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The powerful combination of lean principles and digital technologies accelerates wasteidentification and mitigation faster than traditional lean methods. The new digital lean (also referredto as Lean 4.0) solutions incorporate sensors and digital equipment, yielding innovative solutionsthat extend the reach of traditional lean tools. The tracking of flexible and configurable productionsystems is not as straightforward as in a simple conveyor. This paper examines how the informationprovided by indoor positioning systems (IPS) can be utilised in the digital transformation of flexiblemanufacturing. The proposed IPS-based method enriches the information sources of value streammapping 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 isprovided to show how the proposed method can be employed for monitoring and improvingmanufacturing efficiency.
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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.
Academic Editor: Radmehr P.
Received: 12 April 2021
Accepted: 4 June 2021
Published: 7 June 2021
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1MTA-PE ‘Lendulet’ Complex Systems Monitoring Research Group, Department of Process Engineering,
University of Pannonia, Egyetem u. 10, POB 158, H-8200 Veszprem, Hungary; (T.-A.T.); (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
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.
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 [
]. 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 [
]. The tools of Industry 4.0 that advance LM to the next level include
simulation and optimization [
], process mining [
], data mining [
], data analyt-
ics [
], big data analysis [
], digital twins [
], machine learning [
], virtual
reality [
] and cyber-physical systems (CPSs) [
]. An integrative model for
LM and Industry 4.0 was studied in [
], 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 [
]. 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.
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) [
]. 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 [
]. A typical IPS is an indoor wireless
positioning technology [
] that works with radio-frequency, optical, or acoustic tags
and chips [
]. The IPS tags are always-active and continuously broadcast signals to
beacons [
]. Tags and fixed reference points can be transmitters, receivers, or both,
resulting in numerous possible technology combinations [
]. 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 [
], monitoring production line
activities [
], logistics management [
], pallet management [
], safety management [
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 [
], 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 [
], 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
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 [
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 [
]. Utilizing RFID tags within an IPS is a favourable approach in different
industries, such as construction [
], fast-moving consumer goods production [
automotive part manufacturing [
], automobile assembly manufacturing [
], agriculture
equipment machine part manufacturing [
] and the job shop floor environment [
In the manufacturing shop floor environment, IPS can be beneficial as it can enrich data
acquisition for LM [
] 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]
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
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]
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
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
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
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
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. (
) 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. (
) 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.
Figure 5.
A production flow model discovered by process mining based on IPS and MES data. (
) The frequency of the
material flows. The colours of stations represent utilisation of the workstations. (
) 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.
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. (
) Gantt diagram based on MES data. (
) 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.
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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... Sensor Technology Tracked Objects Purpose Technique of Analysis [14] RFID products job scheduling in machines simulation [15] RFID products re-design of workflow simulation [16] RFID products production scheduling simulation [17] RFID products production scheduling multi objective optimisation [18] Fixture workstations fault detection quadratic programming [19] UWB products lead time prediction semantic enriching [20] Lidar and UWB workers safety monitoring supervised machine learning [21] UWB products bottleneck identification process mining and value stream mapping [22] UWB workers safety monitoring supervised machine learning [23] UWB products bottleneck identification value stream mapping Our paper UWB workers worker capacity allocation process mining and simulation ...
... [26] also includes product information. Perhaps, the most relevant study to our paper is by Tran et al. [21], which we note in Table 1, who also uses position data from localisation sensors to extract event logs for process mining. However, in all these studies, the event logs are based on traces of products, whereas our methodology is based on tracking workers. ...
... Combining process mining with activity recognition is crucial for manufacturing, as noted in [27]. Our methodology uses the facility layout to detect activities related to manufacturing tasks using worker position data, similar to [19,21]. A relevant study in the context of tracking worker movement for process mining to discover process models in manufacturing is [28]. ...
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This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others.
... For this reason, the application of real-time monitoring is appropriate to the operations of production and logistics as well as VSP, especially the documentation of impacts on the value stream, emerging from applied improvement measures. [22], [41]- [43] 2.2.5. Data / Process Mining During the production process different types of data are gathered. ...
