Systems, Volume 8, Issue 4 (December 2020) – 21 articles

The main purpose of this paper was to introduce procedural changes based on a type of human resources management tool using Lean Leadership (LL). We sought to determine which Lean Tools (LTs) are able to promote a rapid impact on a company; namely, we aimed to determine which LTs are able to facilitate fast and positive results for companies after their implementation, thus allowing for quick wins. This study was conducted at a medium-sized Portuguese company in the metalworking sector. Results were assessed from two surveys (involving 17 people) regarding perceptions of employee awareness in the machining area for Quality, Maintenance/Equipment, Process/Products, Leadership/Management by Objectives, and Trust and Training. The surveys were conducted at the beginning of the study and at the end of a quarterly period. LTs were used over a period of three months, thus enabling changes to the company’s manufacturing processes which could lead to improved organization, orientation, consistency and viability. Three months were considered an adequate time period to measure whether the tools were effective in the production of quick wins or not. LL allowed for greater awareness of the importance of LTs, the most important feature of which is the aggregation of all of the employees’ energies, which culminates in better operational efficiency and an increase in the employees’ overall positive perceptions of their company’s principles and mindset. The introduction of LL, as well as the implementation of 10 LTs, which are considered to be Quick-Win Tools, enabled employees to improve their perceptions of their company by 6.1%. Lean tools can be implemented quickly and are easily understood by their intended users. These benefits provide additional motivation for their use and for the achievement of highly satisfactory operating results across production areas. This study aims to identify which lean tools allow companies to obtain benefits/gains quickly. This outcome is the main contribution of this study to the scientific community and to companies. A Quick Win means a rapid improvement in a short period of time. As such, lean tools that provide Quick Wins also contribute to economic development. Due to the restricted time period imposed by the main purpose of this study, statistical analysis of the results was unfeasible. Full article

Dynamical systems are known to exhibit sudden state transitions, with abrupt shifts from one stable state to another. Such transitions are widely observed, with examples ranging from abrupt extinctions of species in ecosystems to unexpected financial crises in the economy or sudden changes in medical conditions. Statistical methods known as early warning signals (EWSs) are used to predict these transitions. In most studies to date, EWSs have been tested on data generated using equation-based methods that represent a system’s aggregate state and thus show limitations in considering the interactions of a system at the component level. Agent-based models offer an alternative without these limitations. This study compares the performance of EWSs when applied to data from an equation-based and from an agent-based version of the Ising model. The results provide a reason to consider agent-based modelling a promising complementary method for investigating the predictability of state changes with EWSs. Full article

This paper combines the good regulator theorem with the law of requisite variety and seven other requisites that are necessary and sufficient for a cybernetic regulator to be effective and ethical. The ethical regulator theorem provides a basis for systematically evaluating and improving the adequacy of existing or proposed designs for systems that make decisions that can have ethical consequences; regardless of whether the regulators are humans, machines, cyberanthropic hybrids, organizations, or government institutions. The theorem is used to define an ethical design process that has potentially far-reaching implications for society. A six-level framework is proposed for classifying cybernetic and superintelligent systems, which highlights the existence of a possibility-space bifurcation in our future time-line. The implementation of “super-ethical” systems is identified as an urgent imperative for humanity to avoid the danger that superintelligent machines might lead to a technological dystopia. It is proposed to define third-order cybernetics as the cybernetics of ethical systems. Concrete actions, a grand challenge, and a vision of a super-ethical society are proposed to help steer the future of the human race and our wonderful planet towards a realistically achievable minimum viable cyberanthropic utopia. Full article

Many authors have sought to summarize what they regard as the key features of “complexity”. Some concentrate on the complexity they see as existing in the world—on “ontological complexity”. Others highlight “cognitive complexity”—the complexity they see arising from the different interpretations of the world held by observers. Others recognize the added difficulties flowing from the interactions between “ontological” and “cognitive” complexity. Using the example of the Covid-19 pandemic in the UK, and the responses to it, the purpose of this paper is to show that the way we understand complexity makes a huge difference to how we respond to crises of this type. Inadequate conceptualizations of complexity lead to poor responses that can make matters worse. Different understandings of complexity are discussed and related to strategies proposed for combatting the pandemic. It is argued that a “critical systems thinking” approach to complexity provides the most appropriate understanding of the phenomenon and, at the same time, suggests which systems methodologies are best employed by decision makers in preparing for, and responding to, such crises. Full article

