Industry 4.0 in the contemporary operating context carries important sources of complexity. This context generates both traditional risks and emerging risks that must be managed. The management of these risks includes both industrial risks and occupational risks, since they are heavily interlinked. The human factor can be considered the main link between both types of risks. Thus, understanding risks originating from human errors and organizational weaknesses as causes of accidents and other disruptions in complex systems requires elaborating sophisticated modeling approaches. Therefore, the objective of this paper is to propose an organizational and human performance approach to improve the emerging risk management linked to the complex systems, like as Human-Machine Interactions (HMI) and Human-Robot Interaction (HRI). To fulfill this objective, we first introduce the concept of emerging risk linked to human factor. Then, we introduce the concept of emerging risk management in the Industry 4.0 context. Under this complex context, we expose the concept considering the current models of risk management. Finally, we discuss how enhancing human and organizational performance can be achieved through risk management in complex systems linked to Industry 4.0. Therefore, we conclude that while Industry 4.0 brings numerous advantages, it must contend with emerging risks and challenges associated with organizational and human factors. These emerging risks include industrial risks as well as occupational risks. Moreover, the human factor aspect of Industry 4.0 is directly linked to industrial emerging and occupational emerging via context of operations. To cope with these new challenges, it is necessary to develop new approaches. One of such approaches is Complex System Governance. This approach is discussed along with the need for adequate organizational and human performance models dealing with, for example, experience from other domains such as nuclear, space, aviation, and petrochemical.
The concept “Industry 4.0” has its origin in a “strategic initiative” of the German government in 2011 [
In contrast to conventional forecast based production planning, Industry 4.0 enables real-time planning of production plans as well as dynamic self-optimisation [
However, there is scarcity of literature discussing issue of complexity in the context of Industry 4.0. Table
Number of scientific publications on complexity and Industry 4.0 (Results from the Web of Science. Timespan: 2010-2018; All databases; Field tag: Topic).
Year | Complexity | Industry 4.0 | Industry 4.0 and complexity |
---|---|---|---|
2018 | 70810 | 1555 | 69 |
2017 | 73548 | 1136 | 63 |
2016 | 82644 | 561 | 48 |
2015 | 67325 | 191 | 9 |
2014 | 59552 | 69 | 4 |
2013 | 55519 | 23 | 3 |
2012 | 51013 | 4 | 0 |
2011 | 48151 | 0 | 0 |
2010 | 46884 | 0 | 0 |
In relation to enterprise production and operation, Industry 4.0 has four objectives [
Suffice to say that traditional industry has its own problems. When these problems are coupled with emerging paradigm of Industry 4.0 along with emerging complex, there emerges a need for development of rigorous and sophisticated approaches for risk management (i.e., traditional and emerging risk management). Roig and Brocal [
Interestingly, despite an increase in number of scientific publications on the subject of “Industry 4.0” (Table
Number of scientific publications on risk and emerging risk in the Industry 4.0 (Results from the Web of Science. Timespan: 2010-2018; All databases; Field tag: Topic).
Year | Risk | Risk management | Industry 4.0 and risk | Emerging risk | Management and emerging risk |
---|---|---|---|---|---|
2018 | 335.842 | 7155 | 66 | 84 | 19 |
2017 | 324.537 | 8226 | 55 | 88 | 21 |
2016 | 310.952 | 7762 | 27 | 90 | 17 |
2015 | 289.990 | 7210 | 7 | 71 | 19 |
2014 | 265.015 | 6461 | 1 | 81 | 20 |
2013 | 247.840 | 6173 | 0 | 74 | 17 |
2012 | 224.108 | 5884 | 0 | 66 | 16 |
2011 | 207.076 | 6050 | 0 | 62 | 14 |
2010 | 193.041 | 6092 | 0 | 79 | 11 |
Number of scientific publications on safety and occupational safety in the Industry 4.0 (Results from the Web of Science. Timespan: 2010-2018; All databases; Field tag: Topic).
