A model-based strategy for developing sustainable cosmetics small and medium industries with system dynamics

Amrina, Uly; Hidayatno, Akhmad; Zagloel, Teuku Yuri M Article A model-based strategy for developing sustainable cosmetics small and medium industries with system dynamics Journal of Open Innovation: Technology, Market, and Complexity Provided in Cooperation with: Society of Open Innovation: Technology, Market, and Complexity (SOItmC) Suggested Citation: Amrina, Uly; Hidayatno, Akhmad; Zagloel, Teuku Yuri M (2021) : A model-based strategy for developing sustainable cosmetics small and medium industries with system dynamics, Journal of Open Innovation: Technology, Market, and Complexity, ISSN 2199-8531, MDPI, Basel, Vol. 7, Iss. 4, pp. 1-21, https://doi.org/10.3390/joitmc7040225 This Version is available at: https://hdl.handle.net/10419/274285 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Open Innovation: Technology, Market, and Complexity Article A Model-Based Strategy for Developing Sustainable Cosmetics Small and Medium Industries with System Dynamics Uly Amrina, Akhmad Hidayatno * and T. Yuri M. Zagloel   Citation: Amrina, U.; Hidayatno, A.; Zagloel, T.Y.M. A Model-Based Strategy for Developing Sustainable Cosmetics Small and Medium Industries with System Dynamics. J. Open Innov. Technol. Mark. Complex. 2021,7, 225. https://doi.org/ 10.3390/joitmc7040225 Received: 22 September 2021 Accepted: 28 October 2021 Published: 8 November 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Industrial Engineering, Faculty of Engineering, University of Indonesia, Depok 16424, Indonesia; uly[email protected] (U.A.); [email protected] (T.Y.M.Z.) *Correspondence: [email protected] Abstract: Global customer consciousness for more sustainable products and government requirements for a more sustainable industry have motivated cosmetics small and medium industries (SMIs) to innovate the strategy by integrating sustainability principles into their manufacturing processes. However, the dynamic complexity of balancing sustainability efforts, stakeholders’ interests, and uncertainty in material pricing require a conceptual reference model to help managers and decisionmakers cope with the transition process. This work therefore proposes a model-based strategy using system dynamics to assist managers and stakeholders in SMIs to clarify their possible pathways and to offer a framework to understand, guide, and generate future strategies. In multiactor, multistakeholder conditions, the proposed methodology can provide insights into how stakeholders can effectively intervene to improve sustainability through open innovation dynamics models. The case study presented here on a personal care cosmetics company demonstrates several leverage points and obstacles, thereby allowing each stakeholder to understand their strategic role in realizing sustainable cosmetics SMIs. Keywords: sustainability; model-based strategy; system dynamics (SD); cosmetics; small and medium industries (SMIs); open innovation dynamics 1. Introduction The cosmetics industry is a leading industry and is estimated to have significant future growth. Despite the 2% decrease in global cosmetics sales in 2020 caused by the Coronavirus (COVID-19) pandemic, international surveys predict growth in the global beauty and personal cosmetics market in 2021 if companies can meet shifting customer expectations and demand for personal care cosmetics, rather than beauty cosmetics [ 1 ]. These products focus on maintaining or restoring health and wellness, which has become essential to many during the pandemic. Changes in customer expectations have accompanied the improvement in the sales outlook [ 2 ]. Consumers have increasingly demanded safe, qualified, and environmentally friendly products [ 3 ] and placed more importance on environmental and long-term health during product selection, in addition to a product’s value for money [ 4 ]. Governmental regulatory bodies have also increasingly required transparent manufacturing processes in compliance with international standards for good manufacturing practices (GMPs) [ 5 ]. The GMPs are in line with the development of sustainable cosmetics industries, which aim to meet the needs of today’s society without compromising the ability of resources to meet the needs of future generations. The characteristics of sustainable development include the use of environmentally friendly technology, using work procedures that support environmental sustainability, and collaboration between stakeholders to jointly develop and determine business alternative strategies that ensure ecosystem sustainability in the future. Currently, ordinary cosmetics small and medium industries (SMIs) only focus on generating shortterm profits without considering their production processes’ environmental and social J. Open Innov. Technol. Mark. Complex. 2021,7, 225. https://doi.org/10.3390/joitmc7040225 https://www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2021,7, 225 2 of 21 impacts. They do not manage waste and the emissions produced, which will damage the environment and public health in the long run. The damage will affect the product’s image and society as cosmetics customers will not buy their product in the future. The government could even close their cosmetics business down. Cosmetics SMIs should aim to meet the customers’ environmental-friendly product preferences and government sustainable development requirements by moving toward sustainability, balancing financial strategies to pursue profits, preserving the environment, and protecting public health [ 6 ]. To achieve this sustainability aim, SMIs must have a robust strategy model. The model should help SMIs’ managers and stakeholders understand the problem more comprehensively and determine alternative strategies, thus triggering sustainability goal achievements. Cosmetics SMIs face challenges when transitioning toward sustainability owing to their smaller scale as managers are still struggling to increase production-side productivity by consistently producing efficient and high-quality products [ 7 ]. Furthermore, due to limited workforce skills and old equipment, SMIs face difficulties producing goods without waste [ 8 ]. This focus on the production side causes decision-makers to disregard the environmental pollution caused by their manufacturing process and conduct environmental impact evaluations rarely to avoid sacrificing profits [9]. With SMIs considered the driving force of the economy in various countries, attention and support from stakeholders should be mapped and directed to strategize the transition and push SMIs toward sustainability. These issues require conceptual models detailing their complexity; these models would then serve as the basis for understanding, guiding, and developing the necessary global strategies and individual strategies for each stakeholder. By definition, a model-based strategy uses a strategic planning model that helps managers and other stakeholders understand the problem structure from a broad perspective and decide what strategy changes will be beneficial [ 10 , 11 ]. A model-based strategy aims not to get the best results but to develop a tool to facilitate dialog between stakeholders [ 12 ]. In