Duck Box: Sensor-Based Material Flow Optimization for

Economically and Energy-Efficient Intralogistics

Christoph Ecker

, Martin Riester

and Sebastian Schlund

Fraunhofer Austria Research GmbH, Theresianumgasse 7, Vienna, Austria

Institute of Management Science, TU Wien, Theresianumgasse 27, Vienna, Austria

Keywords: Intralogistics, Material Flow Optimization, Forklift, LoRaWAN, IPS, Energy Efficiency.

Abstract: Small batch sizes, increasing variant diversity and short delivery times raise the complexity of intralogistics

in manufacturing companies. As a result, these challenges lead to economically- and energy-inefficient

material flows. To address this issue, companies conduct material flow optimization. The decisive factor is

that the planning often cannot be data driven. This is because there is no or insufficient data on internal

material flows and optimization potentials cannot be fully exploited. The aim of this paper is to create

economically- and energy-efficient material flows in manufacturing companies. The focus is on the

development of a sensor prototype for the localization and utilization measurement of forklifts to close the

current data gaps. Based on the current state of the art, the sensor prototype Duck Box is developed. This

Indoor Positioning System (IPS) uses LoRa to determine the position of forklifts and an ultrasonic sensor to

identify the utilization. The recorded and in real time analysed data is transmitted to a database via Long

Range Wide Area Network (LoRaWAN). Finally, the developed prototype is applied and evaluated in a case

study. The results of this sensor-based approach show the significant added value for the economically- and

energy-efficient optimization of material flows.

1 INTRODUCTION

Manufacturing companies are continuously faced

with new challenges. These include small batch sizes,

increasing diversity of variants and short delivery

times. To meet these, companies in most cases react

by building up additional transport and personnel

capacities. Since most internal transports are carried

out manually, this increases internal logistics costs

and reduces the company's success (ten Hompel and

Kerner, 2015; Zajac and Rozic, 2022).

In recent years, manufacturing companies have

therefore invested heavily in optimizing the internal

material flow by eliminating weaknesses such as long

transport distances, high shares of empty runs and low

utilization of forklifts (Martin, 2016).

To address these issues, data is obtained and

analysed, weaknesses are identified and optimization

potentials are developed in the course of material

flow planning (VDI 2498, 2011). This approach is

shown in the following references.

https://orcid.org/0000-0002-8142-0255

Radhwan et al. (2019) conduct systematic, graph-

based layout planning to increase material flow

efficiency by up to 20 percent. A method for

optimizing material provision is presented in Herbert

et al. (2021). Krajcovic et al. (2019) develop a genetic

algorithm to optimize material flows through layout

planning. Belic et al. (2018) reduce internal material

flow costs by optimizing layout, lead times and

employee utilization. Chayaphum et al. (2019)

determine the optimal number of forklifts in

warehouse processes by using a simulation model.

In addition to the efficiency improvements

required, the importance of sustainability goals as

well as energy efficiency is rising (Ene et al., 2016,

Zajac and Rozic, 2022). Intralogistics accounts with

about 11 percent for a significant share of the logistics

industry's greenhouse gas emissions, which in turn

accounts for about 13 percent of total global

greenhouse gas emissions. Since the forklift is an

essential element of intralogistics, the scientific

community has increasingly focused on the topic of

Ecker, C., Riester, M. and Schlund, S.

Duck Box: Sensor-Based Material Flow Optimization for Economically and Energy-Efﬁcient Intralogistics.

DOI: 10.5220/0011968100003491

In Proceedings of the 12th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2023), pages 113-120

ISBN: 978-989-758-651-4; ISSN: 2184-4968

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

113

sustainable and energy-efficient use of forklifts

(Khoei et al., 2022).

Zajac and Rozic (2022) find that transport

parameters such as transport speed or curve radius are

directly related to forklift energy consumption and

derives a calculation method for quantification.

Pashkevich et al. (2019) and Ziólkowski et at. (2022)

show that the utilization rate and transport distances

significantly affect energy-efficient forklift use.