... For this reason, the technique is suitable for the initial, but also continuous VSM as well as VSP. [17], [23], [41], [44] 2.2.6. Simulation-based Decision Making The implementation of a digital value stream map opens opportunities in regard to simulative techniques. ...
Conference Paper
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Value Stream Management in its conventional methodology underlies several disadvantages, especially in the context of effort and flexibility. The approach is characterized as time-and resource-consuming due to a mainly pen-and-paper based procedure, causing inflexibility in an increasing dynamic environment. Taking these findings into account the term static VSM in contrast to a dynamic VSM or VSM 4.0 is introduced in some studies. The fundamental principles of VSM are recognized as still valid, whereas the procedure requires improvements in regard to flexibility and accuracy to ensure a future-viability in today's environments. Recent studies show the relevance and importance of this topic and provide proposals for improving the methodology by the application of information and communication technologies. All reviewed studies have in common a selective consideration of one or more technologies for improving the methodology. Missing is a holistic analysis of the potentials of a digital value stream map, implemented as data-based model with reference to the Value Stream Management phases value stream mapping (VSM), value stream analysis (VSA), value stream design (VSD) and value stream planning (VSP). Based on a literature review for analyzing the state of research in a holistic way, the paper aims at providing a framework, consolidating all recent researches in one model.
... Gemba walk is the most popular observational method for Occupational, Safety, and Health (OSH) practitioners [2] in Lean doctrine. As these techniques are knowledge-and experience-dependent [3], many research proposed innovative usages of technology to replace the traditional ways of self-reports and observations [4,5], to facilitate the digital Lean [6,7]. Motion Capture (MoCap) technologies are preferred [8], along with the development of advanced algorithms such as filtering and Machine Learning (ML). ...
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The human worker is an in-disposable factor in manufacturing processes. Traditional observation methods to assess their performance is time-consuming and expert-dependent, while it is still impossible to diagnose the detailed movement trajectory with the naked eye. Industry 4.0 technologies can innovate that process with smart sensors paired with data mining techniques for automated operation and develop a database of frequent movements for corporate reference and improvement. This paper proposes an approach to automatically assess worker performance with skeleton data by applying pattern mining methods and supervised learning algorithms. A use case is performed on an electrical assembly line to validate the approach, with the skeleton data collected by Kinect sensor v2. By using supervised learning, the movements of workers in each workstation can be segmented, and the line performance can be assessed. The work movement motifs can be recognized with pattern mining. The mined results can be used to further improve the production processes in terms of work procedures, movement symmetry, body utilization, and other ergonomics factors for both short and long-term human resource development. The promising result motivates further utilization of easy-to-adopt technology in Industry 5.0, which facilitates human-centric data-driven improvements.
... However, there is a connection between their location and the processes performed as within the industrial facilities there are predefined activities in every manufacturing cell and a specific order to execute the activities. A recent research examined how the information provided by a RTLS can be utilized in the digital transformation of flexible manufacturing (Tran et al. 2021). The authors elaborated on how to transform the information sources of value stream mapping and positioning data into key-performance indicators used in Lean Manufacturing. ...
... A dead-reckoning technique called "signal-free solutions" in IPS uses commercially available mobile sensors to detect changes in location. Received signal strength (RSS), time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) are few of the IPS concepts for sensing radio signals [3]. Due to its simplicity of implementation and lack of additional hardware requirements, RSS has been commonly employed for IPS design. ...
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This paper offers a method for developing an efficient indoor navigation system with the consideration of the shortest path between source and destination. The challenge for the indoor navigation system is to provide personal navigation information and the optimal route. Applications of indoor navigation systems need consideration of the Shortest Path problem. The shortest pathways can be used to find solutions to the current problems using Dijkstra’s algorithm. Based on the issue with the indoor navigation system, the shortest way and the best path are calculated. This is crucial to navigation systems since it can aid in making wise decisions and time-saving choices. The primary goal is to obtain the implementation at an affordable price. These applications and services are made available indoors, where the GPS does not function. The goal of indoor navigation is to direct users inside buildings. Dijkstra’s algorithm for locating objects and for moving along the shortest path in an indoor setting are examined in this work. Experimental results of indoor navigation systems were carried out on my organization’s indoor environment and verified the applicability of the presented Indoor Navigation System. The techniques provided include map digitization, locating a user, and choosing the shortest route. This is accomplished through a mobile application created for the Android operating system, and indoor navigation is carried out by using Dijkstra’s algorithm. The proposed method is implemented in our college academic block, and the experimental results show that our navigation method is feasible and effective. To verify the reliability of the algorithm, the proposed application fulfils the criteria of an indoor navigation system to produce the optimal route between two points when applied to a map of our college’s indoor terrain.
