under the initial value setting. The red line (the line has the number 2) is the PLAN 11 model, which represents the positive change of the influencing factors involved in the environmental subsystem variables, i.e., government preferences are changed from 500 to 1000 in the original model, laws and regulations are changed from 0.8 to 2 in the original model, platform construction is changed from 0.8 to 2 in the original model, enterprise culture is changed from 0.6 to 1 in the original model, and government subsidies are changed from 0.5 to 1 in the original model. The green line (the line has the number 3) is the PLAN 12 model, which represents the negative change in the influencing factors involved in the environmental subsystem variables, i.e., government subsidies change by 100, laws and regulations change by 0.2, platform construction change by 0.2, enterprise culture change by 0.1, and government subsidies change by 0.1. From the dynamic simulation results in the above figure, it can be seen that under the PLAN 11 model, the curve of the environmental subsystem is stable and does not change greatly, but the value of the environmental subsystem under this model is the largest under the three models. This indicates that under this model, the environmental subsystem has a greater impact on the production performance level of logistics enterprises, in which changes in the influencing factors such as government subsidies, platform construction, laws and regulations, etc. will cause corresponding changes in the production performance of logistics enterprises. Under the PLAN 12 model, the value of the environmental subsystem is the smallest of the three models, which indicates that under this model, the environmental subsystem has a small impact on the production performance level of logistics enterprises, in which changes in factors such as government preferences and corporate culture do not cause significant changes in the production performance level of logistics enterprises. V. CONCLUSIONS AND DISCUSSION This study firstly introduces the overview of the logistics enterprise, puts forward some reasonable assumptions on the constructed model based on the actual situation of the logistics enterprise, and assigns the values of parameters such as constants, state variables, variable functions, and auxiliary variables required by the model through field observation, inquiry and so on combined with the data processing function of VENSIM software itself. On this basis, the computer was used to simulate the model, and the simulation results were analyzed, while the parameter settings in the system were adjusted to analyze the changing dynamics of the input subsystems, output subsystems, and environmental subsystems under different parameter states. Based on the comparative analysis of different scenarios, the following conclusions were drawn: First, in the input subsystem, reducing direct labor, direct materials, manufacturing costs, sales, and advertising costs, and expenditures involving research and development costs by shortening the promotion time of employees and reducing the rate of employee separation can effectively reduce the cost of inputs, and under the premise of the stability of the output subsystem, it can effectively improve the level of the production performance of logistics enterprises [28] . Second, in the output subsystem, through improving the product qualification rate, strengthening the staff skill level and other measures to improve the product qualification output, and delivery output level, under the premise of the input subsystem is unchanged, it can effectively improve the level of production performance of logistics enterprises [29] . Thirdly, in the environmental subsystem, the production performance level of logistics enterprises can be adjusted by changing the values of government preferences, government subsidies, talent subsidies, laws and regulations, platform construction, corporate culture, and other impact indicators. Stronger environmental subsystems can positively and significantly affect the production performance level of logistics enterprises, however, weaker environmental subsystems have less influence on the production performance level of logistics enterprises [30] . Overall, the production performance simulation model of logistics enterprises constructed in this paper generally plays a desirable effect and analyzes the production performance level of logistics enterprises from the input subsystems, output subsystems, and environmental subsystems. However, there are some shortcomings in this paper, due to the limitations of the study, future research can be carried out in the following aspects: firstly, the setting of the simulation system, future research can further optimize the simulation model set up in this paper, especially the impact variables involved in the environmental sub-systems, and did not set up the complex formula indicators [31] (e.g., refining the impact indicators, replacing the model setting unit); secondly, this study only divided the logistics enterprise production performance into input, output and environmental sub-systems. Secondly, this study only classifies the production performance of logistics enterprises into three levels: input, output, and environment, and future research can also study the production performance of logistics enterprises from different levels [32] (e.g., from the links involved in logistics activities, i.e., transport, distribution, warehousing, etc.). Declarations Data availability Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. Acknowledgments Fund Projects: 2019 National Social Science Foundation Project "Development Zone in the Background of Innovation Ecosystem" Research on Knowledge Intensive Economic Growth (19FGLB059); National Natural Science Foundation-Research on Risk Management of Green and Energy-saving-Oriented Old Industrial Building Function Transformation (51678479); and partly by the research on the improvement of emergency response capacity of urban grassroots communities in Sichuan Province (2023ZHYJGL-16). Author contributions Wang Jie completed the literature review and simulation research part of the paper. Wu Zenghai completed the topic selection of the paper. Xie Han completed the formula setting part of the paper. Luo Sheng completed the optimization of language expression in the first three chapters of the paper. Fan Min completed the optimization of language expression n the last two chapters of the paper. 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The Impact of Third Party Logistics Service Capability on Enterprise Performance in China. J Shandong Univ (Philosophy Social Sci Edition), (01):98–109 Zhou Jing S (2015) Jian. Performance Evaluation of Cold Chain Logistics Enterprises Based on AHP-DEA Model. J Social Sci, (05):114–119 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6200753","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":454382585,"identity":"fe92e0e5-84f6-4603-82c6-29bf7f61e31a","order_by":0,"name":"JIE WANG","email":"","orcid":"","institution":"Xihua University","correspondingAuthor":false,"prefix":"","firstName":"JIE","middleName":"","lastName":"WANG","suffix":""},{"id":454382586,"identity":"2254d5eb-f9ab-4687-be48-00d53727abba","order_by":1,"name":"ZENGHAI 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chart\u003c/p\u003e","description":"","filename":"image23.png","url":"https://assets-eu.researchsquare.com/files/rs-6200753/v1/1948feb3ae90e524db5ee3d1.png"},{"id":104574232,"identity":"a8df468f-b260-4ce4-b4dd-fc8c76d1e225","added_by":"auto","created_at":"2026-03-13 13:26:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2068050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6200753/v1/ca625df1-7793-4851-83cb-6a983b26f435.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"System simulation research on production performance of logistics enterprises","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003e \u003cem\u003eA. RESEARCH BACKGROUND\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAs we all know, ‘logistics’ refers to the process of transporting materials or commodities needed by customers from the supplier's location to the customer's location, involving transport, loading and unloading, distribution and processing, as well as information transfer and other logistics activities. On the one hand, the transformation and upgrading of the logistics industry, also makes the shortcomings of the previous rough logistics revealed, such as the logistics supply chain produces more resource waste. At the same time, in the development trend of intelligent logistics, the logistics industry for research and development personnel, high-end technical personnel demand gradually increased, the level of technological innovation has become a measure of the current logistics enterprises, one of the main indicators of the strength of the competitiveness of the weak. On the other hand, good or bad production performance often determines whether an enterprise in the industry can survive for a long time, logistics enterprises as an important body of economic development, especially so. In today's society, logistics enterprises in the pursuit of low-cost production, at the same time also affect the quality of the product, which is affected by the decline in customer satisfaction and other issues. In the market economy, logistics enterprises, as an important part of today's social and economic development, all aspects of its objectives are aimed at creating greater economic profitability for the enterprise itself, to achieve greater profitability. To enhance the effective competitiveness of logistics enterprises in today's fierce economic environment, but also to effectively achieve the development goals of logistics enterprises' upgrading and transformation, and to promote the long-term sustainable development of logistics enterprises, the study of the production performance of logistics enterprises seems to be very necessary.\u003c/p\u003e \u003cp\u003eTherefore, it can be seen from the above, that logistics enterprises play a crucial role in the development of China's economy, and become a major growth point for economic development. However, the traditional logistics enterprises are subject to their scale, capital scale, and business management style, the production performance level is relatively low. At the same time, in the management of production performance, managers have not paid sufficient attention and unique insights, the lack of systematic, effective production performance management methods, and specific practical operation processes have not been perfected\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Therefore, for the logistics enterprise production performance research has great significance in time, this paper is based on the theory of system dynamics, the construction of input subsystems, output subsystems, and environmental subsystems, the use of VENSIM PLE software simulation model analysis and research, comprehensive measurement of logistics enterprise production performance level in the era of intelligent logistics, to provide practical and feasible development direction for the development of the future logistics enterprise upgrading and transformation of the production performance, while at the same time, the specific operation process has not been perfected\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. To provide a practical development direction for the future development of logistics enterprises in production performance upgrading and transformation, and at the same time, to provide a certain reference for the improvement of production performance management.