According to the analyses of Fuc et al. (2016), for

electric-powered forklifts, empty trips cause only up

to 5 percent less energy consumption compared to

trips loaded with one ton; diesel- and gas-powered

forklifts show larger deviations. Ene et al. (2016) and

Burinskiene et al. (2018) show that route optimization

algorithms can reduce energy demand in warehouses

and increase value creation.

One of the biggest challenges in material flow

optimization is that planning often cannot be data-

driven. This is because no or only insufficient data

regarding the current material flows are available

(Martin, 2016; VDI 2689, 2019). This inadequate

data basis can significantly limit the level of detail,

validity and objectivity of the methods presented and

their results (Schwab et al., 2016). Efficient collection

of actual, real-world data regarding utilization and

transport distances travelled is an additional

challenge (Li et al., 2021). Approaches to create the

required data basis are offered by sensor solutions

(Pashkevich et al., 2019) and Indoor Positioning

Systems (Pascher et al., 2019).

The aim of this paper is to create energy- and

economically-efficient material flows in

manufacturing companies. The focus is on the

development of a sensor prototype for the localization

and utilization measurement of forklifts to close the

current data gaps in the planning of internal material

flows.

To achieve this objective, the following structure

is chosen for this paper. Section 2 explains the state

of the art of indoor positioning systems in

intralogistics. Based on this, LPWAN and

specifically LoRaWAN will be presented as an

Internet of Things (IoT) technology suitable for

logistics applications. These fundamentals serve as

the basis for the development of the sensor prototype

in section 3. The concept and its components are

described in detail. In section 4, the resulting

prototype is applied and evaluated in a case study in

the building materials industry. Finally, the results are

summarised in the conclusion and an outlook on

further research is given.

2 RELATED WORKS

2.1 Indoor Positioning Systems (IPS)

IPS are defined as a wireless network of

intercommunicating systems that are used to

determine the position of objects or people in indoor

areas. These systems consist of three main

components: the location technology, the location

method, and algorithms for data evaluation (Pascacio

et al., 2019; Kaluža et al., 2017).

Compared to outdoor applications, indoor

applications show high complexity. This is because

the industrial environment has a significant influence

on the reliability and robustness of measurement

results. Furthermore, accuracy and range

requirements differ significantly depending on the

use case. For each specific use case, a suitable

location technology (infrastructure-less or

infrastructure-based), an appropriate location method

(e. g. Time of Arrival - ToA, Received Signal

Strength Indicator - RSSI) and target-oriented

evaluation algorithms (e. g. triangulation) must be

selected based on the prevailing requirements and

restrictions (Pascher et al., 2019; Pascacio et al.,

2021, Zheng et al., 2010). Therefore, many different

indoor positioning systems in intralogistics appear in

literature (Kaluža et al., 2017).

Chai et al. (2017) and Wang et al. (2014) use

active radio-frequency identification (RFID) with

reference tags to locate vehicles in warehouses.

Motroni et al. (2021) developed a stationary RFID

gate solution to identify forklifts and their direction

of travel.

Burinskiene (2015) implements a combination of

active RFID to locate forklifts and passive RFID to

identify the material on the fork. Mel et al. (2016) and

Müller et al. (2018) focus exclusively on tracking

materials using passive RFID.

Barral et al. (2019), Halawa et al. (2020), Pilati et

al. (2021), Frankó et al. (2020) and Gladysz et al.

(2017), on the other hand, use Ultra-Wide Band

(UWB) to collect intralogistic key figures from

transport systems. Zhao et al. (2020) link UWB

technology for locating forklifts with passive RFID

for identifying materials and loading conditions.

Jung (2018) and Borstell (2016) are developing

camera-based tracking systems for forklifts using

reference markers. Li et al. (2021) use machine vision

to measure utilization rates of forklifts. Kanakaraja et

al. (2021) and Bencak et al. (2022) use Bluetooth Low

Energy (BLE) to locate intralogistics objects.

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The essential characteristics of all these

applications are that, in addition to meet the intended

use to 100 per cent, they should be realised cost-

effectively and with little implementation effort. The

ongoing technological progress of digitalisation

strengthens the trend of IPS deployment in

intralogistics (Pascher et al., 2019; Zheng et al, 2010;

Ficco et al., 2013).