Risk perception can be quantified in measurable terms of risk aversion and sensitivity. While conducting research on the quantization of programmatic risk, a bridge between positive and normative decision theories was discovered through the application of a novel a priori relationship between objective and subjective probabilities and the application of Bernoulli’s expected utility theory. The Entropy Decision Risk Model (EDRM) derived using the Kullback–Liebler entropy divergence from certainty serves as a translation between objective and subjective probability, referred to as proximity, and has proven its applicability to various positive decision theories related to Prospect Theory. However, EDRM initially assumes the validity of the standard exponential power utility function ubiquitous to positive decision theory models as the magnitude of a choice to isolate and validate proximity. This research modifies the prior model by applying Daniel Bernoulli’s expected utility as the measure of choice magnitude in place of power utility. The revised model, EDRM Utility (EDRM-U), predicts the subject choices for both small and large ranges of values and shows that Prospect Theory’s neutral reference point is actually centered about an assumed initial wealth value, called neutral wealth, that correlates to a power utility exponent value. This hypothesis is confirmed by demonstrating that EDRM-U presents an equivalent or better correlation with prior research in eleven landmark studies of college students spanning more than 26 years and comprising over 300 problems, including those with widely varying values. This research contributes to the fields of risk management and decision engineering by proposing a decision model that behaves according to both positive and normative decision theories and provides measures of risk perception. Full article

Today, there is a great need for greener urban freight transportations due to their ever-increasing environmental impact. The planet’s climate has been significantly affected as the temperature is constantly rising and extreme weather events are occurring more and more often. Aiming to reduce the environmental impact of freight transportation in urban areas, an advanced vehicle routing and scheduling system for improving urban freight transportations, has been developed. This paper presents the functionality of the advanced system, while also analyzing its subsystems and demonstrating its use in a case study. The system is provided as an integrated cloud-based software to support the needs of logistics companies, in order to efficiently schedule their deliveries and perform the routing of their vehicles. The utilized multi-objective algorithm produces solutions that minimize either the distribution cost or the environmental emissions or a combination of these parameters. An application of the system is performed for validation purposes, concerning the comparison of the system’s results with corresponding real-life data provided by a medium-sized logistics company. The results of the testing reveal its significant contribution to the reduction of the environmental impact of the company’s distribution services. Full article

Previous research has established that embodied modeling (role-playing agents in a system) can support learning about complexity. Separately, research has demonstrated that increasing the multimodal resources available to students can support sensemaking, particularly for students classified as English Learners. This study bridges these two bodies of research to consider how embodied models can strengthen an interconnected system of multimodal models created by a classroom. We explore how iteratively refining embodied modeling activities strengthened connections to other models, real-world phenomena, and multimodal representations. Through design-based research in a sixth grade classroom studying ecosystems, we refined embodied modeling activities initially conceived as supports for computational thinking and modeling. Across three iterative cycles, we illustrate how the conceptual and epistemic relationship between the computational and embodied model shifted, and we analyze how these shifts shaped opportunities for learning and participation by: (1) recognizing each student’s perspectives as critical for making sense of the model, (2) encouraging students to question and modify the “code” for the model, and (3) leveraging multimodal resources, including graphs, gestures, and student-generated language, for meaning-making. Through these shifts, the embodied model became a full-fledged component of the classroom’s model system and created more equitable opportunities for learning and participation. Full article

The purpose of this study was to investigate how computational modeling promotes systems thinking for English Learners (ELs) in fifth-grade science instruction. Individual student interviews were conducted with nine ELs about computational models of landfill bottle systems they had developed as part of a physical science unit. We found evidence of student engagement in four systems thinking practices. Students used data produced by their models to investigate the landfill bottle system as a whole (Practice 1). Students identified agents and their relationships in the system (Practice 2). Students thought in levels, shuttling between the agent and aggregate levels (Practice 3). However, while students could think in levels to develop their models, they struggled to engage in this practice when presented with novel scenarios (e.g., open vs. closed system). Finally, students communicated information about the system using multiple modalities and less-than-perfect English (Practice 4). Overall, these findings suggest that integrating computational modeling into standards-aligned science instruction can provide a rich context for fostering systems thinking among linguistically diverse elementary students. Full article