Year | Safety | Industry and safety | Industry 4.0 and safety | Occupational safety | Industry 4.0 and occupational safety |
---|---|---|---|---|---|
2018 | 291.426 | 13.270 | 81 | 1940 | 7 |
2017 | 262.601 | 14.352 | 58 | 2248 | 7 |
2016 | 246.359 | 11.658 | 23 | 1858 | 1 |
2015 | 228.041 | 11.239 | 7 | 1536 | 1 |
2014 | 216.289 | 10.651 | 3 | 1606 | 0 |
2013 | 182.296 | 10.390 | 1 | 1596 | 0 |
2012 | 154.996 | 9.189 | 0 | 1690 | 0 |
2011 | 124.767 | 8.616 | 0 | 1343 | 0 |
2010 | 109.836 | 7.259 | 0 | 1229 | 0 |
The management of these emerging risks includes both industrial risks and occupational risks, since they are heavily linked [
Thus, understanding risks originating from human errors and organizational weaknesses as causes of accidents and other disruptions in complex systems requires elaborating sophisticated modeling approaches. Therefore, the objective of the present research is to define an approach for organizational and human performance that can be used to improve the emerging risk management linked to the complex systems under paradigm of Industry 4.0. To obtain this objective, the rest of this paper is organized as follows: Section Section Section
The definitions and risk models used in the professional and scientific fields are numerous. In this regard, Aven [
Main models on risk used in the professional and scientific fields (adapted from [
Model | Description | |
---|---|---|
(1) | R=E | Risk=Expected value (loss) |
(2) | R=P&C | Risk=Probability and scenarios/Consequences/severity of consequences |
(3) | R=C&U | Risk=Consequences/damage/severity of these + Uncertainty |
(4) | R=U | Risk=Uncertainty |
(5) | R=OU | Risk=Objective Uncertainty |
(6) | R=C | Risk=Event or consequence |
(7) | R=ISO | Risk=Event or consequence |
From a standardized perspective, ISO 31000:2018 standard indicates that a risk is usually expressed in terms of risk sources, potential events, their consequences, and their likelihood [
The application of these definitions and models needs adaptations and new approaches when dealing with emerging risks, which are discussed in the following sections.
Generally, when the term “emerging risk” is mentioned, this term refers to any risk that is new and/or increasing. However, other perspectives do exist. For example, the International Risk Governance Council [
Brocal et al. [
From this framework, Brocal et al. [
Possible combinations between the Ci conditions and the risk components (model (8)) that can form a NER: NR and/or IR (adapted from [
Conditions | Risk Components (model 8) | |||||
---|---|---|---|---|---|---|
Source of Risk (SR) | Causes | Events | Consequences | Likelihood | ||
NEW | C1: New technological or organizational variable | (SR,C1) | (C,C1) | — | — | — |
C2: New social perception | (SR,C2) | (C,C2) | (E,C2) | (CO,C2) | — | |
C3: New scientific knowledge | (SR,C3) | (C,C3) | (E,C3) | (CO,C3) | — | |
INCREASING | C4: Increase in the number of sources of risk | (SR,C4) | — | — | — | — |
C5: Increase in the likelihood of exposure | — | — | — | — | (L,C5) | |
C6: Increase health consequences | — | — | — | (CO,C6) | — |
Given that a NER is any risk that is new and/or increasing, Brocal et al. [
Considering the above aspects, Table
The terms ‘‘new and emerging risks (NERs)” and ‘‘emerging risks” are used as equivalent in this paper. However, some significant differences can be found in the technical and scientific literature. These differences, according to Brocal [
It would be desirable that the terminology regarding these risks (i.e., “new and emerging risks,” “emerging risks”) is standardized. In this case, the CWA 16649: 2013 document may prove to be the first step. Currently, International Organization for Standardization (ISO) is developing the ISO 31050 standard—Guidance for managing emerging risks to enhance resilience [
The effects of Industry 4.0 on OH&S generate advantages and drawbacks that could generate emerging risks [
Consequently, one often tries to use mental and intellectual “shortcuts” in finding “easy” explanations or solutions taking into account directly visible parts of the whole context only. The complexity of modern systems generates the opacity where some significant risks become systemic and may be well hidden and lurking beneath until conditions reunite for their full display. One of direct consequences of those changes is the nature of the risks which continue to occur. While undesirable events such as industrial accidents, process and supply chain disruptions, or bankruptcies formerly occurred from known causes and factors, contemporary events usually originate from unanticipated interactions between elements with no visible links [
The technological evolution, including the introduction of the concept of Industry 4.0, and the contemporary operating context significantly contribute to increasing complexity [
