Development of a Business Assessment and Diagnosis Tool That Considers the Impact of the Human Factor during Industrial Revolutions

Abstract

Over time, the satisfaction of needs and the ability to meet them have consistently increased. However, the world of the 21st century is one in which the basic needs of millions of human beings are still not satisfied. Why? To an extent, nonprofit organizations such as charities play essential roles in the needed improvement of this situation. In this regard, the human factor within an organization is key influence in organizational performance and societal impact. Human beings within organizations make decisions based on their own motives, so the ethical values of each person are significantly important. Therefore, it is necessary to use analyze the potential of the human factor in the fourth industrial revolution and to analyze its influence in the previous industrial revolutions. This research was aimed to conduct such analyses for a nonprofit charity. Moreover, the authors of this paper also analyzed the industrial revolution potentials of the charity case study using system dynamics. The relevance of the presented paper was ensured by the aforementioned combination of topics. The results showed how greater impacts, higher expenses, and higher stocks were not necessarily able to quantitatively satisfy food needs in a timely manner if the human factor and global effectiveness and efficiency were not optimized. When these aspects were optimized, our hypothesis was proven, as the models set for further industrial revolutions were shown to provide better results in the satisfaction, efficiency, and economic indicators with a lower financial need; therefore, this model can be used to satisfy other needs of Maslow’s pyramid. In conclusion, this proposed approach empowers welfare organizations to increase their CSR consideration, thus enabling them to use internal mechanisms to secure viability in the pursuit of a high-performance CSR approach.

1. Introduction

Throughout history, human beings have pursued ways to meet their needs in effective and efficient ways by developing sciences that enabled the accumulation of the knowledge that we now call culture. This culture has continued to evolve to this day. However, we currently do not use all potentials of this culture due to a process of substitution, such as when old agricultural machinery is replaced by automated machinery.

Furthermore, the ways in which we have met societal needs have changed throughout history. First there were groups or communities, then agriculture and livestock appeared and the first communities in fixed locations were established, and then there was long period until the first industrial revolution in which the economy was based on agriculture, livestock, and procedures to produce artisanal goods such as military weapons. Accordingly, commerce had a local and regional character until the appearance of global routes across the oceans—such as those initiated in the era of discoveries in Portugal and Spain. Finally, the first two industrial revolutions allowed for mass production and international trade in goods, and the third allowed for information services to become accessible in a decentralized manner. Over this historical evolution, the satisfaction of needs has had varied responses, though always with a pattern: the possibilities and capabilities to meet them have increased. However, the world of the 21st century is one in which the basic needs of millions of human beings are still not satisfied. Why? For centuries, states have presented social organizations and measures to alleviate the lack of distribution of goods and services, but states, companies, and non-governmental organizations have been able to meet appropriate welfare indexes in vast areas of our planet. Research carried out on control in public and private sector organizations has mainly focused on the effective control of labor to ensure that management objectives are met [1]. Lupton and Miller argued that when we do for those in need what they have the capacity to do for themselves, we disempower them. We must empower those in need to finally find their own solutions [2]. In this context, Lupton suggested limiting the need for aid and promoting entrepreneurship [3]. Speckbacher observed that these nonprofit organizations have specific organizational characteristics in their management and control [4].

On the other hand, in the last two decades, the challenge for companies has shifted from deciding whether to be socially responsible to how to carry out CSR in a strategic and effective manner, with a clear and demonstrable predictions of the beneficial impacting on society. However, many companies are facing significant challenges, mainly for two reasons [5]:

• The effectiveness of CSR efforts is often difficult to observe due to a lack of transparency in objectives.
• Second, CSR covers multiple dimensions, and conflicts may arise between interest groups. Managers face challenges in the prioritization and balance of CSR aspects.

The first issue regards the measurement of effects, and the second issue regards the influence of the human factor in the prioritization and decision making of managers. Human motivation has a significant impact on the latter. Therefore, the human factor positively or negatively influences the performance of an organization within its area of influence.

Today, managers can assess the effectiveness but have difficulties in assessing the behavior and efficiency of their employees. Moreover, in big companies, it can be tough to track the reasoning and/or errors and deviations of multi-department decision making. In addition, optimization projects (internal or with external consultancy) generally require long analysis periods and mapping processes to determine weak points and potentials. In this regard, the human factor within an organization is key for its organizational performance and impact on society. Human beings within organizations make decisions based on their motives. Therefore, the ethical values of each person has significant influence.

Today, large amounts of information regarding subjects such as the rates of satisfaction of needs and the values of goods and services are provided by states, private companies, and charities. Based on this information, a normative and methodological model needs to be developed to distribute resources without excess or scarcity, such as in food crisis situations. In this context, sharing the vision of pure voluntarism is a way to link the third sector with the central cultural values of meaning and service. This could connect the third sector with the economic realities of providing jobs and services, with the ultimate goal of the nonprofit sector to identify service gaps and the special needs of particular groups in society; in turn, the sector can be rewarded by both donors and the government for these valuable social contributions [6].

In this sense, analyses are needed to define the potential of the human factor in the fourth industrial revolution, as well to analyze its influence in the previous industrial revolutions. By doing so, the paper was intended to provide a framework to model the human factor within organizations based on historical evolution and to quantify its influence on the food supply chain. To achieve this goal, the authors of this paper researched a case study of a charity to identify the human factor’s impact on the satisfaction of food needs and the efficiency of the organization over the four industrial revolutions. The extended goal of the research was to guide the managers of any organization by monitoring the effectiveness, efficiency, and behavior of their employees, as well as provide hints within a sequence model. This research was intended to test the following hypotheses.

• A new concept for modeling and simulating the human factor within decision making by applying system dynamics will enable the analysis of its impact within organizations such as charities.
• Managers and stakeholders with decision-making capabilities who consider CSR can enable the related welfare organizations to be more efficient than organizations with managers only partially considering the concept.
• Industrial management techniques have provided methods and tools to improve the efficiency of welfare organizations during industrial revolutions, thus enabling the greater satisfaction of needs.

In order to test these hypotheses, we used a system dynamics simulation approach for a welfare organization regarding its global food supply area. By applying this model, we identified potentials for improvement by determining causes of deviations or errors, sped up analysis, found a solution, and converted it into a competitive advantage of the welfare organization.

This proposed approach can empower organizations to increase their CSR consideration, thus enabling them to use internal mechanisms to secure viability.

This paper is structured as follows. First, the need to study the third sector and welfare organizations is described, along with gaps in the research of the human factor. The second section presents an extensive literature review regarding the human factor, industrial revolutions, supply chain management, NPOs, CSR, and system dynamics simulation. The third section explains the methodology for building the model, and the fourth section describes its application in the design of the simulation model. Finally, the simulation results are discussed. Based on the results of the case study, a set of suggestions is provided as guidance for managers to increase the effectiveness, efficiency, and behavior of their teams, followed by the conclusions and potential future research areas.

