Evaluating the contribution of in-line metrology to mitigate bullwhip effect in internal supply chains

Information Control Problems in Manufacturing Proceedings,16th IFAC Symposium on Proceedings,16th IFAC Symposium on Bergamo, Italy, June 11-13, 2018 Information Control Problems Proceedings,16th IFAC Symposium on Available online at www.sciencedirect.com Information Control Problems in in Manufacturing Manufacturing Bergamo, Italy, June 11-13, 2018 Proceedings,16th IFAC Symposium on Information Control Problems in Manufacturing Bergamo, Italy, June 11-13, 2018 Information Control in Manufacturing Bergamo, Italy, JuneProblems 11-13, 2018 Bergamo, Italy, June 11-13, 2018

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IFAC PapersOnLine 51-11 (2018) 1714–1719 to mitigate bullwhip effect in Evaluating the contribution of in-line metrology Evaluating the contribution of in-line metrology Evaluating the contributioninternal of in-linesupply metrology to mitigate mitigate bullwhip bullwhip effect effect in in chainsto Evaluating the contributioninternal of in-linesupply metrology to mitigate bullwhip effect in chains chainsto mitigate bullwhip effect in Evaluating the contributioninternal of in-linesupply metrology internal supply chains Lucia Baur*, Enzo M. Frazzon** internal supply chains  Lucia Baur*, Enzo M. Frazzon**