In this regard, concepts such as Human-Machine Interactions (HMI) and Human-Robot Interaction (HRI) can be considered among the most important [
The increase of this intelligent equipment can lead to connecting the causes of human error with the “smart machine error” [
Brocal et al. [
The increase in organizational complexity in manufacturing processes is changing from centralized decision-making towards decentralized instances. In decentralized instances, decision-making can be taken by workers or by equipment where artificial intelligence is integrated [
Based on the work of Brocal et al. [
From a global level, IRGC [
In relation to risk management, ISO 31000:2018 standard provides guidelines and a common approach to managing any type of risk. This standard defines risk management as coordinated activities to direct and control an organization with regard to risk. SRA [
In OH&S field, the ISO 45001:2018 standard defines management system as a set or interrelated or interacting elements of an organization to establish policies and objectives and processes to achieve those objectives; and OH&S management system is a management system or part of a management system used to achieve the OH&S policy.
IRGC [
The IRGC [ ENISA: European Union Agency for Network and Information Security EFSA: European Food Safety Authority Swiss Re SONAR system Dutch framework (emerging risks related to the use of chemicals) CEN workshop agreement (CWA) 16649:2013 (emerging risks related to technology)
CWA 16649:2013 proposes the Emerging Risk Management Framework (ERMF). The whole process is based on the concept that emerging risks go through a maturation process [
As discussed above, the introduction of the concept of Industry 4.0 brings numerous advantages, but also some new issues. It includes, among other things, emerging risks related to rising complexity of technological systems. One has limited knowledge upon them due to lack of long-term observation data. This situation is fairly challenging for management, organizations, and individual workers as a whole.
This section presents a discussion on how to enhance human and organizational performance aiming at improving risk management in complex systems linked to Industry 4.0.
Komljenovic et al. [
A constant deviation toward danger or failure seems to be one of their key characteristics. The latter is practically impossible to grasp in traditional of chain-of-event analyses.
All this requires overcoming the traditional static approach to risk, through the development of dynamic risk management models oriented towards the organizational and human performance, which is strongly linked to the complex systems characteristic of Industry 4.0.
Industry 4.0 provides digital management of operations in new technological devices, improves working conditions, and generates a safe manufacturing environment for workers [
In the work environment of Industry 4.0, a wide range of examples of smart materials, smart personal protective equipment, and other advances technological applications is improving the OSH [
Thus, during the last few years, new approaches and methodologies have been developed for risk assessment and management considering the dynamic evolution of risk [
It seems that the understanding of events is changing given that one of the main sources of risks (SR) nowadays is the organization [
Barriers that allow normal operation progressed with both the complication of work and the more involved persons. Therefore, this new context complicates the detection of flaws in these barriers, leading to undesirable events and failures. Such situations bring degradation of operational and safety margins. They may be locally and individually acceptable, but the sum of effects may have important unanticipated consequences that are not captured by a local analysis. The complexity of the operating environment involves a solution at the organizational level in order to cope with new challenges [
There are also studies investigating the hypothesis that modern enterprises depend on the deployment of cognitive capacities [
The behavior of people is basically shaped by their milieu. Marais et al. [
Some research works propose the framework of complexity leadership theory which may help getting a better human and organizational performance [
As far as the rationality of decision-making is concerned, several research works indicate that one cannot assume that it is always rational [
The complexity and the opacity of modern systems bring difficulties to the staff to predict its overall behavior as a function of its individual components. The complexity is a system property and results from interactions between its components/subsystems, humans, HMI, HRI, etc. It generates unanticipated and emergent comportment of the system, often intensified by ill adapted operator’s actions to those situations.