2. Fundamental Definitions and State of Research

2.1. Human Factor and Human Needs within Society

Social structures are the results of humanity’s actions in society, and these structures allow them to meet their needs [7]. Maslow studied the motivation of individuals without considering rewards or unconscious desires [8]. Maslow stated that people are motivated to meet certain needs. When a need is met, a person seeks to satisfy the following need, and so on [9]. Analyses of Maslow’s hierarchy within organizational studies remain “extremely rare” and therefore have high potential [10]. The hierarchy of needs can be divided into three types of needs: basic needs, psychological needs, and self-fulfillment needs [11]. It can be more detailed divided into five categories: physiological needs, safety and security needs, love and belonging needs, self-esteem needs, and self-actualization needs [12].

Controversy arises when analyzing Maslow’s hierarchical pyramid regarding humanistic psychology. Is there enough evidence to support this hierarchy in relation to peoples’ emotional development? The main argument is that anyone in society can go back or value an alternative aspect of the hierarchical pyramid. For example, some cultures may be more obsessed with security or belonging needs. To respond to these challenges, many experts believe that Abraham Maslow’s hierarchy does not always follow its original sequence because all people are in different stages of development and all are self-realized in some way [11].

According to Pérez López, each person has a scale of internal preferences and seeks the satisfaction of their needs within their perceived perceptions and possible interactions. He defined three types of motivations by which each person in each decision is simultaneously motivated [13].

• Extrinsic motivation: incentives we expect from the environment when acting (money, car, a good house, power, etc.).
• Intrinsic motivation: any result that we hope to internally experience by acting (learning, feeling responsible for something, recognition, etc.).
• Transcendent motivation: the effects of an action that one performs on others (such as helping others).

This theory was chosen as the basis for the modeling of the human factor in the subsequent simulation models.

2.2. Industrial Revolutions

The first industrial revolution is generally characterized by mechanization [14], the second industrial revolution is linked to the increasing rationalization and division of labor in manufacturing companies, and the third industrial revolution brought electricity and increased productivity through advanced electronics that increased calculation and storage capabilities [15]. Following the 1970s, industrial technological advances were only incremental or evolutionary. In 2011, an initiative called “Industry 4.0” was presented by representatives of companies, politics, and academics [16] that promoted the idea of digitalization in tandem the autonomy and self-control of machines as an approach to strengthen the competitive power of the German manufacturing industry [17]. This fourth wave of technological advancement has been driven by nine fundamental technological advances [18], most of which are not recent innovations. However, it is the combination of technologies, business processes, and data processes that makes Industry 4.0 a novelty [19]. Industry 4.0 has enormous potential to meet customer requirements, improve flexibility, optimize decision making, improve productivity and resource efficiency, and create new services and answer social questions regarding demographic changes, the balance between work and personal life, and the maintenance of a competitive high-wage economy [20].

The effect of the human factor over time was first analyzed for management practices at the beginning of the 20th century following the experiences of the first industrial revolution. It was found that the consideration of the human factor was introduced during the second industrial revolution. Then lean concepts were applied to production systems in the third industrial revolution, and new technologies have enabled the digital modeling, planning, and control of organizations in the fourth industrial revolution.

2.3. Basics of Supply Chain Management

The main objective of logistics is to provide the right products in the right quantity and quality with complete information at the right time in the right place. The goals are low inventory levels, short delivery periods and reaction times, reliable supply service, punctuality, complete delivery, and high supply flexibility. However, there are irremediable conflicts among these objectives. As a consequence, not all objectives can be equally achieved [21]. Logistics management areas include procurement, production, and distribution [22]. Supply chain management and logistics are often used as synonyms, although supply chain management encompasses a broader meaning than logistics [23]. Therefore, the management of the supply chain can be defined as the integration of the organizational units of a supply chain and the coordination of material, information, and financial flows in order to comply with the requirements of end customers and improve the competitiveness of the entire supply chain [24] while considering both the planning and execution processes [25]. In this context, distribution logistics acts as a link between corporate production logistics and customer acquisition logistics [22]. As such, it is intended to be used to guarantee a demand-based market supply in terms of temporal, spatial, and quantitative criteria, thus striving for an optimal relationship between service and costs [26].

2.4. Non-Profit Organizations and Charities

The third sector encompasses all nonprofit organizations, including charities and foundations [27]. Charity is something that is given to an organization or individual to help or benefit them [28], and a charity is an organization that performs services for the satisfaction of society’s needs. Considering that this type of organization does not include economic benefit as a main objective, it is essential to monitor performance and efficiency in order to improve its impact on the social and economic development of society [27].

Services can be defined as the capacity and willingness of a provider in the form of a promise of performance. However, this promise can only be realized in the context of the demand of a client. In other words, since the sale of the service occurs after the actual creation of the need for it [29]. The degree of service orientation, the contribution of services to income and benefits, and customer loyalty increase as the organization moves from selling the product to totally solving the problem [29]. The processes of a service organization include active value creation and support activities in the form of business, support processes, and management processes [30]. However, most Lean Six Sigma applications have focused on private industry, manufacturing, and healthcare; these approaches and related resource allocation and utilization improvements have not yet been applied to nonprofit organizations [31].

2.5. Corporate Social Responsibility (CSR)

Corporate social responsibility (CSR) is defined by the EU commission as “a concept by which companies integrate social and environmental concerns in their business operations and in their interaction with their stakeholders voluntarily” [32]. In addition, it can be defined as “the commitment of companies to contribute to sustainable economic development, working with employees, their families, the local community and society in general to improve their quality of life” [33]. Currently, CSR has become an area of scientific research carried out by not only psychologists, philosophers, sociologists and economists but also specialists in business administration and engineers. Most research papers related to the concept have provided analyses of the idea from several points of view [34].

In this context, CSR plays a key role due to the fact that companies have a responsibility to society and a broader number of interest groups beyond their shareholders [35]. Today, more than 8000 companies in 150 countries are part of the United Nations Global Compact, which deals with human rights, labor standards, environment initiatives, and anti-corruption initiatives [36]. CSR has become increasingly frequent and visible within companies as a mechanism to motivate interest groups and to manage the perceptions and expectations of society about the role and utility of companies in societies and communities beyond the main function of producing and selling goods to a defined consumer market [37].

Currently, for CSR to be accepted as a business entity, it must be framed in such a way that it covers the full range of responsibilities of a business organization. In this way, it is suggested that four types of social responsibilities constitute total CSR: economic, legal, ethical, and philanthropic. These types of responsibilities have always existed to some extent, but only in recent years have ethical and philanthropic functions been prioritized. Each of these four categories deserves detailed consideration [38]. Finally, to implement a CSR strategy at a company while successfully communicating the benefits of truly strategic CSR initiatives, senior managers must express their commitment to these initiatives in a clear and consistent manner—ultimately, an organization must choose to adopt CSR [39]. According to Carroll (1991), “Social responsibility can only become a reality if more managers become moral instead of amoral or immoral” [38].

Today, researchers are analyzing existing regulations, adaptations to current situations, and reasons why social, ethical, and moral regulations are necessary [40].

The evolution of CSR research indicates that discussions of this matter have shifted from existential issues regarding the mission of organizations and the values of shareholders to the processes through which corporations conceptualize and promulgate their social obligations. That is, in recent years, research of CSR has been more broadly interpreted and conceptualized. Studies have emphasized the potential for companies as positive and responsible contributors to society, as well as their potential as tools with which to shape and facilitate social change [41].