2.2 LPWAN and LoRaWAN

A radio-based technology that has rarely been used

for localization in intralogistics is the Low Power

Wide Area Network (LPWAN). This IoT technology

is particularly suitable for sensors and applications

with low data volumes and low transmission

frequencies. Furthermore, LPWAN is characterized

by the ability to be used in different environmental

conditions as well as for data transfer over long

distances with low energy consumption (LoRa

Alliance, 2015; Pieper et al., 2019).

As Herion et al. (2020) state, LoRaWAN, a standard

of LPWAN, shows itself to be a particularly suitable

technology for the use in intralogistic warehouse

processes due to these characteristics. First

LoRaWAN demonstrators and pilot applications in

the logistics context have been developed, as

Kanakaraja et al. (2021), Pieper et al. (2019) and

Fottner et al. (2021) have shown.

LoRaWAN serves as a communication protocol

and system architecture. This standard uses LoRa as

a wireless modulation that enables long-range

communication links in the free regional ISM or SRD

frequency bands (e. g. in Europe at 867-869 MHz).

As shown in Figure 1, LoRaWAN networks are

star shaped (LoRa Alliance, 2015). They consist of

the following components:

 End Nodes are bidirectional, asynchronous IoT

devices that send data when it is available.

 Gateways receive the end node data and

forward it to the network server via 3G, WiFi

or Ethernet interface.

 The network server contains the data

aggregation and processing.

 Application servers are defined as the end

applications such as databases or web

applications (Herion et al., 2020).

A special feature of this technology is that the end

nodes are not assigned to a single gateway. Therefore,

transmitted data is usually forwarded several times to

the cloud-based network server, which then analyses

and filters it. The advantage is that mobile end-

devices do not have to be registered with new

gateways and are therefore network-independent. A

disadvantage of LoRaWAN is the low available data

rate, which in turn has the advantage of low energy

consumption (Herion et al, 2020; Pieper et al, 2019;

LoRa Alliance, 2015).

Figure 1: Design of LoRaWAN networks based on LoRa

Alliance (2015).

3 SENSOR-BASED MATERIAL

FLOW OPTIMIZATION

In the continuation of this paper, the described

theoretical fundamentals are used to develop a

LoRaWAN-based sensor prototype, called Duck

Box, for the localization of internal transport systems,

in this case forklifts. The aim is to develop a

temporary real-time IPS that creates a location-based

database that allows holistic material flow

optimization. For this purpose, the concrete

requirements are defined by the project team as well

as by means of expert interviews, which are used for

the design of the prototype:

 The IPS should be able to locate the transport

flows of forklifts between sources and sinks.

 In addition, it should be possible to

differentiate between empty and loaded runs to

be able to calculate utilization levels.

 Furthermore, process and handling times

during material pick-up and drop-off are to be

identified.

 The sensor has to be flexibly usable on a wide

range of forklift types and models.

 The components of the IPS should have an

independent energy supply and be able to be

used for at least 3 weeks without charging

batteries.

 The system should be able to be set up and

removed in a short time and no intervention in

the IT infrastructure of the companies should

be necessary.

Duck Box: Sensor-Based Material Flow Optimization for Economically and Energy-Efﬁcient Intralogistics

115

 Furthermore, the implementation of the system

should not cause any restrictions in the ongoing

production and logistics processes.

 The generated data should be evaluated locally

and in real time by the sensor and then

transferred to an SQL database.

3.1 Tracking System

Based on the defined requirements, the conception of

the IPS is started. Due to its optimal characteristics

for mobile logistics applications, the LoRaWAN

standard with its LoRa communication technology is

selected as the location technology. Since LoRaWAN

also transmits the Received Signal Strength

Indicators, the RSSI method is defined as the location

method. The algorithms for data evaluation are

calculated locally by the microprocessor of the

sensor, as defined in the requirements.