Lucia Baur*, Enzo M. Frazzon**  Baur*, Enzo M.(KIT), Frazzon** **Karlsruhe Lucia Institute of Technology Karlsruhe, 76133, Lucia Baur*, Enzo M.(KIT), Frazzon**  e-mail: **Karlsruhe Institute of Technology Karlsruhe, Germany (Tel: +49 176 20234071; [email protected]). **Karlsruhe Institute of Technology (KIT), Karlsruhe, 76133, 76133,  **Karlsruhe Institute of Technology (KIT), Karlsruhe, 76133, (Tel: +49 176 20234071; e-mail: [email protected]). **Dept. of Germany Industrial and Systems Engineering, Federal University of Santa Catarina (UFSC), Germany (Tel: +49 176 20234071; e-mail: [email protected]). **Karlsruhe Institute of Technology (KIT), Karlsruhe, 76133, Germany (Tel: +49 176 20234071; e-mail: [email protected]). **Dept. of Industrial and Systems Engineering, Federal University of Santa Florianópolis, SC 88040-900, Brazil (e-mail: [email protected]) **Dept. of Industrial and Systems Engineering, Federal University of Santa Catarina Catarina (UFSC), (UFSC), (Tel: +49 176 20234071; e-mail: [email protected]). **Dept. of Germany Industrial and Systems Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC 88040-900, Brazil (e-mail: [email protected]) Florianópolis, SC 88040-900, Brazil (e-mail: [email protected]) **Dept. of Industrial and Systems Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC 88040-900, Brazil (e-mail: [email protected]) Florianópolis, 88040-900, Brazil to (e-mail: Abstract: Advances in metrologySC show great potential [email protected]) improvements in quality control within Abstract: Advances in metrology show great potential potential toon support improvements in processes quality control control withina productionAdvances systems. in The analysis show of referred advancesto integrated productionin embodies Abstract: metrology great support improvements quality within Abstract: Advances in metrology show great potential to support improvements in quality control within production systems. The analysis of referred advances on integrated production processes embodies relevant scientific and praxis-oriented interest. In order to analyse the potential impact of new in-line production systems. The analysis of referred advances on integrated production processes embodies aa Abstract:scientific Advances in metrology show great potential toon support improvements inimpact qualitywas control withina production systems.and The analysis ofofreferred advances processes embodies relevant and praxis-oriented interest. Ina order order to analyse theproduction potential of new in-line metrology solutions on internal flow materials, model ofintegrated an automotive manufacturer developed. relevant scientific praxis-oriented interest. In to analyse the potential impact of new in-line production systems. The analysis of referred advances on integrated production processes embodies relevant scientific praxis-oriented Inaa order toof the potential impact of new in-line metrology solutions on internal flowcurrent of interest. materials, model of analyse an automotive automotive manufacturer was developed. By varying demandand andinternal comparing and future behaviour, possible benefits of implementing newa metrology solutions on flow of materials, model an manufacturer was developed. relevant scientific and praxis-oriented interest. In order to analyse the potential impact of new in-line metrology internal flowcurrent of materials, a model of an automotive manufacturer wasbe developed. By varying varyingsolutions demand on and comparing current andanalysis future behaviour, possible benefits of implementing implementing new solutions were identified. Theand substantiated that benefits lead time can reduced By demand and comparing future behaviour, possible of new metrology internal flowcurrent ofand materials, a model ofidentified. an automotive manufacturer was developed. By varyingsolutions demand and comparing andanalysis future behaviour, possible benefits of implementing new metrology solutions were identified. The analysis substantiated that lead time time can be reduced significantly, output on can be increased bottlenecks better Metrology solutions support wellmetrology solutions were identified. The substantiated that lead can be reduced By varying countermeasures demand comparing current andanalysis futurebetter behaviour, possible benefits ofsuch implementing new metrology solutions were identified. substantiated that lead time can be bullwhip reduced significantly, output and can be increased and bottlenecks identified. Metrology solutions support wellestablished to reduce theThe undesirable effects of quality oscillations, assupport the significantly, output can be increased and bottlenecks better identified. Metrology solutions wellmetrology solutions were identified. The analysisbetter substantiated that lead time can be bullwhip reduced significantly, output can be increased and bottlenecks identified. Metrology solutions support wellestablished countermeasures to reduce the undesirable effects of quality oscillations, such as the effect also within a single company. established countermeasures to reduce the undesirable effects of quality oscillations, such as the bullwhip significantly, output can becompany. increased solutions wellestablished countermeasures to reduceand the bottlenecks undesirable better effectsidentified. of qualityMetrology oscillations, such assupport the bullwhip effect also also within within single effect aa single company. © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Manufacturing system, Automobile Industry, In-line metrology; Quality management; established countermeasures to reduce the undesirable effects of quality oscillations, such as the bullwhip effect also within a single company. Keywords: Manufacturing system, Industry, Integrated Internal supply Automobile chains. effect also production; within a single company. Keywords: Manufacturing system, Automobile Industry, In-line In-line metrology; metrology; Quality Quality management; management; Keywords: Manufacturing system, Automobile Industry, In-line metrology; Quality management; Integrated production; Internal supply chains. Integrated production; Internal supply chains.  Industry, In-line metrology; Quality management; Keywords:production; Manufacturing system, Integrated Internal supply Automobile chains. Management (SCM). Within this field modelling is a  Integrated1.production; Internal supply chains. INTRODUCTION Management (SCM). this field modelling is common technique understand dynamics  Management (SCM).to Within Within this its fieldinherent modelling is aa 1. INTRODUCTION  1. INTRODUCTION Management (SCM). Within this field modelling is a common technique to understand its inherent dynamics (Aslam, Ng, and Karlsson 2014). Each simulation emerges Throughout the last decades variation and upstream- common technique to understand its inherent dynamics 1. INTRODUCTION Management (SCM). Within this field modelling is bya common technique to understand its inherent dynamics (Aslam, Ng, and Karlsson 2014). Each simulation emerges from an existing mental model and aims to change it Throughout last decades and upstreamamplification of material flows – variation the bullwhip - have (Aslam, Ng, and Karlsson 2014). Each simulation emerges 1. INTRODUCTION Throughout the the last decades variation andeffect upstreamcommon technique to understand its inherent dynamics (Aslam, and Karlsson 2014). Each simulation from existing mental model and aims to it creating new valuable insights (Größler, Thun, and emerges Milling Throughout last Various decades andeffect upstreamamplification of material flows –– variation the -- have been widely the studied. originating from an an Ng, existing mental model and aims to change change it by by amplification of material flows countermeasures the bullwhip bullwhip effect have (Aslam, Ng, and Karlsson 2014). Each simulation emerges from an existing mental model and aims to change it by Throughout the last Various decades variation andeffect upstreamcreating new valuable insights (Größler, Thun, and Milling 2008). To achieve this goal, first a suitable model has to be amplification of material flows – the bullwhip have been widely studied. countermeasures originating from the pioneer work by Burbidge (1961) were applied and new valuable insights (Größler, Thun, and Milling been widely studied. Various countermeasures originating creating from anTonew existing mental model and aims to change it are by creating valuable insights (Größler, Thun, and Milling amplification of material flows – the(1961) bullwhip effect - have 2008). achieve this goal, first a suitable model has to developed and validated before then various scenarios been widely studied. Various countermeasures originating from the pioneer work by Burbidge were applied and were able to reduce these effects. Most authors focus on 2008). To achieve this goal, first a suitable model has to be be from the pioneer work by Burbidge (1961) were applied and creating valuable Thun, and Milling 2008). achieve this goal, first(Größler, a suitable model has to be developed and before then various scenarios are been the widely studied. Various countermeasures originating created To tonew examine the insights model’s behaviour. These four steps from pioneer work by Burbidge (1961) were applied and were able to reduce these effects. Most authors focus on inter-company (e.g. the famous beer game) rather than and validated validated before then various scenarios are were able to reduce these effects. Most authors focus on developed 2008). To achieve this first athis suitable model has to be developed andwithin validated before then various scenarios are from the pioneer work byflows, Burbidge (1961) were applied and created to examine the model’s These four steps are conducted thegoal, course ofbehaviour. article. were able to reduce these effects. Most authors focus on inter-company (e.g. the famous beer game) rather than intra-company material but industry cases such as created to examine the model’s behaviour. These four steps inter-company (e.g. the famous beer game) rather than on developed and validated before then various scenarios are created to examine the model’s behaviour. These four steps were able to reduce these effects. Most authors focus on are conducted within the course of this article. inter-company (e.g. the(1999) famousand beer game) rather than the on intra-company material flows, but industry cases such as presented by Taylor Klug (2013) conducted within thethe course of this article. intra-company material flows, but industry casesshow such as are In the to next chapter current state-of-the-art both in created examine the These four steps are conducted within themodel’s course ofbehaviour. this article. inter-company (e.g. the(1999) famous beer game) rather than on intra-company material flows, but industry cases such as presented by Taylor and Klug (2013) show the relevance of the topic within one company as well. Causes presented by Taylor (1999) and Klug (2013) show the bullwhip In the next chapter the current state-of-the-art both effect research and metrology technology is are conducted within the course of this article. intra-company material flows, intra-company butcompany industry cases such as In the next chapter the current state-of-the-art both in in presented bythe Taylor (1999) and Klug (2013) show the relevance of topic within as well. Causes and countermeasures flow relevance of the topic against within one one company asmaterial well. Causes In the next chapter the current state-of-the-art both in bullwhip effect research and metrology technology presented. It is followed by the development of the presented bythe Taylor (1999) and Klugto (2013) show the bullwhip effect research and metrology technology is is relevance of topic against withinare one company asmaterial well. Causes and intra-company flow variations and amplifications similar those known from In the nextmodel, chapter the current state-of-the-art both in and countermeasures countermeasures against intra-company material flow bullwhip and technology is presented. It followed by the development of the conceptual presentation the real case selected relevance of the topic against within one company asWhang well. Causes presented. effect It is is research followed byof metrology the development of and the and countermeasures intra-company material flow variations and amplifications are similar to those known from inter-company relations (Klug 2013). Lee and (2006) effect research and metrology technology is variations and amplifications are similar to those known from bullwhip presented. It is followed by the development of the conceptual model, presentation of the real case selected and information on the data used for modelling. In this step also and countermeasures against intra-company material flow conceptual model, presentation of the real case selected anda variations and are similar those known from inter-company relations (Klug 2013). Lee and Whang emphasize theamplifications importance further examine this (2006) intrapresented. It is followed by the development of the inter-company relations (Klugto 2013). Leeto and Whang (2006) presentation the real case selected and information on used for modelling. In this step validation of the data model conducted. theaa variationsview, and amplifications are further similar to those known from conceptual informationmodel, on the the data used is for of modelling. InAfterwards this step also also inter-company (Klug Lee and Whang emphasize the importance to examine this intracompany as the usual aggregation process for the(2006) intermodel, presentation of the real case selected and emphasize the relations importance to2013). further examine this intra- conceptual information on the data used for modelling. In this step also validation of the model is conducted. Afterwards simulation results obtained are presented and interpreted with inter-company relations (Klug 2013). Lee and Whang (2006) of the model is conducted. Afterwards the thea emphasize the as importance to further examine intra- validation company the usual aggregation process for the material flow implies information loss regarding information onpossible the data usedare forpresented modelling. InAfterwards this technology step also company view, view, as the usual aggregation process forthis the interintervalidation of theobtained model is thea simulation results and interpreted with respect to the impact ofconducted. new metrology emphasize the importance to further examine this intrasimulation results obtained are presented and interpreted with company view, as the usual aggregation process for regarding the inter- validation of the model is conducted. Afterwards the material flow information context behaviour on implies intra-company level. loss companyand material flow implies information loss regarding simulation results obtained are presented and interpreted with respect to the possible impact of new metrology technology as a countermeasure for the internal bullwhip effect. A final company view, as the usual aggregation process for regarding the inter- respect to the possible impact of new metrology technology companyand flow information loss context behaviour on intra-company level. simulation results are presented and interpreted with context andmaterial behaviour on implies intra-company level. systems to the of newbullwhip metrology technology as for the A evaluation of possible theobtained approach applied and aneffect. outlook on The further development of cyber-physical (CPS) respect company material flow implies information loss regarding as aa countermeasure countermeasure forimpact the internal internal bullwhip effect. A final final context and behaviour on intra-company level. respect to the possible impact of new metrology technology as a countermeasure for the internal bullwhip effect. A final evaluation of the approach applied and an outlook remaining challenges paves the way for further research. The further development of systems (CPS) and cyber-physical production context and behaviour on intra-company level.systems of the approach applied and an outlook on on The specifically further development of cyber-physical cyber-physical systems(CPPS) (CPS) evaluation as a countermeasure for thethe internal bullwhip A final evaluation of the approach applied and aneffect. outlook on remaining challenges paves way for further research. The further development of cyber-physical systems (CPS) and specifically cyber-physical production systems (CPPS) will surely support the traditional countermeasures (Drossel remaining challenges paves the way for further research. and specifically cyber-physical production systems (CPPS) evaluation of the approach applied and an outlook on remaining challenges paves the way for further research. The further development of cyber-physical systems(Drossel (CPS) 2. THEORETICAL FOUNDATION and specifically cyber-physical production systems (CPPS) will surely support the traditional countermeasures et al. 2016). One of their core characteristics, immediate data will surely support the traditional countermeasures (Drossel remaining challenges paves the way for further research. and specifically cyber-physical production systems (CPPS) 2. will support countermeasures (Drossel et 2016). One of their core immediate data processing are necessary prerequisites 2. THEORETICAL THEORETICAL FOUNDATION FOUNDATION et al. al.surely 2016).and Oneauto-adjustment, of the theirtraditional core characteristics, characteristics, immediate data will surely support the traditional countermeasures (Drossel 2. THEORETICAL FOUNDATION et al. 2016). One of their core characteristics, immediate data processing and auto-adjustment, are necessary prerequisites for information sharing – the most studied countermeasure effect in general and specifically in internal processing and auto-adjustment, are necessary prerequisites 2.1 The bullwhip 2. THEORETICAL FOUNDATION et al.information 2016). Oneauto-adjustment, of their core characteristics, immediate data processing and are necessary prerequisites for sharing – the most studied countermeasure against the bullwhip effect (Wang and Disney 2016) and both The bullwhip effect in general and reverse supply chains for information sharing – the most studied countermeasure 2.1 2.1 The bullwhip effect in general and and specifically specifically in in internal internal processing andif auto-adjustment, are necessary prerequisites for information sharing the most studied against the effect (Wang and Disney 2016) both only possible, there is a– suitable metrology solution. Lately bullwhip effect in general and specifically in internal and reverse supply chains against the bullwhip bullwhip effect (Wang and Disneycountermeasure 2016) and and both 2.1 The and reverse supply chains for information sharing – the most studied countermeasure against the bullwhip (Wang 2016) and both The only possible, if is metrology solution. Lately technological developments in thisand areaDisney have been impressive 2.1 The bullwhip effectdescribes in general specificallywhere in internal and reverseeffect supply chains bullwhip a and ‘phenomenon order only possible, if there thereeffect is aa suitable suitable metrology solution. Lately against theimpact bullwhip effect (Wang and Disney 2016) and botha only possible, if there is a suitable metrology solution. Lately technological developments in this area have been impressive and their on integrated production systems within and reverse supply chains The bullwhip effect describes a ‘phenomenon where variability increases as the orders move upstream the technological developments in this area have been impressive The bullwhip effect describes a ‘phenomenon whereinorder order only their possible, if there is a suitable metrology solution. Latelya technological developments in this area have been impressive and impact on integrated production systems within company has not been studied profoundly so far. The bullwhip effect describes a ‘phenomenon where order variability increases as the orders move upstream in supply chain’(SC) (Wang and Disney 2016). First described and their impact on integrated production systems within a variability increases as the orders move upstream in the the technological developments in profoundly this area have been impressive and their has impact on integrated production systems within a The company not been studied so far. bullwhip effect describes a ‘phenomenon where variability increases as the orders upstream inorder the supply chain’(SC) (Wang and Disney 2016). First described in macroeconomic literature almost amove century ago, it gained company has not been studied profoundly sosolutions far. supply chain’(SC) (Wang and Disney 2016). First described Studying the impact of new metrology on the and their has impact on integrated productionsosystems within a variability increases as the orders move upstream in the company not been studied profoundly far. supply chain’(SC) (Wang and Disney 2016). First described in macroeconomic literature almost a century ago, it gained immense popularity in Operations Management in the 80ies in macroeconomic literature almost a century ago, it gained Studying the impact new metrology the bullwhip within aof is a matter of Supplyon Chain company has been studied sosolutions far. Studying effect the not impact ofcompany newprofoundly metrology solutions on the immense supply chain’(SC) (Wang andalmost Disney 2016).ofFirst described in macroeconomic literature a century ago, it gained popularity in Operations Management in 80ies (Adenso-Díaz et al. Main causes the effect as immense popularity in2012). Operations Management in the the 80ies Studying the impact of new metrology solutions on the bullwhip effect within a company is a matter of Supply Chain bullwhip effect within a company is a matter of Supply Chain immense in macroeconomic literature almost a century ago, it gained popularity in Operations Management in the 80ies (Adenso-Díaz et al. 2012). Main causes of the effect Studying the impact of new metrology solutions on the et al. 2012). Main causes of the effect as as bullwhip effect within a company is a matter of Supply Chain (Adenso-Díaz immense popularity in Operations Management in the 80ies Copyright 2018within IFAC a company is a matter of Supply Chain1774(Adenso-Díaz et al. 2012). Main causes of the effect as bullwhip © effect 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier rightsMain reserved. (Adenso-Díaz et Ltd. al. All 2012). causes of the effect as Copyright © 2018 IFAC 1774 Copyright 2018 responsibility IFAC 1774Control. Peer review©under of International Federation of Automatic Copyright © 2018 IFAC 1774 10.1016/j.ifacol.2018.08.209 Copyright © 2018 IFAC 1774