HRI can be a paradigmatic industrial and occupational example of this complex and challenging context, where Vasic and Billard [
Several research works highlight the importance of detecting and cautiously analyzing warning flags, precursors, near-misses, and “low-level” events in order to avoid system level break-downs, process interruptions, and/or major accidents. Therefore, organization should have enough organizational, economic, and technological resilience and flexibility applicable in a large number of different and (un)anticipated situations [
As far as human performance is concerned, it is important to understand the error itself. Some research works have shown that both success and failure pathways apply the same intellectual processes, and only the consequence changes. So, the undesired outcome qualifies an action as an error, and it is essential to find its cause. Analyses shall find out why an event occurred (“direct cause” related to preventive and mitigating barriers as well as error precursors) and why it was not stopped (“fundamental cause”). It also has to investigate the organization and its performance (expanded fundamental causes). Considering that it is almost impossible to determine a true causality in complex systems, those analyses become a difficult undertaking in a modern industrial setting. Stock and Seliger [
As discussed above, the introduction of the concept of Industry 4.0 brings numerous challenges. To cope with those new challenges, it is necessary to implement both a systematic return of experience (internal and external) and a continuous improvement process and to increase organizational resilience and robustness to unexpected events. However, increasing resilience shall be thought about wisely in order to preserve competiveness, further development, sustainability, and economic viability of an organization.
The organizational resilience is a developing concept. It will not be discussed in detail here, but there are some suggested references upon the topic [
Understanding impacts and risks of humans and organizations as contributors to mishaps, disruptions, and accidents in complex systems requires an adequate model. The model has to go further than the “simple” approach of linearly analyzing preventive and mitigating barriers, which provides quite narrow insights of the events.
Although some models exist [
In this regard, it is necessary to adequately take account of the complexity of today’s organizations as well as their operating context. This complexity necessitates a new way of reasoning and managing contemporary organizations. The traditional approaches in modeling, analyzing, and management are not entirely adequate to do it, and new methods are necessary [
Actually, there are numerous research works suggesting that contemporary organizations should be considered, analyzed, modeled, and managed as Complex Adaptive Systems (CAS) or Complex Adaptive Systems of Systems (CASoS) [
A possible approach for dealing with complexity in organizational setting is the application of emerging research of Complex System Governance. Complex System Governance is an emerging field, representing an approach to improve system performance through purposeful execution of design for and evolution of important metasystem roles [
CSG’s overall description based on Keating and Bradley’s [
Areas of concern | Metasystem function | Primary role |
---|---|---|
System identity | M5: Policy and identity | Focusing on overall steering and trajectory for the Industry 4.0 systems in the fulfillment of their missions. Maintaining identity and balance between current and future focus. |
M5 | Focusing on the specific context within which the metasystem of an industry is embedded. Context is the set of circumstances, factors, conditions, or patterns that enable or constrain execution of systems in Industry 4.0 setting. | |
M5’: Strategic system monitoring | Focusing on oversight of the Industry 4.0 performance indicators at a strategic level. This includes identification of performances that exceed as well as those the fail to meet the established expectations. | |
| ||
System development | M4: System development | Developing and maintaining current and future models of systems in question within the Industry 4.0 schema as well as concentrating on the long-range development for the industry to enable the realization of future feasibilities. |
M4 | Focusing on facilitation of learning based on correction of design errors (first order learning) in the metasystem roles of the industry as well as planning for revolution of the industry (second order learning). | |
M4’: Environmental scanning | Focusing on designs, and monitoring systems that can be used to sense operating environment of the industry to detect environmental trends, patterns, or events that can have implications on the current and future state of the industry. | |
| ||
System operations | M3: System operations (M3) | Focusing on the day to day execution of industry functions (operations) |
M3 | Monitoring system performance to identify and assess aberrant conditions, exceeded thresholds, and/or irregularities. | |
| ||
System information | M2: Information and communications | Creating designs and mechanisms that enable the information flow and consistent interpretation of exchanges information and data necessary to execute industry functions. |
CSG metasystem functions (modified from [
Keating and Bradley [
Pyne et al. [
A potential area of practice is Industry 4.0. Again, if one takes Industry 4.0 to mean the current trend of automation and data exchange in manufacturing technologies and encompassing CPS, IoT, cloud computing, and cognitive computing, there remains a case to be made for the utility of CSG in the various areas of Industry 4.0. At the onset, it appears CSG might be used to address issues related to themes of viability, governance, control, communication, coordination, and integration, as well as malfunctions (pathology) that may emerge in the different facets of Industry 4.0.