\u003c/p\u003e \u003cp\u003e \u003cb\u003eB. RESEARCH SIGNIFICANCE\u003c/b\u003e \u003c/p\u003e \u003cp\u003e1) THEORETICAL SIGNIFICANCE\u003c/p\u003e \u003cp\u003eFirst of all, the logistics industry, as a pillar industry of China's national economic development, has been used by many scholars for related efficiency research, such as operational efficiency, innovation efficiency, etc., and most of the research is carried out from the perspective of static efficiency, and few scholars pay attention to the production efficiency of logistics enterprises. From the perspective of system dynamics, there are even fewer studies that construct simulation models to comprehensively analyze the production performance level of logistics enterprises\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Secondly, most of the studies on the production performance level of logistics enterprises either construct empirical models from the perspective of inputs or outputs, and most of the scholars include the environmental impact factors as control variables in the research models. When constructing the research model, fewer scholars include environmental influences as the main influence variables in the complete research model.\u003c/p\u003e \u003cp\u003eTherefore, based on system dynamics theory, this paper constructs input subsystems, output subsystems, and environmental subsystems from an integrated perspective, and conducts simulation research using simulation software to measure the production performance level of logistics enterprises.\u003c/p\u003e \u003cp\u003e2)PRACTICAL SIGNIFICANCE\u003c/p\u003e \u003cp\u003eFirst of all, this paper analyses and applies the knowledge related to system dynamics theory to construct the causality diagrams of various subsystems of production performance, including input subsystems, output subsystems, and environmental subsystems, as well as the overall system flow diagrams, and carries out the system dynamics simulation analyses on the production performance of logistics enterprises\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. As a result, the managers of logistics enterprises can have a clearer understanding of the intrinsic connection between the various parts involved in the production performance process, and can quickly and effectively identify the bottlenecks in the process of achieving the production performance goals, enhance the management efficiency of the managers, and at the same time, provide a certain basis for the improvement of the production performance of logistics enterprises\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Secondly, the power model of the production performance simulation system of logistics enterprises constructed in this paper is not only applicable to logistics enterprises but also applicable to other industries of the same type under the premise of considering different influencing indexes, which can provide certain reference and guidance for other enterprises of the same type in the study of production performance level\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e "},{"header":"II. THEORETICAL FOUNDATIONS AND RELATED CONCEPTS","content":"\u003cp\u003e \u003cem\u003eA. THEORETICAL FOUNDATIONS\u003c/em\u003e \u003c/p\u003e\u003cp\u003e1) SYSTEM DYNAMICS THEORY\u003c/p\u003e\u003cp\u003eSystem dynamics, as a branch of system science, is a branch of a hundred schools of thought, reflecting the characteristics of the cross-fertilization of different disciplines. At the same time, it reflects the characteristics of information system theory and control theory, can find system problems and solve system problems in time\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, used to explore the information feedback within the system as well as the system itself. In 1956, Professor Forrest of the Massachusetts Institute of Technology put forward the term ‘system dynamics’, which formally gave birth to the theory of system dynamics, which reflects the intricate relationship between the system itself, and the different variables within the system, these variables are often between the birth of system dynamics, and the system dynamics theory\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Dynamics theory, which embodies the system itself, the intricate relationship between different variables within the system, these variables often show the characteristics of high-order non-linear, and with time in the dynamic change, it can be subjective qualitative analysis and objective quantitative analysis of the organic fusion, to provide a comprehensive and comprehensive analysis of the system itself and the relationship between different variables within the system.\u003c/p\u003e\u003cp\u003eOn the one hand, the scope of application of system dynamics is very wide, initially mainly used in industrial enterprises in the field of production management, with the development and progress of the economy, so far all kinds of systems, involving various fields: social, economic, ecological, agricultural, environmental protection and so on\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. On the other hand, the role of system dynamics is also very powerful, mainly by a large system of various subsystems or various factors affecting the causal feedback relationship between the establishment of the system dynamics model, the model can be used to carry out the management of prediction, control optimization, etc., not only to explore the system itself, the system within the dynamics of the relationship between the different variables over time but also to coordinate the relationship between a variety of variables, so the system dynamics compared to other simple system dynamics\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. System dynamics has its unique advantages compared with other pure linear regression and linear programming methods. Based on previous research on system dynamics, this paper summarizes the connotation of system dynamics as follows:\u003c/p\u003e\u003cp\u003eFirst, the system involved in system dynamics has the characteristic of information feedback. Information feedback means that there is a meaningful connection between the system itself and different variables within the system, so the theory of system dynamics can be gradually improved based on cybernetics and can achieve real-time dynamic prediction research on the system dynamics simulation model.\u003c/p\u003e\u003cp\u003eSecond, the research object of system dynamics has a certain degree of complexity. The integrated system constructed based on system dynamics theory includes several subsystems, and there is a causal relationship between the subsystems and different variables within the subsystems, which can produce the corresponding logical relationship, and explore the relationship between the integrated system and subsystems and the relationship between different variables within the subsystems under the macro perspective.\u003c/p\u003e\u003cp\u003eThirdly, the development of system dynamics research relies on the analysis of computer simulation models\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Firstly, the influencing factors within the system are analyzed through theory, the stock flow diagram is constructed based on the causal logical relationship between the integrated system and the subsystems and the different variables within the subsystems, and formulas and model equations are assigned to each variable to construct the computer simulation tests and analyses through the basic data. At the same time, to ensure the reasonableness of the model and the reliability of the data, it is also necessary to carry out different validations of the model, such as validity tests, etc. Finally, analyzing the results of the simulation data and carrying out sensitivity analyses, etc., will provide a certain basis for strategic decision-making for the improvement of the system and the improvement of its efficiency.\u003c/p\u003e\u003cp\u003e2)efficiency theory\u003c/p\u003e\u003cp\u003e‘Efficiency’ was originally a concept in the field of physics, used to express the ratio of output energy to input energy when a machine does work. With the development of the economy and technological progress, in the field of management, efficiency refers to the amount of work done per unit of time and the most effective use of social resources to meet human needs; while in the economic field, efficiency refers to the pursuit of maximizing economic benefits under the conditions of given inputs, not wasting or maximizing the use of economic resources, and achieving the optimal allocation of economic resources\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Economists are committed to improving the efficiency of the use of scarce resources and exploring how to maximize the satisfaction of production and living needs under limited conditions\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. At present, academic research on ‘efficiency theory’ mainly has two mainstream directions, one is the classical economic efficiency theory, and the second is the neoclassical economic efficiency theory; there is also some research on allocation efficiency and technical efficiency\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe first is the classical theory of economic efficiency. The main representative, Adam Smith, first put forward the idea of division of labor in his monograph The Wealth of Nations and systematically elaborated on the important role of division of labor in enhancing labor efficiency and increasing national wealth. On this basis, Adam Smith pointed out three advantages of the division of labor: firstly, it enhances workers' proficiency in their work; secondly, the division of labor can greatly reduce the time lost due to the transfer of work; and thirdly, the division of labor will promote the advancement of machinery and equipment, and at the same time improve the efficiency of machinery and equipment\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNext is the neoclassical economic efficiency theory. The neoclassical theory of economic efficiency was developed based on the theory of free market competition initiated by Adam Smith, and gradually formed by the continuous reasoning and improvement of many Western economists\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. One of the most representative is the proposal of Pareto efficiency, Pareto efficiency is also the main standard to measure whether the allocation of resources is efficient. The theoretical study of allocation efficiency in neoclassical economics can be divided into two factions: one is the theory of allocation efficiency proposed by economist Marshall using the partial equilibrium of supply and demand. In Marshall's view, he takes a perfectly competitive producer as a case study enterprise, and the marginal cost function of this case study enterprise is the supply function, and the supply function of the whole market is the algebraic sum of the supply functions of all perfectly competitive enterprises\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The marginal utility function of the customer is the familiar demand function, and the equilibrium price and the corresponding equilibrium output, which represent the equilibrium state of the market, are calculated by linking the supply function with the demand function. Since a perfectly competitive market conforms to the premise that equilibrium price and marginal cost are equal, it is possible to optimize the efficiency of the current allocation\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The second is the Pareto theory of efficiency, which was developed by the economist Pareto based on general equilibrium analysis. Pareto then in the general equilibrium theory of Vallas based on the efficiency of the market for the rationalization of the allocation of resources, in the absence of an optimal allocation of resources, it is necessary to make everyone enjoy the benefits of the allocation of resources at least the same as they are in the initial, there can not be a reduction in the resources of the man-made mastery of the situation, and at least one of the people to get the better than the initial, if this is the case, then there must be a man-made mastery of resources, and at least one person to get better than the initial. If this is the case, then it proves that society has the most rational allocation of the resources it possesses. Later scholars called this kind of allocation efficiency ‘Pareto efficiency’, and a perfectly competitive market is a sufficient and necessary condition for realizing Pareto efficiency, neo-classical economics has proved that a perfectly competitive market can produce Pareto efficiency, thus making the concept of ‘invisible hand’ has become more and more important\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The concept of the ‘invisible hand’ has become clearer.