IN4PL 2023 Abstracts

Area 1 - Industry of the Future
Full Papers Short Papers
Area 2 - Logistics
Full Papers Short Papers

Area 1 - Industry of the Future

Full Papers
Paper Nr: 14
Title:

Harmonizing Heterogeneity: A Novel Architecture for Legacy System Integration with Digital Twins in Industry 4.0
Authors:

Aaron Zielstorff, Dirk Schöttke, Antonius Hohenhövel, Thomas Kämpfe, Stephan Schäfer and Frank Schnicke
Abstract: The transition towards Industry 4.0 requires the modernisation of legacy systems. However, this transformation introduces complexity, mainly due to the variety of data formats and interfaces resulting from the heterogeneity of the used components. For seamless interoperability in Industry 4.0 applications, a harmonised data base is of vital importance. The Asset Administration Shell (AAS), as a standardised digital twin of assets, plays a key role in facilitating interoperable, data-centric solutions in the Industry 4.0 landscape. The prospect of automated data integration offers the potential to reduce errors and optimise the process of digitising legacy systems. This raises the question of what extensions and components within the AAS infrastructure are necessary to realise such automation. In response, this paper presents an architectural concept for integrating legacy systems into the AAS framework. Using an articulated robot as a tangible example, the process of interconnecting data points via the OPC UA protocol is illustrated. Additionally, a prototype is presented, capable of enabling vertical data integration via the BaSyx DataBridge, thus showcasing the notable advantages of automating the incorporation of legacy devices into the AAS. The shown solution retains flexibility and is readily applicable to a variety of systems and scenarios as required.
Paper Nr: 26
Title:

Industrial Application Use Cases of LiDAR Sensors Beyond Autonomous Driving
Authors:

Olaf Poenicke, Maik Groneberg, Daniel Sopauschke, Klaus Richter and Nils Treuheit
Abstract: This paper is giving an overview on different industrial application fields for LiDAR sensors beyond the field of autonomous driving. With insights to three specific use cases, different approaches to process the LiDAR point cloud data are described and referring results and findings of the developed applications are summarized. The application fields of the described use cas-es are the surveillance of industrial process environments of automated fenceless cells, the provision of visual assistance and location information for crane operators and the monitoring of the storage space occupancy in a port terminal. Based on the information from the three use cases and further general LiDAR related background an initial morphological box is drafted to enable the classification of industrial LiDAR use cases. The paper concludes with a brief overview on future work.
Short Papers
Paper Nr: 11
Title:

Measuring the Phase Shift Between Hall Signals and Phase Voltages for Purpose of end Quality Control of BLDC Motor Production
Authors:

Jernej Mlinarič, Boštjan Pregelj and Janko Petrovčič
Abstract: BLDC motors for demanding applications require a sensor system for electronic commutation to determine the current rotor position. Often, three Hall magnetic sensors, angularly displaced by 60º or 120º, are used for this purpose. Ideally, commutation should be performed precisely at the rotor position where the back induced electromotive force (BEMF) crosses the zero value. However, in reality, the deviation (phase shift between the voltage crossing the zero value and the HALL sensor switching) is different from 0º and depends on the mechanical tolerances of motor manufacturing, sensor soldering position, and rotor magnetization repeatability. To perform comprehensive final testing of motors, a dedicated measuring method had to be developed, as professional measuring instruments (Frequency counters) are not suitable for this purpose. This is due to winding voltage not being sinusoidal but pulse-width modulated. It is also not possible to rotate the motor during testing with an additional drive. Consequently, that is why the measurement can only be performed during the coast-down period, when the rotational speed exponentially decreases. In this paper, we present a solution, developed for the EOL (end-of-line) quality assessment production of electric motors (BLDCs) at Domel company. It consists of a PCB for signal preparation, a fast USB module for simultaneous acquisition of analog signals, software for data processing, result analysis and result presentation and company database for results storage.
Paper Nr: 12
Title:

5G and MEC Based Data Streaming Architecture for Industrial AI
Authors:

Telmo Fernández De Barrena Sarasola, Juan Luis Ferrando Chacón, Ander Garcia and Michail Dalgitsis
Abstract: Availability of computation capabilities and real-time machine data is one key requirement of smart manufacturing systems. Latency, privacy and se-curity issues of cloud computing for Industrial artificial intelligence (AI) led to the edge computing paradigm, where computation is performed close to the data source. As on-premise edge deployments require companies to allo-cate budget and human resources to acquire and maintain the required in-formation technologies (IT) infrastructure and equipment, they are not feasi-ble for several companies. However, 5G can merge advantages of previous alternatives. Multi-Access Edge Computing (MEC) servers deployed at the edge of the 5G network close to the final user, offer security, privacy, scala-bility, high throughput and low latency advantages. MECs are suitable for in-dustrial AI, while industrial companies do not face the burden of acquiring and maintaining servers and communication infrastructures. This paper pro-poses a real-time high-frequency data streaming architecture to deploy Indus-trial AI applications at MECs. The architecture has been successfully validat-ed with data sent through a 5G network to a Kafka broker at the MEC, where different microservices are deployed in a Kubernetes cluster. The perfor-mance of the architecture has been investigated to analyze the capabilities of 5G and MEC to cope with the requirements of Industrial AI applications.
Paper Nr: 15
Title:

Approach of a Ticket-Based Test Strategy for Industry 4.0 Components with Asset Administration Shells
Authors:

Dirk Schöttke, Aaron Zielstorff, Thomas Kämpfe, Vasil Denkov, Fiona Büttner, Stephan Schäfer and Bernd Tauber
Abstract: The importance of comprehensive automated component test solutions has been underlined by the growing need for adaptable, demand-driven manufacturing plants. Software solutions, which play a key role in the flexibility and complexity of a plant, contribute significantly to this trend. A fundamental aspect of this is the interoperability of Industry 4.0 components. Despite the availability of numerous software environments for the configuration of Industry 4.0 components, there is a lack of generally accepted definitions for the implementation of test strategies. In particular, the integration of legacy systems requires critical support. Ideally, this should be done semi or fully automated. This paper presents a test environment concept based on established software engineering test methodologies and the use of the Asset Administration Shell (AAS). A testing strategy for Industry 4.0 components using a ticket system is proposed. The approach uses descriptors to facilitate test execution without further software development effort within the native system component environment.
Paper Nr: 17
Title:

A Scenario Generation Method Exploring Uncertainty and Decision Spaces for Robust Strategic Supply Chain Capacity Planning
Authors:

Raphaël Oger, Achille Poirier and Cléa Martinez
Abstract: Strategic Supply Chain Capacity Planning (SSCCP) is an essential activity for companies to prepare their future. However, since uncertainty became an essential factor to consider in this decision-making process, existing solutions to support this process do not fully satisfy their needs anymore. Especially in terms of uncertainty space coverage while exploring and assessing scenarios associated with uncertainty sources and decision options. Therefore, this paper introduces an approach to overcome the complexity of scenario exploration and improve the uncertainty space coverage, to better support SSCCP decision-making. This approach includes a bi-objective metaheuristic that first explores a probabilityimpact matrix to define a relevant subspace to consider in this uncertainty space, and then uses this subspace to explore the decision space and define a relevant subspace of this decision space to assess and display to decision-makers. Then, an implementation and experiment are described and discussed, and finally avenues for future research are suggested.
Paper Nr: 23
Title:

Bridging the Operationalization Gap: Towards a Situational Approach for Data Analytics in Manufacturing SMEs
Authors:

Stefan Rösl, Thomas Auer and Christian Schieder
Abstract: The emergence of Industry 4.0 (I4.0) technologies has significant implications for small and medium-sized enterprises (SMEs) in the manufacturing sector. Current research highlights the benefits of I4.0 technologies but often overlooks the unique challenges and needs of SMEs, particularly in the transition from implementation to routinization of data analytics (DA) in the context of I4.0 initiatives. Our paper addresses this gap by introducing a prototype of an integrated data analytics model (iDAM) specifically designed to help SMEs incorporate DA as part of I4.0. Our model was developed based on a comprehensive review of existing frameworks and methodologies. It covers three key areas (the project situation, the organization’s maturity level, and the application landscape) and proposes a situational process model to bridge the implementation-routinization gap. We demonstrate and evaluate our approach using a practical, real-world use case of a multi-stage manufacturing process in an SME. The iDAM provides a structured and tailored approach to guide SMEs in operationalizing DA based on their individual maturity level and promote the use of DA methods.
Paper Nr: 24
Title:

AutoPose: Pose Estimation for Prevention of Musculoskeletal Disorders Using LSTM
Authors:

Francesco Bassino-Riglos, Cesar Mosqueira-Chacon and Willy Ugarte
Abstract: Office work has become the most prevalent occupation in contemporary society, necessitating long hours of sedentary behavior that can lead to mental and physical fatigue, including the risk of developing musculoskeletal disorders (MSDs). To address this issue, we have proposed an innovative system that utilizes the NAO robot for posture alerts and camera for image capture, YoloV7 for landmark extraction, and an LSTM recurrent network for posture prediction. Although our model has shown promise, further improvements can be made, particularly by enhancing the dataset's robustness. With a more comprehensive and diverse dataset, we anticipate a significant enhancement in the model's performance. In our evaluation, the model achieved an accuracy of 67\%, precision of 44\%, recall of 67\%, and an F1 score of 53\%. These metrics provide valuable insights into the system's effectiveness and highlight the areas where further refinements can be implemented. By refining the model and leveraging a more extensive dataset, we aim to enhance the accuracy and precision of bad posture detection, thereby empowering office workers to adopt healthier postural habits and reduce the risk of developing MSDs.
Paper Nr: 20
Title:

Virtual Try-On Networks Based on Images with Super Resolution Model
Authors:

Franco Gallegos, Sebastian Contreras and Willy Ugarte
Abstract: The main job of a virtual imaging try-on is to transfer a garment to a specific area of an individual's body part. Trying to deform said garment so that it fits in a part of the desired body. Despite some research, the vast majority use a low-quality image resolution of 192 x 256 pixels, limiting the visual satisfaction of online users. Analyzing this visual limitation, we find that the vast majority of the algorithms use these mentioned measures to obtain better performance and optimization during their training, since while the number of pixels is smaller, in the same way, their execution time will be less in the generation. of segments or masks of garments and body parts. Despite having better performance and optimization, quality and pixel size are also of the utmost importance, since it is in the final resolution that the result for the user is appreciated. To address this challenge, we propose a super-resolution extension module, added to the ACGPN model. Such a module gets the resulting image from the ACGPN model, and then with the help of computer vision aims to increase the resolution to 768 x 1024 pixels with minimal loss of quality. For this, a comparison of models that perform this task of increasing the resolution is made. Finally, it is quantitatively shown that the proposal obtains better results.
Paper Nr: 25
Title:

Towards Circular Systems: The Role of Digital Servitization in an Italian Extended Partnership
Authors:

Elena Beducci, Federica Acerbi, Anna de Carolis, Sergio Terzi and Marco Taisch
Abstract: “Made in Italy” products and Italian manufacturing are worldwide recognized for their quality. Nonetheless, businesses and societies are evolving, affected by structural transformations. To maintain their competitive ad-vantage, Italian companies are called to move towards a transformation aligned with global call for actions addressing critical issues, such as cli-mate change. The transition of manufacturing companies, in particular Small and Medium Enterprises (SME), towards circular economy should be sup-ported by adequate investments. To answer a national call, the Extended Partnership (EP) “Made in Italy Circolare e Sostenibile” was established. The EP aims to provide research and innovation resources to enable circular manufacturing practices in Italian companies, developing best practices to be adopted by SMEs. One of the main themes that the EP is investigating is the one of Product Service Systems (PSS), which appear as a viable path to achieve environmental sustainability. Nonetheless, resources and research-es to support manufacturing companies in the path of servitization are still required, thus motivating the creation, in the context of the EP, of projects to support companies in the development of PSS business models, in particular leveraging the opportunities offered by digital technologies.
Paper Nr: 27
Title:

When the Learning Lab Embraces Digitalisation: The Development of a Digital Learning Lab for SMILE
Authors:

Marco Dautaj, Franco Chiriacò, Sergio Terzi, Margherita Pero, Nizar Abdelkafi and Maira Callupe
Abstract: The educational approach, with the impact of COVID-19 and rapid digitalization, has needed a fundamental shift. In response to these evolving circumstances, SMILE (Smart Manufacturing Innovation, Learning-Labs, and Entrepreneurship) has undertaken a comprehensive initiative in order to change the traditional learning lab into a digital one. This digital transformation aims to enhance students' learning experiences and problem-solving capabilities by leveraging technologies and innovative approaches. Digital Learning Nuggets are central to this paradigm shift, which are small units of interactive and engaging educational content. These nuggets have proven instrumental in augmenting students' comprehension and retention of subject matter while fostering a more personalized learning journey. Furthermore, the integration of MIRO, a collaborative software platform, further drives students' learning by facilitating interactive discussions and fostering teamwork in a virtual environment. SMILE’s DLL serves as the foundation for the upcoming Hackathon, an event characterized by the collaboration between academia and industry. In collaboration with various companies, this experiential learning initiative offers students the opportunity to tackle problems posed by these organizations. Participants gain invaluable insights into real-world applications of their knowledge and are better equipped to address complex issues in a professional context. The Hackathon represents the link between academia and industry, fostering a dynamic environment where students can apply theoretical concepts in order to solve real problems. This immersive learning experience not only fosters their critical thinking and analytical skills but also nurtures creativity, adaptability, and teamwork, paramount attributes for today’s competitive job market.

