=Paper=
{{Paper
|id=Vol-2428/paper11
|storemode=property
|title=Sensor-enabled wearable process support in corrugation industry
|pdfUrl=https://ceur-ws.org/Vol-2428/paper11.pdf
|volume=Vol-2428
|authors=Stefan Schönig,Andreas Erner,Michael Market,Stefan Jablonski
|dblpUrl=https://dblp.org/rec/conf/bpm/SchonigEMJ19
}}
==Sensor-enabled wearable process support in corrugation industry==
https://ceur-ws.org/Vol-2428/paper11.pdf
Sensor-enabled Wearable Process Support in
Production Industry
Stefan Schönig1 , Michael Market2 , Andreas Ermer2 , and Stefan Jablonski1
1
Institute for Computer Science, University of Bayreuth, Germany
{stefan.schoenig,stefan.jablonski}@uni-bayreuth.de
2
Maxsyma GmbH & Co. KG, Floß, Germany
{aermer,mmarket}@maxsyma.de
Abstract. In this industry paper we describe a BPM case supporting
production processes in corrugation industry. Due to increasing automa-
tion and staff reduction, less operators are available to control a cor-
rugated paper production line. Hence, interactions between users and
machinery require several location changes of users between control pan-
els that result in delayed information flows. The general goals of the
project carried out are to increase operators productivity in terms of re-
ducing stop times and increasing production speed and to faciliate the
breaking-in of new employees through transparent process knowledge.
Therefore, we implemented a sensor-enabled wearable process manage-
ment combining collected sensor data, wearable interfaces and executed
BPMN models. First evaluations show that the solution improves the
certainty of how and when specific work steps should be carried out and
reduces the delay between work steps through mobile and sensor-enabled
real-time task provision.
Keywords: Sensor-based Process Execution, Internet of Things, Wear-
ables, Production Industry
1 Introduction
Business processes are executed within application systems that are part of the
real world involving humans, cooperative computer systems as well as physical
objects [1–3]. The Internet of Things (IoT) enables continuous monitoring of
phenomena based on sensing devices, e.g., wearables, machine sensors, etc. Pro-
cess execution, monitoring and analytics based on IoT data can enable a more
comprehensive view on processes. Embedding intelligence by way of real-time
data gathering from devices and sensors and consuming them through Business
Process Management (BPM) technology helps businesses to achieve cost savings
and efficiency. In literature, several concepts are emerging on combining IoT and
BPM [4–7]. Still, there are many open challenges to be tackled [8].
In this industry paper we describe a BPM case implemented within a produc-
tion industry scenario. More precisely, we introduced BPM support for several
production processes of corrugation industry plants where paper is glued to-
gether to produce corrugated paper as raw material for cardboard boxes. Due
Copyright © 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
Fig. 1: Overview of the implemented solution
to increasing automation and staff reduction, less operators are available to con-
trol a corrugated paper production line. Hence, interactions between users and
machinery require several location changes of users between control panels that
result in delayed information flows. These delayed reaction times are frequently
the reason for increased deficient products. Furthermore, corrugation plants cur-
rently have to face a high fluctuation of employees such that process knowledge
is lost over time. As a result frequently new employees have to learn a basic
understanding of production process control from scratch.
Based on these issues the general goals of the project carried out are (i) to
increase operators productivity in terms of reducing stop times and increasing
production speed, (ii) to faciliate the education and onboarding of new employees
through transparent process knowledge and (iii) to ensure traceability of work
steps. These goals have been approached in several phases as visualised in Fig. 1:
– Introduction and implementation of a wearable production information sys-
tem, providing up-to-date sensor-based process information and process con-
trol capabilities on a smartwatch interface for production operators.
– Modelling the existing production processes of the corrugation plants using
the Business Process Model and Notation (BPMN).
– Combining collected sensor data, wearable interfaces and executed BPMN
models to realize a sensor-enabled wearable process management in corru-
gation industry.
The described solution has been rolled out in different plants in Germany and
the United Kingdom in 2018 and 2019. In total, fourty production operators have
been equipped with smartwatch devices and assigned a user in the BPM system.
Our approach demonstrates process innovation in three dimensions:
– Feasibility: we introduce an innovative wearable process user interface based
on smartwatches and a sensor-enabled process management solution
– Desirability: the presented case demonstrates the first process-based and
mobile production information system in corrugation industry.
