Hierarchical Management Research Articles

Optimizing Hierarchical Condition Category-Risk Adjustment Factor Management in Population Health Using Rapid Process Improvement Methods.

Centers for Medicare & Medicaid Services provides reimbursement through Hierarchical Condition Category (HCC) coding. Medical systems strive toward risk adjustment optimization, often implementing costly chart review processes. Previously, our organization implementing countermeasures through workflows was complex and performed in silos. Our goal was to put in place HCC-Risk Adjustment Factor (RAF) improvement tools to optimize HCC-RAF management in Population Health using rapid process improvement methods. In this quality improvement analysis (IRB#806198), we used Lean methodology to develop tools and implement streamlined processes for providers to manage, document, and code high-risk HCC conditions. Rather than applying costly countermeasures, Transformational Healthcare conducted a Rapid Process Improvement Workshop (RPIW), with workgroups implementing proposed changes, to improve processes. Each of these tools was embedded in standard work, for teams to use in practice. Tools included the development of RPIW-inspired work groups, a Provider Education website, tip sheets, clinical champions, trainings, audits, practice alerts, smart phrases, schedule view tools, severity scores, reports, dashboards, on-screen decision-support tools, coding expertise, and HCC standard work. Quantitatively, Year 1 showed enterprise HCC-RAF scores improved by 4.1%. We were able to develop tools for providers and team members to allow for more optimized pathways. Although quantitatively we realized an improvement in enterprise HCC-RAF score, our overall aim was to improve process flow and limit waste. Leveraging Lean improvement methods for the collective design of tools has supported culture change. In the end, we found that providers are indeed willing to adopt these newly built tools. These tools have optimized operations, allowing providers to work smarter, not harder.

Multi-Scale Effects of Supply–Demand Changes in Water-Related Ecosystem Services Across Different Landscapes in River Basin

To promote sustainable hierarchical management, it is essential to understand the complex relationships within and underlying causes of supply–demand changes in water-related ecosystem services (WESs) across different spatial scales and landscape patterns. Consequently, the Optimal Parameters-based Geographical Detector (OPGD) and Multi-Scale Geographically Weighted Regression (MGWR) are used to analyze the factors influencing changes in WESs supply–demand. The findings indicate that (1) at the macroscale, population size, and economic activity are the main driving factors, while at the microscale, precipitation becomes the primary factor influencing fluctuations in WESs supply–demand. (2) Furthermore, over time, the influence of social factors becomes increasingly significant. (3) The explanatory power of a single factor typically increases as it interacts with other factors. (4) Abundant precipitation helps in the generation and maintenance of WESs, but intense human activities may have negative impacts on them. Therefore, we have made significant progress in identifying and analyzing the natural and human-induced driving forces affecting changes in WESs by deeply integrating long-term multi-source remote sensing data with the OPGD and MGWR models.

Hierarchical Game Theory-Based Optimal Power Management for Both Seaports and Ships

Hierarchical Game Theory-Based Optimal Power Management for Both Seaports and Ships

Research on safety control technology of high-speed railway combined test based on threatening event analysis

PurposeSafety management is a key point and poses a challenge in joint testing. To detect and address potential accidents' hidden dangers early, this paper conducts research on the safety control technology for high-speed railway joint tests by incorporating the concept of hazardous events.Design/methodology/approachAiming at ensuring the safety of high-speed railway combined inspections and trials, this paper starts from the dual prevention mechanism. It introduces the concept of threatening events, defines them and analyzes the differences between threatening events and railway accidents. The paper also proposes a cause model for threatening events in high-speed railway combined inspections and trials, based on three types of hazard sources. Furthermore, it conducts research on the control strategies for these threatening events.FindingsThe research on safety control technology for high-speed railway combined operation and testing, based on the analysis of threatened events, offers a new perspective for safety management in these operations. It also provides theoretical and practical support for the transition from passive prevention to active risk pre-control, which holds significant theoretical and practical value.Originality/valueThe innovation mainly includes the following three aspects: (1) Building on the traditional dual prevention mechanism, which includes risk hierarchical management and control as well as hidden danger investigation and management, a triple prevention mechanism is proposed. This new mechanism adds the management of threatening events as the third line of defense. The aim is to more comprehensively identify and address potential security risks, thereby enhancing the efficiency and effectiveness of security management. (2) In this paper, the definition of a railway threatening event is clarified, and the causative model of a high-speed railway threatening event based on three kinds of danger sources is proposed. (3) This paper puts forward the control strategy of the high-speed railway combined operation and trial, which includes five key links: identification, reporting, analysis, rectification and feedback, which provides a new perspective for the safety management of the high-speed railway combined operation and trial and has important theoretical and application value.