Based on these definitions, an IPS is developed

that consists of three components:

 The repeater is used to define the sources and

sinks of the material flows. For this purpose, it

is mounted at the transfer points by means of

cable ties or magnets. This component

transmits a unique ID via LoRa, which is

subsequently used for localization.

 The Duck Box is the mobile component of the

tracking system. It is mounted on the front of

the forklift using industrial magnets. The

information sent by the repeater is received and

evaluated by the Duck Box. By developing

various algorithms, the material flow relation

between the source and the sink of the transport

as well as the dwell time at the source are

calculated. Furthermore, based on these

algorithms, it is possible to distinguish whether

material handling is carried out at the source or

whether the forklift has only passed the source.

After calculating these key figures, the data is

transmitted to the gateway via LoRaWAN

standard.

 The gateway receives the data from the Duck

Box and forwards it via 3G to the network

server. A workflow is set up on this server,

which saves the generated data in a SQL

database.

Figure 2 shows the cooperation of the individual

components in the LoRaWAN network. The grey

arrows show the data transfer between repeater, Duck

Box and gateway. It is important to add that both the

reception ranges of the Duck Box and the

transmission strength of the repeater can be

individually adjusted according to requirements. This

is shown in Figure 2 by means of the blue ellipses.

This prevents several receiving areas from

overlapping.

3.2 Loading Status and Degree of

Utilization

For identifying the loading status of the forklift, the

ultrasonic technology is used. Ultrasonic sensors emit

ultrasonic signals at regular intervals, which in turn

are reflected by objects on the fork of the truck. The

reflected ultrasonic signals are received by the sensor.

Based on the time difference between sending and

receiving the ultrasonic signal and multiplying it by

the speed of sound, it is possible to determine the

distance between the object and the ultrasonic sensor.

If this distance is greater than the length of the fork,

the forklift is travelling empty, otherwise it is

travelling under load. Based on the identification of

the empty and loaded runs and in combination with

the location concept, this enables analyses of the

utilization levels per transport relation. The source,

the sink, the dwell time at the source, the transport

route incl. intermediate stops and the loading status of

the transport relation thus form the data set.

The effect of the direct assignment of the loading

status to the transport relation is also used to minimize

the data volume to be transmitted and to comply with

the restrictions of the LoRaWAN standard.

Therefore, the change of the loading state at a repeater

serves as event, which triggers the data transmission

via LoRaWAN. This functional principle can be seen

in Figure 2. The forklift equipped with a Duck Box

(D) starts its unloaded transport at source R1.

Arriving at the reception area of the sink R2, the

forklift picks up material. The loading status changes

to loaded. This event then triggers the data

transmission to the gateway (G).

Figure 2: Tracking system Duck Box.

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3.3 The Developed Prototype: Duck

Box

For the implementation of the concepts, suitable

hardware modules were selected, programmed,

designed and developed:

 An Arduino-based microcontroller is used to

locally evaluate the measured data of the

positioning and the loading by means of the

developed algorithms.

 A Murata LoRa chip, which operates at a

frequency of 868 MHz, is directly connected to

the microcontroller, and is used for the

positioning system as well as for real-time data

transmission. This component is supplemented

by a dipole antenna to achieve greater ranges.

 In addition, the commercially available

ultrasonic sensor US100 is connected to the

microcontroller to measure the utilization of

the forklift.

 Since these modules require a nominal voltage

of 3.3 volts, suitable protected Li-Ion batteries

were selected for this purpose. With a capacity

of 13,800 mAh, the required runtime of about

three weeks can be guaranteed.

 To protect the sensor modules and the power

supply from the harsh operating conditions in

industrial environments, a robust housing

consisting of two halves was designed and

produced by means of additive manufacturing.

A CAD model of the developed solution is

shown in Figure 3.

 Industrial magnets are screwed into the housing

for easy mounting on the forklift. For this

purpose, hexagon nuts are inserted into the

housing. This screw connection also serves to

close the two halves of the housing.

The repeater consists of the same modules as the

Duck Box except the ultrasonic sensor. In addition to

the existing freely available gateways, a RaspberryPi-

based device equipped with a 3G SIM card is

temporarily placed in the area of operation.

