Because an experiment involves examining the relationship between independent variables and changes in one or more dependent variables, defining what is measured—dependent variables—is crucial. The dependent variables are what can be measured and relate to the outcomes described in the research questions.
The research questions are often stated in terms of theoretical constructs, where constructs describe abstract entities that cannot be measured directly. Common constructs in human factors studies include: workload, situation awareness, fatigue, safety, acceptance, trust, and comfort. These constructs cannot be measured directly and the human factors researcher must select variables that can be measured, such as subjective ratings and performance data that are strongly related to these constructs. To assess how smartphones affect driving, the underlying construct might be safety and the measure that relates to safety might be error in lane keeping where the car’s tire crosses a lane boundary. Safety might also be measured by ratings from the drivers indicating how safe they felt. Subjective ratings are often contrasted with objective performance data, such as error rates or response times. The difference between these two classes of measures is important, given that subjective measures are often easier and less expensive to obtain, with a larger sample size. Both objective and subjective measures are useful. For example, in a study of factors that lead to stress disorders in soldiers, objective and subjective indicators of event
stressfulness and social support were predictive of combat stress reaction and later posttraumatic stress disorder. The subjective measure was a stronger predictor than the objective measure [68].
In considering subjective measures, however, what people rate as “preferred” is not always the system feature that supports best performance [69]. For example, people almost always prefer a color display to a monochrome one, even when color undermines performance.
Furthermore, people cannot always predict how they would respond to surprising events in different conditions, like during system failures. Human factors is much more than intuitive judgment (of either the designer OR the participant). It is for this reason that objective data from controlled experiments are needed to go beyond the expert judgments in heuristic evaluations and subjective data.
Subjective and objective dependent variables provide important and complementary information. We often want to measure how causal variables affect several dependent variables at once.
For example, we might want to measure how use of a smartphone affects a number of driving performance variables, including deviations from the lane, reaction time to cars or other objects in front of the vehicle, time to recognize objects in the driver’s peripheral vision, speed, acceleration, and so forth. Using several dependent variables helps triangulate on the truth—if all the variables indicate the same outcome then one can have much greater confidence in that outcome.

P3.28 For an evaluation of a vehicle entertainment system, identify possible dependent variables.

P3.29 What are the benefits of subjective measures?

P3.30 What are the limitations of subjective measures?

P3.31 What is the relationship between a construct and a measure?

be familiar with the range of methods that are available and know which methods are best for specific types of design questions. It is equally important for researchers to understand how practitioners ultimately use their findings. Ideally, this enables a human factors specialist to work in a way that will be useful to design, thus making the results applicable. Selecting an evaluation method that will provide useful information requires that the method be matched to its intended purpose.

3.1 Purpose of Evaluation In Chapter 2 we saw how human factors design occurs in the understand-create-evaluate cycle. Chapter 2 focused on understanding peoples’ needs and characteristics and using that understanding to create prototypes that are refined into the final system through iteration. Central to this iterative process is evaluation. Evaluation identifies opportunities to improve a design so that it serves the needs of people more effectively. Evaluation is both the final step in assessing a design and the first step of the next iteration of the design, where it provides a deeper understanding of what people need and want. Evaluation methods that serve as the first step of the next iteration of the design are termed formative evaluations. Formative evaluations help understand how people use a system and how the system might be improved. Consequently, formative evaluations tend to rely on qualitative measures—general aspects of the interaction that need improvement. Evaluation methods that serve as the final step in assessing a design are termed summative evaluations. Summative evaluations are used to assess whether the system performance meets design requirements and benchmarks. Consequently, summative evaluations tend to rely on quantitative measures—numeric indicators of performance. The distinctions between summative and formative evaluations can be described in terms of three main purposes of evaluation: • Understand how to improve (Formative evaluation): Does the existing product address the real needs of people? Is it used as expected? • Diagnose problems with prototypes (Formative evaluation): How can it be improved? Why did it fail? Why isn’t it good enough? • Verify (Summative evaluation): Does the expected performance meet design requirements? Which system is better? How good is it? Each of these questions might be asked in terms of safety, performance, and satisfaction. For Erika’s analysis, predicting the effect of smartphones on driving safety is most important: how dangerous is talking on a phone while driving?

