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Digitalized milk-run system for a learning factory assembly line
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ScienceDirect ScienceDirect Procedia Manufacturing 00 (2019) 000–000
Available Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com
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Procedia Manufacturing 00 (2019) 000–000
Procedia Manufacturing 31 (2019) 175–179 Procedia Manufacturing 00 (2017) 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
9th Conference on Learning Factories 2019
www.elsevier.com/locate/procedia
9th Conference on Learning Factories 2019
Digitalized milk-run system for a learning factory assembly line Digitalized milk-run system for a learning factory assembly linea a a a, a
Sascha Gotthardt , Maria Hulla , Matthias Eder *, Hugo Karre , Christian Ramsauer Manufacturing Engineering Societya International Conference 2017, MESIC 2017, 28-30 a a, a June Sascha Gotthardt , Maria Hulla , Vigo Matthias EderManagement, *, Hugo Karrea, Christian Ramsauer Graz University of Technology, Institute of Innovation and Industrial Kopernikusgasse 24/II, 8010 Graz, Austria 2017, (Pontevedra), Spain a
Graz University of Technology, Institute of Innovation and Industrial Management, Kopernikusgasse 24/II, 8010 Graz, Austria
a
Costing models for capacity optimization in Industry 4.0: Trade-off Abstract between used capacity andas increasing operational efficiency In order to face current trends and challenges in industrial companies such number of variants, shorter product life cycles and higher Abstract
product complexity, production logistics is one of the main areas for optimizing assembly systems. To increase the efficiency of the internal In order to face current and challenges in industrial companies such asItincreasing numberinofliterature variants,that shorter product life cycles and higher material supply, varioustrends lean system such as the milk-run were introduced. has been shown a a,* b bdigitalization technologies can be product complexity, logistics is one of is thecurrently main areas for of optimizing assembly systems. To increase the efficiency the internal used to improve leanproduction systems. Nevertheless, there a lack knowledge about the digitalization of milk-run systems,ofespecially in material factories. supply, various lean system such as the milk-run were introduced. Itsystem has been shown Factory, in literature that digitalization technologies can be learning Therefore, this work is introducing a digitalized milk-run in LEAD the learning factory of Graz University of a University of Minho, 4800-058 Guimarães, Portugal used to improve systems. there is currently a lack of generate knowledge about the adigitalization milk-run systems, Technology. Thislean system relies Nevertheless, on the RFID technology to automatically orders from warehouse toofa workstation. In the especially warehouseina b Unochapecó, 89809-000 Chapecó, SC, BrazilFactory, the learning factory of Graz University of learning factories. Therefore, work introducing a digitalized milk-run system pick-to-light system is used tothis show theislogistics employee on a screen which partsintoLEAD pick. To efficiently deliver the order to the workstation, Technology. Thisissystem reliesusing on the RFIDplanning technology to automatically orders from a warehouse a workstation. the warehouse the shortest path calculated a path algorithm that is usedgenerate in robotics. The paper shows (1)todevelopments ofInmilk-run systems,a pick-to-light system used to show the logistics onFactory a screenand which parts to pick. within To efficiently delivertrainings. the order to the workstation, (2) the concept for a is digitalized milk-run system atemployee the LEAD (3) its application digitalization the shortest path is calculated using a path planning algorithm that is used in robotics. The paper shows (1) developments of milk-run systems, (2) the concept for a digitalized milk-run system at the LEAD Factory and (3) its application within digitalization trainings. Abstract
A. Santana , P. Afonso , A. Zanin , R. Wernke
© Authors. Published Published by by Elsevier Elsevier B.V. B.V. © 2019 2019 The The Authors.
Under theunder concept of "Industry 4.0", production processes willonbe pushed to be increasingly interconnected, Peer review the of committee of the 9th Conference Learning Factories. Peer review theresponsibility responsibility ofthe thescientific scientific © 2019 The under Authors. by Elsevier B.V. committee of the 9th Conference on Learning Factories. information basedPublished on a real time basis and, necessarily, thmuch more efficient. In this context, capacity optimization Peer review under the responsibility thedigital; scientific committee of the 9 factory; Conference on Learning Factories. Keywords: digitalization; milk-run; leanof and production; rfid; learning goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. Indeed, lean management continuous approaches suggest capacity optimization instead of Keywords: digitalization; milk-run; leanand and digital; production;improvement rfid; learning factory; maximization. The study of capacity optimization and costing models is an important research topic that deserves 1. Introduction contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical 1. Introduction model capacity management based on different models (ABC and genericonmodel has beena Everfor fiercer competition in the manufacturing industrycosting is making the elimination of TDABC). non-addingAactivities the shop-floor crucial objective. Production logistics areidle among the most important optimization targets forthe themaximization task of enhancing the performance developed and it was used to analyze capacity and to design strategies towards of organization’s Ever fiercer competition in the manufacturing industry is making the elimination of non-adding activities on the shop-floor a of assembly Since transports are generallyvsregarded as waste, these areisseparated from value-adding activities such as the value. The systems. trade-off capacity maximization operational highlighted and ofit enhancing is shown that capacity crucial objective. Production logistics are among the most importantefficiency optimization targets for the task the performance assembly and amight so-called milk-run or Mizusumashi has also been introduced. [1, 2] Digitalization offers various potentials in the optimization hide operational inefficiency. of assembly systems. Since transports are generally regarded as waste, these are separated from value-adding activities such as the