Advancements in colorectal cancer detection: The role of immuno‐positron emission tomography, immuno‐single‐photon emission computed tomography, and machine learning applications

AbstractColorectal cancer (CRC) ranks as the world's second most prevalent cancer and third in mortality. Detection and diagnosis are crucial in research and clinical settings. While colonoscopy and computed tomographic colonography are widely used for identifying organic lesions, positron emission tomography (PET) and single‐photon emission computed tomography (SPECT) offer superior visualization of molecular changes. These immuno‐PET and immuno‐SPECT techniques surpass conventional [18F] Fluorodeoxyglucose PET/CT in specificity and sensitivity, improving CRC diagnostics and supporting therapeutic strategies. This review emphasizes the role of immuno‐PET/SPECT in CRC diagnosis and establishing a foundation for therapeutic strategies, facilitating hierarchical management through the identification of treatment‐responsive populations, prediction of therapeutic outcomes, and support for intraoperative imaging. This review introduces the preclinical and clinical utility of immunoconjugates for detecting colorectal adenomas, and primary, metastatic, or recurrent CRC, focusing on specific CRC cell targets like the epidermal growth factor receptor and carcinoembryonic antigen. The review also covers various mAb‐based immunoconjugates and engineered mAb fragments, including diabodies and minibodies. Finally, it looks into the great promise of machine learning in PET or SPECT and it addresses the challenges of translating preclinical successes into clinical practice for colorectal adenoma diagnosis, proposing potential solutions and directions for future research.

Association of Triglyceride-Glucose Index, Triglyceride to High-Density Lipoprotein Cholesterol Ratio, and Related Parameters With Prehypertension and Hypertension.

The objective of this study was to investigate the association between triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-c), triglyceride-glucose index (TyG), and related parameters (TyG-BMI, TyG-WC) with prehypertension and hypertension over 45years old. According to the blood pressure diagnosis, the enrolled individuals were divided into two groups, which were prehypertension and hypertension. In multivariate logistic regression analysis, after adjusting for confounders, the highest quartile groups of TG/HDL-c, TyG, and related parameters showed a significantly increased risk of hypertension compared to the lowest quartile groups, and there was associated with hypertension when comparing the highest TG/HDL-c to the lowest TG/HDL-c and corresponding ORs were 1.416 (1.234, 1.625) and 1.029 (0.893, 1.187), respectively. Furthermore, when comparing the fourth quartile to the first quartile of TG/HDL-c, TyG index, and related parameters, respectively, both corresponding ORs of hypertension were higher than prehypertension. Elevated TyG, TyG-BMI, TyG-WC index, and TG/HDL-c ratio levels were associated with hypertension in individuals over 45years. Moreover, the receiver operating characteristic curve was used to compare the predictive ability of each parameter in identifying people with hypertension suggested that the TyG-WC index ([Area under the curve] AUC: 0.601 [CI: 0.588-0.615]), TyG-BMI, and TyG were more significant than TG/HDL-c in distinguishing hypertension. However, in the prehypertension population, the area under the ROC curve for TyG-BMI (0.543 [CI: 0.530-0.556]) was better than that of other parameters. They have the potential to become cost-effective monitors in the hierarchical management of hypertension.

A learning‐based hierarchical energy management control strategy for hybrid electric vehicles

AbstractIn this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car‐following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real‐time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.