The data from the gateway is transmitted to the

open-source network server The Things Network. To

be able to store the generated data, an interface to a

cloud-based SQL database was created by means of a

webhook integration and an automated online

workflow. The database contains the source-sink

relationships, the dwell times and the loading states

of the transport relations. This data is accessed using

a Tableau dashboard to visualise the collected data

appropriately. As part of the following case study, the

individual functions of the dashboard will be shown.

Figure 3: CAD model of the Duck Box.

4 CASE STUDY

4.1 Execution of the Case Study

The developed IPS was applied and evaluated during

a case study at a company in the building materials

industry. At the company's Austrian location, various

building materials such as mortar, adhesives and

sealants are produced, temporarily stored and loaded

into trucks. Due to the growth in production and the

associated growth in structures, transparency in

intralogistics was increasingly lost.

Since there were no applicable postings in

logistics, it was not possible to optimize the

company's material flows to increase the efficiency

and to achieve the sustainability goals in intralogistics

such as socially evenly distributed personnel

deployment or reduced energy demand.

For this reason, the developed prototype Duck

Box was used over a period of three weeks to create

the missing data basis. All five forklifts of the

finished goods storage were equipped with a Duck

Box. Fifteen material handling positions (sources and

sinks) were equipped with repeaters. The gateway

was used for data transmission.

The data evaluation was done using the developed

Tableau dashboard, which is shown in figure 4. This

dashboard consists of a layout in which the sources

and sinks are drawn by means of circles. The larger

the diameter of the circle, the longer the dwell time at

this position during material handling. In addition,

this figure is supplemented with a distance intensity

diagram. The further the distances and the more

frequently this transport relation is travelled, the more

critical this transport relation is. The most frequent

transport relations are displayed at the bottom of

figure 4. The light blue bars represent the empty runs,

the dark blue ones the loaded runs.

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Figure 4: Visualization of the results.

Based on these results, the following optimization

potentials were identified:

 The greatest transport intensity is caused by the

storage of finished goods in storage area Q13.

Due to the frequency of this transport in

combination with the long transport distance, a

concept was developed to minimize these

efforts by reorganizing the finished goods

storage areas. As a result, the entire warehouse

layout at the site was revised. Q13 was placed

closer to production to be able to reduce

transport intensities. Sinks that were only used

very rarely were placed at a greater distance

from production.

 Furthermore, the utilization of all forklifts

showed that very often paired transports occur,

loaded in one direction and unloaded in the

other. As solution, it is planned to increase the

loading factor. Therefore, a forklift that

performs storage operations should be

equipped with long forks to be able to transport

two pallets per transport.

 All forklifts are assigned to defined tasks. By

comparing them, it was found that the transport

tasks were very unevenly distributed due to this

fixed assignment. This resulted in high

workloads for some forklift drivers. A concept

has been developed for more even workloads

for forklift drivers, for example by no longer

loading trucks and swap bodies separately.

 Finally, it was found that at source Q12, the

pick-up of finished goods, the dwell times were

twice as high compared to the other material

handling positions. By rearranging and

adapting the racks used there, handling

activities could be significantly reduced. In

particular, the short forward and reverse setting

of the forklifts was improved. This has made a

key contribution to increasing energy

efficiency in this area.

By implementing the specified measures, the

following optimizations could be calculated for this

specific case study:

 Transport intensities could be reduced by up to

25 percent.

 The utilization rates of the forklifts were

improved by at least 18 percent.

 The energy efficiency of material transport in

the finished goods area was improved by up to

20 percent.

4.2 Validation of the Prototype

Regarding the Duck Box IPS, it can be concluded that

during the case study all components of the tracking

system operated stably and without interruption over

a period of three weeks. The data transmission via

LoRaWAN also worked without any problems. No

intervention in the company IT network was

necessary. The implementation effort was limited to

about one day. Although the Duck Box was mounted

on different forklift models, there was no impact on

the company's operational processes. The evaluation

within the case study shows that all defined

requirements are fulfilled by the Duck Box.

Compared to state-of-the-art IPS, the setup within

a day and without interfering the operations in

warehouse has shown to have significant potential.