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described by Lee, Padmanabhan, and Whang (1997) are: ‚demand signal processing, rationing game, order batching, and price variations’. Depending on their research focus other researchers have identified additional causes, for example unpredictability of demand and lead time (Wang and Disney 2016). Overall great consensus on the causes of the bullwhip effect can be observed. Countermeasures as lead time reduction, special purchase contracts to stabilize prices and shared capacity to avoid shortage gaming (Lee, Padmanabhan, and Whang 1997) target them. Throughout the last years information sharing in SCs has been the most studied approach, but still the bullwhip effect was not completely eliminated by this countermeasure (Coppini et al. 2010; Wang and Disney 2016). Throughout the years the field itself broadened and further SC issues, such as reverse logistics, were also investigated and evidence on the existence of the bullwhip effect within them was found, even though being less intense than in regular SCs. But this branch of research stayed relatively small and requires further attention (Adenso-Díaz et al. 2012; Cardoso, Barbosa-Póvoa, and Relvas 2013). The traditional model of a SC as a connection of several independent actors has also been challenged and the bullwhip effect was observed within a single company consisting of several workstations (Klug 2013; Taylor 1999; Wangphanich, Kara, and Kayis 2010). Depending on a company’s internal structure for example price variations as part of internal billing systems and competing use of resources as rationing situation might increase the bullwhip effect. Klug (2013) named this ‘dynamic distortion and amplification of material demand […] along the in-house SC, starting from the workplace and point of material use within the factory’ the internal bullwhip effect. Despite extensive research effort practitioners across industries still experience the bullwhip effect, which justifies further research on the topic (Wangphanich, Kara, and Kayis 2010). In the course of extensive research a broad set of methodologies was applied and categorized: empirical, experimental, analytical and – as applied in this article - simulation-based approaches (Wang and Disney 2016). The relatively small and interdisciplinary field of Operations Management, which incorporates SCM, often ‘imports’ methodologies from other fields of research. Amundson (1998) formulated four criteria to ensure the applicability of an external theory in Operations Management. A in depth evaluation would exceed the scope of this article but in accordance with Größler, Thun, and Milling (2008) a fit for the application of a simulation-based approach can be assumed. Indeed the application of simplified SC models for investigations has a rich tradition in the field (Wang and Disney 2016). One of the most famous models is the beer game, first proposed by Sterman in 1989 and afterwards applied both in traditional SCs (e.g. Aslam, Ng, and Karlsson 2014; Coppini et al. 2010) and in reverse SCs (e.g. AdensoDíaz et al. 2012; Cardoso, Barbosa-Póvoa, and Relvas 2013). Modelling and simulation enable researchers to ‘describe, explain and predict the behaviour of a system’ (Wangphanich, Kara, and Kayis 2010) within SCM.