\u003c/p\u003e\u003cp\u003eFinally, several efficiency theories have been studied outside the mainstream. For example, Fayol constructed the marginal production function and then elaborated the theory of marginal efficiency. Zhou Yong\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e suggests that Marx's labor theory of value can be used to measure not only the value of commodities but also the economic efficiency of products. Yao Yu and Yue per Yu \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e argued that the main focus of Western research on equity and efficiency economics is on equity of opportunity and individual efficiency and that there are inherent flaws in these two theories. Marxism, in a way, fills the original Western countries based on the historical view of the materialism of individual private ownership, and this is the change of Marxism to the theory of fairness and efficiency proposed by Western scholars\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. While scholars in the West regarded individual private ownership as unchanging, scholars with Marxism as their guiding philosophy regarded it as historical, and thus the concept of efficiency, recognized by the secular world, slowly began to emerge.\u003c/p\u003e\u003cp\u003e \u003cb\u003eB. DEFINITION OF RELEVANT CONCEPTS\u003c/b\u003e \u003c/p\u003e\u003cp\u003e1) logistics\u003c/p\u003e\u003cp\u003eThe earliest origin of logistics is in the military field, the United States Army, to ensure the timeliness and safety of material supply and research on how to efficiently and cost-effectively transport materials. With the development of the economy and technological progress, logistics began to be used in the field of enterprise production. In 1985, the American Society of Logistics Management defined the concept of ‘logistics’: to meet the needs of customers for planning, implementation, and control steps to achieve goods, services, and related information from the place of supply to the place of consumption, efficient and low-cost flow and storage\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The concept of logistics is defined as the efficient and inexpensive flow and storage of goods, services, and related information from the place of supply to the place of consumption through planning, implementation, and control. The concept of logistics was first introduced in China in the 1990s. China's national standard ‘Logistics Terminology’ (GB/T18354-2021) defines ‘logistics’ as follows: according to the actual demand, the basic functions of transport, storage, loading and unloading, handling, packaging, distribution processing, distribution, information processing, etc. are organically combined, to make the goods physically flow from the place of supply to the place of receipt.\u003c/p\u003e\u003cp\u003e2) logistics company\u003c/p\u003e\u003cp\u003eLogistics enterprises are not limited to transport, distribution, etc. In today's society, logistics enterprises refer to professional organizations engaged in a series of logistics activities such as transport, warehousing, distribution, loading and unloading, distribution and processing, etc. \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Since the 21st century, the name ‘logistics’ has only begun to appear in China's economy and society, and the services provided are still limited to the traditional logistics model \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In 2005, China formally defined the concept of ‘logistics enterprise’: based on the goal of obtaining the maximum benefit under the premise of ensuring the lowest cost, the core business focuses on the basic functions of logistics, such as transport, warehousing in Germany, and business management through the logistics information system, providing specialized and standardized logistics services\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Under the new era, with the rapid development of the economy and the improvement of technological innovation level, modern logistics enterprises began to intelligent, intelligent transformation and upgrade, the use of modern science and technology to the whole process of logistics activities, to provide customers with more high-quality, professional services\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. From the point of view of the secondary market segment of logistics, the business scope of logistics enterprises includes but is not limited to air transport, road transport, pipeline transport, warehousing, loading and unloading, and handling, as well as agency services\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e3) Production performance\u003c/p\u003e\u003cp\u003eProduction performance refers to the performance of the personnel in the production department of an enterprise in fulfilling the set objectives under specific resources and conditions and is an assessment and feedback on the degree of fulfillment of the production objectives as well as the efficiency of their fulfillment \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Generally speaking, production performance is measured by production effectiveness, which focuses on evaluating the extent to which the enterprise has achieved the set objectives within a certain period\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Production performance is closely linked to production management activities, which are carried out through the basic management functions of planning, organizing, coordinating, and controlling. In detail, the production department of the enterprise, according to the enterprise's established goals and plans, makes full use of planning, organization, command, coordination and control and other management functions, reasonable and effective allocation of human, financial, and material resources, to achieve the external market on the quality of the output, the cost of inputs, the product delivery time and other aspects of the requirements of the product, to produce products in line with market expectations\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTABLE I\u003c/p\u003e\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eProduction performance measurement indicators\u003c/span\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetrics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplain\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost investment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCost is a collection of various expenditures incurred by an enterprise in the activities of manufacturing products. The cost will greatly affect the total efficiency of the entire enterprise\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e][\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The relatively high cost will inevitably decline the net profit of the enterprise. Therefore, cost performance management has become an important aspect of the production performance management of logistics enterprises\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality output\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuality is the foundation of a company's survival and an important indicator to measure the production performance of logistics companies\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduct delivery time\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn the fierce modern market environment, efficiency and speed are an important indicator to determine whether you can effectively grasp business opportunities\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Delayed delivery will not only lead to the loss of corporate reputation but also have a largely direct negative impact on customers' business activities, leading to customer loss. On-time delivery can win a large number of customers for logistics companies and better meet customer needs\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e][\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Therefore, product delivery has also become an important indicator to measure the production performance of logistics companies.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor logistics enterprises, production performance includes transport, storage, loading and unloading handling, packaging, consolidation, and other functions, which are commonly used to measure indicators such as the volume of goods transported, the rate of transport, the rate of cargo damage, etc. This paper refers to the research of scholars such as Wang Haiyan\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e on the production performance of the manufacturing industry and attributes the indicators of the production performance of logistics enterprises to the cost of inputs, quality outputs, and product delivery time\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"III. METHOD","content":"\u003cp\u003e\u003cem\u003eA. CAUSAL ANALYSIS AND MODELLING\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA causal feedback diagram is the basis for constructing the system stock-flow diagram, but also an important tool for system dynamics modeling, which can accurately sort out the logical relationship between the systems, the complete causal feedback diagram contains three important elements: variables, causal chain, and polarity presented by the causal chain, as shown in Fig. 1, each causal chain has polarity, which is represented by \u0026lsquo;+\u0026rsquo; and \u0026lsquo;-\u0026rsquo; indicates that the causal chain on the left has a positive polarity and becomes a positive feedback loop; the same is true for the right side\u003csup\u003e[26]\u003c/sup\u003e\u003csup\u003e[27]\u003c/sup\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1)Input subsystems\u003c/p\u003e\n\u003cp\u003eFrom the above figure, it can be seen that the following negative feedback loops are included in the model:\u003c/p\u003e\n\u003cp\u003e01) Promotion time \u0026rarr; new employee promotion rate;\u003c/p\u003e\n\u003cp\u003e02) Separation rate of mature workers \u0026rarr; number of mature workers;\u003c/p\u003e\n\u003cp\u003e03) Separation rate of new employees \u0026rarr; promotion rate of new employees;\u003c/p\u003e\n\u003cp\u003e04) Material wastage \u0026rarr; quantity of raw materials;\u003c/p\u003e\n\u003cp\u003e05) Depletion of raw materials \u0026rarr; quantity of raw materials;\u003c/p\u003e\n\u003cp\u003e06) Depreciable life \u0026rarr; depreciation rate \u0026rarr; number of equipment. (The depreciation rate follows the double-declining balance method. Note: Annual depreciation rate = 2 \u0026divide; Estimated depreciable life \u0026times; 100%,)\u003c/p\u003e\n\u003cp\u003eFrom the figure above, the following positive feedback loops are included in the model:\u003c/p\u003e\n\u003cp\u003e01) Base rate \u0026rarr; Recruitment rate \u0026rarr; Number of new employees \u0026rarr; Number of mature workers \u0026rarr; Direct labor \u0026rarr; Cost input \u0026rarr; Input subsystem \u0026rarr; Production performance level of the logistics company;\u003c/p\u003e\n\u003cp\u003e02) Base rate \u0026rarr; recruitment rate \u0026rarr; number of new employees \u0026rarr; direct labor \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; production performance level of the logistics firm;\u003c/p\u003e\n\u003cp\u003e03) Unit labor cost \u0026rarr; direct labor \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; logistics firm production performance level;\u003c/p\u003e\n\u003cp\u003e04) Base increase \u0026rarr; purchase increase \u0026rarr; raw material quantity \u0026rarr; direct material \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e05) Basic growth rate \u0026rarr; equipment growth rate \u0026rarr; number of equipment \u0026rarr; equipment maintenance costs \u0026rarr; manufacturing costs \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e06) Industrial value-added rate per unit of time \u0026rarr; equipment growth rate \u0026rarr; number of equipment \u0026rarr; equipment maintenance costs \u0026rarr; manufacturing costs \u0026rarr; cost inputs \u0026rarr; input subsystem \u0026rarr; production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e07) Unit equipment maintenance cost \u0026rarr; equipment maintenance cost \u0026rarr; manufacturing cost \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e08) Equipment failure rate \u0026rarr; equipment maintenance cost \u0026rarr; manufacturing cost \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; logistics enterprise production performance level;\u003c/p\u003e\n\u003cp\u003e09) Depreciation cost per unit of equipment \u0026rarr; equipment depreciation cost \u0026rarr; manufacturing cost \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; logistics enterprise production performance level;\u003c/p\u003e\n\u003cp\u003e10) Training cost \u0026rarr; manufacturing cost \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; logistics enterprise production performance level;\u003c/p\u003e\n\u003cp\u003e11) Sales advertising costs \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e12) Design and R\u0026amp;D costs \u0026rarr; cost input \u0026rarr; input subsystem \u0026rarr; production performance level of logistics enterprises.\u003c/p\u003e\n\u003cp\u003e2) Output subsystems\u003c/p\u003e\n\u003cp\u003eFrom the above figure, it can be seen that the following negative feedback loops are included in the model:\u003c/p\u003e\n\u003cp\u003e01) \u0026nbsp; Operating time \u0026rarr; delivery output;\u003c/p\u003e\n\u003cp\u003e02) \u0026nbsp; Equipment failure rate \u0026rarr; delivery output.\u003c/p\u003e\n\u003cp\u003eFrom the above figure, it can be seen that the model contains the following positive feedback loops:\u003c/p\u003e\n\u003cp\u003e01) \u0026nbsp; new employee skill level \u0026rarr; employee skill level \u0026rarr; delivery output \u0026rarr; output subsystem \u0026rarr; logistics enterprise production performance level;\u003c/p\u003e\n\u003cp\u003e02) \u0026nbsp; Mature worker skill level \u0026rarr; employee skill level \u0026rarr; delivery output \u0026rarr; output subsystem \u0026rarr; logistics enterprise production performance level;\u003c/p\u003e\n\u003cp\u003e03) \u0026nbsp; Employee skill level \u0026rarr; product qualified output \u0026rarr; delivery output \u0026rarr; output subsystem \u0026rarr; logistics enterprise production performance level;\u003c/p\u003e\n\u003cp\u003e04) \u0026nbsp; Product qualification rate \u0026rarr; product qualification output \u0026rarr; delivery output \u0026rarr; output subsystem \u0026rarr; logistics enterprise production performance level;\u003c/p\u003e\n\u003cp\u003e05) \u0026nbsp; Raw material qualification rate \u0026rarr; product qualification output \u0026rarr; delivery output \u0026rarr; output subsystem \u0026rarr; logistics enterprise production performance level;\u003c/p\u003e\n\u003cp\u003e06) \u0026nbsp; Maximum output of new employees \u0026rarr; maximum output of employees \u0026rarr; delivery output \u0026rarr; output subsystem \u0026rarr; production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e07) \u0026nbsp; Maximum output of mature workers \u0026rarr; maximum output of employees \u0026rarr; delivery output \u0026rarr; output subsystem \u0026rarr; production performance level of logistics enterprises.\u003c/p\u003e\n\u003cp\u003e3）environmental subsystem\u003c/p\u003e\n\u003cp\u003eFrom the above figure, it can be seen that the following positive feedback loops are included in the model:\u003c/p\u003e\n\u003cp\u003e01) Government subsidy\u0026nbsp;\u0026rarr;\u0026nbsp;environmental subsystem\u0026nbsp;\u0026rarr;\u0026nbsp;production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e02) Talent policy\u0026nbsp;\u0026rarr;\u0026nbsp;environmental subsystem\u0026nbsp;\u0026rarr;\u0026nbsp;production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e03) Government preferences\u0026rarr;environmental subsystem\u0026rarr;production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e04) Laws and regulations\u0026rarr;environmental subsystem\u0026rarr;production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e05) Platform construction\u0026rarr;environmental subsystem\u0026rarr;production performance level of logistics enterprises;\u003c/p\u003e\n\u003cp\u003e06) Corporate culture\u0026rarr;environmental subsystem\u0026rarr;level of production performance of logistics enterprises.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. RESEARCH HYPOTHESIS AND INITIAL DATA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on the causal relationship between the variables in the previous section, this paper constructs a comprehensive system dynamics model of the production performance of logistics enterprises based on input subsystems, output subsystems, and environmental subsystems, and the following figure shows the system flow diagram of the model.\u003c/p\u003e\n\u003cp\u003e1) RESEARCH HYPOTHESIS\u003c/p\u003e\n\u003cp\u003eBased on the literature base and real conditions, the following basic assumptions are made about the research model:\u003c/p\u003e\n\u003cp\u003eThe system dynamics model of production performance of logistics enterprises constructed in this paper is a continuous and progressive value-added cyclic process;\u003c/p\u003e\n\u003cp\u003eTo reflect the operation of the system more intuitively, the system dynamics model of the production performance of logistics enterprises constructed in this paper does not take into account the problem of time delay:\u003c/p\u003e\n\u003cp\u003e01) \u0026nbsp; The logistics enterprise will not have problems such as bankruptcy in the short term;\u003c/p\u003e\n\u003cp\u003e02) \u0026nbsp;The assets of the logistics enterprise will not be transferred in large quantities;\u003c/p\u003e\n\u003cp\u003e03) \u0026nbsp; Logistics companies do not take into account competitive barriers in the market;\u003c/p\u003e\n\u003cp\u003e04) \u0026nbsp; The relevant coefficients in the model are set at the beginning of the period according to the general regulations of the market industry;\u003c/p\u003e\n\u003cp\u003e05) \u0026nbsp; Significant external policy changes and other unusual systemic environmental changes faced by the logistics company are not taken into account.\u003c/p\u003e\n\u003cp\u003eTo better understand the stock-flow diagram in the system dynamics model, the following will explain the various types of variables within the model. There are four main types of variables in a system dynamics model: state variables (stocks), rate variables (flows), auxiliary variables, and constants. State variables (stocks) are variables that accumulate over time. Rate variables (flows) can directly cause changes in the state variable and can represent the rate of change of the stock. The intermediate variable between the state variable (stock) and the rate variable (flow) is called an auxiliary variable, which can assist in influencing the rate of change of the state variable (stock). Generally, by setting fixed parameter values, these variables do not change with time and other variables and are called constants. Also to be emphasized is the table function (with lookup), a custom function in the VENSIM PLE software, defined in a way that is usually graphical. It is mainly used when there is a special non-linear relationship between two variables that cannot be expressed by conventional functions. In addition, VENSIM PLE software will run with the time parameter as a variable in the model, which can also be set as a shadow variable.\u003c/p\u003e\n\u003cp\u003eBased on the system dynamics model of production performance of logistics companies set up in this paper, it involves 6 state variables, 11 rate variables, 13 auxiliary variables, 24 constants, and 4 table functions, in addition to 2 shadow variables, totaling 60 variables.\u003c/p\u003e\n\u003cp\u003e2) initial data\u003c/p\u003e\n\u003cp\u003eWhen the system dynamics model is created, the VENSIM PLE software will ask the user to determine the initial time, end time, time step, and units by defining the INITIAL TIME, FINAL TIME, TIME STEP, and UNITS FOR TIME so that the model can be computed in chronological order.\u003c/p\u003e\n\u003cp\u003e01) Initial time = 2018;\u003c/p\u003e\n\u003cp\u003e02) End time = 2027;\u003c/p\u003e\n\u003cp\u003e03) Time step = 1;\u003c/p\u003e\n\u003cp\u003e04) Time unit: year.