manufacturing industry in terms by of increasing efficiency, connectivity and transparency [3]. It thus enables lean systems to reach a © 2017 The Published Elsevier B.V. assembly andAuthors. a so-called milk-run or Mizusumashi has also been introduced. [1, 2] Digitalization offers various potentials in the higher level of manufacturing excellence. For instance, real-time data creates new opportunities for existing approaches of lean Peer-review under responsibility of increasing the scientific committee of the Manufacturing Engineering Society International Conference manufacturing industry in terms of efficiency, connectivity and transparency [3]. It thus enables lean systems to reach a production by detecting and solving problems faster [4]. Metternich et al. (2017) proposed that the digitalization of lean production 2017. higher level of manufacturing excellence. For instance, real-time data creates new opportunities for existing approaches of lean should improve lean approaches rather than replace them [5]. Very little literature is currently available on the topic of digitalized production by detecting and solving problems faster [4]. Metternich et al. (2017) proposed that the digitalization of lean production milk-run systems withABC; a focus on learning factories. A possible application of a milk-run system is to deliver orders just-in-time as Keywords: Cost Models; TDABC; Capacity Management; Idle Capacity; should improve lean approaches rather than replace them [5]. Very littleOperational literature Efficiency is currently available on the topic of digitalized shown by Qu et al. [6]. milk-run systems with a focus on learning factories. A possible application of a milk-run system is to deliver orders just-in-time as shown by Qu et al. [6]. The aim of this paper is to introduce a digitalized milk-run system in the learning factory of the Graz University of Technology. 1. Introduction First, the IIM LEAD Factory will be introduced. Secondly, the theoretical basis (in-plant) milk-run systems will be discussed and The aim of this paper is to introduce a digitalized milk-run system in the learning factory of the Graz University of Technology. an overview of the development on (digitalized) milk-run systems will be given. As the milk-run system relies on the Radio First, thecost IIMof LEAD Factory will introduced. Secondly, the for theoretical basisand (in-plant) milk-run systems will be discussed and The idle capacity is abe fundamental information companies their management of extreme importance an modern overviewproduction of the development on general, (digitalized) milk-run as systems be given. As the milk-run system onmeasured the Radio in systems. In it is defined unusedwill capacity or production potential andrelies can be
in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity Tel.: +43 873 - fax: 7792;+351 fax: +43 873 - 7791. * Corresponding Paulo Afonso.author. Tel.: +351 253(316) 510 761; 253 (0) 604316 741 E-mail E-mailaddress: address:[email protected] [email protected]
* Corresponding author. Tel.: +43 (316) 873 - 7792; fax: +43 (0) 316 873 - 7791. E-mail address: [email protected] 2351-9789 © 2019 The Authors. Published by Elsevier B.V. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. Peer-review responsibility of the scientific of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © under 2019 The Authors. Published by Elseviercommittee B.V. 2351-9789 © 2019 The Authors. Published Elsevier B.V. Peer review under the responsibility of the scientific by committee of the 9th Conference on Learning Factories.
Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. 10.1016/j.promfg.2019.03.028
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Sascha Gotthardt et al. / Procedia Manufacturing 31 (2019) 175–179
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Frequency Identification (RFID), the basic functionality of this technology is described. The core chapter of this paper deals with the implementation of a digitalized milk-run system in the LEAD Factory and the implication of this for the didactical concept of the learning factory will be elaborated.
Fig. 1. Assembly line of the LEAD Factory.
2. IIM LEAD Factory The LEAD Factory is operated by the Institute of Innovation and Industrial Management at the Graz University of Technology. It facilitates hands-on training in an environment close to industrial reality in the areas of lean management, energy efficiency, agility and digitalization (LEAD). The didactical concept in this learning factory is based on three different setup states: 1) the nonoptimized state, which is characterized by inefficient work flows, no levelled work load, unclear instructions for the training participants, insufficient tools etc.; 2) the lean state, where basic lean principles such as 5S, flow principle, value stream mapping, seven types of waste, Heijunka and Kanban are implemented and 3) the digitalized state, in which many technologies and assistance systems are in operation. In the LEAD factory, RFID tags are integrated together with a production control system that keeps track of the production progress. Also, a camera detects the current process step at a workstation by capturing the position of the part and changes the standard operating procedure accordingly. Moreover, a system to control the standard operating procedure (SOP) by capturing the gestures or mimics of the user is used. A smart Andon system that automatically recognizes if there is a time delay in the process is also installed. It signals the delay to a central control station so that everyone who is interested in a smooth production flow is immediately informed about the problem can work on a solving the problem as quickly as possible. During training, participants assemble the 60-parts TUG Scooter in an optimized U-cell shaped assembly line (Fig. 1). The majority of more than 450 trainings have been performed on lean management until today, but the institute has also offered the possibility since summer 2018, to experience the digitalized state of the LEAD Factory which is constantly trying to reach new milestones in implementing the state of the art technologies. 3. Milk-run systems There are several ways to design and organize logistics systems. Because of many benefits such as reducing logistics costs, a milk-run system is preferably used in lean production systems. Milk-run systems are defined as “route-based, cyclic material handling systems that are used widely to enable frequent and consistent deliveries of containerized pats on an as-needed basis from a ventral storage area (supermarket) to multiple line-side deposit points on the factory floor” [7]. Milk-run systems rely on the idea that only material which has been consumed is replenished. The lot size is determined once and can be controlled via Kanban cards. The cycle time and route through the plant are defined in advance [8]. This system originates from the traditional system for selling milk in which the milkman used to go from one customer door to the next as milk bottles were delivered and the empty bottles collected with the dray following a specified route [9]. Two categories of milk-run systems are discussed in literature – the external and the internal (in-plant) milk-run logistic system. This paper focuses on the internal milk-run system for the delivery of material within a plant or assembly line and this will therefore be described in further detail. In-plant milk-run systems were first utilized by automotive companies where a train or manual cart took pins filled with parts from the inventory (supermarket) to the stations in a predetermined route and schedule [10]. Consequently, several stations could be supplied by the same train within the same route and the same service period [11]. In-plant milk-run systems support lean manufacturing by reducing the seven types of waste, mainly the waste of transportation, the waste of waiting time and the waste from inventory [12]. Since the milk-run can transport larger quantities of material than the individual employee with the appropriate
Sascha Gotthardt et al. / Procedia Manufacturing 31 (2019) 175–179 Author name / Procedia Manufacturing 00 (2019) 000–000