– Viability: the introduced case enables customers to realize an integrated
BPM based solution for machine control and maintenance.
First evaluations carried out with operators show that the solution (i) im-
proves their general understanding of the underlying production process, (ii)
improves their certainty of how and when specific work steps should be car-
ried out, and (iii) reduces the delay between work steps through mobile and
sensor-enabled real-time task provision.
This paper is structured as follows: first, we describe the initial situation that
led to the introduction of the solution in Section 2. In Section 3 we describe the
actions that have been taken including technical details and occuring roadblocks
during the introduction. Section 4 describes first evaluation results and finally
concludes the paper.
2 Situation Faced in Corrugation Plant
Due to increasing automation and staff reduction, less operators are available to
control a corrugated paper production line. Hence, interactions between users
and machinery requires several location changes of users between control pan-
els that result in delayed information flows. These delayed reaction times are
frequently the reason for increased deficient products.
Typically, such a corrugation production line is divided into several areas (cf.
Fig. 2). Each area is independent from the others with well-defined interfaces
between them. Each part of the production line has a couple of so-called control
panels (CP ). A CP is needed for different operators O to intervene the produc-
tion processes, sometimes due to errors, but mostly due to maintenance tasks. It
is also typical that error and maintenance information as well as other context
relevant information (CRI) is depicted on one (or a few) central information de-
vices. There is a simple rule of thumb saying that the longer the reaction time of
an operator to take care of the intervention is, the worse it is for the production
process.
In the former setting, the time an operator Oi in a certain area i needed to
operate a control panel CPj is composed of three parts:
– (i) the time to find out whether at all and what control panel intervention
is required, i.e., the time to go from the operators current position to the
information devices (tnoti )
– (ii) the time to select the relevant information CP Ii from the information
device (tread )
– (iii) the time to go from the information device to the control panel (tcont ),
i.e., to walk a certain distance dO−CP
Fig. 2: Conceptual modelling of a production hall
In sum, tintervene = tnoti + tread + tcont is a timespan which is heavily deter-
mined by physical work, i.e., the time elapsed since operators have to walk from
a current position to the information device and then from this information de-
vice to the control panel. A third time component is the time operators need to
select and filter relevant information on the information device since often those
devices are heavily overloaded with status information from a whole produc-
tion line. Fig. 2 illustrates the problem by depicting a situation in a corrugation
plant. In this plant, a production area is about 140 meters long. An operator
is located somewhere in that production area. To be informed about potential
intervention, the operator has to go to the local information device (tnoti ); after
having found relevant status information (tread ), he has to go to a control panel
for intervention (tcont ). Through observations we found out that filtering status
information takes on average about 20 seconds and that an operator on average
covers a distance of 40 meters per intervention. In total, it takes about 2 minutes
for a necessary intervention. Within this reaction time, e.g., deficient products
are produced. Furthermore, we noticed that corrugation plants have to face a
high fluctuation of employees. Frequently new employees have to be taught and
learn a basic understanding of the production process from scratch.
Summing up, we identified different problems and needs on corrugation pro-
duction shop floor:
– The need for wearable user individual production process information and
control systems to diminish the timespan for information provision and pro-
cess intervention.
– The need for transparent process descriptions and active process support to
synchronise and guide production operators.
3 Actions taken
The described BPM case has been implemented in two phases. First, we intro-
duced a wearable production information system that visualises current process
data and allows operators to control production. Second, after still facing re-
maining issues, we enhanced the solution by realizing a process model based
task coordination for all operators.
3.1 Phase 1: Wearable Production Information Systems
To cope with the observed issues we are introducing innovative technology, here
in the form of wearables. These concepts change fully centralized information
and equipment control towards flexible, decentralized production monitoring
and control. In particular, we deploy mobile concepts for (i) process monitoring,
i.e., the provisioning of up-to-date and individual production process and equip-
ment information, and (ii) process control, i.e., actively impacting production
processes from potentially arbitrary locations within the plant.
One of the major advantages of wearables is immediate notification of oper-
ators independent of where the operator is located and where the information
stems from as long as it is part of the information system. This fast notification
enhances the situational awareness of the operators on the shop floor. A second
major benefit is the ability to actively intervene production through a wearable
device, i.e., that a production line could be controlled remotely. Combining the
need for intervention and the chance of immediate notification and remote con-
trol through wearables leads to the idea of using wearables as fast information
medium and control panel for operators. The gain of time fostered by the usage
of wearables can easily be calculated.