The Internet of Things, Data Science, 3D printing, Artificial Intelligence and Machine Learning are some elements on the agenda related to business modernization. These and other digital technologies promote significant changes in the structures of organizations, from strategic planning to the factory floor. However, this process of Digital Transformation is not only materialized in the implementation of digital technologies in the production chain, but is based on the capacity of these enabled technologies to generate new value propositions. This article proposes a discussion about the implications of Lean Thinking in the implementation of Digital Transformation projects. For this, the study analyzed 21 international academic works, from the year 2018, based on the Web of Science and Scopus repositories. The repositories were selected because they are comprehensive and offer intelligent search tools. After a systematic analysis of the documents, a discussion arose about the central elements capable of expanding the understanding of the components of Lean Thinking and its relationship with Digital Transformation projects.
Purpose The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process. Design/methodology/approach The approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators. Findings It was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators. Practical implications Through the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible. Originality/value The authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.
Companies have been adopting lean management for decades to increase productivity, customer satisfaction and profitability. A foundational and powerful tool in lean management is value stream mapping (VSM). VSM is still widely used as a static pen and paper-based tool. However, as industrial processes have become more advanced and digitalized, static VSM methods struggle to handle the complexities related to flexible and changeable elements in production. Factors including increasing range of parts, processes, and dynamic elements challenge the economic application of static VSM approaches. In the meantime, wireless connectivity together with low-cost sensors in the context of Industry 4.0 have enabled advanced industrial infrastructures, allowing for dynamic VSM (DVSM) approaches. While the potential benefits are promising, research and application in this interdisciplinary field are just evolving. Addressing this gap, this study sets out to map the DVS research landscape. More specifically, we (1) identify the requirements to overcome the limitations of static VSM, (2) analyze and structure existing DVSM approaches, (3) map DVSM approaches against requirements and (4) derive a research agenda for developing DVSM approaches. This study contributes by providing requirements for and a taxonomy of existing DVSM approaches, guiding the development of new DVSM approaches.KeywordsDVSMDynamic Value Stream MappingIndustry 4.0Lean ProductionValue Stream Mapping
Rising energy costs and the current concern about sustainability issues directly impact the competitiveness and survival of organizations, promoting the search for continuous improvement in industrial production processes. The present study, by identifying the absence in the literature of models based on Value Stream Mapping and integrated with Industry 4.0 technologies that promote better energy efficiency practices in industrial processes, proposes a new Lean Management model called Real-time Energy Flow Mapping. This innovative model, provide capabilities to a visual, digital and sustainable management system of all energy behavior in machines and equipment using fully parameterizable metrics and rules. Also, estimulating stakeholders with the ability to predict and monitor energy costs, as well as identify possible problems due to changes in the behavior of energy consumption in machines, thus helping managers to make decisions faster and more dynamically. As a result, the literature search turned up 17 Value Stream Mapping based models with a focus on energy efficiency, along with 31 metrics grouped on energy, environmental and production processes. After discussions by the Focus Groups, 16 resulting metrics were submitted to expert panel, in which the least relevant were discarded and the 13 most relevant classified using Fuzzy Delphi. An analysis to verify the interrelationships between the metrics is performed using the Fuzzy DEMATEL, being finally applied in the proposed model. A case study of the model was carried out, submitting it operationally at the LabFaber 4.0, empirically validating the study. The Real Time Energy Flow Map fills the gap in the literature on Value Stream Mapping models to improve energy efficiency, monitoring energy consumption through dynamic mapping of the energy flow of production processes, identifying the area with increased potential for energy savings through changes in operating behavior that reduce overall energy consumption and environmental impacts.
