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Article
Indoor Positioning Systems Can Revolutionise Digital Lean
Tuan-Anh Tran 1,2 , Tamás Ruppert 1,3,* and János Abonyi 1
Citation: Tran, T.-A.; Ruppert, T.;
Abonyi, J. Indoor Positioning Systems
Can Revolutionise Digital Lean. Appl.
Sci. 2021,11, 5291. https://doi.org/
10.3390/app11115291
Academic Editor: Radmehr P.
Monfared
Received: 12 April 2021
Accepted: 4 June 2021
Published: 7 June 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
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distributed under the terms and
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Attribution (CC BY) license (https://
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4.0/).
1MTA-PE ‘Lendulet’ Complex Systems Monitoring Research Group, Department of Process Engineering,
University of Pannonia, Egyetem u. 10, POB 158, H-8200 Veszprem, Hungary;
anhtt.vck@vimaru.edu.vn (T.-A.T.); janos@abonyilab.com (J.A.)
2Department of Mechanical Engineering, School of Mechanical Engineering, Vietnam Maritime University,
484 Lach Tray St., Ngo Quyen Dist., Hai Phong City VN-18000, Vietnam
3Sunstone-RTLS Ltd., Kevehaza u., 1-3, H-1115 Budapest, Hungary
*Correspondence: ruppert@abonyilab.com
Abstract:
The powerful combination of lean principles and digital technologies accelerates waste
identification and mitigation faster than traditional lean methods. The new digital lean (also referred
to as Lean 4.0) solutions incorporate sensors and digital equipment, yielding innovative solutions
that extend the reach of traditional lean tools. The tracking of flexible and configurable production
systems is not as straightforward as in a simple conveyor. This paper examines how the information
provided by indoor positioning systems (IPS) can be utilised in the digital transformation of flexible
manufacturing. The proposed IPS-based method enriches the information sources of value stream
mapping and transforms positional data into key-performance indicators used in Lean Manufacturing.
The challenges of flexible and reconfigurable manufacturing require a dynamic value stream mapping.
To handle this problem, a process mining-based solution has been proposed. A case study is
provided to show how the proposed method can be employed for monitoring and improving
manufacturing efficiency.
Keywords:
Industry 4.0; Lean 4.0; indoor positioning system; Lean management; smart manufactur-
ing; real-time locating system; process mining; internal inventories
1. Introduction
Internal Positioning Systems provide the possibility of full traceability of manufac-
turing processes [
1
]. The main research innovation of this work is to highlight that data
provided by IPS systems can be transformed into information that is valuable for Lean-
based process improvement.
Lean management (LM) is a well-known concept that is widely accepted in man-
ufacturing industries due to its effectiveness in cutting waste and improving operation
performance [
2
–
5
]. The tools of Industry 4.0 that advance LM to the next level include
simulation and optimization [
6
,
7
], process mining [
8
–
10
], data mining [
11
–
14
], data analyt-
ics [
15
,
16
], big data analysis [
17
,
18
], digital twins [
19
–
22
], machine learning [
23
,
24
], virtual
reality [
25
–
28
] and cyber-physical systems (CPSs) [
17
,
29
–
31
]. An integrative model for
LM and Industry 4.0 was studied in [
32
], which resulted in a flexible and reconfigurable
manufacturing system. This brand-new generation was labelled Lean 4.0, with the promise
of a different perspective of designing, operating, monitoring and optimizing manufactur-
ing systems [
33
]. Lean 4.0 follows LM principles as it is built upon a strong foundation
of communication and connectivity between equipment and personnel, which allows the
key performance indicators (KPIs) to be automatically collected, analysed and modified
according to LM measures. Recently, the Lean Industry 4.0 concept has been introduced to
surpass the production context within enterprises and to cover the extended supply chain
and the logistics network [34].
The tracking of flexible and configurable production systems is not as straightforward
as in a simple conveyor, which provides difficulties in controlling internal inventories.