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC. \u0026nbsp; \u0026nbsp;EQUATION SETTING\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e01) \u0026nbsp; Production performance level of logistics companies = INTEG [(input subsystem - output subsystem) * environmental subsystem, 1000], in million yuan;\u003c/p\u003e\n\u003cp\u003e02) \u0026nbsp; Input subsystem = cost input * 0.9, in million yuan;\u003c/p\u003e\n\u003cp\u003e03) \u0026nbsp; Output subsystem = delivery output * 0.9, in million yuan;\u003c/p\u003e\n\u003cp\u003e04) \u0026nbsp; Environment subsystem = Government subsidy * Talent subsidy * Government preference * Laws and regulations supervision * Platform construction * Corporate culture, unit: ten thousand yuan;\u003c/p\u003e\n\u003cp\u003e05) \u0026nbsp; Number of new employees = INTEG (Recruitment rate - Promotion rate of new employees - Separation rate of new employees, 2000), unit: person;\u003c/p\u003e\n\u003cp\u003e06) \u0026nbsp; Number of Mature Workers = INTEG (Promotion Rate of New Employees - Separation Rate of Mature Workers, 5000), unit: person;\u003c/p\u003e\n\u003cp\u003e07) \u0026nbsp; New Employee Separation Rate = Number of New Employees * New Employee Separation Rate, unit: none;\u003c/p\u003e\n\u003cp\u003e08) \u0026nbsp; New Employee Promotion Rate = New Employee Separation Rate * Promotion Time, Unit: None;\u003c/p\u003e\n\u003cp\u003e09) \u0026nbsp; Mature Worker Separation Rate = Number of Mature Workers * Mature Worker Separation Rate, Unit: None;\u003c/p\u003e\n\u003cp\u003e10) \u0026nbsp; Recruitment rate = base rate * 0.8, unit: none;\u003c/p\u003e\n\u003cp\u003e11) \u0026nbsp; Base rate = 100*Time, unit: none;\u003c/p\u003e\n\u003cp\u003e12) \u0026nbsp; Direct Labor = Unit Labor Cost * (Number of Mature Workers + Number of New Hires), in;\u003c/p\u003e\n\u003cp\u003e13) \u0026nbsp; Quantity of Raw Materials = INTEG (Increase in Purchases - Depletion of Raw Materials - Depletion of Materials, 5000), in;\u003c/p\u003e\n\u003cp\u003e14) \u0026nbsp; Purchase Increase = Quantity of Raw Materials - Base Increase, in million;\u003c/p\u003e\n\u003cp\u003e15) \u0026nbsp; Material Loss = Raw Material Quantity * Raw Material Depletion Ratio, in million;\u003c/p\u003e\n\u003cp\u003e16) \u0026nbsp; Raw Material Loss = Raw Material Quantity * Raw Material Consumption Ratio, in ten thousand yuan;\u003c/p\u003e\n\u003cp\u003e17) \u0026nbsp; Direct materials = raw materials * 1.5, unit: ten thousand yuan;\u003c/p\u003e\n\u003cp\u003e18) \u0026nbsp; Equipment growth rate = Industrial value-added rate per unit of time + base growth rate, in nil;\u003c/p\u003e\n\u003cp\u003e19) \u0026nbsp; Number of equipment = INTEG (equipment growth rate - equipment depreciation rate, 5000), unit: ten thousand yuan;\u003c/p\u003e\n\u003cp\u003e20) \u0026nbsp; Equipment depreciation rate = 2 / depreciable life, unit: none;\u003c/p\u003e\n\u003cp\u003e21) \u0026nbsp; Equipment maintenance costs = unit equipment maintenance costs * number of equipment * equipment failure rate, unit: million;\u003c/p\u003e\n\u003cp\u003e22) \u0026nbsp; Manufacturing cost = training cost * equipment depreciation cost * equipment maintenance cost, in;\u003c/p\u003e\n\u003cp\u003e23) \u0026nbsp; Equipment depreciation cost = number of equipment * unit equipment depreciation cost, unit: ten thousand yuan;\u003c/p\u003e\n\u003cp\u003e24) \u0026nbsp; Employee skill level = mature worker skill level + new employee skill level, unit: none;\u003c/p\u003e\n\u003cp\u003e25) \u0026nbsp; Qualified product output = Employee skill level * Qualified rate of raw materials * Qualified rate of output, unit: ten thousand yuan;\u003c/p\u003e\n\u003cp\u003e26) \u0026nbsp; Employee Maximum Output = Mature Worker Maximum Output + New Employee Maximum Output, unit: million;\u003c/p\u003e\n\u003cp\u003e27) \u0026nbsp; Delivery Output = (Product Qualified Output + Employee Maximum Output)*Operation Time, unit: ten thousand yuan;\u003c/p\u003e\n\u003cp\u003e28) \u0026nbsp; Cost inputs = Manufacturing costs * Direct labour * Direct materials * Design and R\u0026amp;D costs * Sales and advertising costs, in million yuan;\u003c/p\u003e\n\u003cp\u003e29) \u0026nbsp; Industrial value-added rate per unit of time = WITH LOOKUP ([(2018,0)-(2027,0.5)], (2018,0.22), (2019,0.24),(2020,0.19),(2021,0.16),(2022,0.17),(2023,0.2),(2024,0.25 ),(2025,0.28),(2026,0.29),(2027,0.3) ) in n/a;\u003c/p\u003e\n\u003cp\u003e30) \u0026nbsp; Talent Allowance = WITH LOOKUP ([(2018,0) - (2027,5000)], (2018,787), (2019,899), (2020,921), (2021,1123), (2022,1326), (2023,1468), (2024,2548), (2025. 3296), (2026,3649), (2027,4205) ) in $ million;\u003c/p\u003e\n\u003cp\u003e31) \u0026nbsp; Basis Increase = WITH LOOKUP ([(2018,0)-(2027,7000)], (2018,2441), (2019,2266), (2020,2280), (2021,2436), (2022,3628), (2023,4021), (2024,5421), ( 2025,6311),(2026,5675),(2027,6840) ) in $ million;\u003c/p\u003e\n\u003cp\u003e32) \u0026nbsp; DESIGN RESEARCH COSTS = WITH LOOKUP ([(2018,0)-(2027,4000)], (2018,560), (2019,600), (2020,1020), (2021,940), (2022,640), (2023,860), (2024,1267), (2025. 1783), (2026,2230), (2027,3100) ), in $ million;\u003c/p\u003e\n\u003cp\u003e33) \u0026nbsp; Turnover rate of new employees = 0.3, in nil;\u003c/p\u003e\n\u003cp\u003e34) \u0026nbsp; Promotion time = 0.6, unit: year;\u003c/p\u003e\n\u003cp\u003e35) \u0026nbsp; Separation rate of mature workers = 0.2, Unit: None;\u003c/p\u003e\n\u003cp\u003e36) \u0026nbsp; Yield pass rate = 0.8* raw material pass rate, in nil;\u003c/p\u003e\n\u003cp\u003e37) \u0026nbsp; Semi-finished product rate = 0.2, Unit: None;\u003c/p\u003e\n\u003cp\u003e38) \u0026nbsp; Corporate culture = 0.6, Unit: none;\u003c/p\u003e\n\u003cp\u003e39) \u0026nbsp; Operating time = 3, unit: hour;\u003c/p\u003e\n\u003cp\u003e40) \u0026nbsp; Unit labor cost = 500, in;\u003c/p\u003e\n\u003cp\u003e41) \u0026nbsp; Depreciation cost per unit of equipment = 600, in: million;\u003c/p\u003e\n\u003cp\u003e42) \u0026nbsp; Maintenance cost per unit of equipment = 500, in million yuan;\u003c/p\u003e\n\u003cp\u003e43) \u0026nbsp; Raw material pass rate = 0.8, in nil;\u003c/p\u003e\n\u003cp\u003e44) \u0026nbsp; Raw material consumption ratio = 0.4, in nil;\u003c/p\u003e\n\u003cp\u003e45) \u0026nbsp; Raw material consumption ratio = 0.2, in nil;\u003c/p\u003e\n\u003cp\u003e46) \u0026nbsp; Training cost = 860, in;\u003c/p\u003e\n\u003cp\u003e47) \u0026nbsp; Platform construction = 0.8, Unit: Nil;\u003c/p\u003e\n\u003cp\u003e48) \u0026nbsp; Skill level of mature workers = 800, Unit: million;\u003c/p\u003e\n\u003cp\u003e49) \u0026nbsp; Mature worker turnover ratio = 0.2;\u003c/p\u003e\n\u003cp\u003e50) \u0026nbsp; Depreciation life = 5, in years;\u003c/p\u003e\n\u003cp\u003e51) \u0026nbsp; Government concessions = 500, in million;\u003c/p\u003e\n\u003cp\u003e52) \u0026nbsp; Government subsidy = 0.5, in nil;\u003c/p\u003e\n\u003cp\u003e53) \u0026nbsp; Skill level of new employees = 400, in million;\u003c/p\u003e\n\u003cp\u003e54) \u0026nbsp; Maximum output of new employees = 500, in million;\u003c/p\u003e\n\u003cp\u003e55) \u0026nbsp; Turnover rate of new employees = 0.3, Unit: None;\u003c/p\u003e\n\u003cp\u003e56) \u0026nbsp; \u0026nbsp;Regulation of laws and regulations = 0.8, Unit: None;\u003c/p\u003e\n\u003cp\u003e57) \u0026nbsp; Equipment failure rate = 0.3, Unit: None;\u003c/p\u003e\n\u003cp\u003e58) \u0026nbsp; Sales and advertising costs = 1,200, in millions of yuan;\u003c/p\u003e\n\u003cp\u003e59) Semi-finished goods ratio = 0.2, unit: n/a；\u003c/p\u003e\n\u003cp\u003e60) Maximum output of mature workers = 900, in million.\u003c/p\u003e"},{"header":"IV. MEASUREMENTS","content":"\u003cp\u003eOnce the parameters and equations of the model are perfected, they need to be tested, which is a necessary part of constructing the system dynamics model. The running simulation of the model is a simulation of the real world, then the model must first have usability, so it is necessary to build a series of tests on the model, the most basic is the model running test, as shown in Figure 8, which shows that the model runs successfully. Given the complexity of the system, three common important tests are made, including gauge consistency test, model validity test, and sensitivity analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eA. GAUGE CONSISTENCY TEST\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gauge consistency test is the most basic in the SD model, which mainly ensures that the equations are constructed with a uniform scale and there is no logical error. In this paper, the \u0026lsquo;Model Check\u0026rsquo; function of the software is used to carry out the test, and the results show that there is no problem with the scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eB. VALIDITY TEST\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current academic use of system simulation methods is more recognized as running simulations of the model, which is done to assess the accuracy of the results. The most commonly used method is validity testing, which aims to verify that the results obtained from the model reflect the actual system model characteristics and the actual effects of change and that the analytical investigations of the model lead to an adequate investigation and understanding of the problem\u003csup\u003e[26]\u003c/sup\u003e\u003csup\u003e[27]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e1) Input subsystems\u003c/p\u003e\n\u003cp\u003e01) \u0026nbsp; direct labor\u003c/p\u003e\n\u003cp\u003eFrom the above chart, we can see the trend of the number of new employees, the number of new employees shows an increasing trend year by year. From 2018 to 2019, the number of new employees rose sharply, this is due to the largest number of recruiters during this period, resulting in a sharp rise in the number of new employees. Afterward, due to the stabilization of production, the number of mature workers increases and the need for new hires gradually decreases. Since the number of hires is approximately equal to the sum of the number of promotions and the number of departures, the number of new hires gradually shows a stable trend.\u003c/p\u003e\n\u003cp\u003eSimilarly, the trend of the number of mature workers is also increasing year by year. The number of mature workers is stable between 2018 and 2019 since the departure rate of mature workers is similar to the promotion rate, which keeps the number of mature workers stable. The fastest growth rate of mature workers in 2019-2020 is since the number of new hires is the largest in 2018-2019, which is the result of a one-year growth and training period, which transforms new hires into new employees. Training period, resulting in the transformation of new hires into mature workers, which leads to a significant increase in the promotion rate of new hires in 2019-2020, thus advancing the growth of mature workers. In the following years, the promotion rate of new employees decreases year by year, but it is still higher than the separation rate of mature workers, resulting in the number of mature workers showing a rising trend year by year, but the growth trend is slowing down.\u003c/p\u003e\n\u003cp\u003eIn this simulation model, it is assumed that the unit labor cost remains unchanged, thus, the direct labor input of logistics enterprises increases year by year at a slower rate. In the current era of intelligent logistics, although the number of front-line logistics personnel or grass-roots logistics personnel is reduced, with the investment in high-tech logistics, logistics information system development, research and development personnel, the number of high-end technicians is also higher, so in the long run, the direct labor input of logistics enterprises still shows a year-on-year trend of increasing.\u003c/p\u003e\n\u003cp\u003e02) \u0026nbsp; direct material\u003c/p\u003e\n\u003cp\u003eAs can be seen from the above chart: the quantity of raw materials for logistics companies shows a year-on-year decreasing trend, and similarly, the direct materials for logistics companies also show a year-on-year decreasing trend.\u003c/p\u003e\n\u003cp\u003e03) \u0026nbsp; Manufacturing cost\u003c/p\u003e\n\u003cp\u003eAs can be seen from the above chart, in the traditional product cost structure: the largest proportion of raw material costs is one of the important factors leading to high costs, which is due to the existence of a large number of logistics enterprises\u0026apos; inventory, resulting in a large increase in inventory costs due to the search for high-quality and inexpensive suppliers can reduce the cost of production of products to a large extent, and more importantly, to increase the turnover of raw materials to reduce inventories. Although the labor costs of production workers are also increasing year by year, the relative stability of labor costs on the finished product is relatively minimal. Manufacturing costs are increasing at a faster rate, so the control of manufacturing costs is particularly necessary.