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means of transport, it can supply several workstations simultaneously. The system thus minimized the total transportation distance thereby making transport more efficient and reducing costs [11]. At the same time, the rhythmic supply leads to a reduction in stocks, since it is no longer necessary to compensate for short-term fluctuations by means of buffers on the line [13]. Another advantage of in-plant milk-run systems is the more accurate goods delivery with just-in-time systems. The availability of the right number of material maximizes the efficiency of continuous flow systems [14]. 4. Radio frequency identification RFID is a transmitter-receiver technology designed to identify and locate an object automatically and contactless using radio signals. RFID is a low cost and effective technology only using a tag (chip connected to an antenna) attached to an item, specific embedded information and a reader, which constantly emits electromagnetic waves in a given frequency and amplitude powering the tag and simultaneously reading its information. [15]. A significantly improved performance compared to classic barcodes is achieved with modern RFID chip design, since it is possible to store up to 1000-fold data in the same storage space. Additionally, susceptibility to failure is decreased while offering a readability over an increased distance [16]. This and many other features of RFID have made it a widely-used key technology in the internet of things, offering real-time updates and increased traceability of objects and machinery in state of the art industrial applications [17]. The software of the reader, which manages the entire reading process, and a RFID middleware with interfaces to other IT systems and databases [18]. In learning factories RFID is used mainly for tracking and tracing of parts and products as well as the enables manufacturing execution systems [19, 20]. 5. Digitalized milk-run system in the LEAD Factory The production control in the LEAD Factory is accomplished via a RFID system. On the basis of this system, it is known which employee is working on which product at which workstation at any given time. Fig. 2 illustrates a schematic presentation of the LEAD Factory including RFID tags and antennas. On workstations 1 to 4 the product (TUG Scooter) is assembled and on workstation 5 the scooter is packaged. RFID transponders are attached to the product, to the worker cards and to the boxes which contain parts for the assembly of the product that are stored in the material shelves at every work station as well as in the supermarket. There is a RFID receiver at each workstation, which can identify both the product at the station and the worker assembling the product. Moreover, using the RFID tag on the boxes it can be signaled that the workstation is running out of parts and parts need to be transported from the supermarket to the workstation with the milk-run wagon. The unique identification number of the RFID transponders is stored in a central database. As a result, a complete observation of the process is possible. In addition, the RFID system also takes over the recording of defective parts in the production line. For each group of parts there is a RFID chip, which when pulled over the antenna tells the system that there is a problem with the part. This allows the system to generate statistical data about the reject rate and hence also about the material quality.
Fig. 2. RFID in the LEAD Factory.
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5.1. Implementation of a digitalized milk-run system The basic concept of the digitalized milk-run system was implemented in the LEAD Factory during a six-month project. Each box containing parts for assembly of the LEAD Factory product (the TUG Scooter) is equipped with a RFID chip. If all parts in the box are consumed, it is pushed under the workstation by the employee so that it can be taken along during the next supply run. In the digitalized milk-run concept a RFID antenna under the workstation reads the chip and immediately forwards the data to a SQL database where data such as the position of the RFID transponder at a specific time is stored. Every few seconds, the central control station in the warehouse queries the main server for new orders. If an empty box is under the workstation, a new order is generated and forwarded to the control station. Here, the order is immediately shown on a screen to the logistics employee. Every part in a shelf is stored in the database. Thus, the control station does know where the part can be found and thanks to a pick-tolight system, which is a visual indicator of where to find the part, the logistics employee can immediately see where the required part is on the shelf and can thus pick it faster. A visualization example of the control station when there is a lack of black wheels on a workstation is illustrated in Fig. 3a. The highlighted position of the order in the upper right corner implies that the part is on the shelf also in the upper right corner. Thus, it is once more an indication of the real position on the shelf. The number “3” tells the logistics employee to pick three pieces of the part. The limitation for picked orders in the LEAD Factory is five due to the fact that the LEAD Factory is relatively compact in size. Additionally, it is a good opportunity for the training participants to use up all five levels on the supply wagon separately for each order. In previous trainings the authors could see that only the top level of the supply wagon was used by the participants, because it is in a comfortable height and offers an open space for putting boxes. The design of a better supply wagon should therefore also be considered and to do so, we need data on the usage of all five levels. After picking all parts for the open orders, the logistics employee confirms the task to be done and the optimal route through the factory for the supply run appears on the screen (Fig. 3b). The worker can thus be back at the control station and ready top pick the next orders in the shortest time possible. The algorithm which is used to find the shortest route is named A* (A-star) and was published in 1968 by Peter Hart et al. [21] It is based on the 1959 published algorithm by Edsger Dijkstra [22]. The advantage of A* is that it assumes the shortest path between a starting point and a destination point must be a direct one and therefore does not search into the broad range of possibilities but tries to get as near as possible to the goal as fast as possible without knowing the path ahead. Only when the algorithm is no longer able to make direct progress is an alternative path calculated in depth. This algorithm is mainly used in robotics, where it is particularly important for a robot to find the right path quickly. In Fig. 3b an example of an optimal route from the warehouse to the workstation where the order is needed is shown. Light grey fields indicate the walkway. Other colors indicate different functions, the warehouse is indicated in blue, the five workstations (WP) are illustrated in green, and the logistics employee with the milk-run wagon is indicated in red. The layout of the plant can also be visualized as a graph where the obstacles are nodes and the path between nodes is a weighted edge. We decided to use a grid view instead, however, in order to provide an easier to grasp and give a more comprehensive visualization. The complete implementation part of the project was done in C# 6.0 and was developed on a 64bit Windows 8.1 Enterprise edition. The project runs on an Intel Core i55200U CPU @ 2.20GHz and 8GB of RAM.
