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Systemic Optimization in Sustainable Business Operations: Theory and Practice
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Systemic Optimization in Sustainable Business Operations: Theory and Practice

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Theory and Methodology".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 8449

Special Issue Editor

Dr. Helen Cai

E-Mail Website
Guest Editor
Business School, Middlesex University, London, UK
Interests: sustainable business; circular economy and patents; innovation and strategy; marketing and operations

Special Issue Information

Dear Colleagues,

I invite you to contribute to this Special Issue of Systems “Systemic Optimization in Sustainable Business Operations: Theory and Practice”, which aims to address notable gaps in the current literature around sustainability and systemic optimization. While there has been progress in research related to sustainable business practices, many studies have primarily focused on isolated aspects of sustainable operations, often overlooking the interconnectedness that characterizes complex economic systems. Additionally, the existing literature on systemic optimization tends to overlook the practical integration of ecological, economic, and social dimensions, creating theoretical frameworks that are not always applicable or scalable.

This Special Issue seeks to broaden our understanding of systemic optimization in sustainable business operations by encouraging contributions that present comprehensive theoretical and practical insights into how system dynamics can enhance sustainable practices. Authors are encouraged to identify and address the limitations of existing studies, offering new approaches that provide more integrated, holistic models of business operations. The aim is to not only challenge conventional methodologies but also to develop systemic frameworks that can be practically applied to improve sustainable outcomes. Authors should explore how established theories such as systems thinking, complexity theory, and adaptive management can be used to advance sustainable business practices. There is a growing need for models that capture the dynamic interplay between various components, such as resource management, supply chains, and corporate social responsibility. Furthermore, demonstrating the integration of profitability with sustainability is crucial to ensure practical and adaptable strategies that can handle changing economic and environmental conditions.

In addition to addressing these gaps, submissions should discuss the relevance and applicability of theoretical frameworks to real-world scenarios. Contributions should provide practical insights into how businesses can implement these systemic models to enhance their sustainable operations and strategic management practices. This includes highlighting the challenges faced during their implementation and strategies to navigate them, making the research valuable to both academic scholars and business practitioners.

The Special Issue aims to bring together a collection of 10–12 high-quality papers that push the boundaries of current academic inquiry, contributing significantly to the development and application of systemic optimization. We look forward to submissions that offer novel perspectives and evidence-based strategies to improve our understanding of sustainable business operations, ultimately helping bridge the gap between theory and practice.

Dr. Helen Cai
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • systemic optimization
  • sustainable business
  • adaptive management
  • practices for sustainability
  • economic sustainability
  • sustainable operations
  • supply chain management
  • corporate social responsibility
  • socio-economic factors

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Published Papers (8 papers)