transitioning SMIs to sustainability, a model can shed light on issues, unknowns, and differences in stakeholder perspectives and understand them together, especially for managers and other decision-makers in SMIs. In addition, model-based strategies can anticipate the results of an intervention to prevent errors in decision-making [13]. Researchers have explored establishing sustainability in SMIs via multifactorial analysis showing the complexity of SMIs’ problems [ 7 , 14 , 15 ]. Unfortunately, the relationship between the multifactor is considered linear and did not show a dynamically interrelated connection as experienced by SMIs nowadays. This dynamic complexity relationship will undoubtedly trigger the different alternative strategies. However, efforts to explore and model the dynamic complexities of the current issues facing SMIs combined with alternative strategies toward sustainability are limited. Therefore, this research aims to develop a strategy recommendation for sustainable cosmetics SMIs based on the system dynamics (SD) modelling method. The SD modelling methodology offers a deeper understanding of the system’s problems because its steps require the development of a conceptual model in the earlier stage and the ability to predict the results through the final computer model, which would help improve the quality of the strategy recommended. Through this approach, this model-based strategy will provide deeper insights and findings due to a better understanding of the system’s problems. This model is beneficial as a reference for SMIs managers and stakeholders in determining policy factors that influence selecting alternative strategies to support SMIs’ sustainability output criteria. Those criteria include manufacturing excellence, financial growth, environmental protection, and social responsibilities. The SD modelling methodology consists of some essential steps. First, the conceptual model of sustainable cosmetics SMIs that explores the objective parameters, stakeholder interests, input factors, and process variables, and their interactions and influence on manufacturing output performances is defined. A visual and descriptive conceptual model also provides easier transferability into a simulation. The SD model is formulated and validated; the developed model is used as a reference while experimenting with alternative strategies and changing conditions. Based on the insights and findings during the development J. Open Innov. Technol. Mark. Complex. 2021,7, 225 3 of 21 and use stages, promising alternative strategies to achieve sustainable cosmetics SMIs are then discussed. The rest of this work is structured as follows. The concept of sustainable cosmetics SMIs and how SD could navigate a model-based strategy to achieve sustainable SMIs is discussed in Section 2. The developed conceptual model is then detailed in Section 3; the formulated model and a case study are presented in Section 4. The simulation results are discussed in Section 5, and conclusions are drawn in Section 6. 2. Literature Review 2.1. Sustainable Performance Criteria and Factors for Cosmetics Small and Medium Industries The manufacturing industries, including SMIs, must improve their sustainability to demonstrate their care and responsibility for future generations [ 16 ]. Sustainable manufacturing can be accessed via the three sustainability measurements: economic stability, environmental sustainability, and social welfare [ 17 ]. The economic pillar comprises several interrelated performance criteria regarding manufacturing and financial factors [ 18 ], including productivity and quality [ 19 ]; manufacturing costs and profitability are the main quantitative measures [ 20 , 21 ]. Resource consumption and emissions are the environmental pillar’s main parameters [ 22 , 23 ], whereas performance criteria of the social pillar comprise human resource availability, welfare, and public health [ 24 , 25 ]. Overall, several factors affect the sustainability of cosmetics SMIs [ 26 ]; these factors, summarized in Figure 1, have an inter and causal relationship that supports sustainable performance. Meeting demands with high-quality products and minimal waste increases the productivity index and supports profitability ratio targets. Met profitability ratio targets smooth the company’s manufacturing process in the next period and employee welfare with additional income, motivating employees to increase productivity. High productivity minimizes environmental impact and creates a good product image, strengthening customer and social trust. By improving the manufacturing process, improving social benefits, and minimizing environmental impact, cosmetics SMIs increase demand and their financial performance. Further, the government can benefit from this improvement through increased tax revenue. However, these interactions between factors in sustainable SMIs require comprehensive strategic planning; models can thus help support effective strategic decision-making. J. Open Innov. Technol. Mark. Complex. 2021, 7, x FOR PEER REVIEW 3 of 23 on manufacturing output performances is defined. A visual and descriptive conceptual model also provides easier transferability into a simulation. The SD model is formulated and validated; the developed model is used as a reference while experimenting with alternative strategies and changing conditions. Based on the insights and findings during the development and use stages, promising alternative strategies to achieve sustainable cosmetics SMIs are then discussed. The rest of this work is structured as follows. The concept of sustainable cosmetics SMIs and how SD could navigate a model-based strategy to achieve sustainable SMIs is discussed in Section 2. The developed conceptual model is then detailed in Section 3; the formulated model and a case study are presented in Section 4. The simulation results are discussed in Section 5, and conclusions are drawn in Section 6. 2. Literature Review 2.1. Sustainable Performance Criteria and Factors for Cosmetics Small and Medium Industries The manufacturing industries, including SMIs, must improve their sustainability to demonstrate their care and responsibility for future generations [16]. Sustainable manufacturing can be accessed via the three sustainability measurements: economic stability, environmental sustainability, and social welfare [17]. The economic pillar comprises several interrelated performance criteria regarding manufacturing and financial factors [18], including productivity and quality [19]; manufacturing costs and profitability are the main quantitative measures [20,21]. Resource consumption and emissions are the environmental pillar’s main parameters [22,23], whereas performance criteria of the social pillar comprise human resource availability, welfare, and public health [24,25]. Overall, several factors affect the sustainability of cosmetics SMIs [26]; these factors, summarized in Figure 1, have an inter and causal relationship that supports sustainable performance. Meeting demands with high-quality products and minimal waste increases the productivity index and supports profitability ratio targets. Met profitability ratio targets smooth the company’s manufacturing process in the next period and employee welfare with additional income, motivating employees to increase productivity. High productivity minimizes environmental impact and creates a good product image, strengthening customer and social trust. By improving the manufacturing process, improving social benefits, and minimizing environmental impact, cosmetics SMIs increase demand and their financial performance. Further, the government can benefit from this improvement through increased tax revenue. However, these interactions between factors in sustainable SMIs require comprehensive strategic planning; models can thus help support effective strategic decision-making. Figure 1. Sustainable factors, criteria, and inter-connections within cosmetics small and medium industries (SMIs) [26]. Figure 1. Sustainable factors, criteria, and inter-connections within cosmetics small and medium industries (SMIs) [26]. 