During the period from 2000 to 2010, there was a notable increase in process-based articles that emphasized the organization of CSR activities. Although there were some articles on this topic in the sixties and seventies, studies about CSR processes in those periods were mostly descriptive in nature. The resurgence of process-based research reflects the growing interest in the understanding of corporate decision making and CSR implementation [42]. In another sense, it is clear that there has been a conceptual focus shift from financial results to non-financial, social, and organizational results. This trend demonstrates interest in broader interpretations of the role of companies and companies in society. Some examined non-financial areas include customer satisfaction, CEO succession, and executive compensation [43]. The new directions of research on CSR, which are expected to improve and expand academic knowledge while also addressing the practical challenges facing executives and companies, are as follows [44].

• Equilibrium between interest groups and their goals.
• Management of CSR complexity in multinational companies.
• Risk management and limits of social activities related to CSR.

2.6. System Dynamics and Simulation

System dynamics is a simulation technique for the study, management, and solution of complex feedback problems with a focus on analysis and policy design [45]. System dynamics is an effective method for obtaining useful information regarding situations of dynamic complexity. It has been increasingly used to design more successful corporate policies and public policy adjustments [46]. System dynamics deals with the behavior of a system and how it influences its own future evolution, which can be considered as the strategic issues that affect the top management of organizations [47].

Human mental models for complex systems usually lead us in the wrong directions This happens because our environment usually gives us examples of systems with unexpected behavior [48], as the human mind is not able to interpret systems with multiple non-linear loops such as social systems because the understanding of them has not been necessary for man until recent historical times [49]. Therefore, only the study of an entire system as a feedback system leads to correct results [50] since the feedback structure of a system generates its behavior [45].

In addition, the dynamics of all systems arise from the interaction of only two types of positive and negative feedback loops. Intuition can allow us to predict the behavior of isolated loops. However, it is not easy to predict dynamics when several loops interact. When intuition fails, computer simulation can helps to deduce system behavior [46].

System Dynamics was developed in the 1950s as part of a research project of the MIT Sloan School of Management in collaboration with General Electric [51]. The basic components of the dynamics of a closed feedback system include cause and effect relationships, causal loops, and stock and flow diagrams. Causal loop diagrams (CLDs) are an important tool for representing the structures of feedback systems [46]; causal refers to cause–effect relationships and loop refers to a closed chain of cause and effect [48]. A CLD contains the key elements of a system and the causal connections between them [52]. Stocks provide information on the state of a system, upon which decisions and actions are based, and rates determine current flows between levels. Rate equations define the decision functions that depend on the information available on all levels [53].

3. Research Methodology

To define out methodology, two fundamental concepts were used: systems theory and modeling theory. Systems theory is a basic science that describes many individual phenomena through general basic concepts. In the theoretical–cybernetic approach of a system, organizations are understood as open, dynamic, and complex systems that are subject to permanent changes and their own goal-oriented dynamics [54]. The methodological approach for this research paper is shown in Figure 1 and explained later.

The methodological elements of the conceptual model are described as follows.

• Industrial revolutions: four models based on the four known revolutions were developed. The differences are explained in the fourth section of this article.
• Human factor: We considered decision makers as free entities based on their motives. It was found that that the motivations and interests of managers played essentials role in the results of the model.
• Corporate social responsibility: The economic, legal, ethical, and philanthropic responsibilities of an organization depend on the actions of the human beings that compose it. Therefore, compliance to these guidelines was simulated for the human factors within the organization.
• Welfare organization: The charity in the model represents an organization with a global footprint seeking the improvement of human living conditions. Among areas with global impact, the food functional area has autonomy of decision; as such, we simulated it. The charity in the model was set to consider the financial flow (such as investments, donations, procurement costs, distribution costs, and operational expenses such as salaries), materials flow (using food in calories as the unit), and information flow (data and decisions).
• Food/hunger functional area of the charity: A global operational unit is in charge of dealing with suppliers, distributors, and end customers. End customers are those around the world in need of food to fulfill one of their basic needs. The purpose of the studied charity is to reduce the number of persons whose basic need for food has not been fulfilled. This functional area was divided in six geographical divisions corresponding to the continents of the world, with America divided in two.
• Demand and distribution management: The model was set to consider policies for demand and inventory planning, as well as the management of distribution networks in order to deploy a strategy for the food supply chain of the welfare organization. Demand planning consists of the calculation of future demand and includes the selection of a specific forecasting method and its parameters. Typical demand patterns are stationary, seasonal, trend, and sporadic [21]. Inventory planning refers to the management of a currently existing inventory, and its main task is the definition of the optimal order point [55]. Distribution logistics is a key factor for the overall profitability of a company, since it directly affects both the cost of the supply chain and customer service [56].
• Vensim software: In this article, system dynamics modeling was used due to its tactical–strategic characteristics, for which a high level of abstraction is required [57], to conduct our first approximation. In the market, there are different software packages that allow for the modeling of system dynamics, such as ANYLOGIC, DYNAMO, ITHINK, POWERSIM, STELA, and Vensim [52]. Here, the Vensim simulation software was selected for the simulation. Vensim provides high rigor for writing model equations, as well as powerful tools for the optimization of multiparameter simulation results in the determination of the most convenient policy options by parameterizing these policies.

The research scope of study was limited to the management of services (including the management of the demand or needs of society), the production and distribution of resources, and transportation means for the execution of these processes. Only the food functional area of the charity was considered in the simulation model, so only global food need was set as to be satisfied. Accordingly, the limited scope of study was set based on the following criteria.

• Type of need: Needs may include food derived from malnutrition problems, but the simulation focused on dietary need.
• Type of service: The studied services included both the delivery of products and solving societal food needs, which comprised the identification and quantification of these needs and the distribution, assessment, and guidance provision for the people receiving the service.
• Type of manufacturing product: Elements under production, such as rice, could have a “make-to-stock” typology.
• Degree of service orientation: The studied organization presents a maximum degree of service orientation, which indicates that it offers a complete solution as a service. This complete solution includes the purchase, distribution, and/or manufacture of the goods associated with the final service.
• Network level: The designed model was set to focus on an organization that offers services, including interfaces with goods suppliers, distribution services, and customers.
• Design object: The authors of the article focused on the design of a management system for service organizations such as charities to increase their efficiency in order to meet the needs of customers.
• Planning context: The model was designed to include strategic, tactical, and operational levels of planning.
• Principles of design: The design principles were the human factor, corporate social responsibility, and the evolution of industrial management concepts over the course of multiple industrial revolutions.
• Business model dimension: The business model was designed to include the capabilities of the service organization, i.e., the charity, and certain capabilities of the organization’s environment such as volunteers and donations.
• Complexity of the system: The developed model presents interrelationships among many factors such as people (along with their needs and interests), suppliers, and organizations. Therefore, we consider the model to be complex.
• Application: The system was designed to reduce a specific application to a specific case that makes the conceptual model viable.
• Evaluation: We used quantitative and partial monetary evaluations within the simulation model for the specific application, followed by qualitative evaluation for the interpretation of results.
• Evaluation tool: The tool used for evaluation was a system dynamics simulation used to represent the cause–effect relationships of the conceptual model for the specific case study.
• Study organizations: This study was focused on charities that offer services for all levels of planning.
• Objective parameters: The objective parameters were divided in two set of performance indicators. The first set was related to increases in the satisfaction of the needs of final customers or individuals. The second was related to the parameters regarding the efficiency of the system to provide the required services.
• Information technology (IT): IT service providers were also addressed because information architecture is developed along with associated interfaces and communication flows.
• Service management system: In this article, the service management system of the studied charity was examined at all levels of strategic, tactical, and operational planning, thus meeting the requirement of integrative planning.