Table 3.1

Table 3.1 shows the example evaluation techniques for three evaluation purposes. The first rows of this table show methods for understanding and diagnosing problems with qualitative data. Qualitative data are not numerical and include responses to openended questions, such as “what features on the device would you like to see?” or “what were the main problems in operating the device?” Qualitative data also include observations and interviews. These data are particularly useful for diagnosing problems and identifying opportunities for improvement. These opportunities for improvement make qualitative data particularly important in the iterative design process, where the results of a usability test might guide the next iteration of the design. The third row of the table shows methods associated with verifying the performance of the system with quantitative data. Quantitative data include measures of response time, frequency of use, as well as subjective assessments of workload. Quantitative data include any data that can be represented numerically. The table shows that quantitative data are essential for assessing whether a system has met its objectives and if it is ready to be deployed. Quantitative data offer a numeric prediction of whether a system will succeed. In evaluating whether there should be a ban of smartphones, quantitative data might include a prediction of the number of lives saved if a ban were to be adopted. The last two rows show how both quantitative and qualitative data can support understanding people’s needs and characteristics relative to the design. Although methods for understanding (Chapter 2) and methods for evaluation (Chapter 3) are presented in separate chapters, there is substantial overlap between them. In this chapter, we focus on diagnosing design problems and verifying its performance, but evaluations often produce data that can also enhance understanding and guide future designs. Beyond evaluating specific systems or products, human factors specialists also evaluate more general design concepts and develop design principles. Such concept evaluations include assessing the relative strengths of keyboard versus mouse or touchscreen or rotating versus fixed maps. Concept evaluation reflects the basic science that supports the design principles and heuristics that make it possible to guide design without conducting a study for every design decision.

P3.1 How is evaluation related to understanding in the human factors design cycle?

P3.2 What are the three general purposes of evaluation?

P3.3 Would qualitative or quantitative data be more useful in diagnosing why a design is not performing as expected?

P3.4 Would qualitative or quantitative data be more useful in assessing whether a design meets safety and performance requirements?

P3.5 What is the role of quantitative and qualitative data in system design?

P3.6 Why is qualitative data an important part of usability testing?

P3.7 Give examples of qualitative data in evaluating a vehicle entertainment system.

P3.8 Give examples of quantitative data in evaluating a vehicle entertainment system.

P3.9 Describe the role of formative and summative evaluations in design.

P3.10 Identify a method suited to formative evaluation and another more suited to summative evaluation

Three recent interventions are presented briefly
to contrast the project and strategic approaches. First it
should be noted that all three companies and projects
had a high degree of similarity:

1

All were threatening closure of the plant
due to competitive pressures

2

All were in the process of talking about
change in manufacturing, but none had
proceeded as far down this path.

3

In all plants, the active cooperation of top
management and union leadership was a
prerequisite of the project.

4

All were plants in mature industries, i.e.
where technological changes in the product
would be less important to the company
survival than manufacturing prowess.

5

All were large plants, with several hundred
operators

In company. A, an ergonomics program was
started as a project. The aim was to teach operators,
foremen and technical staff how to do ergonomics, both
by classroom training and by undertaking a series of
demonstration projects throughout the plant. Each
project was to be assessed by before-and-after measures
to demonstrate how ergonomics increases both system
performance and operator well being. Over a two-year
period, the project was technically successful in that it
did train many people to become users of task analytic
and job redesign techniques. Success could also be
measured by workplace changes successfully
implemented. However, the project’s non-strategic
aspects were constantly in evidence: operators could not always get released for team meetings, promised
changes were rarely completed on time, direct
intervention by the plant manager was often needed to
insure implementation. To date the ergonomics
program has had minimal impact on the plant's goal of
achieving company-wide top status in quality before the
announced deadline of 199 1.