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Therefore, this approach will also be used to address the challenge elaborated in this article: This article applies a simulation-based approach to SCs and reverse SCs within one company. As ideal subject for the model a company with a step-by-step production process and additional high cost pressure leading to the need for highest efficiency (Govindan 2015) was identified: automotive manufacturing. This sector has always been closely linked to metrology and ‘any progress in the metrology is considered a major cause of actual progress in the automotive industry and vice versa’ (Ali 2017). Its challenge in manufacturing are the large parts to be measured, such as entire car bodies. Ideally this should occur within a very short time period, highest precision and – to enable a CPPS – within the production line or so called ‘in-line’ (Kunzmann et al. 2005). In-line measurement of entire car bodies is a recent development, which could revolutionize quality control in the automotive sector and have an impact on the entire production system. This article studies the impact of metrology innovation on an internal supply chain, here also called production system, of an automotive company and its ability to reduce the bullwhip effect. The following chapter provides a brief introduction to metrology in the automotive industry. 2.2 Overview of current metrology solutions Metrology is an interdisciplinary knowledge area applying science’s progress in an advanced engineering context. A note to the International Organisation for Standardisation (ISO)’s definition of metrology highlights its most important characteristics: ‘Metrology includes all aspects, both theoretical and practical, with reference to measurements, whatever their level of accuracy and in whatever fields of science and technology they occur’ (Clifford 1985). Metrology solutions enable Quality Management (QM) to obtain the information needed to perform quality control and assurance (Bauer et al. 2015; Weckenmann, Kraemer, and Hoffmann 2007). This data is a core element of CPPS and therefore will even gain further importance in the near future (Imkamp et al. 2016). Considering that this is a way to reduce production costs, augment productivity, enable future production technologies and supporting environment-friendly production, it is not surprising that manufacturers drive the development of metrology solutions (Bauer et al. 2015). Metrology will not only be necessary for Industry 4.0 but is a prerequisite for it (Imkamp et al. 2016). The main objective of metrology is to ensure ‘the function of the assembly independently from the choice of the individual parts’ (Weckenmann, Kraemer, and Hoffmann 2007). Various technical setups can lead to this result and the most common ones in automotive industry can be divided into two groups: Coordinate Measuring (Coordinate Measuring Machines (CMMs)) and Optical Scanners (3D Laser Scanners, White-light Scanners and Laser Trackers). Modern metrology solutions in automotive are often not a single application but hybrids, combining the advantages of various approaches (Ali 2017).