The uncertainty, or entropy, of an atom of an ideal gas being in a certain energy state mirrors the way people perceive uncertainty in the making of decisions, uncertainty that is related to unmeasurable subjective probability. It is well established that subjects evaluate risk decisions involving uncertain choices using subjective probability rather than objective, which is usually calculated using empirically derived decision weights, such as those described in Prospect Theory; however, an exact objective–subjective probability relationship can be derived from statistical mechanics and information theory using Kullback–Leibler entropy divergence. The resulting Entropy Decision Risk Model (EDRM) is based upon proximity or nearness to a state and is predictive rather than descriptive. A priori EDRM, without factors or corrections, accurately aligns with the results of prior decision making under uncertainty (DMUU) studies, including Prospect Theory and others. This research is a first step towards the broader effort of quantifying financial, programmatic, and safety risk decisions in fungible terms, which applies proximity (i.e., subjective probability) with power utility to evaluate choice preference of gains, losses, and mixtures of the two in terms of a new parameter referred to as Prospect. To facilitate evaluation of the EDRM against prior studies reported in terms of the percentage of subjects selecting a choice, the Percentage Evaluation Model (PEM) is introduced to convert choice value results into subject response percentages, thereby permitting direct comparison of a utility model for the first time. Full article

The engineering rationale, composed of established logic for the design and development of products, has been confronted by a shift to a circular economy. Digitalization (e.g., Industry 4.0) enables transformation, but it also increases relational complexities in scope and number. In Product–Service Systems (PSSs), the combination of manufactured goods and services should be delivered in new business models based on value-adding digital assistance. From a systems science view, such combinations cannot be managed by the same approach as if they were one uniform system; rather, it is an interdependent mix of technical, social, and digital designs. This paper initializes an updated conceptual discourse on PSSs and provides a reflection on the expected challenges in the transformation from linear to circular models. For example, the role of systems thinking to guide early design stages is discussed and the importance of processes for creating shared visions at different systems levels is suggested to be addressed in future research. The intention is to formulate thoughts about radical cognitive changes in order to realize the PSS paradigm. Full article

There is an ongoing demand for organizations to become more agile in order to prosper amongst their competitors. Many military organizations have declared a renewed focus towards organizational agility. The goal of this research is to isolate the variables needed to measure organizational agility (OA) in military organizations, allowing for the future development of a suitable method to measure OA without the need to interact with outside organizations. This article begins by providing a suitable and formal definition of organizational agility by exploring and analyzing relevant scholarly literature on the subject. Related terms, such as organizational resiliency, flexibility, robustness, versatility, and adaptability are also explored to examine their definition boundaries and any overlapping areas. Existing methods to measure organizational agility are examined and summarized, and the current limitations to their application are highlighted. Previous studies to find characteristics associated with organizational agility were also examined, and an initial set of 88 organizational agility characteristics was built. Since these included possible redundant or overlapping characteristics, the Q-sort method was employed to discover, analyze, and eliminate redundant items from the dataset, ultimately resulting in 64 unique characteristics. The result is a suitable definition for organization agility applicable to military organizations and a list of potential associated characteristics that summarizes related research to date. This groundwork establishes the foundation to conduct a multi-organization study to further refine the characteristic list and ultimately develop a method to measure organizational agility. Full article

Additive manufacturing (AM) opens the vision of decentralised and individualised manufacturing, as a tailored product can be manufactured in proximity to the customers with minimal physical infrastructure required. Consequently, the digital infrastructure and systems solution becomes substantially more complex. There is always a need to design the entire digital system so that different partners (or stakeholders) access correct and relevant information and even support design iterations despite the heterogenous digital environments involved. This paper describes how the design and integration of a digital thread for AM can be approached. A system supporting a digital thread for AM kayak production has been designed and integrated in collaboration with a kayak manufacturer and a professional collaborative product lifecycle management (PLM) software and service provider. From the demonstrated system functionality, three key lessons learnt are clarified: (1) The need for developing a process model of the physical and digital flow in the early stages, (2) the separation between the data to be shared and the processing of data to perform each parties’ task, and (3) the development of an ad-hoc digital application for the involvement of new stakeholders in the AM digital flow, such as final users. The application of the digital thread system was demonstrated through a test of the overall concept by manufacturing a functional and individually customised kayak, printed remotely using AM (composed of a biocomposite containing 20% wood-based fibre). Full article