Fig. 3. (a) Example screen for a picking station; (b) Visualization of the route for the milk-run driver
5.2. Implications for the integration in the didactical concept of the LEAD Factory Prior to the implementation of the digitalized system, the worker had to walk around the production line periodically and search for empty boxes under the workstations. If an empty box was found, the worker had to go back to the warehouse and use it for picking the new parts and bringing them back to the workstation. The implementation of the digitalized system improves the performance of the milk-run system in two ways. The first reason for this is that the logistics employee no longer has to walk the route periodically as before, the employee can now wait for orders at the central control station in the warehouse instead. This
Sascha Gotthardt et al. / Procedia Manufacturing 31 (2019) 175–179 Author name / Procedia Manufacturing 00 (2019) 000–000
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increases the probability that the order will be processed immediately by the logistics employee and no time is wasted. In addition, the delivery time is reduced by half, since the empty boxes no longer have to be picked up at the workplace beforehand, but only need to be replaced with full boxes. Secondly, empty runs are eliminated completely in the process which saves a lot of unnecessarily wasted time. As a result, the training participants learn how to use digitalization in the context of production effectively and what the advantages of such systems are in the industry. 6. Outlook The introduction of a digitalized milk-run system has been presented in this paper. The proposed system uses RFID to realize an automatic order generation for the supermarket where pick-to-light technology supports the employee by showing the position of the requested spare part in the warehouse. Moreover, it is now possible to calculate the shortest routes for the milk-run and therefore also to reduce both transport times and transport costs. For the non-digitalized process in which the milk-run starts at the warehouse, the process of collecting the empty boxes from the workstation, picking the parts at the supermarket and transporting the boxes back to the workstations took an average of 66.6 seconds. On introduction of the digitalized milk-run system, the total time for delivering spare parts took 30.2 seconds. Further possibilities exist to make the milk-run system even more efficient. The picking process could be improved by putting sensors on the pick-to-light bar that can detect how many parts have been removed from the warehouse and whether the parts have been removed from the correct shelf. In addition, current technology offers the possibility of digitally reproducing the entire milk-run and using a simulator to anticipate future orders, so that the control station will know which order is likely to be the next one even before the employee at the workplace knows this. In addition, it is already possible to replace the logistics employees entirely with automated machines. Both the picking process and the delivery of the material are already fully automated in many factories today. In the future, this backlog can also be eliminated in learning factories [23] for the purpose of showing training participants how automated machines that operate 24/7 can cut costs in a factory. References [1] D. T. Jones, D. Roos, Die zweite Revolution in der Autoindustrie, Konsequenzen aus der weltweiten Studie des Massachusetts Institute of Technology. Frankfurt/Main. Campus (1991) 53. [2] H. Taked, Das Synchrone Produktionssystem: Just in Time - Für Das Ganze Unternehmen, Verl. Moderne Industrie, Landsberg am Lech, 2006. [3] H. Hirsch-Kreinsen, Wandel von Produktionsarbeit–„Industrie 4.0 “. WSI-Mitteilungen 67 (2014) 421-429. [4] J. Enke, G. Rupert, A. Kreß, J. Hambach, M. Tisch, J. Metternich, Industry 4.0 – Competencies for a modern production system, Procedia manufacturing 23 (2018) 267-272. [5] J. Metternich, S. Adolph, J. Hambach, C. Hertle et al., Lean 4.0: Durch Digitalisierung die nächste Stufe der Exzellenz erreichen - der Darmstädter Ansatz, Praxishandbuch Industrie 4.0: Branchen - Unternehmen - M&A, 2017. [6] T. Qu, Y. D. Chen, Z. Z. Wang, D. X. Nie, H. Luo, G. Q. Huang, Internet-of-Things-based just-in-time milk-run logistics routing system. IEEE 12th International Conference on Networking, Sensing and Control (2015) 258-263. [7] Y. A. Bozer, D.D. Ciemnoczolowski. "Performance evaluation of small-batch container delivery systems used in lean manufacturing–Part 1: system stability and distribution of container starts." International Journal of Production Research 51 (2013) 555-567. [8] H. Wildemann, A. Niemeyer, Logistikkostensenkung durch auslastungsorientierte Konsolidierungsplanung, PPS-Management 4 (2002) 25-27. [9] S. J. Sadjadi, M, Jafari,, T. Amini, A new mathematical modeling and a genetic algorithm search for milk-run problem (an auto industry supply chain case study), The International Journal of Advanced Manufacturing Technology 44 (2009) 194. [10] M. Knez, B. Gajsek. "Implementation of in-plant milk-run systems for material supply in lean automotive parts manufacturing." International Conference on Logistics and Sustainable Transport. 2015. [11] M. Droste, J. Deuse. A planning approach for in-plant milk-run processes to optimize material provision in assembly systems." Enabling Manufacturing Competitiveness and Economic Sustainability. Springer, Berlin, Heidelberg, (2012) 604-610. [12] J. P. Womack., D. T. Jones, Lean thinking—banish waste and create wealth in your corporation. Journal of the Operational Research Society, 48 (1997) 11481148. [13] K. Kluska, Kamila, P. Pawlewski, The use of simulation in the design of Milk-Run intralogistics systems. IFAC-PapersOnLine 51 (2018) 1428-1433. [14] C. Schulte, "Wege zur Optimierung der supply chain." Auflage, Vahlen-Verlag, München (2005). [15] A. Freitas, "Savings in internal logistics using a RFID-based software system in a lean context." 