The conceptual model was developed over several steps. A model can be seen as a substitute system that serves to make the complexity of reality controllable and therefore make use of theoretical solutions. To establish a model, the attributes of a real system must be reduced to the relevant aspects [58]. At first, a real system is transferred to a model. Within the framework of abstraction, problems may arise regarding the complexity, dynamics, and number of constellations of elements. In order to reduce the complexity of a real system, it is necessary to delineate the scope of the investigation and to restrict the essential characteristics of a system [59]. Second, the model records cause–effect relationships by creating hypotheses, logical derivations, or functional definitions. Last, the model is empirically tested for practical applicability and transferred to a real system. For the application of this methodological approach for a welfare organization’s food functional area, the following steps were performed.

• Defined the service management tasks of the welfare organization according to the levels of the planning horizon.
• Develop an objective system with parameters.
• Define entities and their decision-making alternatives.
• Identify the associations between service management tasks and decision makers.
• Identify interrelationships through CLDs including the human factor’s impact on decision making.
• Identify the necessary information flows for better decision making for charities.
• Derive logical rules for the impact of the human factor by considering its motives and the CSR concept during industrial revolutions.
• Implement potential measures based on industrial management and Industry 4.0 in to the conceptual model of a welfare organization for the improvement of the objective system.
• Develop a quantitative approach based on a system dynamics simulation for charity functional areas such as food.

To empirically test the model, which could not be directly implemented in a real organization, the conceptual model for the studied charity was implemented in a simulation with different compared approaches. The steps of the application of the conceptual model in a simulation were as follows.

• Implementation of logical rules within the simulation.
• Preparation of data entry.
• Programming of the model.
• Validation of the model.
• Execution of the necessary scenarios and experiments.
• Extraction and interpretation of results.

The compared models were as follows.

• Model for the first industrial revolution (1st IR model).
• Model for the second industrial revolution (2nd IR model).
• Model for the third industrial revolution (3rd IR model).
• Model for the fourth industrial revolution (4th IR model).

4. Design and Simulation of a Welfare Organization in the Context of Changing Industrial Revolutions while Considering the Human Factor

4.1. Design and Simulation of a Welfare Organization in the Context of Changing Industrial Revolutions While Considering the Human Factor

The model was developed based on the methodological steps presented below.

Step 1: Service management tasks of the welfare organization according to the levels of the planning horizon: Based on the work of Reyes et al. [60], a selection of service management tasks was considered within the conceptual model.

• Strategic management tasks: definition of service offer, service strategy planning, continuous observation and evaluation of service environment, target system definition, service system design and location distribution, sales planning, and resource planning.
• Tactical management tasks: service requirement planning, supplier selection (procurement planning and distribution planning), and continuous observation of internal performance.
• Operative management tasks: calculation of services volume, contracting of suppliers, order coordination and execution, and measurement and calculation of key performance indicators (KPIs).

Additionally, the model considers the accountability of the economic activities performed by the welfare organization; it considers incomes from donors and expenses from suppliers, infrastructure, and personal salaries.

Step 2: Development of an objective system with parameters for the charity: The target system was derived from an analogy of a supply chain system, i.e., supply and demand for a certain resource need (food in this case), followed by the addition of the related economic parameters. The results were quantitatively evaluated according to the key performance indicators, which were divided into satisfaction indicators and system efficiency indicators including economic parameters.

Satisfaction indicators are the parameters used to measure the effectiveness of the measures and actions performed by the charity within the simulation.

• Average satisfaction rate (%): This refers to the percentage of people with their food needs satisfied in each period.

$$Ø \mathrm{Satisfaction} \mathrm{rate} \left(\%\right)=\frac{\mathrm{People} \mathrm{with} \mathrm{food} \mathrm{need} \mathrm{satisfied}}{\mathrm{World} \mathrm{Population}}\times 100\%$$

(1)

• Average Charity´s Impact (million persons): This is the amount of persons with their food needs satisfied thanks to charity´s action.

$$Ø \mathrm{Charity}´\mathrm{s} \mathrm{Impact} \left(\mathrm{mill}. \mathrm{persons}\right)=\frac{Ø \mathrm{Food} \mathrm{delivered} \mathrm{and} \mathrm{used}}{Ø \mathrm{Food} \mathrm{need} \mathrm{per} \mathrm{person}}$$

(2)

• Service level (%): This represents the percentage of days in which the demand for the continents was satisfied.

$$Ø \mathrm{Service} \mathrm{level} {\left(\%\right)}_{ }=\varnothing \left(\frac{\mathrm{Days} \mathrm{food} \mathrm{need} \mathrm{satisfied} \mathrm{at} \mathrm{continent} \mathrm{i}}{\mathrm{Total} \mathrm{number} \mathrm{of} \mathrm{days}}\right)\times 100\%$$

(3)

• Cumulated Demand (billion calories): This represents the cumulated demand to be satisfied.

$$\mathrm{Cumulated} \mathrm{Demand} {\left(\mathrm{bill}. \mathrm{calories}\right)}_{ }={\displaystyle \sum }_{t=1}^n{\displaystyle \sum }_{i=1}^6\mathrm{Demand} \mathrm{of} \mathrm{continent} \mathrm{i} \mathrm{at} \mathrm{time} \mathrm{period} \mathrm{t}$$

(4)

• Cumulated Consumption (bill. calories): This represents the cumulated consumption of the continents.

$$\mathrm{Cumulated} \mathrm{Consumption} {\left(\mathrm{bill}. \mathrm{calories}\right)}_{ }={\displaystyle \sum }_{t=1}^n{\displaystyle \sum }_{i=1}^6\mathrm{Consumption} \mathrm{of} \mathrm{continent} \mathrm{i} \mathrm{at} \mathrm{time} \mathrm{period} \mathrm{t}$$

(5)

System efficiency indicators are the efficiency parameters that were needed to reach the desired satisfaction rate and service level.