Company B was tackled in a more strategic
manner. First a team of university personnel worked
with the company to establish strategic needs of the
business. This assessment concerned general
management, strategic planning, sales 8z marketing,
financials, labor relations and manufacturing. Human
factors was a small part of the "manufacturing" area.
From this assessment came a series of immediate needs,
represented by projects which, if completed
successfully, would have a major impact of the business.
One of these projects was operator training, using
human factors techniques of knowledge elicitation to
form the basis of a knowledge and skill training
program. The first part of this system has now been
implemented. Company B cites the University's
intervention as a major reason for deciding to stay in the
region, and to locate its new manufacturing facility here

The case of Company C was in many ways an
intermediate case between the two levels. The company
manufactured precision aircraft parts, using small batch
production by skilled machinists. The brief was to
improve manufacturing quality, again using human
factors techniques of process control analysis.
However, the team included specialists to work with
manufacturing management and labor unions at a high
level as well as at shop-floor level to bring some order
to the a11 too typical chaos of high scrap rates, missed
deadlines and the end-of-the-month shipping crisis.
Ergonomics intervention was centered around a
manufacturing cell, and included a complete system for
floor-level process control based on operator input and
human factors techniques. The presence of this working
cell was a major factor in the decision of another
company to but the plant, expand it, and make it their world headquarters for aviation components. Here the
intervention achieved strategic-level results despite a
lack of prior strategic analysis, compens2ted for to some
extent by the non-ergonomic interventions

CONCLUSIONS

In only one company (A) was the intervention
labeled as "ergonomics" and that had the least impact
upon the company's future. In the other two, it is
doubtful whether either management would classify our
interventions as "ergonomics" or "human factors"
despite the role of these disciplines in helping the
company achieve its strategic goals

Case studies in small numbers can never provide
statistical proof of the superiority of one approach over
another, but they can indicate that manufacturing
success may be achieved by allowing our discipline to
be part of a relatively homogeneous team. We may
have to change the way in which we operate, and lose
some of our separate identity, if we truly wish to impact
manufacturing

Abstract

Now that manufacturing has become a respectable topic in industry, an obvious question is how
human factors/ergonomics can contribute to the improvement of manufacturing. The traditional route
for ergonomics intervention has been a Project route, with a set of objectives agreed between the human
factors engineer and people within the company. Projects, however, do not ask the question of whether
human factors intervention is likely to have an impact on the company's strategic objectives, for
example, remaining in the manufacturing of a particular product

Case studies in a variety of industries are used to conrrast the project approach with a more
strategic approach. It is concluded that the project may represent sub-optimization in that a successful
outcome of the project may have no impact upon company survival without a careful examination of the
strategic plans of the company

MANUFACTURING IS CHANGING

The past ten years have seen the realization that
if the USA is to compete successfully in the world,
manufacturing cannot be neglected. Lessons from more
successful manufacturing nations have been learned by
companies of different sizes, and the results are
beginning to be seen in manufacturing excellence

Perhaps the most fundamental change in the way
leading companies treat manufacturing is to focus more
clearly on the ultimate objectives of the company.
Almost every company of any size now has a "Mission
Statement" with fine words about customer satisfaction
and product quality which, if heeded, would radically
change the way the company thinks. This change is
truly radical as it is forces employees at all levels to
evaluate their decisions against very specific outcomes.
These outcomes rarely include the traditional measures
of monthly output, labor cost variances, minimum first
cost of investment, or machine utilization; measures
which most company employees have known to be the
overriding criteria in day-to-day operations

At higher levels, companies are now undertaking
strategic planning to aid the long-term shaping of the
company. Not every opportunity should be pursued,
only those which fit long-term goals, goals which are
themselves based on an honest assessment of the
strengths and weaknesses of the company. Thus a
company may abandon a traditional market for large
scale mass-production of identical components to
concentrate on manufacturing a customized family of components at lower production volumes. In this way it
can be extremely responsive to customer needs, a key
component of customer satisfaction

Such responsiveness cannot be achieved without
changes in traditional production systems. Quality is
essential: any defect will have an immediate impact
upon output, an impact which cannot be hidden from the
customer by large inventories in a highly responsive
manufacturing environment. Responsiveness also forces
decreased reliance on a multi-level decision-making
hierarchy. Hence the modern emphasis on well-trained
self-organizing small groups (or cells) which are
responsive directly to customer needs. When response
time, quality, and training come to the fore in
manufacturing industry, so must human factors
engineering.