For example, the use of the ultrasonic sensor made it

possible to collect utilization rates without applying

information carriers such as RFID tags to products.

The use of LoRaWAN for positioning and data

transmission significantly reduced the complexity of

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the IPS. In addition, the implementation of a separate

IT network without interfering with the company's

infrastructure was beneficial. These measures

increase the usability of the IPS.

Since the developed IPS is designed for temporary

analyses of up to three weeks, the Duck Box IPS

shows disadvantages for permanent applications

compared to state-of-the-art solutions, e. g. due to the

need for regular battery charging. In future case

studies the scalability of the developed system with

increasing number of material handling positions and

forklifts must be evaluated.

The state-of-the-art in conventional material flow

planning shows different approaches to optimize

material flows. These methods usually assume an

optimal data basis. As Schwab et al. (2016) show, the

lack of available data limits the results of these

methods. The developed IPS demonstrates a sensor-

based approach that achieves novel, data-driven

results despite this lack of data, as shown in the course

of the case study. Recorded, actual transports,

utilization rates and dwell times have been used as the

data basis, which are partly difficult to determine with

conventional methods. In summary, the use of sensors

shows a significant added value for the creation of

holistic, valid and efficient material flow planning.

5 CONCLUSIONS

In this paper, a novel sensor prototype called Duck

Box is developed. This LoRaWAN and ultrasonic-

based IPS enables the implementation of sensor-

based material flow optimization. The data generated

in the process creates transparency in the

intralogistics of manufacturing companies by

recording the actual utilization, transport intensities

and material flows as well as dwell times of forklifts

without the need for posting data.

The evaluation of the prototype in a case study in

the building materials industry has shown that the

generated sensor data creates novel and holistic

material flow optimizations. In particular, the actual

utilization rates and determined transport intensities

show a clear added value in planning and have led to

optimization potentials of up to 25 percent in the

specific case study. In conclusion, the sensor used and

the data generated lead to the creation of more

economical and energy-efficient material flows.

The next step is to develop a dynamic

optimization algorithm based on the sensor data. This

algorithm will calculate recommendations regarding

the optimal utilization of forklifts, the ideal

distribution of tasks and the most effective

arrangement of sources and sinks in the layout. The

evaluation of the results is to be standardized

regarding economic and sustainability criteria.

REFERENCES

Barral, V., Suarez-Casal, P., Escudero, C.J., Garcia-Naya,

J.A. (2019). Electronics 2019, 8, 1152. p. 1-23.

Belić, D., Kunica, Z., Opetuk, T., Dukic, G. (2018).

Optimization of the plant layout in the production of the

special transformers: Case study in: FME Transaction,

46, 3, p. 285–290.

Bencak, P., Hercog, D., Lerher, T. (2022). Indoor

Positioning System Based on Bluetooth Low Energy

Technology and a Nature-Inspired Optimization

Algorithm. Electronics 2022, 11, 308. p. 1-27.

Borstell, H. (2016). Bildbasierte Ortung von Gabelstaplern

für sichere manuelle Transport-Prozesse im Umschlag-

Lager. in: Schenk M, editor. Ressourceneffiziente

Produktion und Logistik. Magdeburg: 18.

Forschungskolloquium am Fraunhofer IFF. p. 39-46.

Burinskiene, A (2015). Optimising Forklift Activities in

Wide-Aisle Reference Warehouse. Int. j. simul. model

14. p. 621-632.

Burinskiene, A., Lorenc, A., Lerher, T. (2018). A

Simulation Study for the Sustainability and Reduction

of Waste in Warehouse Logistics. Int. Journal of

Simulation Models 17. p. 485-497.

Chai, J., Wu, C., Zhao, C., Chi, H.L., Wang, X., Ling, B.,

Teo, K. (2017). Reference tag supported RFID tracking

using robust support vector regression and Kalman

filter. Advanced Engineering Informatics 32. p. 1-10.

Chayaphum, A., Supsomboon, S., Butrat, A. (2019). The

Optimal Number of Reach Trucks and Order Picker

Trucks in Warehouse Determining Using Simulation.