Appl. Sci. 2021,11, 5291. https://doi.org/10.3390/app11115291 https://www.mdpi.com/journal/applsci
Appl. Sci. 2021,11, 5291 2 of 14
The potential of digital lean is unlocked by the integration of operational technology (OT)
and information technology (IT) as IT tools can improve the real-time visibility of the value
stream. One of the most promising IT elements that can support Lean 4.0, is the Indoor
Positioning System (IPS) [
35
]. This work aims to present how positional data can enrich
the toolkit of Lean 4.0-based continuous development of flexible manufacturing systems.
The primary function of indoor positioning is similar to GPS, track on a map a
tagged mobile unit that can be an asset or person [
36
]. A typical IPS is an indoor wireless
positioning technology [
37
] that works with radio-frequency, optical, or acoustic tags
and chips [
38
]. The IPS tags are always-active and continuously broadcast signals to
beacons [
37
]. Tags and fixed reference points can be transmitters, receivers, or both,
resulting in numerous possible technology combinations [
39
,
40
]. IPS can identify objects’
location in a closed structure, thus widely applied in an office building, hospitals, facilities,
and warehouses [41].
Compared with other technology including RFID and bar-code scanner, IPS can
exclude human error and systematic flexibility, and is robust to any layout change. Due
to its intrinsic appropriateness for monitoring logistics units within a manufacturing
facility—from items up to packages, transport units, and pallets—IPS has been widely
applied in many aspects such as cycle time optimization [
42
], monitoring production line
activities [
43
], logistics management [
44
], pallet management [
45
], safety management [
46
],
and human resource monitoring [47].
This paper aims to develop a detailed guideline about how the information provided
by IPS can be utilised in lean management. The proposed method is embedded into the
concept of continuous development. The structured DMAIC (Define-Measure-Analyze-
Improve-Control) approach utilised in Six Sigma methodology also follows the concept of
the Plan–Do–Check–Act (PDCA) cycle [
48
], which has been proven effective in reducing
non-value-added activities in the supply chains and assembly lines [49].
Value Stream Mapping (VSM) is the standard tool for recording processes and identify-
ing waste. The challenges of flexible and reconfigurable manufacturing require a dynamic
VSM (DVSM). Our key idea is to handle this problem based on process mining that has
already been applied for VSM of a mixed-model assembly line [
50
], utilised in Six Sigma
projects [51] and also used for the analysis of IPS-based data [37].
According to the structure of the paper, the main contributions are the following:
•
We explored and categorized the possible situations in which the IPS can be applied
in LM in Section 2. The novelty of this section is that it defines how positional data
can be transformed into actionable information for LM.
• We developed a data-based framework to integrate and analyze positional and man-
ufacturing data. As Section 3presents, the novelty of the methodology lies in the
process-mining-based identification of VSMs.
•
We provided a detailed industrial case study with several KPIs to demonstrate the
applicability of the proposed framework in Section 4. Our case study is based on a
real manufacturing problem where the IPS monitors the day by day production, so
the applicability of the developed framework is demonstrated.
2. Utilisation of Location-Information in Lean 4.0
LM is based on the continuous improvement of the processes based on the following
concepts:
• identifying wastes in production processes to eliminate them [52];
• shortening the lead time of production [53,54];
• reducing inventory and stock levels [53,55];
• standardizing tasks and motion to stabilize the output quality [56];
•
developing a continuous flow of information and materials in the organization [
52
,
57
];
• balancing the manufacturing line to avoid bottleneck [55,58];
• employing a comprehensive scheme to maintain productivity [59,60].
Appl. Sci. 2021,11, 5291 3 of 14
The proposed method is based on the assumption that the wastes, the processing and
activity times, and the stocks can be automatically measured by IPS without labour- and
time-consuming measurements. Table 1shows the key performance indicators (KPIs) of the
Lean concepts and their measurement possibilities. The listed measurement systems not
only help organize and optimize the production procedure by easing monitoring activity
with a real-time value stream but are also aligned with LM principles as well as supporting
philosophies such as total quality management (TQM) and just-in-time (JIT) [61].
As Table 1shows, RFID-based systems are particularly suitable for monitoring LM
parameters [
62
,
63
]. Utilizing RFID tags within an IPS is a favourable approach in different
industries, such as construction [
64
,
65
], fast-moving consumer goods production [
66
],
automotive part manufacturing [
67
], automobile assembly manufacturing [
68
], agriculture
equipment machine part manufacturing [
69
] and the job shop floor environment [
70
].