Agent-oriented model of three-level hierarchical minimax management of a regional industrial complex

The developed countries experience indicates the relevance of managing problems of the territorial industrial complexes and the need to create and develop applicable models based on modern approaches that take into account uncertainty and the possibility of management optimizing. The methodological basis of the research was the theory of industrial economics, the theory of economic and mathematical modeling, the theory of optimal control, optimization theory, and game theory. The model uses an agent-oriented approach, methods of economic and mathematical modeling, methods of building management systems of socio-economic systems, a minimax approach. Three-level management model of the regional industrial complex is proposed, its hierarchical subject-object structure is determined. The parameters of industrial facilities are presented in the form of a phase vector, and for each agent the structure of control and information links, algorithms for the choice of control actions are formalized. The problems of forming minimax control actions sets for each agent, solved in the course of using the model, are formulated, and a general consistent algorithm for choosing optimal control actions is proposed. The developed model will make possible to implement it in a software environment and create a tool for modeling the processes of managing the regional industrial complex, and in future — a flexible extensible software toolkit for managing industry at the regional level is to be created.

Adaptive hierarchical energy management strategy for fuel cell mobile robot hybrid power system based on working condition recognition

Energy management of hybrid power is critical to maintain the economical and efficient operation of fuel cell mobile robots. To improve the energy distribution between the proton exchange membrane fuel cell (PEMFC) and battery under different working conditions, this paper proposes an adaptive hierarchical energy management strategy (AHEMS) based on the recognition and management levels. Firstly, the recognition level realizes the identification of different working conditions based on the machine learning (ML) methods including the K-means and KNN. Secondly, the fuel cell hydrogen consumption and efficiency are both optimized by adaptive multi-objective particle swarm optimization (AMOPSO) at the management level. Specifically, an adaptive flight parameter strategy based on the particle dispersity (PD) information is proposed to balance the convergence and diversity of Pareto solutions. Besides, to overcome the parameter uncertainty caused by different working states and improve the system performance, an interval optimization scheme is proposed based on the Pareto solutions. Finally, the fuzzy decision combined with the recognition results and state of charge (SOC) of the battery is performed to find the most appropriate power distribution of the PEMFC and battery. The proposed AHEMS algorithm is compared with different algorithms in the numerical simulation and hardware-in-loop (HIL) experiments. These results demonstrate that the hybrid power system with the proposed optimization scheme performs better than the base model and classical optimization algorithms in terms of the hydrogen consumption and efficiency indexes, revealing the success of this AHEMS approach in solving the energy distribution problem in different working conditions.

Prediction of hemorrhage rate after tonsil surgery in children based on random forest model

Objective:Hemorrhage after tonsil surgery in children is a serious and potentially life-threatening complication. The purpose of this study was to establish a risk warning model for hemorrhage after tonsil surgery in children through a national multi-center retrospective study, providing a basis for hierarchical management after tonsil surgery in children. Methods:Stratified sampling was performed on 8 854 children who underwent tonsillectomy under general anesthesia from 15 research centers in different provinces from January 15, 2022 to May 15, 2023. The sample size of this study was 2 724 cases, including 1 096 males and 1 628 females. Children were divided into bleeding and non-bleeding groups according to whether or not they had bleeding after surgery. The random forest algorithm was used to build a risk warning model. By continuously exploring the optimized model, the accuracy of predicting the postoperative bleeding rate of tonsils in children was improved, and the prediction effectiveness of the model was verified by ten-fold cross-validation. Results:Among 2 724 children, 117 had postoperative bleeding after tonsillectomy, with a bleeding rate of 4.30%. The model constructed by the random forest algorithm for the training set was verified in the test set, and the obtained prediction accuracy was 98.72%, the recall rate was 78.95%, and the area under the ROC curve AUC was 0.96. Conclusion:Although the recall rate of the random forest model needs to be improved, the overall accuracy is quite excellent. It can effectively avoid misjudging positive cases as negative cases. It is a useful tool that can be used to predict the postoperative bleeding rate of tonsils and clinical medical decision-making, laying a good foundation for subsequent optimization and improvement.