Wearables provide a multiplicity of monitoring functions to operators: (i)
visualization and confirmation of alarm and error messages; (ii) observation
of current status information and process parameters of different production
modules; and(iii) communication between different operating users. Thus, re-
sponsible operating and maintenance staff is pointed to current alarm messages
or instructions of machinery in real time on smartwatches on their wrist. Here,
messages and instructions are transmitted to responsible users through visual,
acoustic and, in case of noisy environments through haptic signals like vibration
alarms. By means of configurable user roles or user priority groups, production
or shift supervisors, equipment operators or maintenance staff are able to react
to disturbances and changed situations immediately.
Alongside to observation of process data, it is also possible to actively influ-
ence production processes. Users are able to control functionality that is neces-
sary to operate machinery by means of wearable devices. For example, produc-
tion speed can be adjusted by a corresponding operation on the smartwatch on
the operators’ wrist. Note that both for process monitoring as well as for process
control, functionality and visualized information can depend on the users’ role.
Hence, application specific services and information is only accessible and shown
where they are necessary and needed. This is fundamental for goal-oriented work
and protects users from information overload.
For example wearable devices offer diverse functionality to operators at the
Dry-End (the area where produced corrugated paper leaves the plant), e.g., (i)
remaining time of current production job; (ii) remaining time to next stack
transport; or (iii) current production speed. Information modules that imple-
ment function (i) and (ii) are shown in Fig. 3. Furthermore, users can influence
current process and equipment parameters in realtime via certain scroll bars,
e.g., adjusting the current warp of the corrugated paper. Users at the Wet-End
(the area where original paper is inducted to the plant) receive continuously
information w.r.t. (i) the next necessary roll change or (ii) occurring error and
defects of machinery modules.
3.2 Phase 2: Sensor-enabled Process Support
Despite the introduction of wearable information systems, plant administration
still faced the issue of uncoordinated operators: for example in case of machine
alarms that required operator intervention, either none or more than one person
took care of it. These remaining synchronization problems required a completely
new approach: the introduction of a BPM solution based on well-defined work-
flows and task assignment and coordination.
Therefore, the whole production processes have been defined and modelled
during four months. We observed and documented production as well as operator
tasks over several days and discussed our observations and model drafts together
with operators and production supervisors.
Exemplarily, we describe a subprocess that is executed every time paper
source rolls run empty, i.e., where new paper rolls need to be spliced with the
paper from the low running roll. In order to effectively execute this process, sev-
eral real time interactions with IoT devices, i.e., sensors and operator equipment,
is necessary: the process execution system (BPMS) must be aware of sensor data
which indicates that a splice will happen soon, triggering the splice subprocess.
Operators located somewhere along the machinery need to observe the splice
process to avoid issues. Therefore, they need to be notified in real time to walk
to the splicer. This requires wearable interfaces communicating with the BPMS
over the IoT. Depending on a sensor value indicating the next roll quality, the
BPMS has to execute different paths. In case the environment changes, opera-
tors tasks need to be reordered based on priorities or cancelled by the BPMS.
In addition to current tasks to be executed, operators require context specific
information at hand, e.g., the location of the splicer and the quality of the next
paper roll. Furthermore, operators continuously need to observe viscosity and
temperature of the glue to ensure a successful splice process.
The process is initiated by defining internal variables. Subsequently, the con-
trol flow splits into several branches depending on the priority of tasks and the
machine characteristics. Note that each task makes use of the variable Element
Documentation that captures current machine information and is visualized as
an additional remark below the actual task name.
To directly notify operators when human actions are needed, plant personal
has been equipped with smartwatches (Fig. 3). Therefore, a user-group model
has been defined in the BPMS. Here, available operators were assigned to a spe-
cific area of production that depicts their area of responsibility. Thus, depending
on the area operators are working, the BPMS assigns a different set of tasks.
Furthermore, operators are used more effectively because low priority work is
aborted in order to perform high priority work that could lead to machine stops.
This way, concrete and goal-oriented information in error cases or warning mes-
Fig. 3: Wearables: a) unclaim/complete task; b) tasks; c) and d) context info
sages for supply shortfalls can be transmitted to operators and enhance the over
all process transparency and thus the quality of task execution.