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Research

24 pages, 1717 KiB  
Article
A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus
by Xiangze Wang, Jingqi Deng, Tingting Hu, Dungang Gu, Rui Liu, Guanghui Li, Nan Zhang and Jiaqi Lu
Systems 2025, 13(5), 395; https://doi.org/10.3390/systems13050395 - 20 May 2025
Viewed by 101
Abstract
Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To [...] Read more.
Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To address this gap, this study proposes a comprehensive life-cycle carbon footprint optimization framework that integrates LCA with a mixed-integer linear programming (MILP) model. The framework, while applicable to various production contexts, is validated using a university campus as a case study. In 2023, the evaluated university’s net carbon emissions totaled approximately 24,175.07 t CO2-eq. Based on gross emissions (28,306.43 t CO2-eq) before offsetting, electricity accounted for 66.09%, buildings for 15.55%, fossil fuels for 8.67%, and waste treatment for 8.46%. Seasonal analysis revealed that June and December exhibited the highest energy consumption, with emissions exceeding the monthly average by 19.4% and 48.6%, respectively, due to energy-intensive air conditioning demand. Teaching activities emerged as a primary contributor, with baseline emissions estimated at 5485.24 t CO2-eq. Optimization strategies targeting course scheduling yielded substantial reductions: photovoltaic-based scheduling reduced electricity emissions by 7.00%, seasonal load shifting achieved a 26.92% reduction, and combining both strategies resulted in the highest reduction, at 45.95%. These results demonstrate that aligning academic schedules with photovoltaic generation and seasonal energy demand can significantly enhance emission reduction outcomes. The proposed framework provides a scalable and transferable approach for integrating time-based and capacity-based carbon optimization strategies across broader operational systems beyond the education sector. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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18 pages, 968 KiB  
Article
Is Economies of Scale Driving Everything as a Service?
by Thomas Laudal
Systems 2025, 13(5), 356; https://doi.org/10.3390/systems13050356 - 7 May 2025
Viewed by 187
Abstract
Everything as a Service (XaaS) is commonly understood as the general tendency to replace sales contracts with service contracts. Most research in this area agrees that XaaS is a trend, but it points to many drivers. It could be strategies improving customers’ expense [...] Read more.
Everything as a Service (XaaS) is commonly understood as the general tendency to replace sales contracts with service contracts. Most research in this area agrees that XaaS is a trend, but it points to many drivers. It could be strategies improving customers’ expense model, servitization strategies, customer feedback, mass customisation, and machine learning. However, we do not find contributions considering the relationship between XaaS and economies of scale. When sales contracts are replaced by service contracts, ownership is elevated from the customer to the provider. Thus, possible benefits from economies of scale linked to the ownership of products are then also elevated from the customer to the provider. In this article, we consider the claim that economies of scale may be an underlying driver of the XaaS trend. A review of 140 firms shows that the products with the greatest potential for economies of scale are the ones most frequently provided as a service. This suggests that economies of scale linked to ownership are an underlying driver of XaaS. Thus, ownership-related economies of scale may be a predictor of XaaS. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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23 pages, 4015 KiB  
Article
Enhancing Invoice Processing Automation Through the Integration of DevOps Methodologies and Machine Learning
by Oana-Alexandra Dragomirescu, Pavel-Cristian Crăciun and Ana Ramona Bologa
Systems 2025, 13(2), 87; https://doi.org/10.3390/systems13020087 - 31 Jan 2025
Viewed by 1390
Abstract
In today’s rapidly evolving digital landscape, organizations are increasingly seeking systemic approaches to optimize their financial operations, particularly in invoice processing. Traditional methods of invoice management, which are heavily reliant on manual labor, not only incur significant costs but also contribute to inefficiencies, [...] Read more.
In today’s rapidly evolving digital landscape, organizations are increasingly seeking systemic approaches to optimize their financial operations, particularly in invoice processing. Traditional methods of invoice management, which are heavily reliant on manual labor, not only incur significant costs but also contribute to inefficiencies, delays, and resource wastage. This article presents an integrated framework that combines DevOps methodologies and machine learning (ML) to transform invoice processing into a scalable and sustainable operation. By leveraging system dynamics and automation, the proposed Proof of Concept (PoC) addresses interconnected challenges, such as reducing labor dependency, enhancing operational intelligence, and minimizing environmental impact. The PoC framework includes dynamic model training, testing, deployment, and monitoring, enabling adaptive and resilient solutions aligned with evolving business needs. Findings from a survey highlight the potential of these integrated approaches to streamline processes, reduce errors, and optimize resource utilization while also identifying barriers to widespread adoption. By combining ML’s predictive power with DevOps’ agility, the framework not only advances automation but also provides a path toward sustainable financial operations in an interconnected and data-driven economy. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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20 pages, 875 KiB  
