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3. A new systemic model for man-in-the-loop automation

In order to represent operators’ safety and well-being according
to the recent
findings and theories [10], a systemic model is
required; the model includes the cognitive and physical well-being
aspects of operators interacting with automation [11]. Basically,
the main aspects are:

the human operator: actions, errors, violations, mental models,
expectations, skills, culture;

the team: team cooperation dynamics;

the organization: managerial decisions, policies, culture, vision;

the physical environment: temperature, noise, layout;

the social environment: external pressures;

the tools: technology and automation level;

the rules: procedures, guidelines, checklists, laws;

the task: unexpected, habitual, repetitive, etc.

These features can be regarded as interacting, like fragments of
a bowl that dynamically move in order to
fill in the gaps that may
arise at their borders

The water in the bowl represents the operators’ well-being,
whose optimal level depends on the dynamic interaction among the main aspects of a system; well-being level is determined by the
gaps that the fragments create at their borders

Fig. 1. The well-being bowl

Sometimes an action aimed at increasing well-being could take
into account only one fragment; but changing just one part could
lead to breaking the bowl if the other elements do not adapt to it

Automation design should be guided by this model to promote
harmonization of the fragments’ behaviour by changing its role
according to the degree of control (Table 1). Adaptive automation
could provide a
flexible fragment that copes with the inherent
variability of the production system

coming from human and
organizational factors, process changes and productivity needs.
The constant adaptation enables the system to
fill the gaps
ensuring operators’ well-being

This vision can be translated into a framework that supports a
seamless adoption of a man-in-the-loop automation approach
reducing the risk of negative gaps. Basically, only by making the
most out of the capabilities residing in the human dimension of the
factory, it is possible to unleash automation’s full potential and to
enhance productivity and well-being

Fig. 2 shows the proposed man-in-the-loop automation system
within the factory; the focus is not only on the optimization of
production performances but it includes also the human operator
as a full-fledged part of the whole process. However, it is not
enough to consider the human dimension only as one of the
controlled variables of the automation system; human dimension
must be integrated into the management of the control loop to
couple automation’s efficiency with the
flexible human mind set.
This approach presents a twofold interaction:

the human acts as a decision maker who works in synergy with
the control system at a decisional level;

the human acts also at operational level taking part to the
controlled production process where the interaction is played on
a more operative
field

Fig. 2. Framework for human-in-the-loop factory adaptive automation

The two different cooperation roles could trigger the risks listed
in paragraph 2.1 and cause suboptimal results for the operators’
well-being and the production performance. To reduce the risks
and smooth their negative influences, each role requires a careful
management taking into account its specificity

The proposed man-in-the-loop automation framework requires
to set goals specifically aimed at enhancing the working conditions
by constantly monitoring a stream of physiological measures to detect in real time any deviations from personalized safe patterns
and propose mitigation actions aimed at mediating or mitigating
the cognitive demand that the worker is experiencing. It also
requires reconfigurable automation policies that apply in the
distributed automation structure. to explore and possibly
eliminate the sources of cognitive gaps such as skill mismatching
and alienating duties.

4. Model implementation in demonstration cases

4.1. Adaptive automation in air traffic control

Mental workload in air traffic management is a crucial factor for
safety. Human errors typically occur both in underload and
overload conditions, the former because operators are disengaged
by the task, the latter because they are overwhelmed. In the
interaction with automation, changing the number, quality, rate,
dynamics of stimuli and data presented on the display is crucial to
maintain a proper level of workload

A passive Brain-Computer Interface (pBCI) was developed [14]
in order to track operators’ brain activity (EEG), which is
considered a reliable, sensitive, real-time, and continuous measure
of mental workload. The pBCI was integrated in an air traffic
control simulator and was tested for its capacity to produce
adaptive solutions in real-time, according to the mental workload
measured by means of operators’ brain activity. When operators’
under- or overload is detected, the system automatically triggers
adaptive solutions (e.g. displaying only critical alarms, highlighting
the aircraft currently speaking, animating the icons related to a
short term collision, and displaying only the aircrafts that are
relevant for the task at hand).