3.3 Technology Stack and Implementation
The described approach has been implemented based on a four layer architecture
that is visualised in Fig. 4. It consists of the following layers: (i) IoT objects like
sensors as data sources; (ii) IoT infrastructure and communication middleware;
(iii) the BPMS and (iv) data sinks in form of IoT objects of human process par-
ticipants. The layers are connected based on standard communication protocols.
In order to connect arbitrary sensor objects we make use of the open source
platform Node-RED of IBM. The platform acts as a communication middleware
between various IoT protocols and data sources like TCP sensors and the BPMS.
To allow the IoT objects at layer (i) to communicate with the IoT middleware
at layer (ii) and the BPMS, respectively, a Message Queue Telemetry Trans-
port (MQTT) Broker is used. IoT objects, i.e., sensors or actuators, represent
publishers. They are connected to an IoT gateway using specific architectures
such as Profibus, LAN, WLAN or Bluetooth. A specific IoT variable vx is ac-
quired and published on a MQTT topic /vx /data. Through a MQTT Broker the
acquired data is sent to an acquisition application at layer (ii) that stores IoT
data into a high performant NoSQL database. In our implementation we used
the latest version of the Apache Cassandra database.
A distribution application at layer (ii) keeps the BPMS updated with the
latest sensor values. All running instances of a particular process receive the
corresponding data value. The application cyclically acquires the values from
the database in a key-value structure and sends them to the BPMS. In our
architecture we used the latest version of the Camunda BPMS and therefore
communicated with the workflow engine by means of the Camunda Rest API.
The tools at layer (ii) ensure that process relevant information stemming from
the IoT is up-to-date. Through the acquistion tool, IoT data meta information
Data Sources Data Infrastructure Process Management Data Sinks
IoT Objects and Communication Workflow Engine User
(Mobile Interface)
HTTP/ Communication
MQTT User
Variable Mapping REST Module
IoT Object / Process
TCP /data/vx
IoT Objects Communication HTTP/
Node RED MQTT PUT Data BPMS IoT Objects
(Sensors, ) Protocol REST
...
(Robots, etc.)
IoT
Node RED
Objects
Fig. 4: Integrated communication architecture for sensors and BPMS
is provided that makes clear where the data stems from. Given the current IoT
data values, the engine calculates available activities.
As a mobile user interface we implemented an Android based smartwatch ap-
plication that subscribes to specific MQTT topics. The distribution application
cyclically requests the current user tasks from the Camunda API for each defined
user and publishes to the correct MQTT topic, given the mobile device identi-
fier, i.e., smartwatch device, configured on the BPMS. The application allows
users to start and complete tasks as well as to initiate new process instances.
The process of the device recognizing its configuration is implemented as fol-
lows: the distribution application cyclically checks the user configuration in the
BPMS. When a change is detected, it publishes the new configuration to the
topic /{actor id}. The assignment of a smartwatch to a specific user is imple-
mented by means of a unique device id, i.e., the smartwatch of a certain actor
subscribes to the topic of its specific device identifier. Having established such
connections, the smartwatch communicates with the MQTT broker by subscrib-
ing to the following topics: the current tasks for a specific operator are published
on the topic /{actor id}/tasks. The device sends operators commands, such as
complete task to the topic /{actor id}/command. The content of the message is
forwarded straight to the BPMS using a POST request. In case of active inter-
actions with the IoT environment BPMN Service Tasks are used. Here, we make
use of the Camunda HTTP connector either to directly communicate with IoT
objects that support HTTP communication or to send current variable values
to Node-RED.
3.4 Roadblocks
During the project we were facing several problems and roadblocks. These road-
blocks can be divided into two categories: (i) technical issues and (ii) social and
organisational issues.
First of all, we needed to establish a well working WiFi infrastructure to
ensure full coverage of the production hall for real time communication. For
reasons of blocking machinery, cabins and access restrictions, this turned out to
be difficult and cumbersome. For a long time, we were facing connection problems
resulting in task notification delays. Finally, a network with nine access points
covering the whole shop floor was installed. A wifi controller ensures fast roaming
and hand over such that each device is always connected to the access point with
the best signal for a specific location.
Social and organisational issues turned out to be even more challenging.