\u003c/p\u003e\n\u003cp\u003eIn the selected composition of several manufacturing costs, equipment maintenance costs, and equipment depreciation costs accounted for the vast majority of inputs, so strict control of product equipment damage rate can reduce manufacturing costs to a large extent. Logistics enterprises are different from general manufacturing enterprises, logistics enterprises in the daily operation of the facilities and equipment used in the process, with a high degree of knowledge-intensive, such as large cranes, various types of conveyor devices, and the logistics information platform. Logistics facilities and equipment in the daily operation of the process, once these facilities and facilities are damaged, the maintenance is difficult, and maintenance costs are high, so the logistics enterprise manufacturing costs appear a year-by-year increasing trend.\u003c/p\u003e\n\u003cp\u003eIn summary, reducing inventory, and reducing the damage rate of facilities and equipment can significantly control the production cost of the product.\u003c/p\u003e\n\u003cp\u003e04) \u0026nbsp; Cost of sales advertising\u003c/p\u003e\n\u003cp\u003eAs can be seen from the above figure, in this model, it is assumed that the sales and advertising costs show a stable trend and do not change significantly. As we all know, in the composition of the product cost, sales advertising cost is a proportion that is also a larger part, so the level of sales advertising cost will also determine the level of the final cost input. Under the influence of the current social media, logistics enterprises must pay attention to the investment of sales advertising costs to obtain a better reputation, more stable customer resources, and more diverse types of business. Good sales advertising can play a positive role in promoting the daily operation of logistics enterprises, and the benefits of the economic output of the enterprise outweigh the disadvantages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e05)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDesign development costs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs can be seen from the above figure, in this model, it is assumed that the design and development costs show a stable trend and do not undergo large changes. In the composition of product cost, design R\u0026amp;D cost is a relatively large part, especially in the current era of intelligent logistics, each logistics enterprise is focusing on technology research and development innovation, intelligent logistics platform construction, and other businesses, so the input of design R\u0026amp;D cost will directly determine the daily operating costs of logistics enterprises, for logistics enterprises, better management of design R\u0026amp;D cost is a new direction for future development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e06）Short\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs can be seen from the above figure: cost inputs show a stable trend in 2018-2024 and a decreasing trend after 2024, and the curve of the input subsystem follows a similar trend. Cost inputs are composed of direct labor, direct materials, manufacturing costs, sales and advertising costs, and design and development costs, the direct labor in the previous section shows a year-on-year increasing trend, direct materials show a year-on-year decreasing trend, manufacturing costs also show a year-on-year increasing trend, while sales and advertising costs and design and development costs are assumed to remain unchanged, the above factors together, cost inputs in the late stage shows a year-on-year decreasing trend. This shows that the inputs of direct labor, direct materials, and manufacturing costs will be reduced in the later stage of the cost of logistics enterprises, and the level of daily operational efficiency will be improved.\u003c/p\u003e\n\u003cp\u003e2) Output subsystems\u003c/p\u003e\n\u003cp\u003e01) \u0026nbsp; Qualified Product Yield\u003c/p\u003e\n\u003cp\u003eFrom the above figure, it can be seen that the qualified production volume of products shows a trend of growth year by year. In this model, it is assumed that the product qualification rate, semi-finished product rate, and the skill level of employees will not undergo large changes in the future, and tend to be stable values 2018-2020 the fastest growth in the volume of qualified products, 2018-2019 the high speed of qualified production growth due to the sharp rise in the number of new employees recruited during this period, the rising level of the number of new employees to make up for the lack of their skill level makes qualified. The production of qualified products grows rapidly in absolute terms. 2019 - 2020 qualified production grows at a high rate, during this period, although the growth rate of the number of new employees decreases, the number of mature workers grows the fastest during this period, so the production of qualified products also maintains a high rate of growth. 2020 and beyond, the production of qualified products continues to grow, but the rate of growth each year is gradually slowing down during this period. During this period, the number of new employees decreases year by year, while the number of mature workers shows a slow growth trend, and this trend of qualified production is very similar to the growth trend of mature workers. It can be concluded that mature workers are the backbone of the company\u0026apos;s production and largely influence the production of qualified products.\u003c/p\u003e\n\u003cp\u003e02) \u0026nbsp; Delivery schedule outputs\u003c/p\u003e\n\u003cp\u003eFrom the above figure, it can be seen that the delivery output is increasing year by year between 2018-2027 and the rising trend is slowly decreasing, from the above figure, it can be seen that the qualified output of the product is increasing year by year and the rising trend is also faster, the operation time and the maximum output of the employees are assumed not to be changed in this model, and they tend to be stable.\u003c/p\u003e\n\u003cp\u003eFrom the above figure, it can be seen that the delivery output is increasing year by year between 2018-2027 and the rising trend is slowly decreasing, from the above figure, it can be seen that the qualified output of the product is increasing year by year and the rising trend is also faster, the operation time and the maximum output of the employees are assumed not to be changed in this model, and they tend to be stable.\u003c/p\u003e\n\u003cp\u003eThe stability of product quality and product quality can be well reflected in the delivery output. Delivery output is low at the beginning of the period due to a sharp rise in the recruitment of new employees with low skill levels and high losses of facilities and equipment. 2019-2020 has the highest growth in delivery output, thanks to a sharp rise in the number of mature workers and a large reduction in new employees due to the large promotion of new employees in that period. Delivery output also rises gradually after 2020, but the growth gradually slows down, which coincides with the growth rate of mature workers.\u003c/p\u003e\n\u003cp\u003eFrom the above analysis, it can be concluded that logistics enterprises can effectively improve productivity through the rational and effective use of equipment, reduce the proportion of maintenance time and downtime of facilities and equipment, making it easier for logistics enterprises to meet deadlines and achieve greater delivery output.\u003c/p\u003e\n\u003cp\u003e03) \u0026nbsp; Short\u003c/p\u003e\n\u003cp\u003eFrom the above figure, it can be seen that the output of the delivery period shows an increasing trend year by year, and similarly, the output subsystem also shows an increasing trend year by year. Factors affecting the output of the delivery period include the qualified output of the product, the skill level of the staff, and the operation time. Product-qualified output and production-qualified rate, with technological progress and advanced facilities and equipment input, the logistics enterprise\u0026apos;s production-qualified rate increases year by year, so the product-qualified output increases. In terms of staff skill level, with staff education and training, staff technology upgrading, whether it is a new employee or mature worker, their skill level is showing a higher trend. With the industrial output value of the logistics industry as a whole and the technical upgrading of the logistics enterprises, the actual operating time of the logistics enterprises shows a lower trend.\u003c/p\u003e\n\u003cp\u003eTo sum up, the delivery output of logistics enterprises is increasing year by year, so the level of output subsystem is also getting higher and higher.\u003c/p\u003e\n\u003cp\u003e3）environmental subsystem\u003c/p\u003e\n\u003cp\u003eFrom the previous system flow diagram of the production performance level of logistics enterprises, the environmental subsystem is jointly influenced by six factors: government preferences, government subsidies, talent subsidies, enterprise culture, laws and regulations, and platform construction. Government preferences focus on government organizations directly for certain logistics enterprises, preferential exemptions or capital investment, which is a direct investment for logistics enterprises, can reduce the economic difficulties of daily operations. Government subsidies refer to certain discounts and subsidies given by the government to certain logistics enterprises for certain business investments. Talent subsidy refers to the high-end technical personnel working in the logistics enterprise, in addition to the logistics enterprise itself giving a certain salary, the government will also give this part of the person a certain amount of subsidies and concessions. Laws and regulations refer to certain laws and regulations, such as the Customs Law and the Transportation Law, which are observed by the logistics enterprises in their daily operation. Corporate culture refers to the prevailing cultural atmosphere within the logistics enterprise, a radical or conservative corporate culture, that will lead to different corporate decisions and development direction. Platform construction focuses on the logistics information platform and logistics information system developed and designed by the logistics enterprises themselves, such as RFID and NFC technologies.\u003c/p\u003e\n\u003cp\u003eIn this model, the above factors present a stable trend, only affecting the production performance of logistics enterprises in the daily operation process, and will not have a direct impact on the input and output level of logistics enterprises, so the environment subsystem presents a stable linear trend.\u003c/p\u003e\n\u003cp\u003e4）Production performance level of logistics companies\u003c/p\u003e\n\u003cp\u003eAs can be seen from the above figure: the input subsystem shows a decreasing trend, the output subsystem shows an increasing trend year by year, and the environmental subsystem shows a stable trend. Under the joint effect of the above three subsystems, the level of production performance of logistics enterprises shows a stable trend in 2018-2019 without large changes, while in 2019-2021, it shows a substantial increase. The peak appears near 2021, and after 2021, it shows a small decline until 2027.