Understanding scale effects and differentiation mechanisms of ecosystem services tradeoffs and synergies relationship: A case study of the Lishui River Basin, China

Understanding the scale effect of relationships among ecosystem services (ESs) and the factors influencing these relationships, is vital for sustainable management of ESs. Previous research has focused less on multiple scale units, and the driving mechanisms behind ESs interactions remain unclear. Therefore, in this study, we assessed the spatial distribution of water yield (WY), water purification (WP), and soil conservation (SC) across four spatial scales (3 km × 3 km grid, 5 km × 5 km grid, sub-watershed, and county scales) in the Lishui River Basin of China during 2020. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, geographical weighted regression, and Pearson correlation analysis, we examined tradeoffs and synergies among these ESs at various scales. Additionally, we employed a geographic detector to analyze the reactions and scale effects among these tradeoffs/synergies and social-ecological factors. Our findings revealed the following: (1) Significant spatial heterogeneity in ESs was observed in the basin. The WY decreased from west (2511.65 mm) to east (890.06 mm), with high WP values concentrated in the eastern part, and high SC values (8.30 × 105 t/hm2) found in densely forested and grassland areas surrounding water bodies. Forested and cultivated lands were key contributors to ESs in the basin. (2) Across the four investigated scales, tradeoffs or synergies between the same ESs becoming more pronounced as the scale increased. Notably, most ES relationships displayed their strongest synergies at the county scale, with certain spatial areas demonstrating change in interaction directions. (3) The explanatory power of single- or double-factor interactions increased with scale. ESs interactions were found to be closely linked to climatic factors, as well as topographic features (including slope gradient and elevation), with precipitation exhibiting relatively high explanatory power. The interaction of natural and social factors significantly outweighed the combined interaction of dual social factors. This indicated that socio-ecological factors, especially climate and topography, jointly influenced the intensity and direction of ES tradeoffs/synergies. Scale effects arose from the “peak cutting and valley filling” phenomenon during scale transitions. These insights hold significant value for guiding hierarchical management strategies for regional ESs and informing decisions regarding resource allocation.

Moderate to severe anemia at admission increases the risk of complications in patients over 60 years with hip fracture

BackgroundAnemia frequently occurs in patients with hip fractures and represents a risk factor that can potentially be altered. To evaluate the association between admission anemia and complications in older hip fracture patients while exploring the potential impact of anemia on complications from the perspective of overall, operation and non-operation.MethodsThis retrospective study enrolled in-patients over 60 years old with hip fractures from January 2020 to November 2023. At admission, anemic patients were identified as having a hemoglobin level below 12 g/dL in females and 13 g/dL in males. Anemia was further classified as mild, moderate, or severe. Data encompassing demographics, comorbidities, medications, information on fracture and surgery, and complications were collected.ResultsA total of 462/679 patients had anemia, including 348, 105, and 9 with mild, moderate, and severe anemia, respectively. A total of 281 individuals experienced complications, including 212 and 69 with and without operation, respectively. Multivariate regression analysis identified anemia as a greater risk for acute heart failure (OR = 2.056, p = 0.037, 95% CI 1.043–4.052) than non-anemia. Moderate to severe anemia was a significant risk factor for any complication (OR = 1.584, p = 0.028, 95% CI 1.050–2.390), ≥ 2 (OR = 2.364, p = 0.001, 95% CI 1.443–3.872) or 3 (OR = 2.311, p = 0.022, 95% CI 1.131–4.720) complications, delirium (OR = 2.301, p = 0.018, 95% CI 1.156–4.579), venous thromboembolism (OR = 2.031, p = 0.042, 95% CI 1.025–4.025), and acute heart failure (OR = 2.095, p = 0.016, 95% CI 1.145–3.834), compared with mild to non-anemia. Similar results were observed in operated patients, while anemia and its severity were not associated with complications in non-operated patients.ConclusionModerate to severe anemia caused complications in elderly hip fracture patients, but it was not observed in non-operated individuals. These findings would support orthopedic physicians’ hierarchical management of anemic patients.

A Review of Studies on the Treatment of Premature Ejaculation with Traditional Chinese Medicine

By combing the literature and analyzing the current status of traditional Chinese medicine (TCM) treatment for premature ejaculation (PE), the strengths and shortcomings of the existing studies were identified. Building upon this assessment, we put forward a prospect for the establishment of TCM-specific strategies for PE, including more targeted and individualized comprehensive diagnosis and treatment approaches for PE, the establishment of a TCM hierarchical management program, and the exploration of the most effective entry points for collaborative treatment of PE using both TCM and Western medicine. Indeed, integrating therapeutical strategies from the two perspectives in a multifaceted and complex symptom such as PE, would enhance the possibility of success, possibly also reducing side effects. Moreover, with the help of well-established and new scientific and technological tools, we can explore the key links and targets in treating PE by TCM. At the same time, the mechanism of TCM treatment for PE could and should be examined in depth to lay the foundation for widespread clinical application. Finally, we suggest conducting multicenter, nationwide standardized clinical studies on treating PE with TCM. These studies should employ high-quality randomized controlled trial designs to obtain robust evidence-based medical data.