• Cumulated food delivered and used (billion calories): This is the total food products delivered in a certain period of time.

$$\mathrm{Food} \mathrm{delivered} \mathrm{and} \mathrm{used} \left(\mathrm{bill}. \mathrm{cal}.\right)={\displaystyle \sum }_{t=1}^n\mathrm{Deliveries} \mathrm{used} \mathrm{by} \mathrm{customers}$$

(6)

• Average Efficiency rate (%): This is the percentage of calories sent and used divided by the maximum potential action that could have been made.

$$\varnothing \mathrm{Efficiency} \mathrm{rate} \left(\%\right)=\varnothing \left(\frac{\mathrm{Food} \mathrm{delivered} \& \mathrm{used}}{\mathrm{Max}. \mathrm{Potential} \mathrm{Action}}\right)\times 100\%$$

(7)

• Average Utilization rate of calories (%): This is the percentage of calories consumed by the end customer divided by the original quantity of calories sent.

$$\varnothing \mathrm{Utilization} \mathrm{rate} \mathrm{of} \mathrm{calories} \left(\%\right)=\left(1-\mathrm{transportation} \mathrm{waste}-\mathrm{consumption} \mathrm{waste}\right)\ast 100\%$$

(8)

• Cumulated stock within the distribution network (billion calories): This is the sum of all products stored throughout the distribution process.

$$\mathrm{Cumulated} \mathrm{Stock} \left(\mathrm{bill}. \mathrm{cal}.\right)={\displaystyle \sum }_{t=1}^n\mathrm{Stock} \mathrm{along} \mathrm{the} \mathrm{distribution} \mathrm{network}$$

(9)

• Cumulated distribution costs (million US $): This is the total quantity sent multiplied by the price of distribution.

$$\mathrm{Cum}. \mathrm{Distribution} \cos \mathrm{ts} \left(\mathrm{USD} \mathrm{mill}. \right)={\displaystyle \sum }_{t=1}^n\mathrm{Quantity} \times \mathrm{Distribution} \mathrm{Price}$$

(10)

• Net working capital (NWC) (million US $): This is the difference between a charity´s current assets, such as cash and inventories of raw materials and finished goods, and its current liabilities, such as accounts payable. It is a measure of the charity´s liquidity and operational efficiency. The values are provided at the end of the simulation time.
• Cumulated Donations (million US $): This is the cumulated amount of money received by the charity from donors over the simulation time.

Step 3: Definition of entities and their decision-making alternatives.

The entities within the model were set as the following.

• CEO.
• Procurement manager.
• Distribution manager of the operative divisions.
• Continent administrative units or governments.
• Volunteers.
• Donors.

Each of the above-mentioned entities have “three potential human types” depending on their motivation. The motives explained in the previous section can be used to define three basic types of human beings that were considered within the simulation.

• Extrinsic human type: This type’s extrinsic motive dominates the other two moti-vational types. This human type acts according to the legal and ethical regulations of the charity by trying to reach pre-defined economic performance only if the necessary actions are aligned with its interests; otherwise, this type can act against legal regulations or ethical values in order to follow its reasons of self-interest, such as money, power, or praise. “Use the job for own interest with-out/partially considering legal regulations and ethical values of the organization.” This type can consider legal regulations or ethical values in order to reduce the risks of its actions or when they are a mean for its interest.
• Intrinsic human type: This type’s intrinsic motive dominates the other two moti-vational types. This human type acts according to the legal and ethical regulations of the charity by trying to reach pre-defined economic performance. “Do the job correctly according to legal regulations and ethical values of the organization.”
• Transcendent human type: This type’s transcendent motive dominates the other two motivational types. This human type acts according to the legal and ethical regulations of the charity by trying to reach pre-defined economic performance only if the necessary actions are aligned to the goal of service to other humans and increasing global system efficiency. “Improve the condition of the system and therefore human beings around my position by means of the job.” This type can go even beyond the established regulations and job descriptions in order to distribute resources according to equilibrium of humans as equals.

The decision-making process is influenced by person type, so the human type of each entity within the model could be determined based on these three basic categories. Therefore, alternatives could be derived from the type of humans in certain positions of the system.

Step 4: Associations between service management tasks and decision makers.

• CEO: This person makes decisions regarding service strategy planning.
  • Service strategy planning: The CEO defines which continents are first supplied and which criteria are used to classified continents.
  • Target system: The CEO defines the charity’s desired level of food need satisfaction. These tasks are closely related to the human type of the charity´s CEO.
• Procurement manager: This person makes decision regarding the supplier selection task and procurement planning.
• Distribution manager: This person makes decision regarding supplier selection, distribution planning, and order coordination.
• Continent administrative units or governments: These influence sales planning and resource planning, as well as the continuous observation and evaluation of the charity environment.
  • Sales and resource planning: This entity provides data to the charity, which influences the demand data used for forecasting future needs. Resource planning is performed according to this input.
  • Continuous observation and evaluation of the charity environment: The provided data also influence the observation (and therefore evaluation) of the actions performed by the charity.
• Volunteers: These people influence order coordination and execution tasks, as well as the measurement and calculation of key performance indicators.
• Donors: These people influence the service offer, service strategy, and internal performance of the charity.
  • Service offer: If there are more donations, the service offer can increase.
  • Service strategy: Depending on the donor and its claims, the charity´s CEO can vary the service strategy.
  • Internal performance: This is influenced by the donors if the amount of donations depends on performance.

Step 5: Identification of interrelationships through CLDs including the human factor’s impact on decision making: Based on the previously described factors, the interrelationships between entities and parameters were defined.

• Customer/user management CLD: This represents the parameters that influence the global service to end users around the world; it considers volunteers and charity staff, calories delivered, charity´s image, and the satisfaction rate of global food demand.
• Demand management CLD: This describes the interrelationships between demand information, demand volatility, demand patterns, food production, imports and exports, forecast value and error, food crisis, and global food need or demand.
• Procurement management CLD: This considers the number of suppliers, the price of supplied food, the level of competition, and the efficiency of the supplier operations.
• Distribution management CLD: This considers the number of distributors, the price and lead time of distribution, the efficiency of distribution in terms of distribution or transportation waste, and the stock levels across the distribution network.
• Human factor management CLDs: These CLDs describe the causal relationships influenced by the types of people in certain positions within the charity.
• Economic management CLD: This considers of all factors that impact the economic performance and resources of the charity within the model. It can be seen in Figure 2. It was developed for the other CLDs following the same methodology.

An important step in the research process was the identification of the loops and parameters within the CLDs that were influenced by human type so that we could determine the impact of decision makers on the whole system.

Step 6: Identification of the necessary information flows for better decision making in charities: The information flows for the management of the charity are depicted in Figure 3. Information, material, and financial flows are shown for the strategic, tactical, and operative management tasks including the entities and decision makers of the model.

Step 7: Derivation of logical rules for the impact of the human factor while considering its motives and the RSC concept during the industrial revolutions (IRs): The logical rules within the model are represented by the following logical rule.

• Human type equals 2: The human type of this decision maker is a transcendent human type.
• Human type equals 1: The human type of this decision maker is an intrinsic human type.
• Human type equals 0: The human type of this decision maker is an extrinsic human type.
• According to these values, the decisions made by decision makers are a consequence of the motives of the human type. In the industrial revolution context, the human factor was considered within the model as follows.
• Human factor in the first industrial revolution: The human type for a decision maker during the first industrial revolution was totally random. Moreover, it could be considered that extrinsic motivations were predominant among decision makers. As a consequence, 50% were considered to be extrinsic, 33% were considered to be intrinsic, and 17% were considered to be transcendent.
• Human factor in the second industrial revolution: Reward-based management of the human factor and the standardization of activities were introduced. This caused decision makers to transform from the extrinsic type into intrinsic type due to the fact that the intrinsic type also had financial motives, at least partially. Accordingly, 33% were considered to be extrinsic, 50% were considered to be intrinsic, and 17% were considered to be transcendent.
• Human factor in the third industrial revolution: The lean movement (initiated in the 1950s) influenced the management style of decision makers by considering ethical values such as respect for others, continuous improvement, and prioritizing people. Additionally, CSR has provided guidelines for management decisions since the 1960s. Therefore, 17% were considered to be extrinsic, 50% were considered to be intrinsic, and 33% were considered to be transcendent.
• Human factor in the fourth industrial revolution: With the capabilities of the fourth industrial revolution, the identification of extrinsic leaders and the development of more philanthropic leaders is expected. Accordingly, 50% were considered to be intrinsic and 50% were considered to be transcendent in the simulation model.