2.2. Integrating Company Sustainability Development into Company Strategies It is essential to integrate company sustainability development in the process of determining business strategies. Company sustainability development includes efforts to meet the company’s needs without compromising aspects of natural and human resource preservation. Elaboration of company sustainability development in its strategy requires decision-makers to explore sustainability issues and their impact on the organization, define internal and external drivers, and align with support and hinder factors [ 27 ]. This J. Open Innov. Technol. Mark. Complex. 2021,7, 225 4 of 21 integration provides more explicit guidance on building sustainable development, from determining sustainability goals to implementing them effectively [ 28 ]. Identifying stakeholder interests, translating objectives into sustainability outcome criteria, determining the factors involved, and how the relationship between factors to achieve sustainability goals becomes a sustainability development scheme that must be modelled. The model becomes input, constraint, and feedback to the process of determining strategies. It triggers decision-makers to explore innovative strategic alternatives [29]. The process of developing an effective strategy opens the dynamics of innovation by involving stakeholders in its implementation. The open innovation dynamic strategies benefit companies because they collaborate between internal and external knowledge resources [ 30 ]. Optimal knowledge collaboration performance depends on selecting suitable and competent collaboration partners, considering the criteria for the similarity of knowledge domains between the company and partners, the level of communication, historical cooperation, and the benefits (revenues) obtained by both parties [ 31 ]. With the limitations of SMIs in skills, knowledge, technology, and capital, SMIs can combine open innovation communities in an open innovation dynamics strategy. Open innovation communities bridge knowledge transfer collaboration between companies and external parties by encouraging community members to create innovations based on their knowledge and information [ 32 ]. Open innovation communities allow their members to communicate with various platforms directly or virtually. With open innovation communities, SMIs can involve stakeholders in defining their interests and preferences for sustainable products and processes [ 33 ]. SMIs’ decision-makers translate these stakeholder interests and preferences into sustainability performance indicators. The indicators are then derived into what policy factors can improve their performance. By referring to these policy factors, cosmetics SMIs’ decision-makers can clearly understand and make improvements. The mechanism of open innovation communities facilitates stakeholders with similar interests and preferences to engage in improvement activities [ 34 ]. Customers can become community members and participate in interactive discussions regarding the development of sustainable products and processes. Suppliers can also utilize the information in these discussion forums to prepare the availability of raw materials on time. Research and academic institutions can assist SMIs technically regarding the implementation of continuous improvements. Meanwhile, distributors can provide market development ideas and promotional media in the forum. Government involvement in the community is also essential as a source of policy as well as supporting capital. Finally, cosmetics associations can act as neutral organizations that facilitate conducive and productive discussion forums in generating innovative ideas and ensuring that every community member is committed to implementing them and achieving sustainable SMIs. The process of establishing sustainable SMIs that elaborates on stakeholder interests and preferences, the complexity of sustainability factors and dynamics of their relationship, the performance of output criteria, and the utilization of open innovation communities in determining alternative strategies requires a comprehensive model development. 2.3. A Model-Based Decision Strategy Using System Dynamics A model-based strategy uses accurate replicas of the system, or models, as a tool for decision-makers to understand their problems thoroughly, determine effective strategies to meet goals, and make better use of all available resources [ 35 , 36 ]. Decision-makers can use models to understand more clearly how particular conditions impact the system and intervene based on selective strategies [ 10 ]. These simulation techniques are considered effective and efficient, particularly for systems influenced by complex factors that make analytical solutions difficult to develop [ 37 ]. A model-based approach allows decisionmakers to study the dynamic relationship between the system’s factors and decide the most effective strategy based on the multicriteria performance [ 11 ] and requires computer-based knowledge to support model development and simulation [ 38 ]. Model-based approaches J. Open Innov. Technol. Mark. Complex. 2021,7, 225 5 of 21 are appropriate for SMI managers because of their capability to adapt to environmental changes that are fast, dynamic, and competitive. There are differences between SMIs that apply contemporary strategy models, such as the Balanced Scorecard (BSC) and those referring to SD modelling. The contemporary strategy model has the advantage of a broad, coherent, and measurable perspective. However, the contemporary model has a weakness related to causality, which is only based on linear correlation [ 39 ]. These weaknesses can be covered by an SD model-based strategy, where decision-makers can model interconnection relationships and predict future performance based on model simulations. In addition, it is difficult for decision-makers to find improvement mechanisms in contemporary models [ 40 ], whereas with the SD model-based strategy, SMIs can determine the policy factors that trigger alternative improvement strategies. Developing a model-based strategy requires a comprehensive understanding of problem characteristics, interactions between the factors in the system, and objective criteria considering the stakeholders’ interests [ 41 ]. SMIs have multifactor, complex, and dynamic problems [ 42 ]. SD is a broad-spectrum modeling approach that