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Traditionally CMMs have been widely used in QM in manufacturing but around the turn of the millennium improved optical scanners came onto the agenda and challenged the decade-long dominance of CMMs (Govindan 2015). Car-Body-Inspection with CMMs could only be conducted in a specific metrology lab and was timeconsuming. In-line metrology existed only for smaller parts, but large items as complete Body-In-Whites with small tolerances had to be treated by sample inspection only. This met no longer the needs of QM in a highly competitive, highspeed and high-precision automotive plant – ‘rigorous use of metrology in the production line has become essential to fulfil the quality requirements’ (Kunzmann et al. 2005). These in-line metrology solutions are 3D Laser Scanner based and often include CMM-elements, making them hybrid solutions. The major breakthrough achieved in the last years was the augmented speed and probe size. Now finally also large items as cars can be scanned at a speed, which allows this quality inspection to be conducted in-line. Metrology manufacturers (e.g. Carl Zeiss, Hexagon, and Mitutoyo) offer such solutions and strongly promote them. The dominating topics in operations management, Industry 4.0 and Smart Factory, also assign a new role to metrology: Whether it is seen as an enabler of CPPS or as an integral part of production – the future of manufacturing and metrology are closely tied together. In the upcoming years metrology will have to cope with four core challenges: faster, more precise, more secure and more flexible. (Imkamp et al. 2016). 3 CONCEPTUAL MODEL CPS are becoming more popular in all dimensions of our daily life. Manufacturing, in particular integrated production systems, is no exception to this trend as Demartini et al. (2017) show. Within the production environment ‘computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect

computations and vice versa’ (Lee 2008). For the transition from the traditional factory to the future CPPS-factory two functional components are essential: ‘(1) the advanced connectivity that ensures real-time data acquisition from the physical world and information feedback from the cyber space; and (2) intelligent data management, analytics and computational capability that constructs the cyber space’ (Lee, Bagheri, and Kao 2015). For both, connectivity and real-time data acquisition, powerful and reliable metrology plays a key role. Implementing CPPS faces various challenges: it is risky for production processes on an operational level, installation costs are critical on a financial level and difficulties to quantify its positive impact make it a strategic risk (Wang, Törngren, and Onori 2015). Whereas operational and financial issues are highly individual for each company, the positive impact of in-line metrology on the intra-company material flow can be quantified in a model. To achieve this goal, traditional processes have to be contrasted with the improved future solution, resulting in two models of the internal material flow. For such a study the evolution of the system over time needs to be monitored, so a dynamic model is required (Derler, Lee, and Vincentelli 2012): Discrete Event Simulation (DES) models ‘systems as a network of queues and activities where state changes occur at discrete points of time’ (Tako and Robinson 2011). For our model physical material flows are of major interest and demand changes are discrete events influencing them. To deliver valuable insights a model has to be a good abstraction of the physical world, incorporating essential elements and applying simplifications where appropriate (Derler, Lee, and Vincentelli 2012). The entire production process is represented by several sub-processes and only those affected by the technological change will be different in the traditional model and the future model. Moreover, reverse material flows for quality-related returns are included. In the traditional case, where metrology

Figure 1. Model structure for Model A (traditional) and Model B (innovative).

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solutions are only able to check a sample, it has to be considered, that some defect items pass the control without being discovered. The study aims to show the impact of advanced metrology on intra-company material flows in manufacturing. Effects in terms of reduced variation and less amplification are desired, or so to speak: mitigation of the internal bullwhip effect.

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(4) Demand scheme influence: demand shocks lead to high queues before Stamping. Table 1. Number of items processed for all scenarios. Plant/ production step

4. INDUSTRY CASE AND MODEL KEY COMPONENTS Automotive manufacturing involves several actors, here called plants, within a single company. They are usually located on the same production site, nevertheless it takes few hours to transport the part from one plant to another. There are four main steps in automotive manufacturing of interest for the model: Stamping, Welding, Painting and Assembly (Htay, Sun, and Khaing 2012). The default plant consists of three processes: production, quality control and reworking. Incoming items are first processed in production and then inspected. If a defect is found, items are reworked or - if this is not possible - scrapped. For both cases a constant rate is assumed. Information processing, another core element in the manufacturing process, is highly transparent, meaning that final product demand information is accessible to all actors in the SC. This is based upon the fact, that automotive industry currently undertakes many initiatives ‘to achieve 100 percentage manufacturing efficiency’ (Govindan 2015) with information transparency being one of them. Automotive usually produces make-to-order, which implicates that internal production quantities can be derived directly from end customer demand (Lee 2003). In our model the demand for Body-In-Whites differs from end customer demand in the number of scrapped units.

Model A_ Model B_ Model A_ Model B_ Model constant constant varying varying A_shock

Model B_shock

Stamping Welding Painting Assembly

7251.08 7000.00 6964.78 6011.46

7149.30 7007.76 6709.02 6367.60

7235.06 6991.34 6935.68 5973.54

7348.08 7060.98 6737.24 6339.84

7517.04 7257.86 7005.38 6013.08

7593.76 7262.66 6895.46 6377.92

Reworking Stamping Reworking Welding Reworking Painting Reworking Assembly

296.08

298.04

296.56

299.16

308.54

305.46

295.38

286.92

293.96

288.56

299.24

300.80

220.46

276.86

219.74

275.32

219.80

284.52

215.02

216.64

212.24

215.32

215.02

215.98

Model B_shock

Model A_shock

Model B_varying

Model A_varying

Plant Stamping Welding

Units processed Utilization in % Avg. units in queue Max. units in queue Units processed Utilization in % Avg. units in queue Max. units in queue

Model B_constant

Table 2. Units processed, queue information and plant utilization of in Stamping and Welding of all scenarios. Model A_constant

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7251.08 91.79 26.48

7149.30 91.76 26.34

7235.06 91.50 37.73

7348.08 92.12 39.47

7517.04 95.17 87.03

7593.76 95.07 81.47

119.800

119.26

151.72

152.28

293.46

282.16

7000.00 88.87 27.27

7007.76 88.98 23.63

6991.34 88.82 27.00

7060.98 89.55 23.10

7257.86 92.14 26.11

7262.66 92.18 22.49

94.98

89.36

96.38

89.16

91.52

86.40

For each step a medium production time with triangular distribution is assumed. All production steps also have a maximum capacity assigned, depending on working cycles and number of production lines. A share of 5% of all items produced is defect, of which 20% have to be scrapped, others can be reworked. Figure 1 visualizes our model.

Assumptions (1) and (2) were verified, as Table 1 shows. Assumptions regarding Stamping and Welding (3) and demand scheme influence (4) were proven right as well (see Table 2).

