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6th International Scientific ConferenceManagement of Technology – Step to Sustainable Production11-13 June 2014, Bol, Island Brac, Croatia

Organiser:

Co-organiser:

Editor-in-Chief:Predrag Ćosić

Executive editors:Gordana BarićGoran Đukić

Secretary:Gordana Barić

Published by:Croatian Association for PLM

ORGANIZING COMMITTEE

Predrag Ćosić (Chairman)Gordana BarićGoran ĐukićTihomir OpetukDavor Donevski

HONORARY COMMITTEE

Bachman B. J. (USA) Filetin T. (Croatia) Mikac T. (Croatia)Balič J. (Slovenia) Grubišić I. (Croatia) Mudronja V. (Croatia)Butala V. (Slovenia) Juraga I. (Croatia) Oluić Č. (Croatia)Canen A. G. (Brazil) Kane M. (Belarus) Plančak M. (Serbia)Čala I. (Croatia) Katalinić B. (Austria) Polajnar A. (Slovenia)Čatić I. (Croatia) Kennedy D. (Ireland) Taboršak D. (Croatia)

Čuš F. (Slovenia) Kusiak A. (USA) Udiljak T. (Croatia)Ćosić I. (Serbia) Lombardi F. (Italy) Veža I. (Croatia)Duplančić I. (Croatia) Mamuzić I. (Croatia) Žižmond E. (Slovenia)Ekinović S. (BiH) Marjanović D. (Croatia)

SCIENTIFIC COMMITTEE

Barić G. (Croatia) Ikonić M. (Croatia) Petković D. (BiH)Bouras A. (Qatar) Jerbić B. (Croatia) Poppeova V. (Slovakia)Božič S. (Slovenia) Kladarić I. (Croatia) Raos P. (Croatia)Canen A.G. (Brazil) Kondić Ž. (Croatia) Savescu D. (Romania)Ćosić P. (Croatia) Krajnik P. (Sweden) Sihn W. (Austria)Dolinšek S. (Slovenia) Kunica Z. (Croatia) Šercer M. (Croatia)Đukić G. (Croatia) Majetić D. (Croatia) Štefanić N. (Croatia)Gečevska V. (Macedonia) Milčić D. (Croatia) Štorga M. (Croatia)Guenther H.O. (Germany) Milković M. (Croatia) Tropša V. (Croatia)Guzović Z. (Croatia) Opalić M. (Croatia) Wang Yung-Cheng (Taiwan)

REVIEWERS OF MOTSP 2014 PAPERS

Published categorized papers are peer-reviewed by two independent experts.

Barić G. (Croatia) Katić M. (Croatia) Poppeova V. (Slovakia)Baršić G. (Croatia) Kiss I. (Romania) Požek M. (Croatia)Bouras A. (Qatar) Kljajin M. (Croatia) Plančak M. (Serbia)Baus Z. (Croatia) Kozarac D. (Croatia) Rađenović A. (Croatia)Brezak D. (Croatia) Kožuh S. (Croatia) Runje B. (Croatia)Brozović M. (Croatia) Krajnik P. (Sweden) Sihn W. (Austria)Cajner H. (Croatia) Kuhinek D. (Croatia) Simončić V. (Croatia)Ciglar D. (Croatia) Kunica Z. (Croatia) Sokele M. (Croatia)Cukor G. (Croatia) Lisjak D. (Croatia) Staroveški T. (Croatia)Čala I. (Croatia) Macan J. (Croatia) Stepanić J. (Croatia)Čatić I. (Croatia) Magazinović G. (Croatia) Šercer M. (Croatia)Ćosić P. (Croatia) Majetić D. (Croatia) Šimunović G. (Croatia)Đukić G. (Croatia) Marshall-Ponting A. (UK) Šimunović K. (Croatia)Ernst S. (Germany) Matešić M. (Croatia) Štefanić N. (Croatia)Garašić I. (Croatia) Milčić D. (Croatia) Vučinić D. (Belgium)Gečevska V. (Macedonia) Novaković D. (Serbia) Vujanović M. Croatia)Godec D. (Croatia) Newlands D. J. (France) Wang Y. C. (Taiwan)Grilec K. (Croatia) Perinić M. (Croatia) Jakopčić M. (Croatia) Pilipović A. (Croatia)

All papers are presented in the form which is delivered by authors. The Organizer is notresponsible for statements advanced in papers or spelling and grammar irregularities.