In the manufacturing shop floor environment, IPS can be beneficial as it can enrich data
acquisition for LM [
71
] and it can be used to obtain dynamic spaghetti diagrams, which
are used for the visualization of the value streams [72].
Table 1. The traditional concepts of LM with the potential of IPS.
Lean Concept KPIs Measurement Tools Potential of IPS Relevant Application of IPS
Shortest lead time Average lead and
cycle time
Camera [73,74]; Bar-code
scanner [73]; RFID [62,75];
Machine/Event logs [76];
IPS [37]
high
Real-time monitoring of the
position of semi-finished
products and resources,
calculation of the lead and
cycle times [42,43].
7 wastes elimination
Waste of
Transportation
RFID [66]; Machine/Event
logs [76]; IPS [72]high Real-time spaghetti
diagram [72].
Waste of Inventory Camera [30]; RFID [62,66];
IPS [72]high Tracked items in inventory
areas [77,78].
Waste of Motion Camera [74] low -
Waste of Waiting
Camera [73,74]; Bar-code
scanner [73]; RFID [62,75];
Machine logs [76]; IPS [37]
high
Tracked semi-finished
products, waiting times,
internal stock levels [71].
Waste of
Over-processing Manual audit [79] low -
Waste of
Over-production RFID [66]; IPS [37,72] high
Discovered overproduction
based on the tracked
semi-finished products [71].
Waste of Defect Optical sensors [80];
RFID [81]; [72]medium
Reduced defect based on
IPS-based poke-yoke solutions
and better monitored rework
flows.
Less inventory inventory value RFID [66]; IPS [72] high
Improved control of the
inventory level [78] and
e-kanban solutions reduce
internal inventories [82].
Standardized work Deviation from
standardized work
Camera [74]; RFID [62];
IPS [37]high
IPS based dynamic work
instructions improve operator
work (Smart operator) [83]
Continuous flow Queueing time RFID [62]; IPS [37,72] high Discovered queueing areas
near the workstations [1].
Line balancing Line balance factor Camera [74]; RFID [62];
IPS [37]high
Improved activity time
analyses thanks to sensor
fusion [43].
Quick changeover Set-up and
changeover time
Machine logs [84]; Manual
audit [85]high Supported SMED projects [86]
Appl. Sci. 2021,11, 5291 4 of 14
Locating sensors only may fail to comprehend the actual activities performed in the
production. For example, locating sensors can signal that the product is in a workstation,
but they cannot indicate whether the product is currently processed, especially in a manual
processing step, while the operators were busy looking for tools or reading work instruc-
tions. Therefore, it is beneficial to use multiple sensory data for Lean 4.0, so IPS is mostly
beneficial to enrich the existing information collected by other sensors and data stroded in
an MES system.
3. IPS-Driven Development Framework for Lean 4.0
The previous section presented that the application of IPS in Lean 4.0 could open
new possibilities if the positional data could be transformed into real-time contextualised
and actionable information. This section proposes a framework that we developed for
this purpose.
The proposed framework that also utilizes information sources of a typical Industry
4.0 manufacturing system is presented in Figure 1. In addition to location data, acquired
data from existing technologies such as bar-code scanners, machines and event logs can be
incorporated to provide a comprehensive view of the current system state. The facility in-
formation, such as the overall layout and designated area map, can provide supportive data
to clarify the facility context. The information of the overall layout is added, as well as some
designated areas for buffer inventory, waiting for queues, and maintenance preparation.
After data processing and computation of LM KPIs, different system monitoring techniques
can be applied, such as optimal material route, optimal labour assignment and JIT prepara-
tion. Process- and data mining are performed on the collected and contextualised data to
explore frequent patterns of material flow and states of the production process.