2019 Research, Invention, and Innovation Congress

RI²C. p. 1-5.

Ene, S., Küçükoglu, I., Aksoy, A., Öztürk, N. (2016). A

genetic algorithm for minimizing energy consumption

in warehouses. Energy 114. p. 973-980.

Ficco, M., Esposito, C., Napolitano, A. (2013). Calibrating

Indoor Positioning Systems With Low Effort. IEEE

Transactions on Mobile Computing 99. p. 1-14.

Fottner, J., Clauer, D., Hormes, F., Freitag, M., Beinke, T.,

Overmeyer, L., Gottwald, S.N., Elbert, R., Sarnow, T.,

Schmidt, T., Reith, K.B., Zadek, H., Thomas, F. (2021).

Autonomous Systems in Intralogistics – State of the Art

and Future Research Challenges. Journal Logistics

Research BVL 14. p. 1-41.

Frankó, A., Vida, G., Varga, P. (2020). Reliable

Identification Schemes for Asset and Production

Tracking in Industry 4.0. Sensors 20. p. 3709.

Fuc, P., Kurczewski, P., Lewandowska, A., Nowak, E.,

Selech, J., Ziolkowski, A. (2016). An environmental

life cycle assessment of forklift operation: a well-to-

wheel analysis. Int. Journal of Life Cycle Assessment

for Machines 21. p. 1438-1451.

Duck Box: Sensor-Based Material Flow Optimization for Economically and Energy-Efﬁcient Intralogistics

119

Gladysz, B., Santarek, K., Lysiak, C. (2017). Dynamic

Spaghetti Diagrams. A Case Study of Pilot RTLS

Implementation. Intelligent Systems in Production

Engineering and Maintenance – ISPEM 2017. Springer

International Publishing. p. 238-248

Halawa, F., Dauod, H., Lee, I.G., Li, Y., Yoon, S.W.,

Chung, S.H. (2020). Introduction of a real time location

system to enhance the warehouse safety and operational

efficiency. International Journal of Production

Economics 224, 107541. p. 1-21.

Herbert, M., Heinlein, P., Fürst, J., Franke, J. (2021). A

systematic approach for planning, analyzing and

evaluating internal material provision. 30

International Conference on Flexible Automation and

Intelligent Manufacturing (FAIM 2021). Procedia

Manufacturing 55. p. 447-454.

Herion, L., Hunecke, C., Uckelmann, D. (2020). LoRa-

Technologie in der Logistik. Fabriksoftware 25. p. 41-

44.

Jung, M. (2018). Entwicklung und Evaluierung einer

kamerabasierten Lokalisierungsmethode für

Flurförderzeuge. Technische Universität München.

Kaluža, M., Beg, K., Vukelić, B. (2017). Analysis of an

indoor positioning systems. Zbornik Veleučilišta u

Rijeci 5. p. 13-32.

Kanakaraja, P., Kotamraju, S.K., Nadipalli, L., Kumer, A

(2021). IoT Enabled BLE and LoRa based Indoor

Localization without GPS. Turkish Journal of

Computer a. Mathematics Education 12. p. 1637-1651

Khoei, A.A., Süral, H., Tural, M.K. (2022). Energy

minimizing order picker forklift routing problem.

European Journal of Operational Research. p. 1-23

Krajcovic, M., Hancinsky, V., Dulina, L., Grznar, P., Gaso,

M., Vaculik, J. (2019). Parameter Setting for a Genetic

Algorithm Layout Planner as a Toll of Sustainable

Manufacturing. Sustainability 2019. 11. 2083. p. 1-26.

Li, Z., Lu, K., Zhang, Y., Li, Z., Liu, J.B. (2021). Research

in Energy Efficient Management of Forklift Based on

Improved YOLOv5 Algorithm. Journal of Mathematics

Volume 2021. p. 1-9.

LoRa Alliance (2015). LoRaWAN – What is it? A technical

overview of LoRa and LoRaWAN.

Martin, H. (2016). Transport- und Lagerlogistik.