Hierarchical Approach to the Management of Drinking Water Sludge Generated from Alum-Based Treatment Processes

The management of drinking water treatment plant (DWTP) sludge is challenging for water treatment facilities. Previous studies reported mainly on handling sludge through landfilling, release into water bodies, discharge into wastewater treatment plants, onsite disposal, and incineration methods for the treatment of sludge. The limitations of these sludge-handling methods are well documented. This article focuses on the hierarchical approach as an alternative and comprehensive method for handling DWTP sludge. The core of hierarchical management streamlines the minimization of the generated DWTP sludge; treatment of DWTP sludge to reduce toxicity; changing of the physicochemical form of DWTP sludge; and finally, the reuse, recycling, and recovery of DWTP sludge. The premise is to achieve zero landfilling of DWTP sludge, establish a circular economy, generate job opportunities, and preserve the environment. Thus, this study also proposes two main technologies, which are gravity-based sludge separators for fractionating the sludge and photocatalytic membrane reactors (PMRs) as a technology for the treating and/or recovery of nutrients and minerals from DWTP sludge. Until the chemical deductive or minus approach becomes a reality in water treatment, the use of PMRs and gravity-based sludge separators will enhance the management of DWTP sludge when incorporated into the hierarchical approach.

A hierarchical energy management strategy for DC microgrid hybrid energy storage systems based on fractional-order sliding mode controller

A hierarchical energy management strategy (EMS) for a fuel cell (FC)-supercapacitor (SC)‑lithium battery hybrid energy storage system (HESS), based on a fractional-order sliding mode controller (FOSMC), is proposed to address the nonlinear behavior of low-voltage direct current (DC) microgrid HESS. To fully exploit the energy of the SC, the system management layer divides the EMS into maximum power operation mode and state machine control algorithm operation mode. For the first time in equipment control layer, a FOSMC, more suitable for handling nonlinear characteristics, is employed, and its stability is evaluated using the Lyapunov method. Finally, a 48 V HESS experimental platform is constructed for validation, demonstrating that the proposed EMS enhances the stability, response speed, and robustness of the HESS. Compared to conventional sliding mode controller (SMC) and integral sliding mode controller (ISMC), the adoption of a FOSMC reduces the steady-state error of the bus voltage by at least 9.1 % and increases the response speed by at least 11.1 %, while exhibiting good robustness to external disturbances and uncertainties in controller parameters. This fills the current research gap in parameter robustness validation.

Hierarchical Energy Management Strategy for Plug-in HEVs Based on Historical Data and Real-Time Speed Scheduling

Hierarchical Energy Management Strategy for Plug-in HEVs Based on Historical Data and Real-Time Speed Scheduling

Development of an Intelligent Risk Hierarchical Management System on Pulmonary Aspiration for the Elderly.

For the elderly, the risks associated with pulmonary aspiration are significant, and can severely impact their quality of life. Therefore, the management of pulmonary aspiration risk is crucial in promoting healthy aging, an aspect often overlooked in self-health management for the elderly. To enhance self-management of pulmonary aspiration risk among the elderly, we have organized a multidisciplinary team to develop an intelligent risk hierarchical management system on pulmonary aspiration for the elderly. This system tailors the assessment with different questionnaires based on the elderly individual's environment. Additionally, it utilizes elderly-friendly formats such as pictures, videos and animations, making it easier for seniors to comprehend the occurrence and hazards of pulmonary aspiration. This enables them to proactively take preventive measures to manage the risk, thus achieving self-management of pulmonary aspiration risk.