HUMAN FACTORS IN MANUFACTURING

One arena in which the USA is a major power is
in human factors/ergonomics so that it is natural to
consider how we can use this power to improve
manufacturing. Results to date have been less than
spectacular, with major human factors involvement
mainly in nuclear power production (for cognitive and
behavioral interventions) and in a variety of
manufacturing industries at the level of musculo-skeletal
injury reduction. Results in this context means having
an impact on the company's major decisions of which
markets to pursue, where to manufacture goods and how
manufacturing contributes to overall company goals

On a strategic basis, if the USA has a lead in
human factors, that lead should be exploited in
manufacturing. We have had numerous examples from
around the world of how human factors can be a key
element in manufacturing. Harris & Chaney’s book
(1969) was based on a major implementation of
ergonomics within the quality control function of an
aerospace company. On a smaller scale Hasselquist
(1981) showed how redesigning a line on ergonomics
principles had a productivity payback period of less than
half a year, with the added bonuses of a halving of the
error rate and elimination of musculo-skeletal injuries.
The authors suspect that most ergonomists could
produce similar examples from their files.

But the direct impact of human factors on the
manufacturing system which has been changed is really
not the whole question. For example, a program of
ergonomic changes in a shoe manufacturing company
(Drury & Wick, 1984) was particularly successful in
reducing muscular skeletal injuries in the plant, but it
did not prevent eventual closure of the plant as the
parent company responded to foreign competition.
Even a four-year program of ergonomics throughout the
company (which was estimated to have saved over $6
million in productivity increases and injury reduction)
was not enough to prevent closure of the ergonomics
department with the rest of the engineering functions
after a hostile take-over.

Part of the reason for human factors still not
being of central impact on manufacturing is the way in
which we have traditionally intervened. Whether the
ergonomics expertise comes from a group inside the
company or external to the company, the typical
intervention consists of a project. This project is
defined by both the customer and ergonomists in such a
way that both groups are satisfied with the potential
outcome and the intervention methodology.
Unfortunately, the customers and human factors
engineers may have reached a satisfactory
understanding at the wrong level.

Human factors engineers are trained to ask
technical questions (What is your workhest schedule?
How do you extract information from that display?) but
only have a rudimentary idea of the system functioning
beyond this level in manufacturing industry. Thus, ideas
of customer satisfaction with cost, on-time delivery and
quality are addressed, if at all, in terms of cycle times
and error rates. Our customers are often little better. A
safety manager may wish to reduce lost-time injuries, an
R & D manager may wish to estimate performance of a
prototype system or a quality control manager many
wish to reduce human-caused errors. At these levels,
the two parties can talk comfortably, as the connections
between their variables are reasonably obvious. But
what if the customer’s problem has no impact on the
company’s fortunes: Or what if the ergonomist’s time
should be better spent with another project or another
company? We all have a duty to ensure that what may
be a scarce national resource (human factors talent) is
used to maximum advantage.

In contrast the military typically has learned
many years ago that human factors expertise needs to be
continuously available throughout system development
(e.g. Meister, 1971) where it can impact on the broader
aspects of systems effectiveness. Can we make use of
this model in a manufacturing environment where the
higher level customers are (at best) skeptical about
human factors, and the human factors engineers are
untrained in such areas as strategic planning, cost
accounting or employmenflabor policies?

