Simulation Based Performance Analysis of Productio
Simulation Based Performance Analysis of Productio
Simulation Based Performance Analysis of Productio
*E-mail: kashif.mahmood@taltech.ee
Abstract. Production planning and scheduling rely heavily on the efficient operations of
production logistics and material handling equipment. Industry 4.0 technologies such as Internet
of Things (IoT), Digital Twins, and Artificial Intelligence (AI) can be applied to production
logistics in terms of autonomous mobile robots that facilitate to increase the flexibility and
productivity of the whole production site. However, before the implementation of an automated
production logistics systems, its feasibility must be analysed. This paper describes a simulation-
based approach, including the definition of and comparative analysis of Key Performance
Indicators (KPIs), to analyse the performance of production intralogistics applied to a selected
use case. The presented approach offers a proof of concept on the basis of which decision-makers
can implement mobile robots for intralogistics in their own production environments.
1. Introduction
In the scope of production management, the performance of activities such as obtaining raw materials
to delivering finished goods to customers, need to be jointly studied and analysed. These activities are
highly interconnected, and the analysis of the performance of those activities can help optimize
manufacturing and logistics operations. The improvement of production intralogistics – the internal
transportation of goods within a given manufacturing facility – has a major impact on the production
efficiency of the whole site. As such, the requirement to optimize internal logistics systems in terms of
operational performance, throughput and sustainability arises [1]. Although automation contributes a lot
to business value creation and has already been to some extent introduced into the intralogistics of
manufacturing facilities (e.g. conveyors, fork-lifters and pallet trucks), the aforementioned equipment
allows only for a low degree of flexibility, whilst other tasks, such as loading & unloading, and the
authorization of goods, are still mainly performed manually [2]. High level automation, such as the
introduction of Autonomous Mobile Robots (AMR) into the intralogistics of the facility, offers a more
flexible solution that can lead to a more efficient process of transportation.
Whilst intralogistics automation promises many benefits, any change within the production site
introduces new challenges. For example, to ensure a smooth transition into the new workflow, a
thorough change management course for line operators is recommended to be planned and carried out.
Moreover, internal logistics systems are highly complex, with the deployment of AMRs requiring a
thorough preliminary study and analysis. Therefore, the method of simulation and 3D visualization can
be used to analyse and verify the change. Simulation modelling, paired with the Digital Twin concept
and the setup of KPI (Key Performance Indicator) targets, has become a staple framework in operations
management today, for the insights gained facilitate better decision-making in terms of financial, time
oriented, material and energy savings, as well as the ability to streamline the process activities [3].
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Published under licence by IOP Publishing Ltd 1
Modern Materials and Manufacturing (MMM 2021) IOP Publishing
IOP Conf. Series: Materials Science and Engineering 1140 (2021) 012026 doi:10.1088/1757-899X/1140/1/012026
2. Literature Review
For the literature review, state-of-the-art articles relevant to the field of this study were analysed. Topics
include the automation of production intralogistics through AMRs, the significance of simulation
modelling and 3D visualization as decision-making tools, and a brief explanation of relevant Key
Performance Indicators. Moreover, similar studies and related approaches are referred to in this section.
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Modern Materials and Manufacturing (MMM 2021) IOP Publishing
IOP Conf. Series: Materials Science and Engineering 1140 (2021) 012026 doi:10.1088/1757-899X/1140/1/012026
3.3. Visualization
The exact-scale digital model of the production floor in 3D verifies the work of the real system, ensuring
that the created model behaves as intended. 3D simulation assists users to visualize staff, equipment,
building facility, and other items and processes in the virtual environment. The verification can be
performed by providing real input data to the model and comparing the results with historical data.
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Modern Materials and Manufacturing (MMM 2021) IOP Publishing
IOP Conf. Series: Materials Science and Engineering 1140 (2021) 012026 doi:10.1088/1757-899X/1140/1/012026
Visualization also represents processing data in the form of a dashboard which helps to determine
between strategic alternatives.
4. Case Study
The proposed simulation-based approach was applied to the intralogistics process of a chemical
manufacturer which produces detergents and hand sanitizer. The manually operated transportation of
goods is a key operation in the production facility. Due to the high demand of products and, thus, the
subsequent increase of production capacity and flexibility, the company decided to analyse and improve
the intralogistics process with the intention to automate the production floor logistics by implementing
AMRs. This solution is expected to reduce the transportation time and ultimately increase the process
productivity, as well as cut down on workers' fatigue. The studied production facility consists of four
production lines that fill empty bottles (in containers) of different sizes with liquid, label and cap them.
The intralogistics related activities, planned to be executed on four different stations with the help of an
AMR, are as follows:
1. Loading of products (empty bottles) in warehouse and transportation to production line
2. Unloading of empty bottles at the start of production line
3. Loading of filled bottles at the end of production line and transportation to finished goods area
4. Unloading of filled bottles in Finished Goods (FG) area and moving back to the Warehouse (WH)
The 3D simulation models of the use case were created and analysed in Visual Components 4.2, a
3D manufacturing simulation software. The physical setup of the production lines and routes mapping
of the AMR were constructed on the basis of full-scale production layout. Figure 2 gives a concise view
and a single production line simulation model, where the intralogistics activities were marked and
executed as defined above with the corresponding numbers. Figure 3 is a holistic view of the production
facility and illustrates the transportation of goods using the AMR following the route WH → Production
→ FG → WH. The movement of the AMR was mapped and analysed during the simulation, with the
green-coloured marking showing the movement of the AMR in the production area, the red-coloured
one - to and from the FG area, and the yellow-coloured route – to WH and from WH.