Economics has long been concerned with the development of tools to help understand and describe the interactions among economic actors including the circular flow of economic resources. This paper expands our available toolkit of models, by describing a novel dynamic equilibrium-seeking model of a closed economy. The model retains many of the key features of state-of-the-art Computable General Equilibrium (CGE) models including economic interdependence, input substitution, nested production functions, and so on. A distinguishing feature of this model is that it adopts price-related balancing feedback loops that simulate the self-regulating behaviour of a dynamic economic system. Our modelling shows not only equilibrium states (as per conventional CGE models), but the transition path toward an often-changing equilibrium. This facilitates the investigation of out-of-equilibrium dynamics and behaviour adaptation typical of largescale disruption events. Full article

A key requirement for an integrated digital tool chain is secure access and control of data assets. Not all stakeholders will have the same access to or control over the flow of information, some will be able to input or change data whilst others will only be able to read the data. Simply providing secure access protocols is not sufficient because copied data can quickly become disassociated and modified from its original instantiation, leading to its reuse elsewhere or later in the lifecycle but in an inappropriate way. Therefore, data management mechanisms are required that capture information about the data along with any decisions or modifications it has undergone during the course of its life, thus providing complete traceability for later validation purposes. This undertaking is essential across the systems engineering lifecycle. This pursuit involves controlling who can access and modify data within the lifecycle. This paper describes a solution to this by the introduction of blockchain technology, a relatively new technology that allows digital information to be distributed but not copied, making it an immutable set of time-stamped data managed by a network of connected systems and services. Though blockchain technology is not commonly referred to when discussing Industry 4.0, the technology’s capabilities should add value when applied in a context of data management and security within the lifecycle of a product or services and in conjunction with digital twins, big data, and IoT. This paper describes how permissioned blockchains can be implemented within a systems engineering lifecycle, providing example architecture patterns showing how data provenance can be maintained throughout. Full article

Today’s fast-growing demands at the global level for mobile applications (apps) cause customers to call for the customization of their apps to fit their individualized needs and business realms. Customization is a challenge for apps-development companies when they want to satisfy their numerous users in a crowded competitive market. Moreover, pursuing customization involves additional challenges when ramping up app development projects in order to meet demands at a larger scale. To address this challenge, we proposed a framework to systematize and support mobile apps’ development consistently with a customer-driven approach and modular design philosophy. From a practical point of view, the proposed framework integrates quality function deployment (QFD), axiomatic design (AD) principles, and practices from the ITIL (Information Technology Infrastructure Library) framework. The framework supports a systematic process for translating customer needs into design parameters as well as supporting prioritization of ITIL practices for further development. The effectiveness of the framework was explored in practice through a case study about an app supporting relief in the 2020 COVID-19 pandemic, as well as a survey among potential users. The assessment of the framework indicated an average score ranging between 3.58 and 3.92 in a five-point Likert scale for all of the items used in the survey. Full article

This paper is motivated by the notion that coupling systems allows for mitigating the failure of individual ones. We present a novel approach to determining asymptotic stability and robustness of a network consisting of coupled dynamical systems, where individual system dynamics are represented through polynomial or rational functions. The analysis relies on a local analysis; thus, making it computationally implementable. We present an efficient computational method that relies on semidefinite programming. Importantly, for cases where multiple equilibrium points exist, we show how to determine regions around an asymptotically stable equilibrium point that bounds solutions. These regions increase when systems are coupled as we observe when applying the presented analysis framework to a mathematical model of a continuous stirred tank reactor. Importantly, the presented work has implications to other fields as well. Full article