We applied Gantt diagrams to analyze the periods of operations. As it will be pre-
sented in the application example there are many missing time periods in the Gantt diagram
when the states of the material flow and the resources are not monitored in the MES. Based
on the analysis of positional data, additional states of the manufacturing process can be
defined and assigned to the product and material flows and states of the resources (e.g.,
temporal inventories can be defined). The explored states and additional time-stamps
provided by the IPS process mining algorithms can be utilised to update VSMs. Thanks to
the real-time position of the process flows, motion-based anomalies can be detected.
Figure 1. The proposed framework of Lean 4.0 data-driven development.
Appl. Sci. 2021,11, 5291 5 of 14
Figure 2shows that proposed methodology follows the PDCA (Plan, Do, Check, Act)
cycle of continuous improvement. The core element of the method is the process model
(represented as a VSM in the figure) that contains all the essential information about the
manufacturing process. The proposed improvement cycle continuously updates the model
with the help of IPS data. The motivation is to continuously and automatically monitor the
production. The developed framework can discover the real process model continuously
based on the IPS data with the toolset of process mining. The resulted models are used
to update the VSMs, evaluate the performance of the process by calculating the Lean
KPIs. The most apparent benefit of Lean 4.0 KPIs are the readiness of decision making and
optimisation based on real-time information about the manufacturing system.
Figure 2. IPS data is the key element of the proposed PDCA-cycle-based methodology.
4. Application to the Monitoring of a Flexible Manufacturing Process
In this section, a manufacturing use case is represented to prove the applicability of
the proposed methodology. We introduce the studied production process and the purpose
of the continuous improvement project and show the details of the application of IPS.
4.1. Purpose of the Project
The presented study focuses on the monitoring of CNC machines and assembly
stations used to produce metal parts for an automotive company. There are five CNC
machines, one assembly station, one assembly line and a packaging station. The orders
(tasks) move along different paths during the production, depending on the production
plan. The project aims to reduce transportation waste, identify the waiting and queue-
ing times, and monitor the cycle times. Due to the changing number and variations of
product families, this process is not a one-time activity. One small change in the product
architecture can cause changes in the assembly sequence, which can lead to significant
performance losses.
Traditionally, LM masters will detect these 3M (Muda—Mura—Muri) via eye observa-
tion, then conduct re-calculation and re-arrangement to find a new optimal point. By using
the IPS, the manufacturing activities can be easily tracked and automatized. The positional
data from the moving carts are analyzed to identify whether they are not in a pre-defined
value-added area (like assembly stations). The extracted cycle times are used to find the
potential wastes of human works (changing times, manual work) and focus on these ar-
eas, such as defining a standardized digital work instruction that depends on the current
position information of the semi-finished product.
4.2. Description of the Applied IPS
The hardware architecture of the IPS is illustrated in Figure 3. The applied ultra-
wide band (UWB)-based real-time locating system (RTLS) uses active tags and anchors
for localization. There are 15 anchors installed on the shop floor, which is nearly 2000 m
2
.
Appl. Sci. 2021,11, 5291 6 of 14
The anchors are connected to two central units. The raw sensory data are transferred
into the position calculation server. The calculation of the position is based on the TDoA
(Time Difference Of Arrival) method and applies a Kalman filter to obtain more accurate
information. The IPS is installed to track carts with the shop floor’s semi-finished products.
These carts are moving (manually) between the workstation and the IPS sends information
to the MES if the actual cart with the defined (paired) product has arrived at the actual
station. There are 40 carts, every cart has a dedicated IPS tag. Each time a semi-finished
product was put on a cart, the operator paired the order number with the tag ID with a
timestamp. The positional data accuracy is around 0.5 m, which is sufficient to obtain an
accurate spaghetti diagram (Figure 4a) from each produced order. The shop floor with a
tracked motion of one product is shown in Figure 4a. As this figure illustrates, the analysis
of the positional data allows the identification of the temporary stations and
motion paths.
Figure 3. The hardware architecture of the applied IPS (based on Sunstone-RTLS Ltd.—Hungary).
The proposed lean analysis was performed on the production data from MES and
positional data (where the sample time is three seconds) from IPS. The process-flow was
discovered with a process mining algorithm based on the MES data. We used the Disco
software for process mining, and in the following subsection, the results are shown. The po-
sitional data are used to calculate the loss of process. We analyzed the transportation path,
where we calculate the transportation periods for orders and the hidden temporary storage
and queuing times are identified.