Step 8: Implementation of potential measures based on industrial management during the industrial revolutions in the conceptual model of a charity for the improvement of the target system: Based on the capabilities of the different industrial revolutions, measures for each of them were derived.

Measures for the third industrial revolution, apart from those related to the human factor, were set as follows.

• Demand management: Advanced forecasting techniques are often used while governments and authorities act as intermediaries, with a time delay for providing the required demand information.
• Procurement management: Food prices decrease due to management efficiencies.
• Distribution management: Less transportation waste leads to decreased distribution lead times and prices.

Measures for the fourth industrial revolution, apart from those related to the human factor, were set as follows.

• Demand management: Real-time demand information is available; governments and authorities are not needed as intermediaries for data input.
• Procurement management: Food prices decrease due to the automation of food production plants.
• Distribution management: On-site production with portable production plants and technologies enable no transportation waste and minimum distribution lead times.

Step 9: Development of a quantitative approach based on a system dynamics simulation for a charity functional area such as food: For the quantification of the simulation results, parameters, such as the average calory consumption per person and per day, were determined and used within the simulation.

4.2. Simulation Case Study of the Global Food Supply Area of a Welfare Organization

The four simulation models and their characteristics were as follows.

• 1st Industrial Revolution: As a consequence of the consideration of the human factor of the first industrial revolution, this model partially follows the principles of CSR, which means that people with decision-making power (such as administration authorities) are not all responsible for all four aspects: economic, legal, ethical and philanthropic. In the simulation, this was represented by the fact that the continent’s decision maker increased the region’s demand for food, then argued for higher rejection rates, and made sales on the black market to earn more money. This action was not economically responsible, legal, ethical, or philanthropic.
• 2nd Industrial Revolution: This model considers not only the specialization of activities that reduce production costs and increase quality but also the first human factor for motivation and management.
• 3rd Industrial Revolution: This model considers the human factor and operational efficiencies following the improvements of the industrial revolutions and CSR principles.
• 4th Industrial Revolution: This model considers the principles of CSR and improvements following industrial revolutions. It was built on the basis of the third simulation model, with the addition of the capabilities of the fourth industrial revolution.

First, a series of assumptions were defined to simplify the model to focus on the objective of the simulation.

• There were non-variable material transport times.
• The provision and manufacture of food products was as given.
• There were four models, with no variants within them.
• The need did not change with the quality of customer service.
• The population was considered stable during the simulated period.
• The average food need per day was set to 2250 calories regardless of the region.
• The distribution of food within the continent was considered homogeneous.
• Storage or transport limitations on the continents were not considered.

The following conditions enabled comparisons between the four models.

The same initial stock data: consumption per necessary person (need).

• The same capacity for internal food production in all simulations regardless of the scenario.
• The same percentages of calory loss from production to consumption.
• The same demand and demand patterns.
• The same capabilities for service delivery, such as food delivery.
• Same transport times depending on the location.

The objective of the simulation was to compare the objective parameters of the four models described for the following scenarios.

• Scenario 1: Stable internal production situation for the continents without food crises added to the starting situation.
• Scenario 2: War in Africa: The existing food stock is reduced by 30%, the internal production of the continent is reduced by 1/3, and the transport time increases by 20%. This situation lasts two years.
• Scenario 3: Tornado in North America: The existing food stock is reduced by 50%, the internal production of the continent is reduced by 20%, and the transport time increases by 10%. This situation lasts six months.
• Scenario 4: Tsunami in Asia: The existing food stock is reduced by 50%, the internal production of the continent is reduced by 1/3, and the transport time increases by 20%. This situation lasts six months.

The descriptive model of the simulation model was divided into six geographical areas based on the internal food production capacity and needs associated with the population of each area, as extracted from UN data [61].

• Continent 1: Africa.
• Continent 2: Europe.
• Continent 3: Asia.
• Continent 4: North America.
• Continent 5: Caribbean and South America.
• Continent 6: Oceania and others.

First, the modeler had to define temporal constraints, that is, the time horizon and time units. It was easy to accomplish this step by setting the duration of the simulation. In the case study, it was decided to simulate three years to evaluate influence in the short-, medium-, and long-term.

• Initial time: 0 days.
• Final time: 1100 days.
• Time step: 1 day.
• Units of time: day.

The validation of simulation models was performed using different methods. In this process, some simulation variables were used to observe their behavior and consequently evaluate whether the models were validated. Sterman defined 12 possible methods to validate system dynamics models. One of the most relevant of these is the test of extreme values, which demonstrates that the response of a model is plausible when extreme values are used for different input parameters [62]. The same input and output variables were chosen to analyze and validate all models. The input variables comprised the human factor type of the CEO of the charity, the distribution lead time, and the utilization rate of calories.

After changing these variables, the following results were calculated in order to validate the logic of the simulation model:

• For example, as shown in Figure 4, if the type of CEO was assigned less value, the charity’s impact, average satisfaction rate, and average efficiency rate had to be lower because the decision-making options and therefore the potentials for the most efficient distribution of resources were limited.
• As shown in Figure 5, with a higher distribution lead time, distribution costs and stocks had to be higher and net working capital had to be lower.
• As shown in Figure 6, with a higher utilization of calories, global food need had to be lower and satisfaction rate and service level had to be higher.

Because we obtained the expected and logical values from the analyses of extreme values, the model was validated.

4.3. Simulation Case Study: Results

The simulation results for the four models with the four scenarios are presented in

Table 1. Simulation results for the first scenario: Stable Scenario.

No.	Key Indicator	Simulation Models
		1st Industrial Revolution Model	2nd Industrial Revolution Model	3rd Industrial Revolution Model	4th Industrial Revolution Model
1	Ø Satisfaction Rate (%)	96.27	97.72	99.61	99.84
2	Ø Charity´s Impact (million persons)	665.6	837.8	531.4	436.9
3	Ø Service Level (%)	56.1	49.3	83.3	83.3
4	Σ Demand (bill. calories)	25.39	25.29	23.09	21.34
5	Σ Consumption (bill. calories)	24.45	24.82	22.99	21.30
6	Σ Food Delivered & used (bill. calories)	1.65	2.07	1.32	1.08
7	Ø Efficiency rate (%)	57.6	72.6	94.7	98.8
8	Ø Utilization rate of calories (%)	75.0	75.0	82.5	89.3
9	Σ Stock (bill. calories)	19.22	27.40	5.55	4.40
10	Σ Distribution costs (USD mill.)	12,806	18,184	1823	1380
11	Σ Donations(USD mill.)	34,901	40,437	41,130	35,144
12	Σ Net Working Capital (USD mill.)	4591	2305	30,131	28,202

,

Table 2. Simulation results for the second scenario: War Crisis in Africa.