This work uses cognitive network science to reconstruct how experts, influential news outlets and social media perceived and reported the news “COVID-19 is a pandemic”. In an exploratory corpus of 1 public speech, 10 influential news media articles on the same news and 37,500 trending tweets, the same pandemic declaration elicited a wide spectrum of perceptions retrieved by automatic language processing. While the WHO adopted a narrative strategy of mitigating the pandemic by raising public concern, some news media promoted fear for economic repercussions, while others channelled trust in contagion containment through semantic associations with science. In Italy, the first country to adopt a nationwide lockdown, social discourse perceived the pandemic with anger and fear, emotions of grief elaboration, but also with trust, a useful mechanism for coping with threats. Whereas news mostly elicited individual emotions, social media promoted much richer perceptions, where negative and positive emotional states coexisted, and where trust mainly originated from politics-related jargon rather than from science. This indicates that social media linked the pandemics to institutions and their intervention policies. Since both trust and fear strongly influence people’s risk-averse behaviour and mental/physical wellbeing, identifying evidence for these emotions is key under a global health crisis. Cognitive network science opens the way to unveiling the emotional framings of massively read news in automatic ways, with relevance for better understanding how information was framed and perceived by large audiences. Full article

Systems involving agriculture and natural resources (AGNR) management and representing integrations of biologic, geologic, socio-economic, and climatic characteristics are incredibly complex. AGNR managers purport using a systems-oriented mental model while many observed management and policy strategies remain linear or symptom-driven. To improve AGNR professionals’ systems thinking abilities, two programs, the King Ranch^® Institute for Ranch Management at Texas A&M University-Kingsville (KRIRM) and the Honors College at South Dakota State University (SDSUHC), implemented the famous Production Distribution Simulation Game (a.k.a. the Beer Game) into their programs beginning in 2003 and 2011. A Beer Game database consisting of 10 years of trials or over 270 individual players was compared to seminal work in the literature as well as to one another. We found that AGNR managers and students performed worse than players in a seminal Beer Game study. More interestingly, we found that younger players adapted more readily to inventory surpluses by reducing the order rates and effective inventories significantly when compared to older players (p < 0.10 for retailer and distributors, and p < 0.05 for wholesales and factories). We substantiated our results to those in more recent studies of age-related decision-making and in the context of common learning disabilities. Lastly, we discuss some implications of such decision-making on 21st century AGNR problems and encourage AGNR disciplines to better integrate system dynamics-based education and collaboration in order to better prepare for such complex issues. Full article

For several decades, systems thinking has been a defined body of knowledge that has contributed to many areas of science. Its value has, critically, resided in (meta- or post-) paradigmatic and participative use of one or several systems approaches to help stakeholders’ structure and tackle complex problems. With renewed and (post-)pandemic interest in interdisciplinary work, this paper argues that to continue securing a future, system thinking requires a wider understanding of the dynamics and intertwining of knowledge unfolding and ethics in society. Two different but overlapping scenarios for systems thinking are proposed: (a) One based on inter-(disciplinary, para/professional, group) work and (b) another based on ethical vigilance. The first one is not so different from what has been envisaged for systems thinking in the last few years. Nevertheless, and following the ideas of the sociologist Andrew Abbott, this scenario proposes the explicit inclusion of the goal of knowledge rediscovery to promote a sense of solidarity, mutual understanding and compassion. For the second scenario, Michel Foucault’s notion of governmentality is used to problematize pandemic events and practices, and to offer possibilities for individual critical thinking and action, also leading us to consider the importance of (self-other) compassion. Features, implications, questions and examples of use are provided for each scenario. Full article

Human variability related to physical, cognitive, socio-demographic, and other factors can contribute to large differences in human performance. Quantifying population heterogeneity can be useful for designers wishing to evaluate design parameters such that a system design is robust to this variability. Comprehensively integrating human variability in the design process poses many challenges, such as limited access to a statistically representative population and limited data collection resources. This paper discusses two virtual population modeling approaches intended to be performed prior to in-person design validation studies to minimize these challenges by: (1) targeting recruitment of representative population strata and (2) reducing the candidate design parameters being validated in the target population. The first approach suggests the use of digital human models, virtual representations of humans that can simulate system interaction to eliminate candidate design parameters. The second approach suggests the use of existing human databases to identify relevant human characteristics for representative recruitment strata in subsequent studies. Two case studies are presented to demonstrate each approach, and the benefits and limitations of each are discussed. This paper demonstrates the benefit of modeling prior to conducting in-person human performance studies to minimize resource burden, which has significant implications on early design stages. Full article