Validation verifies the model’s applicability to the case. Core assumptions on the developed model’s behaviour are formulated and then tested. As Sterman highlights, ‘every variable must correspond to a meaningful concept in the real world’ (2000). All models and scenarios passed the examination on the total demand, number of items scrapped and plausibility of the processing and holding times. Additionally, we expect:

All six scenarios were run fifty times and averages, minimum and maximum values of the numbers observed were documented. The most outstanding results are classified into supply-chain-wide, reverse-supply-chain-wide, plant-specific and demand-induced.

(1) Number of items processed by plant: From Stamping to Assembly the number of items, which have already passed, decreases constantly. (2) Number of items processed by reworking: The number of items in reworking station increases constantly from Assembly to Stamping. (3) Similar behaviour in Stamping and Welding: As differentiation between the models only starts with quality control after Welding, until then both models have to show a similar behaviour.

5. SIMULATION RESULTS

Supply-chain-wide a reduction of flow times both in terms of maximum and average duration was observed for handedover and scrapped items, as Figure 2 illustrates. Hence, less items are within the production system at any time, which is a great step towards a lean production system eliminating stocks, reducing oscillations and so smoothening the material flow. For scrapped items it also means, that they occupy less space during their short lifetime within the plant. As we assumed a make-to-order production system this moreover implies, that the re-ordering of this specific item occurs earlier and therefore the final customer faces a shorter delivery time. Most outstanding is, that significantly more items leave the production system (around 300, respectively

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5%) of Model B than of Model A under all demand schemes (Table 3). Figure 2. Average time in system for all items

Whereas queues were reduced by 50% or more in Painting, they increased by factor 3 (constant demand), factor 4 (varying demand) or even factor 6 (demand shock) in Assembly by changing from Model A to new Model B. The effects might be that strong because of the already high utilization of the facilities. With respect to demand-induced results another conclusion could be drawn by comparing the demand schemes varying and shock: the amplitude of demand variation has a stronger effect than the number of occurrences. This thesis should be further investigated by testing a larger demand scheme set. The importance of considering metrology solutions is proven by the positive supply-chain-wide effects observed: faster processing, reduced queues in many process steps both in forward and reverse SC can significantly reduce the bullwhip effect. Especially the earlier scrapping of defect items reduces lead time and so enables faster information processing, in this case earlier re-ordering. This enhanced information transparency is a direct result of the modified metrology solution.

Model B_shock

Model A_shock

Model B_varyin g

Model A_varyin g

Model B_consta nt

Plant

Painting

Units processed Utilization in % Avg. units in queue Max. units in queue

6964.78 92.80 50.47

6709.02 89.40 23.56

6935.68 92.39 64.30

6737.24 89.74 30.92

7005.38 93.28 110.07

6895.46 91.83 42.62

127.08

87.44

156.70

110.68

322.78

137.66

Assembly

Reverse-supply-chain-wide the burden shifts from Painting Reworking to Assembly Reworking. Under all demand schemes queues increased at Assembly Reworking, making it the new bottleneck. Changing the SC’s structure towards a 100% quality control after Welding, loosened the ties between the Welding and Painting and showed another challenge in the reverse material flow. Looking at the queue profiles of reverse material flows in Figure 3 supports this thesis, as the second largest queue occurring in Model A under all demand schemes is always at Assembly Reworking. As outlined previously most significant changes are expected at the Painting and Assembly plants, so the plant-specific look focuses on them. Processing times - minimum, maximum and average – remain constant in all six scenarios. Greatest variation has been observed in queues (Table 3). Figure 3. Average number of items in the queue for the reworking stations as part of the reverse material flow.

Model A_consta nt

Table 3. Units processed, queue information and plant utilization of in Painting and Assembly of all scenarios.

Units processed Utilization in % Avg. units in queue Max. units in queue

6011.46 85.98 17.31

6367.60 90.97 58.90

5973.54 85.44 17.11

6339.84 90.56 85.32

6013.08 85.99 17.31

6377.92 91.12 99.14

52.38

149.80

52.04

214.14

52.46

299.46

But results also indicate, that implementing the new metrology solution is not a stand-alone-project: it improves several plants, but also shifts burden to a new bottleneck: the Assembly Reworking. Any implementation should consider this side-effect and have a plan on hand how to handle this issue. There is still a deeper understanding of the influence of demand schemes necessary to understand their influence on the SC. To determine, whether this technology solution is a real improvement for the automotive industry, the real demand pattern, which the manufacturers face, should be tested for both models as well. A factor not considered so far but crucial for implementation is cost. Real company data is necessary to calculate savings by faster processing, less stocks and earlier re-ordering and metrology solution implementation cost. The attractiveness of such an advanced metrology solution for an automotive company also depends on other factors as the information handling, production system characteristics and potential economies of scale. 6. CONCLUSIONS In-line metrology results in lead time reduction by reducing queues and the total number of items in the production system. This research shows by means of a simulation