The collected positional data contain the tag IDs with the x–y position
[m]
according
to the predefined coordinate system (fitted for the shop floor layout). These data are
updated every three seconds (the sample time can be set—maximum 1 kHz) to ensure
most of the carts’ motion is covered in this production scenario. The factory layout with
the zone (workstation) definitions is provided by the rectangles (Figure 4a) to match the
activity order. This layout is elaborated based on the facility’s overall layout, and with the
designated area represented where the production activities are performed. These areas are
determined with the hardware’s capability, and the system can detect the corresponding
processes that are occurring. The entering and exiting times define the time that a product
spends on one process step. The information (resources, produced pieces, quality issues)
for every Task ID, which includes the Start time when the tag entered the zone (Workplace),
is stored in the MES.
Appl. Sci. 2021,11, 5291 7 of 14
(a)
(b)
Figure 4.
The distribution analysis of the positional data supports the identification of the states of the internal inventories,
and the waiting and cycle times. (
a
) Tracked path of one product on the shop-floor. The rectangles define the workstations
and the dots represent the positional data. The timeline is presented by the colours of the dots. (
b
) The discovered status is
based on positional data. The blue stars are the transportation, while the orange markers are the queueing positions.
4.3. Calculation of the IPS-Based Indicators
This section shows how the data from MES and IPS can be utilised based on the
relevant KPIs.
The IPS sends signals to the MES when the actual cart arrives at the pre-defined station.
The applied process mining algorithm determines the process model of the production flow.
An illustrative result is presented in Figure 5. Due to the flexibility of the manufacturing
process, the extracted model is not trivial and varies over time; therefore, the model is
continuously updated based on the real-time positional data.
The discovered process flows serve as input for work standardisation projects. Accord-
ing to the most frequently conducted steps, a pattern of main material flow is recognized in
Figure 5a, where the main workstations and machines are highlighted in blue. We note that
there is no leading process flow. Along with the material flow map, the average cycle times
are recorded, as illustrated in Figure 5b. The thickness of the arrows represent the time
delay between the two stations. The result can be compared to the manufacturing processes
Appl. Sci. 2021,11, 5291 8 of 14
standard times so that non-conformance stands out. The main process mining-based KPIs
are summarized in Table 2.
(a)
(b)
Figure 5.
A production flow model discovered by process mining based on IPS and MES data. (
a
) The frequency of the
material flows. The colours of stations represent utilisation of the workstations. (
b
) The discovered average cycle and
transition times. The colours of stations represent the cycle times. We can notice that the HELLER stations are the bottlenecks
of the process. The transition times are represented by the arrows, which highlight the possible hidden wastes.
A Gantt diagram has been developed to further study the reason for the long transition
times of the semi-finished products (see Figure 6a). The rows of the Gantt chart show the
orders (Tasks) and colours represent the workstations. We defined the Unknown station
to show the period where we have no data (from MES). These periods are denoted in
Appl. Sci. 2021,11, 5291 9 of 14
Figure 6a with red lanes and these periods could be the source of the long time period
between two stations on the results of process mining and could be the hidden wastes of
the manufacturing process. The Unknown period is the 19.74% of the studied time period.
(a)
(b)
Figure 6.
The Gantt diagrams show the states of the production of a given product. The comparison of the two diagrams
shows the benefit of the additional information of the IPS. (
a
) Gantt diagram based on MES data. (
b
) Gantt diagram based
on IPS and MES data.
In the next step, we discovered the status of thee Unknown periods based on the
positional data. We analysed 22 days of production data for this purpose. Based on the
positional data from IPS, the velocity is calculated to determine the Waste of transportation
periods. An example for that period is shown in Figure 4b with the blue stars. Temporary
storage is the positions that are closely located out of the pre-defined zones (workstations).
When the carts with the products are located in a pre-defined zone (but it is not logged to
the MES, so it is not under production), we assumed these products are queueing before
the actual workstation (see the orange points on Figure 4b). Figure 6b shows the new
Gannt chart with three more defined stations related to queueing, temporary storage,
and transportation.