addresses such dynamic complexity by showing behavioral factors over time in a closed system in the form of a feedback loop [ 43 , 44 ]. The model’s behavior helps managers or other decision-makers predict future relationships and system performance responses [ 45 ] and see the impact of implementing alternative approaches, thereby helping them choose the most effective strategies [ 46 ]. SD modeling comprises four generic steps: model conceptualization, development, validation, and simulation [47]. The conceptual model uses causal loop diagrams (CLDs) to show cause–effect relationships and highlights the endogenous focus of SD [ 48 ]. CLDs describe positive and negative relationships between the connected factors by arrows labeled with a positive (+) or negative ( − ) sign [ 49 ]; (+) indicates a unidirectional pattern of increasing or decreasing between the cause and the effect, whereas ( − ) indicates the opposite. A reinforcing loop (R) is formed when the accumulated relationships between the factors show a positive feedback pattern, and a balancing loop (B) represents the accumulation of negative cause– effect relationships. The relationship between inventory level and production in cosmetics SMIs is visualized as a reinforcing loop in Figure 2. The production amount is influenced by the final inventory amount, demand, and productivity level; the inventory amount is determined by the initial inventory amount, production amount, and the number of goods sold. J. Open Innov. Technol. Mark. Complex. 2021, 7, x FOR PEER REVIEW 5 of 23 use models to understand more clearly how particular conditions impact the system and intervene based on selective strategies [10]. These simulation techniques are considered effective and efficient, particularly for systems influenced by complex factors that make analytical solutions difficult to develop [37]. A model-based approach allows decisionmakers to study the dynamic relationship between the system’s factors and decide the most effective strategy based on the multicriteria performance [11] and requires computer-based knowledge to support model development and simulation [38]. Model-based approaches are appropriate for SMI managers because of their capability to adapt to environmental changes that are fast, dynamic, and competitive. There are differences between SMIs that apply contemporary strategy models, such as the Balanced Scorecard (BSC) and those referring to SD modelling. The contemporary strategy model has the advantage of a broad, coherent, and measurable perspective. However, the contemporary model has a weakness related to causality, which is only based on linear correlation [39]. These weaknesses can be covered by an SD model-based strategy, where decision-makers can model interconnection relationships and predict future performance based on model simulations. In addition, it is difficult for decision-makers to find improvement mechanisms in contemporary models [40], whereas with the SD modelbased strategy, SMIs can determine the policy factors that trigger alternative improvement strategies. Developing a model-based strategy requires a comprehensive understanding of problem characteristics, interactions between the factors in the system, and objective criteria considering the stakeholders’ interests [41]. SMIs have multifactor, complex, and dynamic problems [42]. SD is a broad-spectrum modeling approach that addresses such dynamic complexity by showing behavioral factors over time in a closed system in the form of a feedback loop [43,44]. The model’s behavior helps managers or other decision-makers predict future relationships and system performance responses [45] and see the impact of implementing alternative approaches, thereby helping them choose the most effective strategies [46]. SD modeling comprises four generic steps: model conceptualization, development, validation, and simulation [47]. The conceptual model uses causal loop diagrams (CLDs) to show cause–effect relationships and highlights the endogenous focus of SD [48]. CLDs describe positive and negative relationships between the connected factors by arrows labeled with a positive (+) or negative (−) sign [49]; (+) indicates a unidirectional pattern of increasing or decreasing between the cause and the effect, whereas (−) indicates the opposite. A reinforcing loop (R) is formed when the accumulated relationships between the factors show a positive feedback pattern, and a balancing loop (B) represents the accumulation of negative cause– effect relationships. The relationship between inventory level and production in cosmetics SMIs is visualized as a reinforcing loop in Figure 2. The production amount is influenced by the final inventory amount, demand, and productivity level; the inventory amount is determined by the initial inventory amount, production amount, and the number of goods sold. Figure 2. Causal loop diagram (CLD) with reinforcing loop (R1) in production operation. Figure 2. Causal loop diagram (CLD) with reinforcing loop (R1) in production operation. A quantitative model that is theoretically and logically acceptable and refers to the conceptual model is then developed using a stock and flow diagram (SFD) based on a mathematical equation that can be simulated based on a specified period [ 47 ]. SFDs describe the relationship between inflows and outflows that can increase or decrease stocks, which generate delays by accumulating the difference between the inflow and outflow [ 46 ]. The SFD development in relation to the CLD in Figure 2is shown in Figure 3. J. Open Innov. Technol. Mark. Complex. 2021,7, 225 6 of 21 J. Open Innov. Technol. Mark. Complex. 2021, 7, x FOR PEER REVIEW 6 of 23 A quantitative model that is theoretically and logically acceptable and refers to the conceptual model is then developed using a stock and flow diagram (SFD) based on a mathematical equation that can be simulated based on a specified period [47]. SFDs describe the relationship between inflows and outflows that can increase or decrease stocks, which generate delays by accumulating the difference between the inflow and outflow [46]. The SFD development in relation to the CLD in Figure 2 is shown in Figure 3. Figure 3. Stock and flow diagram (SFD) of goods inventory in production operation. Validation in SD can be carried out through structural-behavioral and statistical evaluation [50]. Some structural-behavior verification tools include expert validation to verify the CLD structure and dimensional consistency and behavior prediction tests to test the SFD [51]. Dimensional consistency tests are used to ensure a balance of dimensions in each equation in interrelated factors. The software used here has a built-in dimensional consistency check that, if not passed, prevents the model from running. Behavior prediction tests qualitatively verify the suitability of a model’s future behavior pattern with the current pattern logic. Statistical validation then uses the absolute mean error (AME) to compare the simulated data with the historical data of relatively uncontrolled factors [52]. The model can be considered valid when the AME, calculated via Equation (1) below, is >30% [53]. 