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Modern Materials and Manufacturing (MMM 2021) IOP Publishing
IOP Conf. Series: Materials Science and Engineering 1140 (2021) 012026 doi:10.1088/1757-899X/1140/1/012026
The results of the simulation analysis can be observed in figure 4. The graph, showing the time spent
and distance covered by the AMR, helps to perceive the idleness and busyness of the robot. One AMR
was used to feed and serve four production lines. For the 8-hour simulation run, performance metrics
such as throughput and utilization were determined. By introducing variations in the simulation model
(like the number of AMRs needed for the current production capacity), the effect of an AMR
implementation to the transportation cost and defects was observed. The impact of the change, i.e. the
automation of the production intralogistics operation, was monitored through previously defined KPIs;
the results are shown in table 2. The deployment of an AMR shows a positive impact on every KPI.
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Modern Materials and Manufacturing (MMM 2021) IOP Publishing
IOP Conf. Series: Materials Science and Engineering 1140 (2021) 012026 doi:10.1088/1757-899X/1140/1/012026
5. Conclusion
The proposed simulation-based approach is intended to help analyse the feasibility of automation of
intralogistics processes and the implementation of AMRs in production logistics. Due to the possibility
of achieving a high level of accuracy in the representation of a real production facility in 3D modelling
and simulation software, the authors of this study recommend using the aforementioned Industry 4.0
tools as part of the decision-making workflow when automating intralogistics processes. The case study
ensured the effective use of 3D simulation and visualization which helped to reduce the installation time
of AMRs and analyse the production capacity to figure out the number of AMRs needed to fulfil the
current capacity requirement. Moreover, with the defined KPI analysis, it is technically feasible to use
AMRs for intralogistics, and it may enhance the proactive decision making as well. Mobile robots are
flexible tools which can be applied in different use cases as needed and can be introduced to a production
facility stage-wise, first testing a solution with just one AMR, and then gradually increasing their number
per required capacity. The simulation-based approach can be replicated in other companies in the future,
especially those that are dealing with similar business processes and production environments.
6. References
[1] Mörth O, Emmanouilidis C, Hafner N and Schadler M, 2020, Cyber-physical systems for performance
monitoring in production intralogistics, J. of Computers & Industrial Engg., vol. 142, pp. 1-10.
[2] Granlund A, and Wiktorsson M, 2014, Automation in internal logistics: Strategies and operational
challenges. International Journal of Logistics Systems and Management, 18(4), 538–558.
[3] Mahmood K, Karaulova T, Otto T and Shevtshenko E, 2017, Performance Analysis of a Flexible
Manufacturing System, Procedia CIRP, vol. 63, 424 – 429.
[4] Riives, J.; Karjust, K.; Küttner, R.; Lemmik, R.; Koov, K.; Lavin, J. (2012). Software development platform
for integrated manufacturing engineering system. Proceedings of the 8th International Conference of
DAAAM Baltic Industrial Engineering, Tallinn, Estonia, 19-21 april 2012. Ed. Otto, T., pp. 555−560.
[5] Klumpp M, et al, 2019, Production logistics and human-computer interaction—state-of-the-art, challenges
and requirements for the future, The International Journal of Advanced Manufacturing Technology, vol.105,
3691–3709
[6] Fragapane G, et al, 2020, Increasing flexibility and productivity in Industry 4.0 production networks with
autonomous mobile robots and smart intralogistics, Annals of Operations Research, Springer.
[7] Laguna M and Marklund J, 2019, Business Process Modeling, Simulation and Design, 3rd Ed, CRC Press,
[8] Straka M, et al, 2017, Application of extendsim for improvement of production logistics' efficiency,
International Journal of Simulation Modelling, vol.16 (3), 422-434.
[9] Masteamssik S, Schulze T, Raab M and Lemessi M, 2016, Comprehensive 3D Visualization of Simulated
Processes in Virtual Factories, International Conf. Modeling, Sim. and Vis. Methods, pp. 50-56.
[10] Hwang G, Lee J, Park J and Chang T, 2017, Developing performance measurement system for Internet of
Things and smart factory environment, International Journal of Production Research, vol. 55, 2590–2602.
[11] Mahmood K, Lanz M, Toivonen V and Otto T, 2018, A Performance Evaluation Concept for Production
Systems in an SME Network, Procedia CIRP, vol. 72, 603 – 608.
[12] Michalos G, Kousi N, Makris S and Chryssolouris G, 2016, Performance assessment of production systems
with mobile robots, Procedia CIRP, vol. 41, 195 – 200.
[13] Fischer M, et al, 2010, Automated 3D-Motion Planning for Ramps and Stairs in Intra-Logistics Material
Flow Simulations, Proceedings of the 2010 Winter Simulation Conference, pp. 1648-1660.