Systematik, Planung, Einsatz und Wirtschaftlichkeit,

Springer Vieweg. Wiesbaden, 10th edition.

Mel, S., Herath, D., McKenzie, D., Pathak, Y. (2016).

Radio frequency (un)identification, Results from a

proof-of-concept trial of the use of RFID technology to

measure microenterprise turnover in Sri Lanka.

Development Engineering 1. p. 4-11.

Motroni, A., Buffi, A., Nepa, P., Pesi, M., Congi, A. (2021).

An Action Classification Method for Forklift

Monitoring in Industry 4.0 Scenarios. Sensors 2021. 21.

5183. p. 1-20

Müller, R., Vette-Steinkamp, M., Hörauf, L., Speicher, C.,

Burkhard, D. (2018). Development of an Intelligent

Material Shuttle to Digitize and Connect Production

Areas with the Production Process Planning

Department. Procedia CIRP 72. p. 967-972.

Pascacio, P., Casteleyn, S., Torres-Sospedra, J., Lohan,

E.S., Nurmi, J. (2021). Collaborative Indoor

Positioning Systems: A Systematic Review. Sensors

21. 1002. p. 1-39.

Pascher, R., Ecker, C., Sihn, W. (2019). Conceptual Design

of a modular, hybrid Sensor System (Duck Box) for the

Implementation of location-based Material Flow

Analysis. Proceedings of XXIII International

Conference MHCL 2019. p. 263-266.

Pashkevich, N., Haftor, D., Karlsson, M., Chowdhury, S.

(2019). Sustainability through Digitalization of

Industrial Machines: Complementary Factors of Fuel

Consumption and Productivity for Forklifts with

Sensors. Sustainability 2019, 11. p. 1-21.

Pieper, C., Lücke, B., Schuba, K., Schriegel, S., Jasperneite,

J. (2019). LoRaWAN-basiertes Intralogistikassistenz-

system. 10. Jahreskolloquium KommA –

Kommunikation in der Automation. Magdeburg. p. 1-12.

Pilati, F., Sbaragli, A., Regattieri, A., Cohen, Y. (2021).

Real time locating system for a learning cross-docking

warehouse. 11

Conference on Learning Factories,

CLF2021. p. 1-6.

Radhwan, H., Shayfull, Z., Farzizuan, M.R., Effendi, M.S.

M., Irfan, A.R., (2019). Redesign of bahulu production

layout to improve the efficiency of process flow. In:

APCOM 2019. Strbske Pleso, Slovak Republic. AIP

Conference Proceedings. 20153. p. 1-7.

Schwab, O., Zoboli, O., Rechberger, H. (2016). A Data

Characterization Framework for Material Flow

Anaysis. Journal of Industrial Ecology. 21. 1. p. 16-25.

ten Hompel, M., Kerner, S. (2015). Logistik 4.0. In

Informatik Spektrum 38. p. 176-182.

VDI 2498 (2011). Part 1: Procedure in material flow

planning. Fundamentals. p. 1-15.

VDI 2689 (2019). Manual of material flow investigations.

p. 1-40.

Wang, H., Cheng, T., Wu, Z.A. (2014). Research on

Positioning Algorithm of Forklift-mounted RFID

Reader. Sensors & Transducers, Vol. 174, Issue 7. p.

164-168.

Zajac, P., Rozic, T. (2022). Energy consumption of forklift

versus standards, effects of their use and expectations.

Energy 239. p. 1-16.

Zhao, K., Zhu, M., Xiao, B., Yang, X., Gong, C., Wu, J.

(2020). Joint RFID and UWB Technologies in

Intelligent Warehouse Management System. IEEE

Internet of Things Journal 7, 12. p. 11640-11655.

Zheng, Y., Baala, O., Caminada, A. (2010). Efficient design

of indoor positioning systems based on optimization

model. Wireless Days (WD) IFIP. p. 1-5.

Ziólkowski, A., Fuc, P., Jagielski, A., Bednarek, M. (2022).

Analysis of emissions and fuel consumption from

forklift by location of operation. Combustion Engines

2022, 189. p. 30-35.

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