𝐴𝑀𝐸 = ∣ 𝑆 − 𝐴 ∣ 𝐴 (1) where S and A represent the value of the simulated and actual data, respectively. Model simulation allows users to experiment with several alternative strategies while testing the model [54]. The SD model’s simulation results are presented in output criteria achievement figures and graphed future behavior patterns [55]. The alternative with the highest performance results and best outcome criteria is the most plausible and practical choice; however, this is only one benefit of using SD simulations. Additionally, SD simulations clarify the behavioral trends caused by modifying each parameter, thereby providing a discussion medium for all relevant stakeholders. 3. Materials and Methods 3.1. Developmental Methods The three interconnected steps of model development using the SD approach are detailed in Figure 4. For each stage, the results of the previous stage act as inputs; the results are cross-checked for compliance with the prior stage and the conceptual model. The approach begins with step 1, model conceptualization, where the problem situation was mapped, stakeholders’ interests were analyzed, each factor and their inter-relationships were determined, and a conceptual model was developed to visualize each of these aspects [47]. CLDs were used in the system diagram to highlight the cause and effect relationships between factors. The system diagram was then verified using an expert validation technique, a focus group discussion (FGD). Theoretically, FGD facilitates dialog between invited experts to enhance understanding of the model’s objectives and verify the model’s structure in greater depth [56]. Here we invited the experts from open innovation Figure 3. Stock and flow diagram (SFD) of goods inventory in production operation. Validation in SD can be carried out through structural-behavioral and statistical evaluation [ 50 ]. Some structural-behavior verification tools include expert validation to verify the CLD structure and dimensional consistency and behavior prediction tests to test the SFD [ 51 ]. Dimensional consistency tests are used to ensure a balance of dimensions in each equation in interrelated factors. The software used here has a built-in dimensional consistency check that, if not passed, prevents the model from running. Behavior prediction tests qualitatively verify the suitability of a model’s future behavior pattern with the current pattern logic. Statistical validation then uses the absolute mean error (AME) to compare the simulated data with the historical data of relatively uncontrolled factors [ 52 ]. The model can be considered valid when the AME, calculated via Equation (1) below, is >30% [53]. AME = |S−A| A(1) where Sand Arepresent the value of the simulated and actual data, respectively. Model simulation allows users to experiment with several alternative strategies while testing the model [ 54 ]. The SD model’s simulation results are presented in output criteria achievement figures and graphed future behavior patterns [ 55 ]. The alternative with the highest performance results and best outcome criteria is the most plausible and practical choice; however, this is only one benefit of using SD simulations. Additionally, SD simulations clarify the behavioral trends caused by modifying each parameter, thereby providing a discussion medium for all relevant stakeholders. 3. Materials and Methods 3.1. Developmental Methods The three interconnected steps of model development using the SD approach are detailed in Figure 4. For each stage, the results of the previous stage act as inputs; the results are cross-checked for compliance with the prior stage and the conceptual model. The approach begins with step 1, model conceptualization, where the problem situation was mapped, stakeholders’ interests were analyzed, each factor and their inter-relationships were determined, and a conceptual model was developed to visualize each of these aspects [ 47 ]. CLDs were used in the system diagram to highlight the cause and effect relationships between factors. The system diagram was then verified using an expert validation technique, a focus group discussion (FGD). Theoretically, FGD facilitates dialog between invited experts to enhance understanding of the model’s objectives and verify the model’s structure in greater depth [ 56 ]. Here we invited the experts from open innovation communities of cosmetics SMIs. In practice, FGDs require 5–12 people [ 57 ]. Accordingly, seven experienced ( ≥ 20 years of experience) cosmetics manufacturers, distributors, and academics participated. The list of triggered questions for discussion with the experts in the FGD is attached to Appendix A. J. Open Innov. Technol. Mark. Complex. 2021,7, 225 7 of 21 J. Open Innov. Technol. Mark. Complex. 2021, 7, x FOR PEER REVIEW 7 of 23 communities of cosmetics SMIs. In practice, FGDs require 5–12 people [57]. Accordingly, seven experienced (≥20 years of experience) cosmetics manufacturers, distributors, and academics participated. The list of triggered questions for discussion with the experts in the FGD is attached to Appendix A. Figure 4. Development of a model-based strategy based on the methodology developed by Sterman [47]. From the system diagram, the SFD was formulated in step 2. The SFD development consists of translating the relationship between factors in the CLD into mathematical equation and evaluating the resulting system behavior. Finally, the SFD structure was validated by dimensional consistency and output behavior checking [58], and the resulting behavior was checked against the conceptual logic behavior. The SFD was used to simulate several alternative strategies in step 3 [59]. The alternative strategies demonstrate the combinations of open innovation communities’ involvement scope. An FGD was again used to review the conformity of the alternative strategies planned initially in the conceptual model. As updates to the alternative strategies can change the designed CLD, system diagram, and SFD, any modification at each stage was continuously cross-checked with the prior stage. Finally, the results from the simulations were used to analyze the conditions and factors that influence performance and must be considered by the stakeholders. Obtained recommendations and analysis results served as feedback to the conceptual model on whether the strategy can effectively contribute to achieving the goals or create new problems [60]. 