No.	Key Indicator	Simulation Models
		1st Industrial Revolution Model	2nd Industrial Revolution Model	3rd Industrial Revolution Model	4th Industrial Revolution Model
1	Ø Satisfaction Rate (%)	93.35	94.16	99.52	99.56
2	Ø Charity´s Impact (million persons)	582.5	676.8	739.4	629.8
3	Ø Service Level (%)	52.4	48.9	87.3	87.7
4	Σ Demand (bill. calories)	25.39	25.29	23.09	21.34
5	Σ Consumption (bill. calories)	23.70	23.91	22.97	21.25
6	Σ Food Delivered & used (bill. calories)	1.44	1.68	1.83	1.56
7	Ø Efficiency rate (%)	47.9	55.8	95.0	95.6
8	Ø Utilization rate of calories (%)	75.0	75.0	82.5	89.3
9	Σ Stock (bill. calories)	17.97	22.41	12.23	9.27
10	Σ Distribution costs (USD mill.)	11,670	14,609	3843	2008
11	Σ Donations (USD mill.)	33,540	36,272	57,543	48,954
12	Σ Net Working Capital (USD mill.)	5903	4200	43,069	40,490

,

Table 3. Simulation results for the third scenario: Tornado in North America.

No.	Key Indicator	Simulation Models
		1st Industrial Revolution Model	2nd Industrial Revolution Model	3rd Industrial Revolution Model	4th Industrial Revolution Model
1	Ø Satisfaction Rate (%)	96.22	97.53	99.82	99.84
2	Ø Charity´s Impact (million persons)	660.9	818.6	551.0	421.4
3	Ø Service Level (%)	55.0	47.8	83.3	83.3
4	Σ Demand (bill. calories)	25.39	25.29	23.09	21.34
5	Σ Consumption (bill. calories)	24.44	24.77	23.04	21.27
6	Σ Food Delivered & used (bill. calories)	1.64	2.03	1.36	1.04
7	Ø Efficiency rate (%)	58.4	72.2	98.2	94.7
8	Ø Utilization rate of calories (%)	75.0	75.0	82.5	89.3
9	Σ Stock (bill. calories)	19.65	27.47	5.85	4.90
10	Σ Distribution costs (USD mill.)	12,750	17,657	2486	1012
11	Σ Donations (USD mill.)	34,805	39,943	44,310	32,631
12	Σ Net Working Capital (USD mill.)	4631	2573	32,611	26,045

and

Table 4. Simulation results for the fourth scenario: Tsunami in Asia.

No.	Key Indicator	Simulation Models
		1st Industrial Revolution Model	2nd Industrial Revolution Model	3rd Industrial Revolution Model	4th Industrial Revolution Model
1	Ø Satisfaction Rate (%)	92.38	94.67	97.49	98.91
2	Ø Charity´s Impact (million persons)	590.9	840.1	615.1	570.6
3	Ø Service Level (%)	56.0	54.0	80.6	79.4
4	Σ Demand (bill. calories)	25.39	25.29	23.09	21.34
5	Σ Consumption (bill. calories)	23.46	24.04	22.51	21.11
6	Σ Food Delivered & used (bill. calories)	1.46	2.08	1.52	1.41
7	Ø Efficiency rate (%)	49.8	69.0	90.3	95.5
8	Ø Utilization rate of calories (%)	75.0	75.0	82.5	89.3
9	Σ Stock (bill. calories)	17.04	19.06	7.90	7.06
10	Σ Distribution costs (USD mill.)	11,246	19,061	2569	2288
11	Σ Donations (USD mill.)	33,364	39,510	43,501	43,748
12	Σ Net Working Capital (USD mill.)	5878	0197	30,917	35,220

.

4.4. Simulation Case Study: Interpretation of Results

In the first simulation scenario, the satisfaction rate of the world population was more than 3% higher for the 4th IR model than the 1st IR model. On the other hand, the charity´s impact was more than 400 million persons lower than that of the 2nd IR model because the 2nd IR model presented a human factor with a more partial alignment with CSR than the 4th IR model, as well as due to more efficient production and distribution processes leading to a lower internal demand because the continents were able to satisfy their needs in higher percentages without external support and thus gain autonomy thanks to industrial revolution measures. Moreover, while the first two industrial revolution models presented service levels of around 50%, the 3rd and 4th IR models reached levels of more than 80% while also securing more than four-fold lower stock levels. Due to these levels and the overall system efficiency, the distribution costs and NWC of the 4th IR model were more than ten times lower and higher, respectively, than those of the 2nd IR model. The results of this simulation showed that greater impacts, higher expenses, and higher stocks are not necessarily better for quantitively and temporally satisfying food needs if the human factor and global effectiveness and efficiency are not optimized. If they are optimized, the hypothesis proposed at the beginning of this paper can be proven, with a remaining NWC of around 30,000 million US $ for future crisis or other society needs that the charity intends to deal with.

In the second scenario, the satisfaction rate of world population was found to be more than 6% higher for the 4th IR model than that of 1st IR model. On the other hand, the charity´s impact was more than 40 million persons lower than that of the 2nd IR model. Moreover, while the first two industrial revolutions presented service levels of around 50%, the 3rd and 4th IR models reached levels of almost 90% while also securing more than two times lower stock levels. Due to these levels and the overall system efficiency, the distribution costs and NWC of the 4th IR model were more than seven and ten times lower, respectively, than those of the 2nd IR model. The results of this scenario also demonstrate how greater impacts, higher expenses, and higher stocks are not necessarily better for quantitively and temporally satisfying food needs.

In the third scenario, the satisfaction rates of world population were approximately 100% and 96% for the 4th and 1st IR models, respectively. On the other hand, the charity´s impact of the 4th IR model was half the impact of the 2nd IR model. Moreover, while the first two industrial revolutions presented service levels of around 50%, the 3rd and 4th IR models presented levels of almost 85% while also securing more than four times lower stock levels. Due to these levels and overall system efficiency, the distribution costs of the 4th and 2nd IR models were slightly above 1000 million and 17,000 million US $, respectively. Accordingly, the NWC was below 5000 million US $ for the 1st and 2nd IR models, while it reached more than 25,000 million US $ for the 3rd and 4th IR models.

In the fourth scenario, the satisfaction rates of world population were approximately 99% and 92% for the 4th and 1st IR models, respectively. On the other hand, the charity´s impact of the 4th IR model was the same as that of the 1st IR model, although the first two industrial revolutions presented service levels of around 50% and the 4th IR model presented levels of almost 80% while also securing more than two times lower stock levels. Due to these levels and the overall system efficiency, the distribution costs of the 4th IR model were more than eight times lower than the distribution costs of the 2nd IR model. Accordingly, the NWC was almost zero for the 2nd IR model but more than 35,000 million US $ for the 4th IR model.

In summation, four scenarios for the four simulation models were studied. In all four scenarios, the satisfaction rate and service level increased over time with the industrial revolutions, with an exception in the service level from the third to the fourth industrial revolutions due to the discrete nature of the parameter and the higher distribution of resources in the fourth industrial revolution. Moreover, the charity´s impact increased from the first to the second industrial revolutions but decreased in the third and fourth revolutions due to the higher utilization rate of calories. This effect occurred because a higher utilization rate of calories led to a decrease in global food demand and, therefore, the maximum potential action that needed to be delivered by the charity.