\u003c/p\u003e\n\u003cp\u003eLogistics enterprises are different from general traditional enterprises, logistics enterprises emphasize high technology, high efficiency, high yield, and technological innovation and scientific and technological progress require greater capital, talent, material resources investment support, although the external environment of government subsidies, government concessions, talent subsidies and so on to the rich, but the cost of inputs is still high. Therefore, for logistics enterprises, logistics enterprises should pay more attention to the research and development of knowledge-intensive technology, high-end logistics information platforms, and high-efficiency logistics and distribution technology, which can to a large extent enhance the level of production performance of logistics enterprises. High technology also corresponds to high output and high returns.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC. SENSITIVITY TEST\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analysis is a key step in the testing of SD models, where the parameters of the main variables in the model are adjusted, the model is run, and the change in the value of that variable before and after the adjustment is compared to determine the extent of its impact. If the model is verified to be insensitive to changes in most of these parameters, then the model is stable and robust. The method of model sensitivity analysis focuses on changing one parameter and observing the degree of change in other variables of the model, i.e. sensitivity. This study refers to the method of Gu Chaolin et al.\u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e1) Input subsystems\u003c/p\u003e\n\u003cp\u003e01) \u0026nbsp; direct labor\u003c/p\u003e\n\u003cp\u003eThe green line (the line has the number 2) is the CURRENT model, representing the simulation results under the initial data settings. The blue line (the line has the number 1) \u0026nbsp;is the PLAN 1 model, which represents the positive change of the influencing factors involved in direct labor, i.e. the turnover rate of new employees is adjusted from 0.3 to 0.8 in the original model, the promotion time is changed from 0.6 to 0.8 in the original model, the turnover rate of mature workers is adjusted from 0.2 to 0.6 in the original model, and the unit cost of labor is changed from 500 to 1,000 in the original model, and the red line (the line has the number 3) is the PLAN 2, which represents the negative change of the influencing factors involved in direct labor. Negative change in the impact factors involved in direct labor, i.e. new hire separation rate changes to -0.3, time to promotion changes to 0.1, mature worker separation rate changes to -0.2, and unit labor cost changes to 100.\u003c/p\u003e\n\u003cp\u003eFrom the dynamic simulation results of the above figure, it can be seen that: under PLAN 1, the growth rate of direct labor is faster and larger. This indicates that when the employee separation ratio becomes larger, the employee promotion time becomes longer, and the unit labor cost becomes larger, the direct labor cost of the logistics enterprise increases, and the cost input is larger, and in the case of constant delivery output, the cost input of the logistics enterprise is higher, and therefore the production performance level of the logistics enterprise is poorer.\u003c/p\u003e\n\u003cp\u003eUnder PLAN 2, the increase in direct labor at the beginning of the period is greater than in the CURRENT model, but at the later stage, the direct labor cost under PLAN 2 will be lower than in the CURRENT model. This indicates that when the employee turnover rate decreases, the promotion time of employees becomes shorter, and the unit labor cost decreases, the direct labor input of the logistics enterprise will be reduced, and the production performance level of the logistics enterprise can be maintained at a high level under the premise of constant delivery output.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e02) \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edirect material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe green line (the line has the number 3) represents the CURRENT model, i.e. the simulation results at the initial value setting. The red line (the line has the number 2) is the PLAN 3 model, which represents the positive change of the influencing factor involved in the direct material variable, i.e., the raw material consumption ratio is changed from 0.4 in the original model to 0.8, and the raw material loss ratio is adjusted from 0.2 in the original model to 0.8. The blue line (the line has the number 1) is the PLAN 4 model, which represents the negative change of the influencing factor involved in the direct material variable, i.e., the raw material consumption ratio is changed to -0.2, and the raw material loss ratio is adjusted to -0.2. Adjusted to -0.2.\u003c/p\u003e\n\u003cp\u003eFrom the dynamic simulation results of the figure above, we can see that the trend of the curve in the original model is similar to that in the PLAN 4 model, but the slope of the curve in the PLAN 4 model is larger, indicating that under the PLAN 4 model, the input of direct materials decreases faster. This shows that when the raw material consumption ratio and the raw material consumption ratio remain low, the investment in raw materials and various logistics facilities and equipment is lower, resulting in a lower direct material investment. When the output remains unchanged during delivery, the production performance level of logistics companies is better.\u003c/p\u003e\n\u003cp\u003eHowever, under the PLAN 3 model, the curve gradually stabilized in the later stage after one to two years of rapid decline in the early stage and did not change significantly. This shows that when the raw material consumption ratio is higher than that of raw material consumption, the material loss and waste rate are higher, and the investment in logistics facilities and equipment is also higher, which ultimately leads to high investment in direct materials. When the output remains unchanged during delivery, the logistics company\u0026apos;s cost investment is higher, so the production performance level of logistics companies is poor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e03) \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emanufacturing cost\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe red line (the line has the number 2) represents the CURRENT model, i.e. the simulation results with the initial values set. The blue line (the line has the number 1) is the PLAN 5 model and represents the positive change in the influencing factors involved in the manufacturing cost variable, i.e., the training cost was adjusted from 860 to 2000 in the original model, the depreciation cost per unit of equipment was changed from 600 to 2000 in the original model, the depreciation life was changed from 5 years to 10 years in the original model, the maintenance cost per unit of equipment was adjusted from 500 to 2000 in the original model, and the equipment failure rate was changed from 0.3 to 0.8. The green line (the line has the number 3) is the PLAN 6 model and represents a negative change in the influencing factors involved in the manufacturing cost variable, i.e. training costs change to 100, depreciation cost per unit of equipment changes to 100, depreciation life changes to 1 year, maintenance cost per unit of equipment changes to 100, and equipment failure rate changes to 0.1.\u003c/p\u003e\n\u003cp\u003eFrom the dynamic simulation results in the above figure, it can be seen that: under the PLAN 5 model, the manufacturing costs show a year-on-year incremental change trend, but the magnitude of the change is slower and the increase is smaller, which indicates that under this simulation setup, the inputs of the manufacturing costs are lower, and the production performance level of the logistic enterprise can be maintained at a higher level in the case that the output of the delivery period remains unchanged. The training cost and equipment maintenance cost are kept low, however, the equipment depreciation cost is higher than under PLAN 6, which may be related to the depreciation life, the longer the depreciation life, the lower the depreciation cost, and the shorter the depreciation life, the higher the depreciation cost.\u003c/p\u003e\n\u003cp\u003eUnder the PLAN 6 model, manufacturing costs show an increasing trend of change from year to year, but the change is slightly larger than PLAN 5. This indicates that when the training costs and equipment maintenance costs are low, the input of manufacturing costs of logistics enterprises is correspondingly low, and under the premise of relatively stable delivery output, the production performance level of logistics enterprises can reach a high level.\u003c/p\u003e\n\u003cp\u003e2) Output subsystems\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e01) Qualified Product Yield\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe green line (the line has the number 3) represents the CURRENT model, i.e., the simulation results at the initial value setting. The red line (the line has the number 2)is the PLAN 7 model, which represents the positive change of the influencing factors involved in the product qualified yield variable, i.e. the skill level of mature workers is adjusted to 2000 from 800 in the original model, the skill level of new employees is changed to 1000 from 400 in the original model, the raw material qualification rate is changed to 0.9 from 0.8 in the original model, and the semi-finished product rate is changed to 0.5 from 0.2 in the original model. The blue line (the line has the number 1) is the PLAN 8 model, which represents the negative charge of the influencing factors involved in the product-qualified yield variable. PLAN 8 model, which represents a negative change in the influencing factors involved in the product qualification yield variable, i.e., a change in the skill level of mature workers to -1000, a change in the skill level of new employees to -2000, a change in the raw material qualification rate to 0.1, and a change in the semi-finished product rate to 0.1.\u003c/p\u003e\n\u003cp\u003eFrom the dynamic simulation results of the above figure, it can be seen that: under the PLAN 7 model, the curve of product-qualified output shows a rising trend year by year, and the level of growth is the fastest under the three models. Among them, the output qualification rate, semi-finished product rate, and staff skill level are higher, which leads to higher qualified output, so in the case of stable cost inputs of logistics enterprises, the output subsystem of logistics enterprises maintains a higher level, the production performance level of logistics enterprises is higher, and the daily operation is in good condition. Under the PLAN 8 model, the product-qualified yield shows a slowly rising trend and the rise is very small. Compared with the PLAN 7 model, the output qualification rate, semi-finished product rate, and employee skill level are lower under this model, which leads to lower qualified product output, which indicates that the output effect of the logistics enterprise is poorer, which is mainly manifested in the lower efficiency of logistics and distribution, higher error of logistics and distribution, etc.\u003c/p\u003e\n\u003cp\u003e02) Delivery schedule outputs\u003c/p\u003e\n\u003cp\u003eThe green line (the line has the number 3) represents the CURRENT model, i.e., the simulation results at the initial value setting. The red line (the line has the number 2) is the PLAN 9 model, which represents the positive change of the influencing factors involved in the delivery output variable, i.e., the maximum output of new employees is changed from 500 to 1000 in the original model, the maximum output of mature workers is adjusted from 900 to 2000 in the original model, and the operating time is changed from 3 to 6 in the original model. The blue line (the line has the number 1) is the PLAN 10 model, which represents the negative change of the influencing factors involved in the delivery output variable, i.e. The maximum output of new employees changes to -1000, the maximum output of mature workers changes to -500, and the operating time changes to -3.