3.2. Conceptual Model of Sustainable Cosmetics Small and Medium Industries According to the sustainable cosmetics SMIs conceptual model developed by Amrina et al. [26], the customers, government, and cosmetics manufacturers are the dominant actors having interests and goals aligned with a business’ sustainable development. These actors have the same concerns regarding developing a productive and sustainable manufacturing process that results in qualified, safe, and profitable cosmetics for society. Four other actors (research and academic institutions, cosmetics suppliers, distributors, and associations) have the same goals but have not played a significant role in promoting sustainable manufacturing. The SMI managers, as problem owners, could not achieve these goals alone. Thus, a transparent conceptual model regarding the intervention needed by each stakeholder is required to support the sustainable cosmetics SMIs, considering the situation analysis, goal setting, multiactor analysis, and inter-relationship factors. These factors are visualized in Figure 5, where the factors in the input box become the references for selecting alternative strategies that influence the CLD output criteria. Figure 4. Development of a model-based strategy based on the methodology developed by Sterman [47]. From the system diagram, the SFD was formulated in step 2. The SFD development consists of translating the relationship between factors in the CLD into mathematical equation and evaluating the resulting system behavior. Finally, the SFD structure was validated by dimensional consistency and output behavior checking [ 58 ], and the resulting behavior was checked against the conceptual logic behavior. The SFD was used to simulate several alternative strategies in step 3 [ 59 ]. The alternative strategies demonstrate the combinations of open innovation communities’ involvement scope. An FGD was again used to review the conformity of the alternative strategies planned initially in the conceptual model. As updates to the alternative strategies can change the designed CLD, system diagram, and SFD, any modification at each stage was continuously cross-checked with the prior stage. Finally, the results from the simulations were used to analyze the conditions and factors that influence performance and must be considered by the stakeholders. Obtained recommendations and analysis results served as feedback to the conceptual model on whether the strategy can effectively contribute to achieving the goals or create new problems [60]. 3.2. Conceptual Model of Sustainable Cosmetics Small and Medium Industries Accordingto thesustainablecosmeticsSMIsconceptualmodeldeveloped by Amrina et al. [26] , the customers, government, and cosmetics manufacturers are the dominant actors having interests and goals aligned with a business’ sustainable development. These actors have the same concerns regarding developing a productive and sustainable manufacturing process that results in qualified, safe, and profitable cosmetics for society. Four other actors (research and academic institutions, cosmetics suppliers, distributors, and associations) have the same goals but have not played a significant role in promoting sustainable manufacturing. The SMI managers, as problem owners, could not achieve these goals alone. Thus, a transparent conceptual model regarding the intervention needed by each stakeholder is required to support the sustainable cosmetics SMIs, considering the situation analysis, goal setting, multiactor analysis, and inter-relationship factors. These factors are visualized in Figure 5, where the factors in the input box become the references for selecting alternative strategies that influence the CLD output criteria. J. Open Innov. Technol. Mark. Complex. 2021,7, 225 8 of 21 J. Open Innov. Technol. Mark. Complex. 2021, 7, x FOR PEER REVIEW 8 of 23 Figure 5. Conceptual model of sustainable cosmetics small and medium industries [26]. The CLD in the center of Figure 5 contains five individual reinforcing (R) and two balancing (B) loops, which combine to configure four main combination loops: the justin-time (JIT) production loop (R1–B1–R2), the waste elimination loop (R2–B1–R3), the productivity loop (R1–B1–R2–R3–B2), and the sustainable manufacturing loop (R1–B1– R2–R3–B2–R4–R5). The JIT production loop shows that SMIs can consistently meet customer demand by proper inventory planning and reducing defective products. The waste elimination loop indicates that production costs will decrease if the company can streamline the JIT loop by increasing quality (i.e., reducing defective products). This loop determines the saving amount in production resource consumption, such as raw material, person hours, electricity, and water. The productivity loop connects the JIT, waste elimination, and balancing loops under a stable price. This loop clarifies that minimizing waste in the production process can increase profits without increasing the product’s selling price. If SMIs fail to streamline their production process, their productivity will decline and slowly erode profits. Finally, the sustainable manufacturing loop integrates the productivity loop with the socio-environment factors in the R4 and R5 loop, thereby integrating all existing loops with output criteria, including productivity ratio, profitability ratio, taxes, environmental impact, and human health. Furthermore, this conceptual model can be used to explore the model-based strategy considering the interaction between parameters to improve output criteria. 3.3. Model-Based Strategy Development Several alternative strategies were then developed based on the loops found in CLD and their effect on achieving the output criteria (see Figure 5). Overall, the output criteria of the model are positively impacted when positive loops produce a more substantial effect than negative loops, as explained in the Theory section regarding CLD. During the performed FGDs with seven experts, policy factors influencing the alternative strategies were discussed, and model scenarios were selected considering the open innovation communities intervention scopes. In particular, experts selected five factors in the loops that allowed stakeholders to intervene to improve output performance: the defect ratio, workforce, electricity consumption, fixed cost amount, and government subsidies. The open Figure 5. Conceptual model of sustainable cosmetics small and medium industries [26]. The CLD in the center of Figure 5contains five individual reinforcing (R) and two balancing (B) loops, which combine to configure four main combination loops: the just-in-time (JIT) production loop (R1–B1–R2), the waste elimination loop (R2–B1–R3), the productivity loop (R1–B1–R2–R3–B2), and the sustainable manufacturing loop ( R1–B1–R2–R3–B2–R4–R5 ). The JIT production loop shows that SMIs can consistently meet customer demand by proper inventory planning and reducing defective products. The waste elimination loop indicates that production costs will decrease if the company can streamline the JIT loop by increasing quality (i.e., reducing defective products). This loop determines the saving amount in production resource consumption, such as raw material, person hours, electricity, and water. The productivity loop connects the JIT, waste elimination, and balancing loops under a stable price. This loop clarifies that minimizing waste in the production process can increase profits without increasing the product’s selling price. If SMIs fail to streamline their production process, their productivity will decline and slowly erode profits. Finally, the sustainable manufacturing loop integrates the productivity loop with the socio-environment factors in the R4 and R5 loop, thereby integrating all existing loops with output criteria, including productivity ratio, profitability ratio, taxes, environmental impact, and human health. Furthermore, this conceptual model can be used to explore the model-based strategy considering the interaction between parameters to improve output criteria. 