4.4. Discussion, Utilisation of the Results
The results are shown in Figure 7, where we note that the Queueing time and Wasting
time are almost 20% of the analysed manufacturing time. Table 2summarises these times
Appl. Sci. 2021,11, 5291 10 of 14
according to the stations and shows that the AO2 workplace has the most significant
queuing time, so the continuous improvement project should focus on that first.
Figure 7. The distribution of the average times calculated based on the IPS and MES data.
Table 2. The cycle and queueing times calculated with the help of the positional data.
Workplace Average Cycle Time
[min] Queueing Time [min] Produced Tasks
Waiting time 119.47 - 54
Waste of transportation 2.73 - 27
AO2 77.73 102.86 56
DAEWOO 84.71 84.05 49
DHOLE 88.69 5.09 163
HELLER1 99.36 91.80 72
HELLER2 228.53 46.74 34
HELLER3 197.16 59.56 32
HELLER4 124.82 42.18 146
M4KTK 61.91 84.61 145
PACK2 30.30 47.72 31
The IPS serves as a non-stop monitoring system that contributes to the everyday work
of Lean specialists. As a first step, an alarm system can be set up at each workstation to
notify if the working or waiting times in that station exceed their predefined limits; so the
line advisor can take required supportive action on time.
The integrated application IPS and process mining supports the redesign of the layout
thanks to its ability to detect hidden stations and states of the process.
5. Conclusions
In this paper, the possible applications of indoor positioning systems in Lean 4.0 are
explored. The proposed IPS incorporates different kinds of sensors to acquire not only
positional data but also other data such as vibration, which enables them to recognize
motion and transportation activities. Along with this IPS architecture, the traditional set of
Appl. Sci. 2021,11, 5291 11 of 14
Lean KPIs are redefined and redesigned to be derived automatically from IPS-based data.
The process mining-based analysis of the collected data can provide insight into the key
factors that determine the productivity and efficiency of production systems.
The proposed method of data acquisition enables further system optimization, which
assists managers in monitoring their system effortlessly and in a stress-free manner. In the
trend of Lean 4.0, the use of such a system is expected to soon be dominant due to its
hardware maturity, as well as the readiness of data and the need from the manufacturer.
The framework for process analysis can provide the basis for further optimization and
enhancement of human–machine activity cooperation, which will constitute our future
research. A case study is conducted in a mechanical manufacturing firm to show the
possible output of Lean 4.0 KPIs, and improvements can be made based on activity data.
The accuracy of the result from the system is much dependent on the hardware char-
acteristics. The most frequent error occurs when the location sensor cannot recognize
which area is between two adjacent ones. Due to the current technology limitation, the de-
fined space of workstations needs to be separated with a distinct distance. Fortunately,
with process mining tools, meaningless noise and error can be excluded. However, the au-
thors believe that this problem can be mitigated soon, with advancements in the new
hardware system.
According to the intensive use of data in monitoring a smart factory, one particular
concern is personal privacy. When a tag is attached to an operator, then every movement
can be tracked. To ensure personal privacy, the tag is only active in the production zones.
Besides, with a large amount of operation data and production monitoring parameters
from the system, the management dashboard needs to be discussed and adjusted at a
managers’ meeting, not by any single person. As the system can improve the facility
operation—through line speed changes, human assignment and dynamic line balancing—
it is unwise to teach it in the wrong way. The consultation of an LM expert in setting the
KPIs and allowable adjustment is necessary.
We believe that the proposed framework and the presented results provide a practical
starting point for lean management practitioners and can initiate further research projects.
Author Contributions:
Conceptualization, T.-A.T., Supervision, J.A.; Code development: T.R.;
Writing—original draft preparation, T.-A.T. and T.R.; funding acquisition, J.A. All authors have read
and agreed to the published version of the manuscript.
Funding:
This work was supported by the TKP2020-NKA-10 project financed under the 2020-4.1.1-
TKP2020 Thematic Excellence Programme by the National Research and by the 2019-1.1.1-PIACI-
KFI-2019-00312 project (Mobilized collaborative robot-based development of a modular Industry 4.0
production system with quality management functions).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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