Article
Knowledge Exploitation, Inventor Characteristics, and Green Innovation Performance in Automotive Firms
by Yu-Tong Liu, Seung-Yoon Rhee and Eun-Jung Hyun
Systems 2025, 13(1), 6; https://doi.org/10.3390/systems13010006 - 26 Dec 2024
Viewed by 734
Abstract
Optimizing knowledge exploitation strategies is a crucial challenge in promoting sustainable technology development. This study investigates how innovation characteristics, inventor attributes, and network structures influence the effectiveness of these strategies in firms’ green transition efforts. Our study focuses on the automotive industry—a sector [...] Read more.
Optimizing knowledge exploitation strategies is a crucial challenge in promoting sustainable technology development. This study investigates how innovation characteristics, inventor attributes, and network structures influence the effectiveness of these strategies in firms’ green transition efforts. Our study focuses on the automotive industry—a sector characterized by long development cycles, high capital intensity, and strong path dependencies—as it undergoes a significant transition towards sustainable technologies. Using patent data from the Korean automotive industry, we uncover nuanced dynamics in the refinement and enhancement of green knowledge. While knowledge exploitation generally boosts innovative performance, this effect is significantly stronger for green innovations. Interestingly, high levels of inventor experience in green technologies weaken the positive impact of exploitation—a finding that challenges conventional expectations. Network structure also plays a pivotal yet often overlooked role: high network cohesion reduces the benefits of exploitation, while a broad network range enhances them. By shedding light on the hidden contributions of individual inventors and their networks, we deepen our understanding of the micro-foundations of green innovation within sustainability transitions. Our findings offer valuable insights for managers seeking to enhance their green knowledge strategies in industries undergoing technological shifts toward sustainability. We highlight previously underappreciated conditions under which exploitation strategies are most effective and factors that can be leveraged to elevate sustainable innovation performance through strategic knowledge enhancement. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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25 pages, 1284 KiB  
Article
Synergies of Heterogeneous Environmental Regulation on the Quality of Foreign Direct Investment
by Zhaoyang Zhao, Yuhong Chen, Chong Ye and Lorenzo Lotti
Systems 2024, 12(12), 586; https://doi.org/10.3390/systems12120586 - 22 Dec 2024
Viewed by 958
Abstract
Expanding a high level of openness and attracting high-quality foreign direct investment (FDI) while preventing foreign-invested enterprises from relocating to host countries to reduce costs and circumvent environmental regulation (ER) in their home countries, which can transform host countries into “pollution heaven”, present [...] Read more.
Expanding a high level of openness and attracting high-quality foreign direct investment (FDI) while preventing foreign-invested enterprises from relocating to host countries to reduce costs and circumvent environmental regulation (ER) in their home countries, which can transform host countries into “pollution heaven”, present a significant challenge for emerging markets such as China. Based on a theoretical analysis that integrates various frameworks, this study constructs a panel regression model to empirically investigate the relationship between ER and the quality of FDI. This analysis is conducted from the perspectives of administrative means and market mechanisms, utilizing panel data from 267 prefectural-level cities in China spanning the years 2005 to 2021. This study reveals the following conclusions: (1) The implementation of ER significantly enhances the quality of FDI within cities, a conclusion that remains robust across various tests. (2) ER improves the quality of FDI through two key pathways: enhancing green competitiveness and fostering green technological innovation. (3) In comparison to the isolated effects of administrative and market mechanism policies, the synergistic effect of these two approaches proves to be more pronounced in elevating the quality of FDI. (4) ER exerts a significant impact on the quality of FDI, particularly within sub-samples of cities characterized by higher levels of environmental protection and a focus on non-resource-oriented activities. (5) ER has a negative spatial spillover effect on FDI quality. This study serves as a valuable guide for emerging markets to enhance environmental policy effectiveness and assess the potential for a new open economic system. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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25 pages, 3383 KiB  
Article
Multi-Objective Optimization of Manufacturing Process Using Artificial Neural Networks
by Katarína Marcineková and Andrea Janáková Sujová
Systems 2024, 12(12), 569; https://doi.org/10.3390/systems12120569 - 17 Dec 2024
Cited by 2 | Viewed by 1405
Abstract
This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected [...] Read more.