3.3. Model-Based Strategy Development Several alternative strategies were then developed based on the loops found in CLD and their effect on achieving the output criteria (see Figure 5). Overall, the output criteria of the model are positively impacted when positive loops produce a more substantial effect than negative loops, as explained in the Theory section regarding CLD. During the performed FGDs with seven experts, policy factors influencing the alternative strategies were discussed, and model scenarios were selected considering the open innovation communities intervention scopes. In particular, experts selected five factors in the loops that allowed stakeholders to intervene to improve output performance: the defect ratio, workforce, electricity consumption, fixed cost amount, and government subsidies. The open innovation dynamic relationship between each strategy and the loop impacted is detailed in Table 1. J. Open Innov. Technol. Mark. Complex. 2021,7, 225 15 of 21 6. Conclusions This work develops a practical and scientific open innovation dynamic model-based strategy to help decision-makers improve sustainability within cosmetics SMIs. The proposed model-based strategy can qualitatively and quantitatively predict future system behavior under various alternative strategies from an early stage. SD modeling methodology was used to provide a qualitative and conceptual CLD and a quantitative SFD. Using an Indonesian personal care manufacturer as a case study, the sustainability output criteria of five factors were analyzed, including the productivity ratio as an operational criterion, the profitability ratio and tax revenue as economic indicators, the environmental impact as an environmental indicator, and the human health impact as a social criterion. The developed SD model was used to evaluate three alternative strategies agreed upon by experts in FGDs. Without government subsidies or outside intervention, the self-lean improvement strategy demonstrated the weakest performance in all criteria; improvements implemented by SMIs alone do not significantly influence sustainable goal achievement. The limited cooperation strategy, in which assistance is provided by suppliers and research and academic institutions and a small investment is required, presented improved environmental and human health output criteria but only moderate improvements to the operation and economic criteria. The comprehensive collaboration strategy demonstrated excellence on all sustainability criteria; however, this strategy requires significant investments. Without stakeholders’ commitment and firm support, a comprehensive collaboration strategy cannot be realized. Various activities were proposed for each stakeholder to assist SMIs in their quest toward sustainability, from suppliers to the government. Overall, this study provides a foundation to develop a model-based, multistakeholder strategy to assist SMIs’ transition toward sustainability by clarifying their role and visually tracing the impact of their contributions in a structured, systematic, and less costly manner. This research is limited to processes in the manufacturing area; further research is needed to explore sustainable collaboration upstream and downstream of the supply chain overall process, especially to examine the effect of changing material formulations and transportation and logistics mechanisms on the sustainability of cosmetics SMIs. In addition, other researchers can also enrich the open innovation dynamic SMIs’ model with government policy scenario perspectives that might change the choice of alternative strategies set out in this study. The development of these topics has the potential to improve the model-based strategy to achieve sustainable SMIs. Author Contributions: Conceptualization, U.A. and A.H.; data curation, U.A.; formal analysis, U.A. and A.H.; funding acquisition, T.Y.M.Z.; investigation, U.A.; methodology, U.A., A.H., and T.Y.M.Z.; project administration, U.A.; software, U.A. and A.H.; supervision, A.H. and T.Y.M.Z.; validation, U.A.; visualization, U.A. and A.H.; writing—original draft, U.A.; writing—review and editing, U.A. and A.H. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the PUTI Doctoral Program of the University of Indonesia, grant number NKB-655/UN2.RST/HKP.05.00/2020. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data will be made available on request from the corresponding author. Acknowledgments: The author appreciates the experts’ contribution during the FGD and the CV Budi Andhika (a small-medium scale cosmetic company) for sharing case study data. Conflicts of Interest: The authors declare no conflict of interest. J. Open Innov. Technol. Mark. Complex. 2021,7, 225 16 of 21 Appendix A Table A1. The triggered questions for discussion with experts in the Focus Group Discussion (FGD). Discussion Topic Question 1. The sustainability goals and how stakeholders involved a. Should cosmetics SMI managers and decision-makers consider the demands of customers, society, and government for a sustainable industry? Please explain your point of view. b. Do managers have the understanding and ability to achieve a sustainable SMI? c. What are the current roles and interests of stakeholders to create sustainable SMIs? 2. The sustainability factors and inter-relationship between factors a. The following are the factors and their relationships based on literature studies and previous research that affect the achievement of sustainable SMIs. Are there factors or relationships that have not been defined here? Are there any opinions or suggestions? b. Are the factors and relationships listed in the conceptual model enough to be a general model for cosmetics SMIs? 3. The sustainability performance criteria These are the output criteria that we define according to the literature and previous research. Are they enough? Are there any other output criteria that might be used as a reference for sustainable Cosmetics SMIs performance indicators? 4. The alternative strategies a. Which input factors can be used as policy factors that managers and stakeholders can intervene in them? b. What are the possible strategies for cosmetics SMIs to achieve sustainability goals and consider the relationship loops in the conceptual model? Appendix B Table A2. List of Equations in Powersim. Factor Unit Powersim Equation AC Opr Hours h/year GRAPHSTEP(TIME;1;1;{1936;1960;1952;1960;1928;1928;1984//Min:1920;Max:1990//} <<h/year>>) AC power kWh/year ROUND(“No of AC” דPower per AC” דAC Opr Hr”) Actual environment impact Pt/year ROUND(“EI Single Score” דGoods Produced”) Additional demand kg/year 0 Additive ratio 1 −“Water Ratio” Capacity standard kg/year 48000 Catch_up production kg/year ROUND(“Production Plan” −“Capacity Standard” + Rework) Change in environmental impact Pt/year “Reference Environmental Impact” −“Actual Environmental Impact” Change in productivity index “Productivity Index” −“Last Year Productivity Index” Comp_Opr_Hours h/year GRAPHSTEP(TIME;1;1;{1936;1960;1952;1960;1928;1928;1984//Min:1920;Max:1990//} <<h/year>>) Company cost Rp/year ROUND(“Production Cost” ×(1 + “Material Price Fluctuation Factors”) + “Marketing Expense”) Company revenue Rp/year ROUND(“Production Revenue” דSelling price Fluctuation Factors”*(1 − “Discount Ratio”)) Computer power kWh/year ROUND(“No of Computer” דPower per Computer” דComp Opr Hr”) Defect kg/year “Defect Ratio” ×Production Defect ratio % 3 Demand changing factors kg/Pt 1 J. Open Innov. Technol. Mark. Complex. 