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modelling, that metrology innovation can work as a countermeasure against the bullwhip effect within one company. The developed model enriches the theoretical work on intra-company material flow oscillations by Klug (2013) and takes the intra-company view Lee and Whang (2006) were asking for. It highlights the impact of metrology innovations on integrated production systems, specifically of inline-metrology, which is a prerequisite for the realization of CPPS (Drossel et al. 2016; Kunzmann et al. 2005). Further studies based upon the role of metrology solutions within CPS and CPPS could show other areas of application. Also this article limits itself to the automotive sector but other industries might be eligible once the costs for the innovative metrology solutions decrease. Further empirical evidence could be collected in a large field research with access to data of various manufacturers. Such a study could also create valuable insights by having the opportunity to compare and contrast different production systems. REFERENCES Adenso-Díaz, B., Plácido Moreno, E. G., and Lozano, S. (2012). An analysis of the main factors affecting bullwhip in reverse supply chains. International Journal of Production Economics, 135 (2), 917–28. Agnihothri, S. R., and Kenett, R. S. (1995). The impact of defects on a process with rework. European Journal of Operational Research, 80 (2), 308–27. Ali, S. H. R. (2017). State-of-the-Art of CMM-Coordinate Metrology in Automotive Industry. Amundson, S. (1998). Relationships between theory-driven empirical research in operations management and other disciplines. Journal of Operations Management, 16 (4), 341–59. Aslam, T., Ng, A. H., and Karlsson, I. (2014). Integrating system dynamics and multi-objective optimisation for manufacturing supply chain analysis. International Journal of Manufacturing Research, 9 (1), 27–56. Bauer, J. M., Bas, G., Durakbasa, N. M., and Kopacek, P. (2015). Development Trends in Automation and Metrology. IFAC-PapersOnLine, 48 (24), 168–72. BMW Group (2017). Werk Regensburg. Accessed May 06, 2017. http://www.bmwgroup-werke.com/de/ regensburg/ unser-werk/standort infos.html. Burbidge, J. L. (1961). The ‘new approach’ to production. Production Engineer, 40 (12), 769–84. Cardoso, S. R., Barbosa-Póvoa, A. P. F., and Relvas, S. (2013). Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty. European Journal of Operational Research, 226 (3), 436–51. Ceglarek, D., and Shi, J. (1995). Dimensional Variation Reduction for Automotive Body Assembly: Case Study. Manufacturing Review, 8 (2), 139–54. Clifford, P. M. (1985). The internat. vocabulary of basic and general terms in metrology. Measurement, 3(2), 72-76. Coppini, M., Rossignoli, C., Rossi, T., and Strozzi, F. (2010). Bullwhip effect and inventory oscillations analysis using the beer game model. International Journal of Production Research, 48 (13), 3943–56. Demartini, M., Tonelli, F., Damiani, L., Revetria, R., and

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Cassettari, L. (2017). Digitalization of manufacturing execution systems: The core technology for realizing future smart factories. Proceedings of the Summer School Francesco Turco, 2017, 326-33. Derler, P., Lee, E. A., and Vincentelli, A. S. (2012). Modeling Cyber-Physical Systems. Proceedings of the IEEE, 100 (1), 13–28. Drossel, W.-G., Ihlenfeldt, S., Penter, L., Zorn, W., Hellmich, A., and Grüning, T. (2016). Das Presswerk auf dem Weg in das Cyberphysische Zeitalter. ZWF, 111 (7-8), 460-65. Govindan, A. (2015). Optical Metrology Solutions: The future of Metrology. Accessed May 02, 2017. http://www.qualitymag.com/articles/92718-opticalmetrology-solutions-the-future-of-metrology. Größler, A., Thun, J.-H., and Milling, P. M. (2008). System Dynamics as a Structural Theory in Operations Management. Production and Operations Management, 17 (3), 373–84. Htay, M. M., Sun, L. B., and Khaing, M. A. S. (2012). Quality Management Information in Auto Manufacturing Process. AMR, 529, 436–40. Imkamp, D., Berthold, J., Heizmann, M., Kniel, K., Peterek, M., Schmitt, R., Seidler, J., and Sommer, K.-D. (2016). Challenges and trends in manufacturing metrology – Industrie 4.0. tm-Technisches Messen, 83 (7-8), 417–29. Klug, F. (2013). The internal bullwhip effect in car manufacturing. International Journal of Production Research, 51 (1), 303–22. Kunzmann, H., Pfeifer, T., Schmitt, R., Schwenke, H., and Weckenmann, A. (2005). Productive Metrology Adding Value to Manufacture. CIRP Annals Manufacturing Technology, 54 (2), 155–68. Lee, H. L., Padmanabhan, V., and Whang, S. (1997). Information Distortion in a Supply Chain: The Bullwhip Effect. Management Science, 43 (4), 546–58. Lee, H. L., and Whang, S. (2006). The bullwhip effect: a review of field studies. In: Carranza Torres, O.A. and Villegas Moran, F.A. (eds.). The bullwhip effect in supply chains – A review of methods, components and cases, New York: Palgrave Macmillan, 57–70. Lee, J. (2003). E-manufacturing—fundamental, tools, and transformation. Robotics and Computer-Integrated Manufacturing, 19 (6), 501–7. Lee, J., Bagheri, B., and Kao, H.-A. (2015). A CyberPhysical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 18–23. Sterman, J. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston: Irwin/McGrawHill. Tako, A. A., and Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52 (4), 802–15. Taylor, D. H. (1999). Measurement and Analysis of Demand Amplification Across the Supply Chain. The International Journal of Logistics Management, 10 (2), 55–70. Wang, L., Törngren, M., and Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517-27.

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