The pBCI is able to activate adaptive automation solutions
during high-workload scenarios, while it does not activate them
during periods of normal workload, in order to avoid underload. In
addition, the pBCI induces a decrease in the perception of mental
workload by the operators when adaptive solutions are activated.
Behavioural performance analysis demonstrated that the task
performance significantly increased when adaptive automation
solutions were triggered (Table 2: C3).

4.2. Automation in the white-goods industry

The white-goods industry is characterised by work-intensive
production environments where humans are mainly employed to
assemble a highly diversified range of products manufactured in
continuously changing and relatively small lots. Such production
and high production pace, due to the automation component of the
line, poses serious cognitive demands to the workers.

The involved white-goods industry uses continuous
flow
production lines with a takt time
fluctuating next to one minute
and a number of product variants exceeding one hundred. In the
past, the company tried to map the workforce capabilities
(experience on the job, management of safety and quality aspects)
in order to establish a personal skill matrix for each worker.
However, the lack of a systemic approach to the humanautomation
interaction prevented any substantial improvement
in adapting the workplaces, and the production system at large, to
the characteristics and condition of the individual workers
 

Adaptive automation and human factors in manufacturing: An
experimental assessment for a cognitive approach

A B S T R A C T

Despite increasing automation levels and digital solutions, production systems still very much rely on the
inescapable contribution of the human factor. The changing relationship between man, the technological
system and the organization framework together with the increased complexity result in high risks for
workers’ safety and their psychophysical health. Adaptive factory automation and management solutions
integrating the man in the loop are proposed in order to achieve production performance, workers safety
and well being in a balanced way in varying boundary and exogenous conditions. Particularly, this paper
presents new methods and the related recent case studies in different sectors

1. Introduction

While automation in manufacturing dates back to the
first
industrial revolution, the increasing manufacturing systems’
complexity and technology advancements require nowadays
reliable tools to plan, assess, and drive the production chain in
order to get the planned goals. Many methodologies have been
developed to face the organisational aspects by considering the
production plant capacity, customer demand and products
characteristics

At the level of production shop-floor, the
growing
flexibility of production means calls for automation
systems whose behaviour is driven by dynamically adapting
management policies

In the last
fifty years automation evolved
dramatically along several generations: from the direct involve-
ment of workers in the manufacturing process, to intelligent
automation systems where workers play a supervisor role

Indeed, the introduction of new technologies impacts on the
complexity of manufacturing system management and requires to
promote harmonisation between automation and the human
factors

especially considering the cognitive workload related to
manufacturing operations at different decisional levels

This paper proposes a methodology, validated in two selected
industrial cases, to integrate cognitive workload into the design of
workplaces to match the human safety and well-being necessities
and tasks’ cognitive requirements. Specifically, a new framework to classify the fabrication tasks of production processes according to
their cognitive complexity and the required capacity enables an
anthropocentric optimisation of the manufacturing activities

2. Automation and human factors

2.1. Interaction challenges

Human factors and human performance limitations, resulting
in errors and violations, are the main contributors to accidents and
injuries in complex systems

A common approach aimed at
reducing this incidence has been to transfer to automation a
variable portion of the tasks that were previously performed by the
human operator

The shared responsibility between human and automation
could be broadly located along a continuum, as represented in
Table 1

Notwithstanding the successful integration, many issues
occur when considering the relationship between humans and
automation; the problems are essentially:

Out-of-the-loop condition: the difficulty of operators to have a
clear and complete picture of the automation states and
processes, lead to a diminished ability to detect possible
automation failures and to regain manual control

Surprising mode transitions: operators may become unaware of
changes in the operating mode performed by automation

Skill loss: pervasive automation will decrease the opportunity for
training manual skills, which will be ineffective in case of an
urgent manual control of the system