In addition, the efficiency rate of the charity´s action increased in time with the industrial revolutions, with the exception in the third scenario from the third to the fourth industrial revolution due to the lower need of action; there was a difference of only 3.5%. Additionally, stocks and distribution costs increased from the first to the second industrial revolutions due to increases in distribution volume. Both later drastically decreased in the third and fourth industrial revolutions.

The results showed that donations increased until the third industrial revolution and decreased in the fourth revolution due to lower global food needs. On the other hand, net working capital was low for the first and second industrial revolutions but high for the third and fourth ones. This means that in the first and the second industrial revolutions, all donations were needed to fulfill global food needs, while in the third and fourth industrial revolutions, there was a donation surplus.

According to these analyses, it can be said that if all current capabilities are applied, there are lower financial needs to satisfy the required food needs, so resources could be used to satisfied other needs of the Maslow´s pyramid or to invest in new or a portable food production plants.

The results also showed that the NPO’s role in satisfying the population’s needs (food in this case) was not directly related to expenses but to the real impact on persons that were supported by the measures. Therefore, the findings clearly demonstrate how an effective and efficient system that aligns the human factor with CSR can enable welfare organizations to fulfill their business functions for social welfare while reducing expenses and delays in service provision, thus allowing for economic surplus for other welfare activities.

Ultimately, the results support our initial hypothesis because the models presented better results regarding the satisfaction, efficiency, and economic indicators over time with further the industrial revolutions. These improvements resulted from the consideration of the human factor in tandem with motives and CSR principles over the course of the industrial revolutions, thus leading to an efficient and effective global system.

5. Discussion

Previous studies have attempted to integrate the human factor into service activities [63]. Barbieri et al. proposed a methodology that allowed them to consider the contribution of human factors and risk profiles in service provision [63], but there was no formal description how human motivations and interests were modelled and how these impacted the service provision. Furthermore, other studies have considered the human factor and competencies within business process management [64], although there has been no description of how human factors were modelled and considered in business decision making; the present research provides a framework to model them and assess their influence. The authors of other studies have analyzed the evolution and impacts of the industrial revolutions, e.g., Trenovski and Merdzan, who studied the impact of the fourth industrial revolution on the global economy [65], though they considered the human factor in terms of production agents and not in terms of the human factor’s effect on decision making. Furthermore, Søbjerg et el. described the challenges of human decision-making processes based on professional judgements; they advocated for the use of statistics to develop risk assessments [66], but they did not present any formal recommendation for quantifying and applying these assessments (which the presented model and application do). In addition, and in contrast to the present research, no previous studies have presented modelling techniques for applying CSR principles to decision-making processes. As a result, our research has satisfied its expected contributions: the effects of the human factor on decision making, as well as resulting implications, were modelled in the context of changing industrial revolutions while considering CSR for welfare organizations. Our results demonstrated that the service provision of the organization was better when human decision-making followed CSR and that the industrial management techniques improved the efficiency of the studied welfare organization over time with changing industrial revolutions, thus enabling the greater satisfaction of needs.

6. Recommendations for Managers

According to our results, the human factor plays a significant role in business operations. Despite the supposed all-importance of leadership, the effects of the motivations and interests of subordinated employees are not often considered in practice. Consequently, many managers cannot assess the efficiency and behavior of their employees. This section presents a set of suggestions for managers for further consideration when managing a planning team within any organization, thus providing a framework for managerial positions to improve their capabilities.

• Break-down department functions into tasks: First, one should know how to convert high-level functions into specific tasks. This can help managers understand the complexity of the full service set they provide to their organization, as well as how the service is realized.
• Define clear input and output data for each task: One should describe tasks as a process flow in which data are received from one department and then processed by another department in order to determine an output that could be a service for the company and the related amount of resources such as money, time, or procurement costs need to create this output.
• Build employee profiles: One should describe employees’ qualifications, past and present job activities, and potential for future activities in accordance with their motivations and interests.
• Assign task to employees: One should determine who performs which tasks within the functional areas. The interrelationships with machines, computers, objects, and related information systems should be taken into consideration.
• Rotating model for each task: One should develop a rotation model for each specific task, i.e., for the time period in which one employee performs specific tasks and other time periods in which other employees perform the same tasks. For this step, it should be ensured that all employees have a same or similar training level for the assigned tasks.
• Evaluate task output and compare performance between employees: This step consists of measuring the indicators defining the decision making and execution activities of an employee while performing their assigned task.
• Assess deviations of the output statistically: One should identify deviations among employees and determine their values and characteristics.
• Identify causes for deviations: One should identify whether a deviation is caused by the input data, the planner, the employee, or the execution team.
• Measures for increasing performance: After having identified the deviation, one should determine measures to prevent the deviation in the future.
• Describe the employee profile of each employee (% intrinsic, % extrinsic, and % transcendent): After having performed the aforementioned steps on a regular basis, one should perform a rolling review of the assessment of each employee profile.
• Go back to step 3.

7. Conclusions and Outlook

This study proves the importance of the design and management of service organizations that consider the human factor. The decision making of human beings influences the planning of tasks from the identification of a need to the distribution and execution of the measures needed to satisfy it. The authors of this paper were able to identify the objectives, indicators, and functions of service management in welfare organizations such as charities. Consequently, the need for new approaches in the coordination of service management in welfare organizations has been proven. The modeling a welfare organization demonstrated that the mission and objectives of corporate social responsibility provide commitments to increase relevance, image, and perception for both end customers and current and potential stakeholders. Therefore, a welfare organization oriented towards CSR goals can create a positive feedback cycle between donations, society, the environment, and the economy. When applied to a charity, this model can help to identify the real needs of people and to enable the more efficient distribution of resources due to economic, legal, ethical, and philanthropic responsibilities. The presented approach was proven by applying it to a system dynamics model with the necessary notation and functions. The case study demonstrated how the developed model can improves adaptability to the needs of society and internal performance to maximize the performance of available resources. According to the results of our comparisons, the application of CSR in decision makers over changing industrial revolutions can result in strong satisfaction, efficiency, and economic indicators. The relevance of the human factor in decision-making processes was also demonstrated by our comparisons. Furthermore, we found that industrial revolution improvements have enabled organizations to deal with current and future challenges in the service sector, especially in charities.

As a result, we have proven that considering the human factor in tandem with the technological and production system innovations of industrial revolutions allows for increases in the welfare of individuals within a given social environment.

Finally, it is important to point out new research paths or new ways to continue improving the current project. The next step of the investigation will be to apply CSR and the potential industry revolution measures in real service organizations such as charities to prove our model’s potential and applicability. Moreover, our analysis can be extended by considering human motives and types in more detail with an in depth literature analysis. In addition, the detailed analyses of the functionalities and comparison of the current management systems in charities can be conducted to improve the current model and its applicability. Finally, a future research area may be expanding the consideration of the human factor by introducing learning effects and experience to the conceptions of the motives that influence the decision-making processes of managers.

In conclusion, this study has proven the impact of the human factor on business efficiency and quantified it in the context of changing industrial revolutions. This study has also shown that the developed model can be used with system dynamics to simulate the human factor in the context of decision making along with the objective of CSR. By using this model, an organization could identify distortion elements and potentials for the application of CSR policies.