One way in which human factors can maximize
its strategic impact on manufacturing is to be part of a
team which operates at the strategic level. This means
that the team interacts with, and reports to, the highest
levels in management and union so that the strategic
aspects of the intervention are explicitly part of the
project. It does mean however that the intervention will
not be seen as an ergonomics intervention, but as an
indistinguishable part of the overall systems
intervention.

the Training Needs Detector, a tool dedicated to the human
resources management that reasons in the long-run by ponder-
ing the gaps between required skills in the production and those
provided in the actual job allocations in order to identify
persistent skill shortages towards the definition of personalized
training paths (Table 2: O2).

For the validation of the new approach and developed tools, two
lines,oneformicrowaveovensandanotherforfridgeproduction,were
modelled relying on the representation capabilities offered by the
KNOW Platform. The single jobs at each workstation were designed
trying at the same time: (i) to balance the workload by shifting the
tasks among workstations to cope with the upper limit set at 90% of
takt time; (ii) to distribute the tasks with critical requirements, to
balance the cognitive burden and increase the possibility that a pool of
workers can effectivelyand completely match the skill demand; (iii) to
reduce the risk of musculoskeletal disorders due to ergonomically
impactingtasksinsomeworkstations.Joballocationtestswerecarried
outoff-lineinvolvingteamleaderstocomparetheirchoiceswiththose
offered by the linear optimizer

The improved workplace adaptability results in the reduction of
cumulative trauma disorders and psychological stress. The
availability of an all-encompassing digital solution capable to
characterize, from a human-centric perspective, both workers and
production lines allows to integrate all necessary human-related
information and permits to support production facilities improve-
ment in terms of skill-matching, ergonomics and safety. Further-
more, the integration of multi-perspective tools provides
environments that promote awareness of all the human-related
aspects at a glance, thus fostering
first-time-right solutions and
reducing the several design iterations that were necessary before.
Finally, the long-term analysis of mismatch at the boundaries of
human-automation interaction allows to redesign a more humanaware
automation and to promote training activities to
fit workers’
profiles to automation needs.

5. Concluding remarks

In the context of emerging smart factories, the reported analysis
examines the potential cooperation that can bring to a synergic
human-automation solution when the roles are modulated on the
basis of the automation level. The described industrial cases elicit
the current issues experienced within an automation dominated
environment that impacts production systems productivity and
workers’ well-being.

The proposed model for worker-aware adaptive shows how
human well-being drivers are harmonised in the automation
design. According to this model, well-being is achievable only by
implementing adaptability and
flexibility within a sociotechnical
system; automation becomes the keystone of this human-in-theloop
adaptive approach to production. The new factory automation
integrates seamlessly human and digital decision-making by monitoring production performances and workers’ physiological
parameters; this scheme permits the reintroduction of the man-inthe-
loop within factory automation.

This new framework has been validated in the experiments
carried out in two different domains showing respectively how:

a dedicated tool can relief mental workload while operating in
the context of adaptive automation;

an integrated toolset, covering different phases of factory design
and operation, can support human-centric automation.

The proposed automation model can address the identified
gaps. The obtained results prove, qualitatively and quantitatively,
that the integration of the human factors analysis, within
automation design, is a compelling condition for a synergic
improvement of manufacturing performance and human wellbeing
at once. Specifically, the experimental assessment presented
in this paper shows the effectiveness of the proposed model that
permits to overcome the trade-offs of automated manufacturing
environment and humans well-being through a synthesis of the
technical and psychological tools

Future work must employ methods and tools presented in this
paper to face the gaps outlined in Table 2, in different
manufacturing contexts and at any phase of the production
process.

Nowadays the human-automation synergy is fundamental for a
sound development of the innovative production scenarios
according to new emerging paradigms, like Industry 4.0 or Society
5.0. Particularly, the acceleration of scientific and technological
innovations will make the automation role more and more
important and pervasive. Consequently, the future evolution of
manufacturing industry should tackle the challenge to properly
balance the industrial targets with the workers well-being, by
means of human-in-the-loop adaptive automation systems.

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.

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)

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