Table 1
The continuum of shared responsibility between human and automation, adapted
from

Automation-induced errors: while automation may compensate
or reduce some typical human errors (counting, remembering,
monitoring, etc.), more automation could lead to new, unex-
pected forms of human errors

Behavioural adaptation: automation may grow the perception of
safety and operators could adapt their behaviour taking higher
risks

Inappropriate trust: trust in automation could change according to
the perception of its reliability. When this perception is biased,
inappropriate trust will result as misuse, disuse and complacency

Job satisfaction: automation could be perceived as a threat to
workers professional profile, especially when it is introduced
without a proper transition and a management care for reskilling
their workers

In order to face the described issues, a comprehensive approach
to map them and an effective strategy at different and relevant
levels, is required. The identification of the intervention areas and
gaps is a crucial step to build a coherent system capable to
harmonize the automated components with their human counterpart.
In current efficiency-driven contexts the cognitive level,
that acts as the interface between human and automation, is
responsible for the workload impacting the worker. Much of this
impact is determined by the decisions taken in the design of the
automation processes as well as by the organizational strategies;
therefore, the organization level must be included in the analysis.
Table 2 shows the gaps and the involved processes for the
cognitive, organizational and technological levels

Table 2
Cognitive, organizational, technological gaps and processes

2.2. Design guidelines

Traditional approaches to enhance the human-automation
interaction are based on the Fitts list

reported in Table 3 Such a
list allocates functions considering the information processing
stage and static conditions. A more effective approach should
assign the functions according to the task, the operator, and the
situation considering the system dynamics: adaptable automation
allows the operator to decide the level of control according to the list reported in Table 1. Adaptive automation can change the
control level by automatically adjusting itself to the operator’s
performance, operator’s state and the system status

Table 3
Relative strengths of humans and automation

RESEARCH METHOD AND RESULTS

This study utilized a survey instrument on the U.S. manufacturing firms who were most likely to adopt AMS. The firms were selected from the following three publications: Moody's Industrial Manual, American Association of Manufacturing Technology (AAMT), and A.MS trade journals. Within each firm, a plant manager was identified as target for the questionnaire. To ensure an appropriate level of respondent knowledge, only participants meeting the following criteria were included in the study: (1) at least six months of AMS use by the organization, and (2) at least six months AMS experience by the individual participant. Those respondents that did not meet these conditions were asked to so indicate and return the questionnaire. This action ensured that each participant was familiar with and able to objectively evaluate the AMS adopted in his/her organization. The respondents were urged to take part in the study only if the requirements stated above were met.

Of the 400 questionnaires mailed out, we received 117 responses (29.3 percent). Nineteen were discarded, eight because they were not completely filled out and eleven because the respondents indicated insufficient experience with AMS. There remained 92 (23 percent) usable responses that were included in the study.

Human factors were investigated by asking respondents to indicate the extent to which they felt the eight human factors discussed earlier were present during and after AMS implementation. Over 37% of the participating firms had a process manufacturing environment while about 30% operated in a repetitive manufacturing environment. The respondents reported that A_MS projects were initiated mostly (over 80%) by management rather than workers or vendors. In about 80% of the cases, the AMS projects were directed by management rather than a steering committee or appointed individuals. Over 38% of the respondents were top management, 38.5% were middle management, and about 19% belonged to other ranks.

The Partial correlation coefficients of the human factors and AMS benefit measures were computed. According to the results, the benefit measures did not indicate any multi-collinearity problem. The Partial Coefficients (r) of 0.1900 or higher are significant at p < .06. The correlation analysis reveals that all human factors positively correlate the benefit measures. The strongest correlation were found between morale and reduced throughput time (r = .4318; p < .000); satisfaction and return on equity (r = .2026, p < .060); reward system and reduced throughput time (r = .3274, p < .002); belief in AMS and improved work conditions (r = .3651, p < .001); top management commitment and enhanced competitiveness (r = .3714, p < .000); response to workers' concerns and better control (r = .3453, p < .001); effective facilitator and better control (r = .2483, p ~ .020); training and improved quality (r = .2305, p < .011).