\u003c/p\u003e\n\u003cp\u003eFrom the dynamic simulation results in the above figure, it can be seen that under the PLAN 9 model, the turnaround output curve shows a rising trend year by year and the growth is also maximum. This indicates that higher product-qualified output and maximum employee output can lead to better delivery output. The good delivery output indicates that the production performance of logistics enterprises can be maintained at a high level under the premise of stable and unchanged inputs, which is more beneficial than detrimental to the long-term development of logistics enterprises.\u003c/p\u003e\n\u003cp\u003eUnder the PLAN 10 model, the output curve of the delivery period shows a slow upward trend with a small increase and a negative value of output. Compared with the PLAN 9 model, the maximum output of the employees under this model is lower, which leads to lower delivery output indicators, which indicates that the production performance level of the logistics enterprise is poor, which is mainly manifested in the poor customer satisfaction of the logistics enterprise, the logistics and distribution time is longer, and so on.\u003c/p\u003e\n\u003cp\u003e3) environmental subsystem\u003c/p\u003e\n\u003cp\u003eThe blue line\u0026nbsp;(the line has the number 1)\u0026nbsp;represents the CURRENT model, i.e., the simulation results under the initial value setting. The red line\u0026nbsp;(the line has the number 2)\u0026nbsp;is the PLAN 11 model, which represents the positive change of the influencing factors involved in the environmental subsystem variables, i.e., government preferences are changed from 500 to 1000 in the original model, laws and regulations are changed from 0.8 to 2 in the original model, platform construction is changed from 0.8 to 2 in the original model, enterprise culture is changed from 0.6 to 1 in the original model, and government subsidies are changed from 0.5 to 1 in the original model.\u003c/p\u003e\n\u003cp\u003eThe green line (the line has the number 3) is the PLAN 12 model, which represents the negative change in the influencing factors involved in the environmental subsystem variables, i.e., government subsidies change by 100, laws and regulations change by 0.2, platform construction change by 0.2, enterprise culture change by 0.1, and government subsidies change by 0.1.\u003c/p\u003e\n\u003cp\u003eFrom the dynamic simulation results in the above figure, it can be seen that under the PLAN 11 model, the curve of the environmental subsystem is stable and does not change greatly, but the value of the environmental subsystem under this model is the largest under the three models. This indicates that under this model, the environmental subsystem has a greater impact on the production performance level of logistics enterprises, in which changes in the influencing factors such as government subsidies, platform construction, laws and regulations, etc. will cause corresponding changes in the production performance of logistics enterprises.\u003c/p\u003e\n\u003cp\u003eUnder the PLAN 12 model, the value of the environmental subsystem is the smallest of the three models, which indicates that under this model, the environmental subsystem has a small impact on the production performance level of logistics enterprises, in which changes in factors such as government preferences and corporate culture do not cause significant changes in the production performance level of logistics enterprises.\u003c/p\u003e"},{"header":"V. CONCLUSIONS AND DISCUSSION","content":"\u003cp\u003eThis study firstly introduces the overview of the logistics enterprise, puts forward some reasonable assumptions on the constructed model based on the actual situation of the logistics enterprise, and assigns the values of parameters such as constants, state variables, variable functions, and auxiliary variables required by the model through field observation, inquiry and so on combined with the data processing function of VENSIM software itself. On this basis, the computer was used to simulate the model, and the simulation results were analyzed, while the parameter settings in the system were adjusted to analyze the changing dynamics of the input subsystems, output subsystems, and environmental subsystems under different parameter states. Based on the comparative analysis of different scenarios, the following conclusions were drawn:\u003c/p\u003e\n\u003cp\u003eFirst, in the input subsystem, reducing direct labor, direct materials, manufacturing costs, sales, and advertising costs, and expenditures involving research and development costs by shortening the promotion time of employees and reducing the rate of employee separation can effectively reduce the cost of inputs, and under the premise of the stability of the output subsystem, it can effectively improve the level of the production performance of logistics enterprises\u003csup\u003e[28]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSecond, in the output subsystem, through improving the product qualification rate, strengthening the staff skill level and other measures to improve the product qualification output, and delivery output level, under the premise of the input subsystem is unchanged, it can effectively improve the level of production performance of logistics enterprises\u003csup\u003e[29]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThirdly, in the environmental subsystem, the production performance level of logistics enterprises can be adjusted by changing the values of government preferences, government subsidies, talent subsidies, laws and regulations, platform construction, corporate culture, and other impact indicators. Stronger environmental subsystems can positively and significantly affect the production performance level of logistics enterprises, however, weaker environmental subsystems have less influence on the production performance level of logistics enterprises\u003csup\u003e[30]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOverall, the production performance simulation model of logistics enterprises constructed in this paper generally plays a desirable effect and analyzes the production performance level of logistics enterprises from the input subsystems, output subsystems, and environmental subsystems. However, there are some shortcomings in this paper, due to the limitations of the study, future research can be carried out in the following aspects: firstly, the setting of the simulation system, future research can further optimize the simulation model set up in this paper, especially the impact variables involved in the environmental sub-systems, and did not set up the complex formula indicators\u003csup\u003e[31]\u003c/sup\u003e(e.g., refining the impact indicators, replacing the model setting unit); secondly, this study only divided the logistics enterprise production performance into input, output and environmental sub-systems. Secondly, this study only classifies the production performance of logistics enterprises into three levels: input, output, and environment, and future research can also study the production performance of logistics enterprises from different levels\u003csup\u003e[32]\u003c/sup\u003e (e.g., from the links involved in logistics activities, i.e., transport, distribution, warehousing, etc.).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eData sharing is not applicable to this article as no datasets were generated or analyzed during the current study.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eFund Projects: 2019 National Social Science Foundation Project \"Development Zone in the Background of Innovation Ecosystem\" Research on Knowledge Intensive Economic Growth (19FGLB059); National Natural Science Foundation-Research on Risk Management of Green and Energy-saving-Oriented Old Industrial Building Function Transformation (51678479);\u0026nbsp;and partly by the research on the improvement of emergency response capacity of urban grassroots communities in Sichuan Province (2023ZHYJGL-16).\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eWang Jie completed the literature review and simulation research part of the paper.\u003c/p\u003e\n\u003cp\u003eWu Zenghai completed the topic selection of the paper.\u003c/p\u003e\n\u003cp\u003eXie Han completed the formula setting part of the paper.\u003c/p\u003e\n\u003cp\u003eLuo Sheng completed the optimization of language expression in the first three chapters of the paper.\u003c/p\u003e\n\u003cp\u003eFan Min completed the optimization of language expression n the last two chapters of the paper.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eEthical approval was not required as the study did not involve human participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent\u003c/p\u003e\n\u003cp\u003eInformed consent was not required as the study did not involve human participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhu Lin On the problems and countermeasures in performance management of high-tech enterprises. 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J Social Sci, (05):114\u0026ndash;119\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Logistics companies, Production performance, System dynamics, Simulation","lastPublishedDoi":"10.21203/rs.3.rs-6200753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6200753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAt present, under the background of global economic integration, the rapid development of science and technology, and other times, the logistics industry has become the mainstream trend of the times by shifting from the traditional development stage to the intelligent and intelligent development stage. It has gradually become the main development goal of China's logistics industry in the future. Based on the actual situation of logistics enterprises in the era of intelligent logistics, this study constructs a system dynamics simulation model based on the system dynamics theory and efficiency theory. It assigns values to the parameters of all kinds of variables required by the model. On this basis, the model is simulated by computer, and the simulation results are analyzed. At the same time, the parameter settings in the system are adjusted to explore the changing dynamics of the input subsystem, output subsystem, and environmental subsystem under different parameter states.\u003c/p\u003e","manuscriptTitle":"System simulation research on production performance of logistics enterprises","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 08:55:39","doi":"10.21203/rs.3.rs-6200753/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"edd32b7b-6ed9-431b-b201-77ca4e7f1d10","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48318146,"name":"Business and commerce/Business and management"},{"id":48318147,"name":"Humanities/Complex networks"},{"id":48318148,"name":"Social science/Business and management"},{"id":48318149,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-03-13T13:25:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 08:55:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6200753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6200753","identity":"rs-6200753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}