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This research examines a case study on the implementation of an effective approach to advanced Lean Six Sigma problem-solving within a pharmaceutical manufacturing site which manufactures acetaminophen (paracetamol containing pain relief) tablets. Though this study was completed in a single manufacturing company, the implementation of this study delivers important application and results that can be deployed in other such manufacturing companies. The manufacturing site faced backlogs in customer orders due to increased demand. Increased demand is due to brand popularity and recognition, product efficacy and a COVID 19 pandemic that intensified the demand for pain relief tablets in an already very busy site. With increased demand, to ensure timely deliveries, customer satisfaction and minimize delays, sources of site productivity losses and wastes needed to be analyzed and reduced or eliminated. Manufacturing Packaging line downtime was identified as one area of concern. The goal of the research was to introduce a problem-solving technique to reduce downtime within a manufacturing site without affecting the production required to fulfil customer demand while increasing product quality. The research utilized an integrated LSS methodology which identifies, stratifies and effectively eliminates non-value adding (waste) activities, by following a 7-step customized problem-solving methodology which resulted in complete elimination of the issue under investigation and savings of just under half a million dollars. The learnings are being deployed and leveraged worldwide across the pharmaceutical organizations parent site and sister sites. The presented results demonstrated that Lean Six Sigma methodology and tools are effective for accurate root causing of problems and enablers of implementation of continuous improvement.
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The losses associated with changeovers are becoming more significant in manufacturing due to the high variance of products and requirements for just-in-time production. The study is based on the single minute exchange of die (SMED) philosophy, which aims to reduce changeover times. We introduced a method for the analysis of these losses based on models that estimate the product- and operator-dependent changeover times using survival analysis. The root causes of the losses are identified by significance tests of the utilized Cox regression models. The resulting models can be used to design a performance management system that considers the stochastic nature of the work of the operators. An anonymized manufacturing example related to the setup of crimping and wire cutting machines demonstrates the applicability of the method.
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Real-time monitoring and optimization of production and logistics processes significantly improve the efficiency of production systems. Advanced production management solutions require real-time information about the status of products, production, and resources. As real-time locating systems (also referred to as indoor positioning systems) can enrich the available information, these systems started to gain attention in industrial environments in recent years. This paper provides a review of the possible technologies and applications related to production control and logistics, quality management, safety, and efficiency monitoring. This work also provides a workflow to clarify the steps of a typical real-time locating system project, including the cleaning, pre-processing, and analysis of the data to provide a guideline and reference for research and development of indoor positioning-based manufacturing solutions.
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In this paper an overview of recent augmented reality (AR) solutions for manufacturing execution systems (MES) is presented. The first part of the paper describes the challenges of integrating AR into MES, while the second part focuses on custom AR solutions. The last part of the paper highlights the advantages of the proposed approaches, as well as real life experimental results. Experiments are described in detail while the code for these applications is made public on author’s website.
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Technologies and techniques of location and navigation are advancing, allowing greater precision in locating people in complex and challenging conditions. These advances have attracted growing interest from the scientific community in using indoor positioning systems (IPSs) with a higher degree of precision and fast delivery time, for groups of people such as the visually impaired, to some extent improving their quality of life. Much research brings together various works that deal with the physical and logical approaches of IPSs to give the reader a more general view of the models. These surveys, however, need to be continuously revisited to update the literature on the features described. This paper presents an expansion of the range of technologies and methodologies for assisting the visually impaired in previous works, providing readers and researchers with a more recent version of what was done and the advantages and disadvantages of each approach to guide reviews and discussions about these topics. Finally, we discuss a series of considerations and future trends for the construction of indoor navigation and location systems for the visually impaired.