Table 1 is a record of the Chi-Square test obtained by cross-tabulating human factors and AMS benefit measures. The association between any two variables was significant if p _< .001 (*) or p< .05 (**). In order to test the hypotheses of the study, Chi Square testes were conducted to determine if there exist any associations between human factors and the benefits of AMS. As shown on Table 1, every human factor considered in this study has significant association with some of the AMS benefits measures.

Of the 400 questionnaires mailed out, we received 117 responses (29.3 percent). Nineteen were discarded, eight because they were not completely filled out and eleven because the respondents indicated insufficient experience with AMS. There remained 92 (23 percent) usable responses that were included in the study.

Human factors were investigated by asking respondents to indicate the extent to which they felt the eight human factors discussed earlier were present during and after AMS implementation. Over 37% of the participating firms had a process manufacturing environment while about 30% operated in a repetitive manufacturing environment. The respondents reported that A_MS projects were initiated mostly (over 80%) by management rather than workers or vendors. In about 80% of the cases, the AMS projects were directed by management rather than a steering committee or appointed individuals. Over 38% of the respondents were top management, 38.5% were middle management, and about 19% belonged to other ranks.

The Partial correlation coefficients of the human factors and AMS benefit measures were computed. According to the results, the benefit measures did not indicate any multi-collinearity problem. The Partial Coefficients (r) of 0.1900 or higher are significant at p < .06. The correlation analysis reveals that all human factors positively correlate the benefit measures. The strongest correlation were found between morale and reduced throughput time (r = .4318; p < .000); satisfaction and return on equity (r = .2026, p < .060); reward system and reduced throughput time (r = .3274, p < .002); belief in AMS and improved work conditions (r = .3651, p < .001); top management commitment and enhanced competitiveness (r = .3714, p < .000); response to workers' concerns and better control (r = .3453, p < .001); effective facilitator and better control (r = .2483, p ~ .020); training and improved quality (r = .2305, p < .011).

Table 1 is a record of the Chi-Square test obtained by cross-tabulating human factors and AMS benefit measures. The association between any two variables was significant if p _< .001 (*) or p< .05 (**). In order to test the hypotheses of the study, Chi Square testes were conducted to determine if there exist any associations between human factors and the benefits of AMS. As shown on Table 1, every human factor considered in this study has significant association with some of the AMS benefits measures.

CONCLUSIONS

The dramatic advancement and the adoption of advanced manufacturing systems (AMS) in organizations can be attributed to its numerous benefits that can improve the competitive position of the manufacturing firms. The results of this study show that the eight human factors considered in this study have positive associations with all the eight AMS benefits. While other factors may play major roles in realization of AMS benefits, sociotechnical or human factors have been shown to be essential ingredients for AMS success. For a manufacturing firm to actualize the full benefits of AMS, steps need to be taken to ensure effective human resource management. The implication is that when a firm takes care of the human needs, the AMS implementation will be well received and understood and as such, the desired effects of AMS will be brought to bear in the firm's production cost. In essence, the manufacturing firm that pays attention to human factors may realize the benefit of shorter throughput time because AMS is properly implemented and efficiently operated. If human factors are ignored in a firm during AMS implementation, there is a good chance that the workers will be discouraged and reluctant to apply themselves and as such, delays may occur in the production schedules. If that is the case, components of the manufacturing system will not be interacting in the most efficient way thereby resulting in a long throughput time.

HUMAN FACTORS AFFECTING THE SUCCESS OF ADVANCED MANUFACTURING SYSTEMS

ABSTRACT

This paper analyzes the data collected from 98 manufacturing companies to investigate the associations between human factors and the success of advanced manufacturing systems (AMS).