This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected key process by employing artificial neural networks (ANNs) which mirror adaptive management principles. Three input and three output parameters significantly impacting the effectiveness of this key process have been systematically identified and experimentally measured. It was necessary to perform multi-objective optimization (MOO), which consisted in achieving the minimum values of cost and process time and the maximum value of the quality index through the optimal setting of the input parameters (cutting speed, feed rate, and volume of removed material). The application of ANNs in MOO in this research study is a novelty in this field. The results obtained through application of the ANN method reveal the optimal values of the examined parameters, which represent the best combination of input technical variables leading to the best results in output economic parameters. This multi-objective optimizing solution facilitates enhanced process efficiency. By integrating Systems Theory, Complexity Theory, and adaptive management, this research advances sustainable process improvements by minimizing resource use, reducing waste, and enhancing overall system efficiency. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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22 pages, 4088 KiB  
Article
An Optimized Method for BMI in Environmental Projects Based on the Value-Oriented AHP
by Yuanyuan Liu and Wei Liu
Systems 2024, 12(12), 519; https://doi.org/10.3390/systems12120519 - 25 Nov 2024
Viewed by 851
Abstract
Effective rural solid waste management (RSWM) is crucial for sustainable rural development, particularly in developing countries, which face dual challenges from economic growth and environmental protection. To build a more sustainable business model for RSWM, this study employs a value proposition analysis approach [...] Read more.
Effective rural solid waste management (RSWM) is crucial for sustainable rural development, particularly in developing countries, which face dual challenges from economic growth and environmental protection. To build a more sustainable business model for RSWM, this study employs a value proposition analysis approach to systematically analyze the multi-level requirements of various stakeholders involved in the current models of RSWM. It then proposes a novel optimizing approach for RSWM models from the perspective of business model innovation (BMI) by integrating the value proposition (VP) theory with the algorithm of the Analytic Hierarchy Process (AHP) to fill the research gap. In this study, an AHP-based evaluating algorithm is firstly proposed based on the viewpoints of multiple stakeholders’ value propositions. Using this method, four typical pilot RSWM models across China are assessed and ranked, followed by a comprehensive analysis of the results and the incorporation of hierarchical criteria from multiple value dimensions. Building on the analysis of the results, optimization strategies for a novel RSWM model are proposed by constructing a conceptual framework of the business model. In addition, the analysis also indicates that both phases of sorting and collection and transportation are the main factors for fulfilling the overall satisfaction of the RSWM models. Lastly, this paper concludes by summarizing the relevant theoretical and managerial implementations of the proposed approach, providing a foundation for the scientific development of appropriate RSWM models by providing a new idea for BMI especially for environmental management projects that include multiple stakeholders. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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33 pages, 6103 KiB  
Article
A Study on the Design of Incentive Contracts for Platform Economy Regulation Based on Dual Principal–Agents
by Ruibi Zhang, Jinhe Zhu and Ming Lei
Systems 2024, 12(9), 343; https://doi.org/10.3390/systems12090343 - 2 Sep 2024
Viewed by 1435
Abstract
A system of incentives can be established to encourage several parties to unite as a community of interest and become jointly committed to the platform economic governance. The platform economy involves progressively more complex subjects of interest and relationships, which are not the [...] Read more.
A system of incentives can be established to encourage several parties to unite as a community of interest and become jointly committed to the platform economic governance. The platform economy involves progressively more complex subjects of interest and relationships, which are not the typical principal–agent one-time cooperative relationship. This study investigates the problem of regulatory incentives in the platform economy, specifically focusing on the relationship between the government, platform enterprises, and merchants. It analyzes this issue under conditions of asymmetric information by constructing and solving a dual principal–agent model. The findings indicate the following: (1) the government’s incentives and regulatory mechanisms can be considered as interchangeable to some extent, with decisions made by evaluating their respective costs; (2) the government’s optimal incentives and regulations ultimately shape the self-regulatory behavior of merchants through platform enterprises; and (3) the optimal level of incentives for both the government and the platform enterprise is influenced by factors such as the ability coefficient, the social transformation coefficient, and the merchants’ reliance on the platform enterprise. Additionally, the optimal effort level of the platform enterprise and the merchants increases with higher levels of the regulatory effort, risk sensitivity coefficient, and ability coefficient. A win–win scenario and a long-term, stable cooperative partnership can be reached by the three parties under the ideal incentive intensity. The study’s conclusions can serve as a theoretical foundation and support for the creation of incentive contracts for platform economy regulation. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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