Technical Editor:Mario Lesar

ISSN 1848-5022

Copyright © Croatian Association for PLM, Zagreb, Croatia, 2014

Home Introduction Contents Authors Sponsors Book of Abstracts

Invited Speakers Session1 Session2 Session3 Session4

Invited Speakers

Wilfried Sihn Industry 4.0: Opportunities And Potentials

Bojan Jerbić New Robotic Challenges

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Session1: Production Management

Stefan Scheifele, Jens Friedrich, Alexander Verl,Armin Lechler

Modular Production System for Flexible and Localized Production

Daniel Coupek, Jens Friedrich, Alexander Verl, ArminLechler

Modular Information Sharing Platform For Real-Time Data Processing And KnowledgeExtraction In Modern Production Systems

Tomaž Berlec, Marko Starbek, Janez Kušar Going Lean Step by Step

Ngoc Anh Tran, Tobias Teich, Holger Dürr, An NinhDuong, Ulrich Trommler

Knowledge-Based Planning Assistant for Technology Optimization Using Process Standards(WATOP)

Arunas Burinskas Performance Conditions of Producers

Rafał Prusak, Zbigniew Skuza, Cezary Kolmasiak The Use of Network Methods and Control Charts in Planning and Control of Production

Wolfgang Unzeitig, Marlene Schafler, AlexanderStocker, Franz Weghofer, Markus Flasch

An Instrument for Reducing Uncertainty in the Early Phase of Production Planning

Tomislav Brnadić Application Of Lean Principles In The Maintenance Of Gas Stations

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Session2: Measurement, Quality, Control, Logistics, Maintenance

Zbigniew Skuza, Rafał Prusak, Cezary Kolmasiak Analysis of Selected of Quality Cost

Yung-Cheng Wang, Lih-Horng Shyu, EberhardManske, Chung-Ping Chang

Quadrature Phase-Shifted Fabry-Perot Interferometer Utilized for Nanopositioning

Florian Frick, Armin Lechler, Alexander Verl Adaptable Control Systems Through User-Reconfigurable SOPC Architectures

Gerald Humenberger, Gerhard Liedl, StefanHeidlmayr

Measurement of the Emitted Thermal Radiation During CO2-Laser Cutting

František Petrík, Andrej Červeňan, Marian Králik Improving the Maintenance System of the Manufacturing Enterprise

Alexander Sunk, Tanja Nemeth, Thomas Edtmayr,Peter Kuhlang, Wilfried Sihn

Increasing Productivity Systematically by Applying Target-Conditions in Logistical ValueStreams

Hrvoje Cajner, Mario Mačković, Nedeljko Štefanić,Nikola Grubešić

Characterization of the Explosion of Flammable Gas in Closed Pipes Using the Design ofExperiments Methodology

Michal Wieczorowski, Thomas Mathia, Serge Carras,Damian Smierzchalski

Surface Topography Inspection in Multisensor Approach

Katarina Cerovšek, Predrag Ćosić Machine Tools Selection In Process Planning Using The AHP Method

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Session3: Product Development, Production Technologies

Mariusz Cygnar, Bogusław Cieślikowski, TomaszMachowski

Modeling Of The Injection And Combustion Processes In The TSI Engine

Kathrine Spang, Carin Andersson Dry or Wet Gear Hobbing a Case Study

Davor Donevski, Diana Milčić, Dubravko Banić Influence of 1D Curves on ICC Profile Accuracy

Grzegorz Budzik, Bogdan Kozik, Jacek Bernaczek,Tadeusz Markowski, Tomasz Dziubek, MałgorzataZaborniak

Analysis Of The Geometric Accuracy Of Gears RP Casting Moulds

Tomaz Kostanjevec, Matej Vogrinčič Improved Product Development Approach with Multi-Criteria Analysis

Damir Belić, Zoran Kunica Development of Wheelchair for People with Special Needs

Nagore Alvarez, Marion Real, Iban Lizarralde, JérémyLegardeur

Ekit’eko: A Serious Game to Support Sustainable Aptitudes During the Development ofEco-Innovations

Davor Petranovic, Ante Marusic Genetic Algorithm Approach To Optimum Wire Busbar Design

Maja Rujnić-Sokele, Ivana Radić Boršić, GordanaBarić

Water Vapour Permeability Of Biodegradable Films

Mario Dakovic, Miro Hegedic Risk Management Approaches In Oil And Gas Onshore Construction Projects (ProjectManagement)

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Session4: Sustainability

Anita Štrkalj, Zoran Glavaš, Krešimir Maldini, DamirHršak, Ivica Šipuš

Kinetic Studies of Adsorption System of the Waste Steel Shot / Cr (VI) Ions

Ivan Dakov, Viktor Hristov, Svetoslav Novkov Innovative Outsourcing Approach for Ensuring IT Clusters’ Sustainable Development