2017. [16] M. Rieback,, C. Bruno, A.Tanenbaum, The evolution of RFID security, IEEE Pervasive Computing 1 (2006) 62-69. [17] T. Fan, F. Tao, S. Denkg, S. Li, Impact of RFID technology on supply chain decisions with inventory inaccuracies, International Journal of Production Economics 159 (2015) 260-272. [18] K. Finkenzeller, RFID handbook: fundamentals and applications in contactless smart cards, radio frequency identification and near-field communication, 2010. [19] B. Schallok, C. Rybski, R. Jochen, H. Kohl, Learning Factory for Industry 4.0 to provide future skills beyond technical training. Procedia Manufacturing 23 (2018) 27–32. [20] N. Gjeldum, M. Mladineo, M. Crnjc, I. Veza, A. Aljunovic. Performance analysis of the RFIF system for optimal design of intelligent assembly line in the learning factory. Procedia Manufacturing 23 (2018) 63–68. [21] P. E. Hart, N. Nilsson, B. Raphael, A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics 4.2 (1968) 100-107. [22] E. W. Dijkstra, A note on two problems in connexion with graphs. Numerische mathematic, 1 (1959) 269-271. [23] M. Scholz., S. Kreitlein, C. Lehmann, J. Böhner, J. Franke, R. Steinhilper, Integrating Intralogistics into Resource Efficiency Oriented Learning Factories, Procedia CIRP 54 (2016) 239-244.
ScienceDirect ScienceDirect Procedia Manufacturing 00 (2019) 000–000
Available Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com
ScienceDirect ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
Procedia Manufacturing 31 (2019) 175–179 Procedia Manufacturing 00 (2017) 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
9th Conference on Learning Factories 2019
www.elsevier.com/locate/procedia
9th Conference on Learning Factories 2019
Digitalized milk-run system for a learning factory assembly line Digitalized milk-run system for a learning factory assembly linea a a a, a
Sascha Gotthardt , Maria Hulla , Matthias Eder *, Hugo Karre , Christian Ramsauer Manufacturing Engineering Societya International Conference 2017, MESIC 2017, 28-30 a a, a June Sascha Gotthardt , Maria Hulla , Vigo Matthias EderManagement, *, Hugo Karrea, Christian Ramsauer Graz University of Technology, Institute of Innovation and Industrial Kopernikusgasse 24/II, 8010 Graz, Austria 2017, (Pontevedra), Spain a
Graz University of Technology, Institute of Innovation and Industrial Management, Kopernikusgasse 24/II, 8010 Graz, Austria
a
Costing models for capacity optimization in Industry 4.0: Trade-off Abstract between used capacity andas increasing operational efficiency In order to face current trends and challenges in industrial companies such number of variants, shorter product life cycles and higher Abstract
product complexity, production logistics is one of the main areas for optimizing assembly systems. To increase the efficiency of the internal In order to face current and challenges in industrial companies such asItincreasing numberinofliterature variants,that shorter product life cycles and higher material supply, varioustrends lean system such as the milk-run were introduced. has been shown a a,* b bdigitalization technologies can be product complexity, logistics is one of is thecurrently main areas for of optimizing assembly systems. To increase the efficiency the internal used to improve leanproduction systems. Nevertheless, there a lack knowledge about the digitalization of milk-run systems,ofespecially in material factories. supply, various lean system such as the milk-run were introduced. Itsystem has been shown Factory, in literature that digitalization technologies can be learning Therefore, this work is introducing a digitalized milk-run in LEAD the learning factory of Graz University of a University of Minho, 4800-058 Guimarães, Portugal used to improve systems. there is currently a lack of generate knowledge about the adigitalization milk-run systems, Technology. Thislean system relies Nevertheless, on the RFID technology to automatically orders from warehouse toofa workstation. In the especially warehouseina b Unochapecó, 89809-000 Chapecó, SC, BrazilFactory, the learning factory of Graz University of learning factories. Therefore, work introducing a digitalized milk-run system pick-to-light system is used tothis show theislogistics employee on a screen which partsintoLEAD pick. To efficiently deliver the order to the workstation, Technology. Thisissystem reliesusing on the RFIDplanning technology to automatically orders from a warehouse a workstation. the warehouse the shortest path calculated a path algorithm that is usedgenerate in robotics. The paper shows (1)todevelopments ofInmilk-run systems,a pick-to-light system used to show the logistics onFactory a screenand which parts to pick. within To efficiently delivertrainings. the order to the workstation, (2) the concept for a is digitalized milk-run system atemployee the LEAD (3) its application digitalization the shortest path is calculated using a path planning algorithm that is used in robotics. The paper shows (1) developments of milk-run systems, (2) the concept for a digitalized milk-run system at the LEAD Factory and (3) its application within digitalization trainings. Abstract
A. Santana , P. Afonso , A. Zanin , R. Wernke
© Authors. Published Published by by Elsevier Elsevier B.V. B.V. © 2019 2019 The The Authors.