The AMS measures and human factors were cross tabulated and the Chi Square values were used to test the hypotheses of the study. The results show that statistically significant, positive associations exit between human factors and the success of AMS implementation. The implications of the findings to the practitioners and researchers are discussed.

INTRODUCTION

The drive to lower operating costs and improve manufacturing efficiency has led many manufacturing companies to implement different forms of advanced manufacturing systems (AMS). The dramatic developments in advanced manufacturing technologies at various organizational levels can be attributed to numerous benefits that improve the competitive position of the company. AMS affects not just manufacturing, but the whole company operations, giving new challenges to a firm's ability to manage both manufacturing and information systems. AMS can be defined as a group of integrated hardware-based and software based technologies which, when properly implemented, monitored, and evaluated, can improve the operating efficiency and effectiveness of the adopting firm. It encompasses a broad range of computer-based technological innovations which are integrated using communication links made possible through advanced computing technologies and are referred to as computer-integrated manufacturing (CIM)

AMS has the potential to dramatically improve production performance and create vital business opportunities for companies that are capable of successfully implementing and managing it. (King and Ramamurthy, 1992). AMS can also provide distinctive competitive advantages in cost and process leadership. Practitioners and researchers have since developed strong interest on how AMS

can be used to combat global competition. A growing number of organizations are now adopting AMS to cope with fragmented mass markets, shorter product lifecycle, and increased consumer demand for customization (Zummuto, et al., 1992). Although AMS can help manufacturers compete under these circumstances, they often serve as a double-edged sword, imposing organizational challenges and, at the same time, providing competitive benefits

The benefits of AMS have been widely reported in the literature and classified as being tangible and intangible (Udo and Ehie, 1996). Although the benefits of AMS are numerous and have been found to have direct links with the firm's operating performance, only a handful of companies have been able to realize the full benefit of AMS. The rate at which these benefits are derived varies to a large extent from one company to another. Beatty (1993) concludes that only half of those companies adopting AMS ever achieve the benefits they sought. Success in AMS implementation becomes a reality when the set goals and objectives stipulated by the adoption strategy are fully realized.

The potential offered by AMS to deal with the emerging realities of the twenty-first century competitive environment is widely recognized, but concerns have also been expressed about the ability of firms to exploit this to their advantage. The literature is replete with arguments in support of the presence of one or more of the critical success factors as requirements for successful AMS implementation. Sociotechnical or human factors axe among the critical factors believed to have some impact on the success of AMS implementation. The purpose of this study is to investigate the extent to which the identified human factors affect the benefits of AMS. This agrees with the observation made by Huber and Brown (1991), who suggest that some empirical research is needed to investigate the impact of sociological variables on the implementation of manufacturing processes.

The eight most cited benefits of AMS considered in this study are return on equity, reduced manufacturing cost, reduced throughput, enhanced competitiveness, better control, quick response, improved working conditions, and improved quality.

The null hypothesis of this study is that there is no association between the AMS benefits and human factors. That is: human factors do not affect the success of AMS implementation

HUMAN FACTORS

Several technical and social changes often take place when a company adopts A_MS. As Hopkins (1989) points out, if an organization focuses solely on the technical issues from the outset of a manufacturing project implementation and at the expense of the human issues, its performance will be less favorable than if it pays attention to both sets of issues. The adoption of AMS certainly changes the social relationship and interactions among employees and their supervisors.