Main industries at risk such as nuclear, space, aviation, and petrochemical have developed different ways of coping with human and organizational performance issues. Their return of experience may be beneficial for analyzing ways to improve it within the context of Industry 4.0. Some experience and practices from the nuclear power industry are depicted below.
The Institute of Nuclear Power Operations (INPO) identifies key aspects to accomplish the quality in the integrated risk management [ Behaviors: the expected actions for the stages of risk management are suggested for all organizational levels from individuals to corporate executives. Organizational characteristics for effective integrated risk management: a set of principles, policies, practices, oversight, and training are recommended for achieving an all-inclusive risk management process which is elaborated and implemented. Integrated risk management warning flags: the warning flags aim at helping the staff and managers to detect undesirable conditions affecting an integrated risk management. The former are categorized by defenses aiming at minimizing risky events. The staff and managers should analyze them for stimulating discussions and draw lessons.
Such an approach may help reinforcing the overall resilience and robustness. This activity also involves a cautious analysis of organizational factors such as incentive systems that influence human performance and impact the risk of errors [
Therefore, improving and managing human performance risks (including those coming from machine-human interfaces) based on the experience from the nuclear power industry may also include other elements such as the following [ Frequently discuss risks, complexity, and their interdependence Perform gap analysis between outlooks and observations of behavior Enthusiastically lobby different opinion to avoid deliberate carelessness (reduce cognitive and motivational biases) Analyze and discuss past behaviors, including informal messages at the corporate level Doubt and uncertainty should not go unchallenged Ask to demonstrate that a system is appropriately safe to function or not sufficiently unsafe to be shut down Be cautious regarding findings from a root cause investigation that one points out to the negligence; in this case, only part of the story has been exposed Enlarge the scope of defense-in-depth concept to embrace the concept of complex systems and their intrinsic nonlinearity
Undoubtedly, Industry 4.0 brings numerous advantages. However, this paradigm also carries emerging risks and challenges related to organizational and human performance. These emerging risks include both industrial risks and occupational risks. Arguably, human factor is the main link between industrial emerging risks and occupational emerging risks in the Industry 4.0 context.
Addressing these issues calls for effectiveness in dealing with traditional static approach to risk, for example, through the development of dynamic risk management models oriented towards the organizational and human performance. However, there is also a need to develop robust approaches capable of dealing with emerging risks associated with Industry 4.0. In this research, Complex System Governance is suggested.
However, there remains a need for case applications, clearly articulating the potential of such approaches. Moreover, there is no need to be limited to such approaches as Complex System Governance. This research can be extended to include technological approaches such as Blockchain Technology and the discovery of deep systemic pathological issues affecting Industry 4.0.
The authors declare that they have no conflicts of interest.
This work was funded by the Spanish Ministry of Economy and Competitiveness, with the title “Analysis and Assessment of technological requirements for the design of a New and Emerging Risks standardized management SYStem (A2NERSYS)” with reference DPI2016-79824-R.