Under theunder concept of "Industry 4.0", production processes willonbe pushed to be increasingly interconnected, Peer review the of committee of the 9th Conference Learning Factories. Peer review theresponsibility responsibility ofthe thescientific scientific © 2019 The under Authors. by Elsevier B.V. committee of the 9th Conference on Learning Factories. information basedPublished on a real time basis and, necessarily, thmuch more efficient. In this context, capacity optimization Peer review under the responsibility thedigital; scientific committee of the 9 factory; Conference on Learning Factories. Keywords: digitalization; milk-run; leanof and production; rfid; learning goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. Indeed, lean management continuous approaches suggest capacity optimization instead of Keywords: digitalization; milk-run; leanand and digital; production;improvement rfid; learning factory; maximization. The study of capacity optimization and costing models is an important research topic that deserves 1. Introduction contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical 1. Introduction model capacity management based on different models (ABC and genericonmodel has beena Everfor fiercer competition in the manufacturing industrycosting is making the elimination of TDABC). non-addingAactivities the shop-floor crucial objective. Production logistics areidle among the most important optimization targets forthe themaximization task of enhancing the performance developed and it was used to analyze capacity and to design strategies towards of organization’s Ever fiercer competition in the manufacturing industry is making the elimination of non-adding activities on the shop-floor a of assembly Since transports are generallyvsregarded as waste, these areisseparated from value-adding activities such as the value. The systems. trade-off capacity maximization operational highlighted and ofit enhancing is shown that capacity crucial objective. Production logistics are among the most importantefficiency optimization targets for the task the performance assembly and amight so-called milk-run or Mizusumashi has also been introduced. [1, 2] Digitalization offers various potentials in the optimization hide operational inefficiency. of assembly systems. Since transports are generally regarded as waste, these are separated from value-adding activities such as the
manufacturing industry in terms by of increasing efficiency, connectivity and transparency [3]. It thus enables lean systems to reach a © 2017 The Published Elsevier B.V. assembly andAuthors. a so-called milk-run or Mizusumashi has also been introduced. [1, 2] Digitalization offers various potentials in the higher level of manufacturing excellence. For instance, real-time data creates new opportunities for existing approaches of lean Peer-review under responsibility of increasing the scientific committee of the Manufacturing Engineering Society International Conference manufacturing industry in terms of efficiency, connectivity and transparency [3]. It thus enables lean systems to reach a production by detecting and solving problems faster [4]. Metternich et al. (2017) proposed that the digitalization of lean production 2017. higher level of manufacturing excellence. For instance, real-time data creates new opportunities for existing approaches of lean should improve lean approaches rather than replace them [5]. Very little literature is currently available on the topic of digitalized production by detecting and solving problems faster [4]. Metternich et al. (2017) proposed that the digitalization of lean production milk-run systems withABC; a focus on learning factories. A possible application of a milk-run system is to deliver orders just-in-time as Keywords: Cost Models; TDABC; Capacity Management; Idle Capacity; should improve lean approaches rather than replace them [5]. Very littleOperational literature Efficiency is currently available on the topic of digitalized shown by Qu et al. [6]. milk-run systems with a focus on learning factories. A possible application of a milk-run system is to deliver orders just-in-time as shown by Qu et al. [6]. The aim of this paper is to introduce a digitalized milk-run system in the learning factory of the Graz University of Technology. 1. Introduction First, the IIM LEAD Factory will be introduced. Secondly, the theoretical basis (in-plant) milk-run systems will be discussed and The aim of this paper is to introduce a digitalized milk-run system in the learning factory of the Graz University of Technology. an overview of the development on (digitalized) milk-run systems will be given. As the milk-run system relies on the Radio First, thecost IIMof LEAD Factory will introduced. Secondly, the for theoretical basisand (in-plant) milk-run systems will be discussed and The idle capacity is abe fundamental information companies their management of extreme importance an modern overviewproduction of the development on general, (digitalized) milk-run as systems be given. As the milk-run system onmeasured the Radio in systems. In it is defined unusedwill capacity or production potential andrelies can be
in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity Tel.: +43 873 - fax: 7792;+351 fax: +43 873 - 7791. * Corresponding Paulo Afonso.author. Tel.: +351 253(316) 510 761; 253 (0) 604316 741 E-mail E-mailaddress: address:[email protected] [email protected]
* Corresponding author. Tel.: +43 (316) 873 - 7792; fax: +43 (0) 316 873 - 7791. E-mail address: [email protected] 2351-9789 © 2019 The Authors. Published by Elsevier B.V. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. Peer-review responsibility of the scientific of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © under 2019 The Authors. Published by Elseviercommittee B.V. 2351-9789 © 2019 The Authors. Published Elsevier B.V. Peer review under the responsibility of the scientific by committee of the 9th Conference on Learning Factories.
Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. 10.1016/j.promfg.2019.03.028
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Frequency Identification (RFID), the basic functionality of this technology is described. The core chapter of this paper deals with the implementation of a digitalized milk-run system in the LEAD Factory and the implication of this for the didactical concept of the learning factory will be elaborated.
Fig. 1. Assembly line of the LEAD Factory.