Sensor-based event invocation heavily depends on the accessibility of sensor and
machinery data. A big part of required data stems from external device providers
that were asked to either provide their data in an accessible way or to implement
an interface such that the data can be collected and be referenced in executed
processes. Both solutions turned out to be difficult to realize and took months
to be established.
Last but not least, the fundamental modelling of running shop floor processes
proved difficult as well. There was no written document of activities and work
steps and operators and supervisors on site were frequently busy. As a result
gathering necessary process information turned out to be a longsome procedure
implying occasionally unpleasant conversations.
4 Results achieved
The established wearable process solution of a german production plant is cap-
tured in Fig. 5. Through the described implementation it was possible to sig-
nificantly reduce reaction time intervals. The amount of deficient products was
decreased and the overall quality of the produced corrugated paper has been
improved. The overall equipment downtime was significantly decreased, since
problems have been prohibited or recognized in advance and were solved proac-
tively. Hence, the overall equipment efficiency could be increased effectively. To
quantify these findings, we analysed process execution. We tracked the corruga-
tion process (i) for five days without operators using wearable devices and (ii)
other five days with operators being notified using smartwatches. In particular,
we measured the average instance throughput time for splice processes. The ef-
fectiveness of the approach has been measured based on machine stop times and
waste reduction. On average, 100 splices are executed per shift, i.e., 8 hours of
production. In case (i) we recorded a total stop time of approx. 180 min, i.e.,
on average 12 min per shift. In case (ii) the stop time has been decreased to
approx. 60 min in total, i.e., 4 min per shift on average. The results show that
the application of the wearable sensor-enabled BPMS leads to less machine stops
because users need less time to recognize work to be done.
Additionally, we performed a qualitative usability evaluation involving oper-
ators of two shifts, i.e., 8 people. A usability evaluation of the wearable process
user interface was performed by calculating its’ System Usability Score (SUS).
A SUS questionnaire consisting of the following ten questions was presented to
operators at the Wet-End group:
1. I think that I would like to use this application frequently
2. I found the application unnecessarily complex
Fig. 5: Exemplary process based user interface in corrugation plant
3. I thought the application was easy to use
4. I think that I would need the support of a technical person to be able to use
this application
5. I found the various functions in this application were well integrated
6. I thought there was too much inconsistency in this application
7. I would imagine that most people would learn to use this application very
quickly
8. I found the application very cumbersome to use
9. I felt very confident using the application
10. I needed to learn a lot of things before I could get going with this application
The evaluation resulted in a system usability score of 93,33 out of 100. Fig. 6
visualises the evaluation results. As can be seen is this score located between the
ratings EXCELLENT and BEST IMAGINABLE, showing that the wearable
process support is easy to use and helpful for operators.
5 Summary and lessons learned
In this paper, we described an innovative BPM case carried out in corrugation
production industry. Within the project, we implemented a sensor-enabled wear-
able process management combining collected sensor data, wearable interfaces
and executed BPMN models. First evaluations show that the solution improves
the certainty of how and when specific work steps should be carried out and
reduces the delay between work steps through mobile and sensor-enabled real-
time task provision. Of course the presented solution can be generalized to other
Fig. 6: Qualitative user experience evaluation according to the System Usability
Score (SUS)
industry types as well. As a summary, we want to outline the most important
aspecs that we learned throughout the project and which factors were mainly
contributing to the successful completion of the case.
– We recognized the advantages that BPM technology yields compared to
traditional information systems for production shoop floor. User specific task
coordination based on sensor data as a process oriented solution can provide
it, seems to be the missing link between production information and operator
guidance.
– Modelling production shop floor process models is a cumbersome task that
requires both deep technical background w.r.t. the production system as well
as w.r.t. the used modelling language, e.g., BPMN. To establish a working
and accepted solution, an expert in both areas has to tackle this job.
– Organizational issues, e.g., with external companies, should be identified at
an early stage of the project to reduce waiting times for adjustments.
– Understanding BPMN modelled processes by production employees is not as
simple as expected. Models have been misinterpreted frequently and several
explanations have been necessary to consolidate a common understanding
of the notation and the defined processes.
– As a result, other than planned, BPMN models turned out to be less impor-
tant as a communication basis for all participating people. Instead, executed
processes and concrete task assignments fostered certainty of operators, with-
out knowing the overall flow of work.
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