Dynamic Evolution of Multi-Scale Ecosystem Services and Their Driving Factors: Rural Planning Analysis and Optimisation

Rural areas provide ecosystem services (ESs) to urban metropolitan regions. These services are threatened by the constant pressure of urbanisation and new interest in rural development. This has heightened the conflict between environmental concerns and developmental needs, thereby presenting significant land management and rural planning challenges. Employing a quantitative measurement and optimisation framework, we investigate six representative ES variables to assess planning strategies that can address this contradiction. We used a suburban rural area around Nanjing, China, as our study area. We collected spatial data from 2005 to 2020 at two scales (village level and 500 m grid) to map ESs, quantify interactions (trade-offs and synergies among ES bundles), and identify the social, ecological, and landscape drivers of rural change. Based on this, rural planning strategies for optimising ESs at different scales have been proposed. Our findings include (1) spatial heterogeneity in the distribution of ESs, (2) the identification of seven synergistic and eight trade-off pairs among ESs, (3) a spatial scale effect in suburban rural areas, and (4) the spatial trade-offs/synergies of ESs exhibiting a ‘Matthew effect’. The identification of key trade-offs and synergistic ES pairs and the categorisation of ES bundles form the basis for a multi-scale hierarchical management approach for ESs in the region. By examining the commonalities and variations in drivers across diverse scales, we established connections and focal points for spatial planning. We use these findings to propose spatial planning and landscape policy recommendations for rural suburban areas on multiple scales. This study aims to provide a comprehensive and detailed spatial optimisation strategy for rural areas that can help contribute to their revitalisation.

Energy management strategy of series–parallel hybrid transmission integrating map information and personalized driving characteristics

The integration of multi-source intelligent and connected information during a driving trip, along with its online application to globally optimized energy management strategies, has emerged as a crucial technical approach for enhancing the energy-saving effectiveness of hybrid transmissions. However, the action mode of such information and the optimization calculation efficiency of existing dynamic programming (DP) methods limit the online application of the aforementioned strategies with global optimization capabilities. To address these problems, the present study proposes a hierarchical energy management strategy that follows the reference trajectory of the battery state of charge (SoC) and comprehensively considers the multi-source information on the driving trip. First, a global speed prediction model based on personalized driving characteristics is proposed to obtain an accurate driving cycle input for the space-domain DP method. Second, the aforementioned tasks as well as the working-mode decision of the hybrid transmission and the multi-power-source torque distribution calculation tasks are deployed in the dual-core controller. Finally, the hierarchical energy management strategy is verified via vehicle testing. Compared with the DP strategy, the proposed strategy has an energy-saving potential of 4.17% that is yet to be realized. Furthermore, compared with the charge-depleting and charge-sustaining (CD–CS) strategy, the proposed strategy reduces fuel consumption by 0.38 L per 100 km, and its energy-saving effect is significant. This study is the first to apply the DP method to the vehicle controller, thereby facilitating the online application of energy management strategies with global optimization capabilities.

ENTERPRISE ECOSYSTEM AND SUSTAINABLE DEVELOPMENT: IMMANENT INTERDEPENDENCE

Global hypercompetition necessitates the development of new models of business collaboration among economic entities. Enterprise ecosystem is one form of organization and support for entrepreneurship that facilitates the transition from competition to cooperation among business entities. The article explores various theoretical approaches to defining the concept of ecosystem that enabled to propose the author’s interpretation. It is substantiated that the entrepreneurial ecosystem offers optimal pathways and effective tools for fostering innovation and economic growth, grounded in the principles of sustainable development. A comparative analysis of organizational models of activity is conducted. Compared to other organisational models, the ecosystem entails the transformation of hierarchical management mechanisms, giving rise to a dynamic environment facilitating the free exchange of knowledge, information, resources, technologies, and competencies. The synergistic model of the enterprise ecosystem within the framework of sustainable development is proposed. The model exhibits several characteristic features: cross-industry interaction, which maximizes synergies more effectively than corporate ties; diversification of activities with a focus on lean innovation; emphasis on environmental sustainability and the commercialization of developments; flexibility of business processes; autonomy of participants and adherence to voluntary cooperation principles. The article substantiates the key improvement strategies that enterprises should implement to achieve synergistic effects and sustainable development goals within the enterprise ecosystem.