2. IIM LEAD Factory The LEAD Factory is operated by the Institute of Innovation and Industrial Management at the Graz University of Technology. It facilitates hands-on training in an environment close to industrial reality in the areas of lean management, energy efficiency, agility and digitalization (LEAD). The didactical concept in this learning factory is based on three different setup states: 1) the nonoptimized state, which is characterized by inefficient work flows, no levelled work load, unclear instructions for the training participants, insufficient tools etc.; 2) the lean state, where basic lean principles such as 5S, flow principle, value stream mapping, seven types of waste, Heijunka and Kanban are implemented and 3) the digitalized state, in which many technologies and assistance systems are in operation. In the LEAD factory, RFID tags are integrated together with a production control system that keeps track of the production progress. Also, a camera detects the current process step at a workstation by capturing the position of the part and changes the standard operating procedure accordingly. Moreover, a system to control the standard operating procedure (SOP) by capturing the gestures or mimics of the user is used. A smart Andon system that automatically recognizes if there is a time delay in the process is also installed. It signals the delay to a central control station so that everyone who is interested in a smooth production flow is immediately informed about the problem can work on a solving the problem as quickly as possible. During training, participants assemble the 60-parts TUG Scooter in an optimized U-cell shaped assembly line (Fig. 1). The majority of more than 450 trainings have been performed on lean management until today, but the institute has also offered the possibility since summer 2018, to experience the digitalized state of the LEAD Factory which is constantly trying to reach new milestones in implementing the state of the art technologies. 3. Milk-run systems There are several ways to design and organize logistics systems. Because of many benefits such as reducing logistics costs, a milk-run system is preferably used in lean production systems. Milk-run systems are defined as “route-based, cyclic material handling systems that are used widely to enable frequent and consistent deliveries of containerized pats on an as-needed basis from a ventral storage area (supermarket) to multiple line-side deposit points on the factory floor” [7]. Milk-run systems rely on the idea that only material which has been consumed is replenished. The lot size is determined once and can be controlled via Kanban cards. The cycle time and route through the plant are defined in advance [8]. This system originates from the traditional system for selling milk in which the milkman used to go from one customer door to the next as milk bottles were delivered and the empty bottles collected with the dray following a specified route [9]. Two categories of milk-run systems are discussed in literature – the external and the internal (in-plant) milk-run logistic system. This paper focuses on the internal milk-run system for the delivery of material within a plant or assembly line and this will therefore be described in further detail. In-plant milk-run systems were first utilized by automotive companies where a train or manual cart took pins filled with parts from the inventory (supermarket) to the stations in a predetermined route and schedule [10]. Consequently, several stations could be supplied by the same train within the same route and the same service period [11]. In-plant milk-run systems support lean manufacturing by reducing the seven types of waste, mainly the waste of transportation, the waste of waiting time and the waste from inventory [12]. Since the milk-run can transport larger quantities of material than the individual employee with the appropriate
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means of transport, it can supply several workstations simultaneously. The system thus minimized the total transportation distance thereby making transport more efficient and reducing costs [11]. At the same time, the rhythmic supply leads to a reduction in stocks, since it is no longer necessary to compensate for short-term fluctuations by means of buffers on the line [13]. Another advantage of in-plant milk-run systems is the more accurate goods delivery with just-in-time systems. The availability of the right number of material maximizes the efficiency of continuous flow systems [14]. 4. Radio frequency identification RFID is a transmitter-receiver technology designed to identify and locate an object automatically and contactless using radio signals. RFID is a low cost and effective technology only using a tag (chip connected to an antenna) attached to an item, specific embedded information and a reader, which constantly emits electromagnetic waves in a given frequency and amplitude powering the tag and simultaneously reading its information. [15]. A significantly improved performance compared to classic barcodes is achieved with modern RFID chip design, since it is possible to store up to 1000-fold data in the same storage space. Additionally, susceptibility to failure is decreased while offering a readability over an increased distance [16]. This and many other features of RFID have made it a widely-used key technology in the internet of things, offering real-time updates and increased traceability of objects and machinery in state of the art industrial applications [17]. The software of the reader, which manages the entire reading process, and a RFID middleware with interfaces to other IT systems and databases [18]. In learning factories RFID is used mainly for tracking and tracing of parts and products as well as the enables manufacturing execution systems [19, 20]. 5. Digitalized milk-run system in the LEAD Factory The production control in the LEAD Factory is accomplished via a RFID system. On the basis of this system, it is known which employee is working on which product at which workstation at any given time. Fig. 2 illustrates a schematic presentation of the LEAD Factory including RFID tags and antennas. On workstations 1 to 4 the product (TUG Scooter) is assembled and on workstation 5 the scooter is packaged. RFID transponders are attached to the product, to the worker cards and to the boxes which contain parts for the assembly of the product that are stored in the material shelves at every work station as well as in the supermarket. There is a RFID receiver at each workstation, which can identify both the product at the station and the worker assembling the product. Moreover, using the RFID tag on the boxes it can be signaled that the workstation is running out of parts and parts need to be transported from the supermarket to the workstation with the milk-run wagon. The unique identification number of the RFID transponders is stored in a central database. As a result, a complete observation of the process is possible. In addition, the RFID system also takes over the recording of defective parts in the production line. For each group of parts there is a RFID chip, which when pulled over the antenna tells the system that there is a problem with the part. This allows the system to generate statistical data about the reject rate and hence also about the material quality.
Fig. 2. RFID in the LEAD Factory.