Given the potential impact on employee attitudes, motivation, and retention, these social changes call for an effective management (Huber and Brown, 1991). When employees are affected, the success of A.MS is likely to be affected as well. In this study, human factors comprise of three components namely: self-interest (four factors), top management (three factors), and preparation (one factor)

(a) Self Interest. It is known that human beings are self-interested in that we tend to strive to succeed on those tasks we believe to be of a personal interest to us. King and Ramamurthy (1992) maintain that no matter how attractive the benefits or the sophistication of technology, if personnel-related aspects (such as motivation, participation, reward schemes, etc) are not planned for, the end result is bound to be a frustrating failure. They discovered in their study that people problems could prove to be more difficult to solve than technical problems and could have serious consequences on AMS implementation. The self-interest factors considered in this study include: general employees' morale, satisfaction levels, personal belief that AMS can lead to personal reward or benefits to the individual, and equitable reward structures

(b) Top Management. Top management support provided in the form of creating project mission; allocation of sufficient resources; establishment of a reward system that fits the project; maintaining project accountability; personnel recruitment, selection, and training; monitoring and feedback functions.

Research and experience support the fact that the degree of management support of a project will lead to significant variations in the degree of acceptance or resistance to the project, and also to the degree of success (Udo and Ehie, 1996). The top management factors included in this study are commitment by top management, effective facilitator, and quick response to workers concerns by management

Preparation. The main preparation needed by the workers in the AMS environment is training. The need for training has been heavily emphasized by Beatty (1993)

5.2.

Collaborative Work

Agility emphasizes a collaborative design process whereby engineering disciplines affected
by design decisions are integral participants in making those decisions (Forsythe
et al., 1995). Collaboration permeates all aspects of an agile product realization process,
with specific steps necessary to assure concurrence is obtained early in design, before
precious time and resource commitments have been made. Such extensive collaboration requires an awareness and appreciation of the interests and contributions of each discipline.
However, this is often difficult to attain due to “engineering arrogance” or the belief
that “what I do is difficult and what you do is easy,” and organizational dynamics that
often allot considerable power, influence, and respect to designers, and substantially less
to supporting disciplines.

Many technical innovations may be applied to support collaborative work. For example,
X applications sharing software allows designers, working at their desktops, with
only a moment’s notice, to open a shared CAD representation of a design that they and
other team members may view, and freely manipulate from within the CAD application.
In this way, X applications software enables collaborative design and decisions, making
codesigners of team members who otherwise would have only been reviewers. In addition,
solid models and animated illustrations of machining and robot assembly processes
allow Manufacturing and Assembly engineers to more readily and clearly communicate
their concerns to designers

5.3.

Enterprise Integration of Information Technologies

Agility requires the removal of information bottlenecks and improvement in the continuity
of information flow through the enterprise. It is unacceptable to have work delayed
because information available at one point in the process has not or cannot be transferred
and used at another. In striving for this objective, there is the need to open channels for
information flow and to remove resistance (e.g., cross-platform, cross-application incompatibility)
to information flow

Information may be transmitted via multiple channels depending on urgency, content,
and distribution (e.g., phone, voice-mail, fax, e-mail, ftp, PDM, http). Product Data Management
(PDM) is of particular significance in that it provides team members a central
information repository that offers automatic notification of file and design changes. Thus,
notification and updating of team members does not require a conscious effort, but is
integrated into the day-to-day interactions with the PDM

Agility is enhanced by a seamless flow of information between software applications,
and between software and production hardware. Burdensome file conversions create intolerable
delays for the production process and waste valuable human resources. Agility
requires that the cognitive resources of project personnel be directed toward the challenges
of design, analysis, and decision, and not spent on mundane activities such as data
entry or recoding. Through development of software routines that translate between software
applications and some standardization to compatible software applications, a production
process may be developed that is seamless, from beginning to end

6

CONCLUSION

Agile manufacturing will proceed, with or without the contributions of human factors.
For the field of human factors, agile manufacturing is, more than anything else, an opportunity.
By raising human factors issues, and applying the knowledge and skills gained
from other domains, there is an opportunity for human factors to assume an important
role, positively influencing the future of agile manufacturing. As of 1997, agile manufacturing
is still immature and not yet fully defined. Agile manufacturing poses many
questions best answered by human factors. Our willingness, as a profession, to address these questions will determine whether our role is in the definition of a paradigm or in the
limited role of after-the-fact, fixing what is broken