Area 2 - Logistics

Full Papers
Paper Nr: 13
Title:

A Classification of Data Structures for Process Analysis in Internal Logistics
Authors:

Maximilian Wuennenberg, Charlotte Haid and Johannes Fottner
Abstract: Data Science plays a crucial role in driving new approaches to process optimiza-tion. With the increasing complexity of internal logistics systems, data-oriented methods have become essential in addressing the challenges that arise. However, standardized process analytics frameworks are lacking due to the heterogeneity of the underlying processes and the resulting data. This article aims to address this complexity by presenting a categorization of internal logistics data, consolidating the current state of the art. The categorization takes into account both real-world and scientifically proposed data architectures, providing a comprehensive over-view. It includes a classification of comparative data fields based on their im-portance, the associated internal logistics processes, and potential usage scenari-os. This classification is designed to cater to different use cases, such as diagnos-tics or prescriptive analytics. By presenting this categorization, the article enables practitioners to effectively leverage generated process data in a more goal-oriented manner. It empowers them to conduct suitable analyses tailored to their specific needs and objectives, based on the provided data architectures. In summary, this article offers valuable insights into internal logistics data categorization, providing a framework for practitioners to make informed decisions and optimize processes using data-driven approaches.
Paper Nr: 16
Title:

Balancing Risks and Monetary Savings when the Crowd Is Involved in Pickups and Deliveries
Authors:

Annarita De Maio, Roberto Musmanno and Francesca Vocaturo
Abstract: The increasing number of requests in the last-mile delivery has led to the introduction of advanced technological solutions to enhance couriers’ services. In addition, innovative strategies like crowd-shipping have been introduced in order to create synergies within the territory and involve ordinary people in the transportation activity with the aim of reducing operational costs and pollution as well as of increasing the service level. We refer to a company that manages a crowd-shipping platform and provides transportation services within time windows through its own fleet of vehicles and occasional drivers. The service requests correspond to pairs of pickups and deliveries. The objective of the company is to maximize the profit by balancing risks and benefits, from an economic perspective, associated with the involvement of ordinary people in fulfilling service requests. We extend the pickup and delivery problem with occasional drivers and regular vehicles, introducing risk and compensation considerations. The computational analysis conducted through an optimization model shows how the use of occasional drivers reduces overall costs. Moreover, a series of managerial insights is provided thanks to a sensitivity analysis on the risk and compensation parameters associated with crowd-shipping service.
Short Papers
Paper Nr: 18
Title:

Proposition and Evaluation of an Integrative Indicator Implementation for Logistics Management
Authors:

Francielly H. Staudt, Maria Di Mascolo, Marina C. Guimarães, Gülgün Alpan, Carlos Manuel T. Rodriguez, Marina Bouzon and Diego Fettermann
Abstract: The growing operation complexity has led companies to adopt many indicators, making complex the evaluation of the overall performance of logistics systems. Among several studies about logistics management, this is the first one to deter-mine an overall logistics performance indicator and evaluate its impact for logis-tics management. The proposed methodology encompasses four main phases: the first one defines the management scope and the indicator set, the second applies statistical tools reaching an initial model for indicators aggregation, the third one determines the global performance model, and the last phase implements the inte-grative indicator with its scale. The methodology is implemented in an outbound process from a Brazilian company with eight logistics KPI’s. Principal Compo-nent Analysis (PCA) is used to stablish the relationships among indicators and an optimization tool is applied to define the integrative indicator scale. The global performance (GP) provided by the integrative indicator has demonstrated that even if important indicators have not reached their goal, it is possible to attain a good global performance with improvements in other areas. Thus, the framework demonstrates to be a useful solution for logistics performance management.
Paper Nr: 19
Title:

Blockchain Subnets for Track & Trace Within the Logistics System Landscape
Authors:

Henrik Körsgen and Markus Rabe
Abstract: The advantages of track & trace solutions to enterprises that move physical goods are widespread. Yet, track & trace solutions are not a standard component of an enterprise’s system landscape. While mostly being triggered by customer requests, track & trace initiatives are often siloed. Seldomly, the entire enterprise and even more rarely the enterprise’s network are considered. The reasons for a siloed implementation of track & trace software are manifold. Different business priorities, adverse data sharing policies, lack of knowledge, and a lack of structured approach are a few to mention. Starting with the structured approach of the digital enterprise architecture (DEA) is a first remedy to these issues. From a technological perspective, embracing a blockchain subnet gives enterprises access to solutions for data ownership, validity, and integrity impediments. The block-chain-based subnet is compatible with a bimodal enterprise architecture. This allows enterprises to connect to their supply chain partners’ logistics systems differently than to their own one. Several steps need to be considered to achieve a functional supply-chain-network-wide track & trace solution. From finding consensus among the blockchain peers to designing the track & trace block, academic input is combined to render the implementation of a blockchain-based track & trace solution possible. From a business perspective linking the requirement catalogue with the purposeful use of the latest technology follows an end-to-end approach.
Paper Nr: 29
Title:

Case Fill Rate Prediction
Authors:

Kamran I. Siddiqui, Madeleine Y. Lee, Thomas Koch and Elenna Dugundji
Abstract: Stockouts present significant challenges for Fast-Moving Consumer Goods (FMCG) companies, adversely affecting profitability and customer satisfaction. This research investigates key drivers causing Case Fill Rate (CFR) to fall below target levels and identifies the best model for predicting future CFR for the sponsor company. By utilizing hypothesis testing and feature importance techniques including decision tree matrix root cause analysis and Shapley additive explanations (SHAP) value plots, we conclude demand forecast error is the most critical driver influencing CFR. Machine learning classification and regression techniques were deployed to predict shipment cut quantity. To improve longer-term forecasts, a combination of models should be incorporated, along with extended historical data, promotions data, and consideration of exogenous variables. In conclusion, companies should prioritize forecasting accuracy and optimize inventory policy to improve CFR in the long run.
Paper Nr: 30
Title:

A Continuous Review Policy for Warehouse Inventory Management
Authors:

Andrew Mohn, Charles Snow, Yusuke Tanaka, Thomas Koch and Elenna Dugundji
Abstract: A continuous review policy (CRP) was used to simulate the inventory of finished product, raw material and packaging material at a warehouse. The ordering points were simulated based on the minimum order quantity and the production forecast. The safety stock levels did not impact the ordering points in this scenario because the demand was known and did not deviate. The simulation results showed that the average weekly number of pallets received at the warehouse was 341, with a standard deviation of 115 pallets. The aggregated ordering volume was fairly volatile, but the warehouse inventory levels were fairly uniform over time. The overall bin occupancy remained below 1,700, less than 20% of the total warehouse capacity. This suggests that the CRP is a good inventory management policy for products with known and stable demand. However, the volatile ordering volume could be a problem if there are constraints on processing incoming deliveries to the warehouse.
Paper Nr: 21
Title:

Assigning Products in a Vertical Lift Module Supermarket to Supply Production Lines
Authors:

José Oliveira, António Vieira, Luís Dias and Guilherme Pereira
Abstract: This paper presents the development of a mathematical model for product assignment in a Vertical Lift Module (VLM), which are increasingly employed in the industrial sector due to their advanced technology and efficient parts-to-picker process. Mathematical modelling plays a crucial role in addressing the complexity of these problems and providing intelligent and innovative solutions. Despite being a tactical problem with medium-term implications, the competitive nature of the industrial environment demands quick adjustments driven by the mass customization paradigm. This requires continuous evaluations to reconfigure the supermarket accordingly, which can be efficiently accomplished through the rapid application of artificial intelligence using advanced mathematical methods. The proposed integer linear programming, which is based on the well-known transportation problem, features a simple objective function aimed at minimizing the number of trays in the VLM. Additionally, five constraints are included to ensure the applicability of the model to real-world scenarios. The simplicity of the AMPL implementation of the mathematical model is emphasised. Experimental computation using real data validates the proof of concept and assesses the impact of introducing new rules for product assignment. This research also explores the potential for optimising warehouse operations and suggests avenues for further investigation.