2021,7, 225 17 of 21 Table A2. Cont. Factor Unit Powersim Equation Discount ratio 0.1209 EI single score Pt/kg 0.0449 EI target Pt/kg 0.0449 Energy consumption kWh/year ROUND(“Energy Waste” + “Normal Energy Consumption”) Energy cost Rp/year “Energy Consumption” דEnergy Price” Energy price Rp/kWh 1500 Energy waste kWh/year “Waste of Computer” + “Waste of AC Power” + “Waste of Machine Power” Fixed material price Rp/kg GRAPH(TIME;1;1;{17000;17000;21000;17000;17000;18000;18000;21000;23000;23000; 25000;25000;25000;27000;27000;27000;29000;29000;29000;31000;31000;31000;33000; 33000;33000//Min:17000;Max:30000//}<<Rp/kg>>) Flow of additional demand kg/year “Potential Demand Change” −“Additional demand” Goods produced kg/year Production −Defect + Rework Goods sold kg/year “Simulated Demand” Government subsidies Rp 0 HR add income Rp/year “Overtime Fee” + “Reward −Incentives” HR cost Rp/year (“No of HR” דHR Rate”) + “HR add Income” HR rate Rp/(person ×year) GRAPH(TIME;1;1;{37300000;38400000;38700000;43300000;44800000;46400000; 50500000;50500000;50500000;52000000;53500000;55000000;56500000;58000000; 59500000;61000000;62500000;64000000;65500000;67000000;68500000;70000000; 71500000;73000000;74500000//Min:35000000;Max:80000000//}<<Rp/(person × year)>>) Human health damage DALY/year “Human Health Factor” דGoods Produced” Human Health Factor DALY/kg 2.63333333333333 ×10−6 Inventory kg/year 3141 Lamp operation hours h/year GRAPHSTEP(TIME;1;1;{1936;1960;1952;1960;1928;1928;1984//Min:1920;Max:1990//} <<h/year>>) Last year productivity index 0,95 Lighting power kWh/year ROUND(“Power per Lamp” דNumber of Lamp” דLamp Opr Hr”) Mach opr. hour h/year GRAPHSTEP(TIME;1;1;{1936;1960;1952;1960;1928;1928;1984//Min:1920;Max:1990//} <<h/year>>) Mach opr. ratio GRAPH(TIME;1;1;{62,5;62,5;62,5;62,5;62,5;62,5;62,5;62,5;31,3; //Min:30;Max:63//}<<%>>) Machine power kWh/year “No of machine” דPower per Mach” דMach Opr Hr” ×Mach_Op_Ratio Maintenance cost Rp/year GRAPH(TIME;1;1;{6235000;6235000;6235000;5579000;7433000;7945000;9744000// Min:5000000;Max:10000000//}<<Rp/year>>) Marketing expenses Rp/year GRAPH(TIME;1;1;{352578000;273100000;352578000;337036000;284033000;291114000; 280521000;293396000//Min:200000000;Max:400000000//}<<Rp/year>>) Material consumption kg/year “Normal Material Consumption” + “Material Waste” Material cost Rp/year ROUND(“Material Consumption” דFixed Material Price”) Material price fluctuation % GRAPH(TIME;1;1;{7;40; − 13;13;41;31;20;40;28;28;23;23;23;20;20;20;17;17;17;15;15;15;14; 14;14//Min:−15;Max:50//}<<%>>) Material waste kg/year ROUND(Rework דAdditive Ratio”) No of AC pieces GRAPHSTEP(TIME;1;1;{8;8;8;6;6;6;6;5//Min:1;Max:8//}<<pieces>>) No of Admin HR people GRAPH(TIME;1;1;{8;8;8;8;8;8;7;7;6;6;6;6;6;6;6;6;6;6;6;6;6;6;6;6;6//Min:5;Max:9//} <<person>>) No of Computer pieces 6 No of HR people “No of Prod_Log HR” + “No of Admin HR” No of machine machines 9 No of Prod_Log HR people GRAPH(TIME;1;1;{10;10;10;10;10;10;9;7;7;7//Min:5;Max:10//}<<person>>) No of waste machine machines GRAPHLINAS(TIME;1;1;{3;4;3;1;1;1;1//Min:0;Max:5//}<<mach>>) No of water pump pieces 2 Normal energy consumption kWh/year “AC Power” + “Computer Power” + “Lighting Power” + “Machine Power” + “Water Pump” Normal material consumption kg/year ROUND(“Additive Ratio” ×(“Goods Produced” + Inventory)) Number of lamps Pieces 10 J. Open Innov. Technol. Mark. Complex. 2021,7, 225 18 of 21 Table A2. Cont. Factor Unit Powersim Equation Ord_demand kg/year GRAPHCURVE(TIME;1;1;{72167;57792;73751;58655;46813;47750;50344;40275;41081; 41902;42741;43595;44467;45357;46264;47189;48133;49095;50077;51079;52100;53142; 55268//Min:40000;Max:75000//}<<kg/year>>) Order lead time h/kg 0.08 OT fraction h/kg 0.03 OT hours h/year ROUND(“OT Fraction” דCatch_Up Production”) OT rate Rp/hour 17000 Other fixed costs Rp/year GRAPH(TIME;1;1;{395615000;301421000;319640000;241928000;302614000;366015000; 330992000//Min:200000000;Max:400000000//}<<Rp/year>>) Overprocessing h/year “Waiting hours” + “Reprocess hours” Overtime fee Rp/year “OT Hours” דOT Rate” Plan productivity index IF(“Last Year Productivity Index”>1;0,97;”Last Year Productivity Index”) Potential demand change kg/year “Change in Environmental Impact”*”Demand Changing Factors” Power per AC kW/pieces 0.75 Power per computer kW/pieces 0.1 Power per lamp kW/pieces 0.02 Power per machine kWh/mach 0.424 Power per water pump kW/pieces 0.25 Production kg/year “Production Plan” Production cost Rp/year “Material Cost” + “HR Cost” + “Maintenance Cost” + “Energy Cost” + “Other Fixed Cost” −“Government Subsidies” Production plan kg/year (“Simulated Demand”/“Plan Prod Index”) −Inventory Production revenue Rp/year “Goods Sold” דSelling Price” Productivity ratio “Production Revenue”/“Production Cost” Profitability ratio “Company Revenue”/“Company Cost” −1 Reference environmental impact Pt/year ROUND(“Goods Produced” דEI target”) Reprocess hours h/year Rework דRework Time” Reward budget Rp/year/person 1000000 Reward-incentives Rp/year “No of HR” דReward Budget” Rework kg/year Defect Rework time h/kg 0.02 Selling price Rp/kg GRAPH(TIME;1;1;{25000;27000;27000;27000;32000;34000;34000;34000;35000;35000; 35000;35000;35000;36000;36000;36000;36000;36000;37000;37000//Min:20000; Max:50000//}<<Rp/kg>>) Selling price fluctuation factors GRAPHSTEP(TIME;1;1;{191;199;198;218;206;189;181;190;185;185;194;194;194;198;198; 198;208;208;208;214;214;214;221;221;221//Min:175;Max:230//}<<%>>) Simulated demand kg/year Ord_Demand + “Additional demand” Tax paid Rp/year IF(“Profitability Ratio”>0;”Tax Ratio” דCompany Revenue”) Tax ratio % GRAPHSTEP(TIME;1;1;{1;1;1;1;1;1;0,5;0,5;0,5;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1//Min:0; Max:1.5//}<<%>>) Waiting hours h/year Rework דOrder Lead Time” Waste AC Pieces 4 Waste computer Pieces 3 Waste of AC power kWh/year ROUND(“Waste AC” דPower per AC” ×Overprocessing) Waste of computer power kWh/year ROUND(“Waste Comp” דPower per Computer” ×Overprocessing) Waste of machine power kWh/year ROUND(“No of Waste Machine” דPower per Mach” ×Overprocessing) Water pump power kWh/year “No of Water_Pump” דPower per Water Pump” דWater Pump_Opr_Hours” Water pump opr hr h/year GRAPHSTEP(TIME;1;1;{1936;1960;1952;1960;1928;1928;1984//Min:1920;Max:1990//} <<h/year>>) Water ratio 0.44 J. Open Innov. Technol. Mark. Complex. 2021,7, 225 19 of 21 Appendix C Table A3. Statistical Validation Using Absolute Mean Errors. No Year Actual Data (A) Simulation Result (S) Absolute Mean Error (AME) 1 2013 Rp. 3031.7 million Rp. 3029.4 million 0% 2 2014 Rp. 2765.8 million Rp. 2729.4 million 1% 3 2015 Rp. 3498.2 million Rp. 3466.0 million 1% 4 2016 Rp. 3150.7 million Rp. 3035.0 million 4% 5 2017 Rp. 2766.2 million Rp. 2712.8 million 2% 6 2018 Rp. 2719.6 million Rp. 2697.4 million 1% 7 2019 Rp. 2778.7 million Rp. 2723.6 million 2% 8 2020 Rp. 2222.5 million Rp. 2287.2 million 3% Absolute mean error (AME) 1% References 1. Villena, K. World Market for Beauty and Personal Care. Available online: https://www.euromonitor.com/article/world-market- for-beauty-and-personal-care-2 (accessed on 1 August 2021). 2. Ramli, N.S. Green Marketing: A New Prospect in the Cosmetics Industry. 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