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Industry 4.0 has brought forth many advantages and challenges for the industry players. Many organizations are strategizing to take advantage of this industrial paradigm shift, thus improving the sustainability of the enterprise. However, there are many factors such as talent development, machinery advancement and infrastructure development which involve huge investment that need to be considered. This paper presents an enhanced adaptive model for the implementation of the lean and green (L&G) strategy in processing sectors to solve dynamic industry problems associated with Industry 4.0. A feature of this enhanced adaptive model is that it combines experts’ experience and operational data as input in dealing with real industry application. A lean and green index is coupled in the model to serve as a benchmark and process improvement tracking indicator. This allows the industrialist to set a lean and green index target for effective process improvement. From this integrated model, an ensemble of backpropagation optimizers is then used to identify the best-optimized strategy. This ensemble optimizer is formulated to perform operation improvement and update the targeted lean and green index automatically when a higher index is achieved for continuous improvement. A case study on a combined heat and power plant is performed and reflects an improvement of 18.25 % on the lean and green index. This work serves as a practical transition strategy for the industrialist desiring to improve the sustainability of the facility with Industry 4.0 elements at minimum investment cost.
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The enterprises to be competitive are constantly looking for continuous increase of productivity, quality and level of services. With the development of Industry 4.0 concept, manufacturers are more confident about new advantages of automation and systems integration. Lean management is well developed and empirically proven effective managerial approach. Combining Lean and Industry 4.0 practices seems to be necessary evolutionary step for further raise the level of operational excellence (exploitation of finance, workload, materials, machines/devices). There is an increasing number of Industry 4.0 solutions used to reduce waste (as known from Lean Management). Therefore, the main objective of this article aims at presenting the results of a literature review on the concept of ‘Lean Industry 4.0’. Dynamic methodology called ‘Systematic Literature Network Analysis (SLNA)’ was used. It combines the Systematic Literature Review approach with the quantitative analysis of bibliographic networks to detect emerging topics and the dynamic evolution of the topics. The paper is a comprehensive systematization and rationalization of knowledge about the integration of LM and I4.0 concepts, identifies the most important research trends and defines directions for future research. The article contains a framework that presents the current state of knowledge in the area of Lean Industry 4.0. Keywords: bibliometrics, Industry 4.0, lean management, lean manufacturing, systematic literature network analysis
A cyber-physical system is one of the integral parts of the development endeavor of the smart manufacturing domain and the Industry 4.0 wave. With the advances in data analytics, smart manufacturing is gradually transforming the global manufacturing landscape. In the Resistance Spot Welding (RSW) domain, the focus has been more on the physical systems, compared to the virtual systems. The cyber-physical system facilitates the integrated analysis of the design and manufacturing processes by converging the physical and virtual stages to improve product quality in real-time. However, a cyber-physical system integrated RSW weldability certification is still an unmet need. This research is to realize a real-time data-driven cyber-physical system framework with integrated analytics and parameter optimization capabilities for connected RSW weldability certification. The framework is based on the conceptualization of the layers of the cyber-physical system and can incorporate the design and machine changes. It integrates data from the analytics lifecycle phases, starting from the data collection operation, to the predictive analytics operation, and to the visualization of the design. This integrated framework aims to support decision-makers to understand product design and its manufacturing implications. In addition to data analytics, the proposed framework implements a closed-loop machine parameter optimization considering the target product design. The framework visualizes the target product assembly with predicted response parameters along with displaying the process parameters and material design parameters simultaneously. This layer should help the designers in their decision-making process and the engineers to gain knowledge about the manufacturing processes. A case study on the basis of a real industrial case and data is presented in detail to illustrate the application of the envisioned cyber-physical systems framework.
The ability to predict the remaining cycle-time in industrial environments is of major concern among production managers. An accurate prediction would enable managers to handle undesired situations with more control, thereby preventing future losses. However, making such predictions is no trivial task: there are many methods available to cope with this problem, including a recent research stream in process mining. Process mining provides tools for automated discovery of process models from event logs, and eventually, extend those models in driving predictions. In general, predictive models in process mining generally deals with business processes, and not directly with the industrial environment, which contains a full prism of particularities. In this paper we propose a hybrid predictive model based on transition-systems and statistical regression which is “product-oriented”, tailored to better predict online cycle-times on industrial environments. We propose a weight for each method, optimized by a linear programming model. We tested our new approach on an artificially created log that emulates an industrial environment, and on a real manufacture log. Results showed that our approach provides better accuracy measures for both test instances.