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the Job Allocator, a decision support tool based on linear
optimization that suggests to the team leader, for each shift,
the worker-workplace allocations by matching the skills,
knowledge and capacities residing with those required by the
production plan; the tool considers also the personal allocation
preferences defined by the workers (Table 2: O1);

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.