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5.1. Implementation of a digitalized milk-run system The basic concept of the digitalized milk-run system was implemented in the LEAD Factory during a six-month project. Each box containing parts for assembly of the LEAD Factory product (the TUG Scooter) is equipped with a RFID chip. If all parts in the box are consumed, it is pushed under the workstation by the employee so that it can be taken along during the next supply run. In the digitalized milk-run concept a RFID antenna under the workstation reads the chip and immediately forwards the data to a SQL database where data such as the position of the RFID transponder at a specific time is stored. Every few seconds, the central control station in the warehouse queries the main server for new orders. If an empty box is under the workstation, a new order is generated and forwarded to the control station. Here, the order is immediately shown on a screen to the logistics employee. Every part in a shelf is stored in the database. Thus, the control station does know where the part can be found and thanks to a pick-tolight system, which is a visual indicator of where to find the part, the logistics employee can immediately see where the required part is on the shelf and can thus pick it faster. A visualization example of the control station when there is a lack of black wheels on a workstation is illustrated in Fig. 3a. The highlighted position of the order in the upper right corner implies that the part is on the shelf also in the upper right corner. Thus, it is once more an indication of the real position on the shelf. The number “3” tells the logistics employee to pick three pieces of the part. The limitation for picked orders in the LEAD Factory is five due to the fact that the LEAD Factory is relatively compact in size. Additionally, it is a good opportunity for the training participants to use up all five levels on the supply wagon separately for each order. In previous trainings the authors could see that only the top level of the supply wagon was used by the participants, because it is in a comfortable height and offers an open space for putting boxes. The design of a better supply wagon should therefore also be considered and to do so, we need data on the usage of all five levels. After picking all parts for the open orders, the logistics employee confirms the task to be done and the optimal route through the factory for the supply run appears on the screen (Fig. 3b). The worker can thus be back at the control station and ready top pick the next orders in the shortest time possible. The algorithm which is used to find the shortest route is named A* (A-star) and was published in 1968 by Peter Hart et al. [21] It is based on the 1959 published algorithm by Edsger Dijkstra [22]. The advantage of A* is that it assumes the shortest path between a starting point and a destination point must be a direct one and therefore does not search into the broad range of possibilities but tries to get as near as possible to the goal as fast as possible without knowing the path ahead. Only when the algorithm is no longer able to make direct progress is an alternative path calculated in depth. This algorithm is mainly used in robotics, where it is particularly important for a robot to find the right path quickly. In Fig. 3b an example of an optimal route from the warehouse to the workstation where the order is needed is shown. Light grey fields indicate the walkway. Other colors indicate different functions, the warehouse is indicated in blue, the five workstations (WP) are illustrated in green, and the logistics employee with the milk-run wagon is indicated in red. The layout of the plant can also be visualized as a graph where the obstacles are nodes and the path between nodes is a weighted edge. We decided to use a grid view instead, however, in order to provide an easier to grasp and give a more comprehensive visualization. The complete implementation part of the project was done in C# 6.0 and was developed on a 64bit Windows 8.1 Enterprise edition. The project runs on an Intel Core i55200U CPU @ 2.20GHz and 8GB of RAM.
Fig. 3. (a) Example screen for a picking station; (b) Visualization of the route for the milk-run driver
5.2. Implications for the integration in the didactical concept of the LEAD Factory Prior to the implementation of the digitalized system, the worker had to walk around the production line periodically and search for empty boxes under the workstations. If an empty box was found, the worker had to go back to the warehouse and use it for picking the new parts and bringing them back to the workstation. The implementation of the digitalized system improves the performance of the milk-run system in two ways. The first reason for this is that the logistics employee no longer has to walk the route periodically as before, the employee can now wait for orders at the central control station in the warehouse instead. This
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increases the probability that the order will be processed immediately by the logistics employee and no time is wasted. In addition, the delivery time is reduced by half, since the empty boxes no longer have to be picked up at the workplace beforehand, but only need to be replaced with full boxes. Secondly, empty runs are eliminated completely in the process which saves a lot of unnecessarily wasted time. As a result, the training participants learn how to use digitalization in the context of production effectively and what the advantages of such systems are in the industry. 6. Outlook The introduction of a digitalized milk-run system has been presented in this paper. The proposed system uses RFID to realize an automatic order generation for the supermarket where pick-to-light technology supports the employee by showing the position of the requested spare part in the warehouse. Moreover, it is now possible to calculate the shortest routes for the milk-run and therefore also to reduce both transport times and transport costs. For the non-digitalized process in which the milk-run starts at the warehouse, the process of collecting the empty boxes from the workstation, picking the parts at the supermarket and transporting the boxes back to the workstations took an average of 66.6 seconds. On introduction of the digitalized milk-run system, the total time for delivering spare parts took 30.2 seconds. Further possibilities exist to make the milk-run system even more efficient. The picking process could be improved by putting sensors on the pick-to-light bar that can detect how many parts have been removed from the warehouse and whether the parts have been removed from the correct shelf. In addition, current technology offers the possibility of digitally reproducing the entire milk-run and using a simulator to anticipate future orders, so that the control station will know which order is likely to be the next one even before the employee at the workplace knows this. In addition, it is already possible to replace the logistics employees entirely with automated machines. Both the picking process and the delivery of the material are already fully automated in many factories today. In the future, this backlog can also be eliminated in learning factories [23] for the purpose of showing training participants how automated machines that operate 24/7 can cut costs in a factory. References [1] D. T. Jones, D. Roos, Die zweite Revolution in der Autoindustrie, Konsequenzen aus der weltweiten Studie des Massachusetts Institute of Technology. Frankfurt/Main. Campus (1991) 53. [2] H. Taked, Das Synchrone Produktionssystem: Just in Time - Für Das Ganze Unternehmen, Verl. 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