Silvia Barbero, Eleonora Fiore The Flavours of Coffee Ground – The coffee waste as accelerator of new local businesses

Iztok Slejko, Slavko Božič, Erika Gombač Sustainable Production and Look of Wind Energy in Our Country

Neven Lovrin, Željko Vrcan The Influence of Engineering Ethics on Ecology and Sustainable Development

Andrea Di Salvo, Andrea Gaiardo, Luca Giuliano Electric Vehicle (EV) and Sustainable Mobility: An Innovative Interface

Max Regenfelder Complexity in Re-Use, Remanufacturing and Recycling Businesses

Janez Kopač, Franci Pušavec, David Homar Mechanical Engineering Approaches As Guaranty For Sustainable Production

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INCREASING PRODUCTIVITY SYSTEMATICALLY BY APPLYING TARGET-CONDITIONS IN LOGISTICAL VALUE STREAMS

Alexander SUNK1,2, Tanja NEMETH1,2, Thomas EDTMAYR1,2, Peter KUHLANG1,2,

Wilfried SIHN1,2 1Vienna University of Technology, Faculty of Mechanical and Industrial Engineering, Institute of

Management Science Theresianumgasse 27, Vienna, Austria

2Fraunhofer Austria Research GmbH, Division Production and Logistics Management Theresianumgasse 27, Vienna, Austria

Abstract

In the entire value stream of original spare part supply, packing is one of the main issues in the distribution system and its productivity is mainly affected by a particularly high proportion of manual work. This paper presents an approach to support practical improvement work at value streams and shows from theoretical and practical point of view, how performance-enhancing and learn-enhancing target-conditions can be derived and defined from the ideal-state and its characteristics in order to increase productivity by continuous and discontinuous improvements at the packing value stream of original spare parts.

Keywords: value stream, logistics, productivity, target-condition, methods-time measurement

1. INTRODUCTION

The after-sales service and in particular the supply of original spare parts is an important branch in the automotive industry, which enables original equipment manufacturer (OEM) to generate additional revenues with high growth potential in the competitive market environment because of market demands [1,2,3,4].

Considering the total value stream of supplying original spare parts, packaging is one of the most important activities in the distribution system as well as in the supply chain [5]. Packing is the moving of customer specific original spare parts out of a package into a cartonage. It starts after picking and ends with ready set cartonages of customer ordered parts and quantities ready for delivery [6].

Several authors point out the intensive manual work structure in picking as well as in packing, because handling and moving of both original spare parts and cartonages is difficult to standardise due to their variability and thus hardly automatable. Consequently, packaging costs (direct manual work and material) account for a substantial portion of a product’s manufactured cost [5,7,8,9]. At packing, especially value adding and non-value manual adding activities affect productivity and must therefore be systematically planned and improved [6,7,10].

Hence, the resulting cost pressure for companies in high-wage countries gets even stronger because of globalisation, the challenging competitive situation and the current re-industrialisation. Thus, especially for packing of original spare parts, new and higher requirements arise for productivity management of companies. So, systematic application and further development of modern improvement methods and improvement procedures are necessary to meet these requirements [11].

As practice shows, value stream management has proven to be suitable for improvement work and consequently for increasing productivity because of applying lean principles target-oriented. This paper points out how the packing value stream in the supply of original spare parts gets developed towards an ideal-state by defining performance-enhancing and learn-enhancing target-conditions.

1.1 Fundamentals

Productivity is the yield of the production factors “workforce”, “machine” and “material”. This yield is represented by the ratio “performance divided by factor input”. When calculating productivity, output

Measurement, Quality, Control, Logistics, Maintenance

(=performance) is represented by a specific quantity, e.g. produced goods. Input is quantified by the use of production factors; e.g. the figure for workforce productivity by number of workers [12,13,14,15].

Upon closer examination of factors influencing the productivity, it becomes obvious that for human and machinery resources especially the dimensions “work method design”, “level of performance provided” and “degree of utilisation of resources” affect productivity [16]. Anyway, work method(s) design is the most important dimension for influencing productivity [16,17].

The paradigm of striving for an ideal-state – it can be considered as a vision – is the basis of the improvement of a value stream and its processes. The ideal-state describes the condition of a value stream with zero losses so that added value is generated at minimum costs [18]. This ideal-state is used as a navigation link (“true north”) or orientation guide and represented by characteristics like 100% added value, continuous one-piece-flow, zero defects and lack of impairment for employees [19]. The ideal-state gives direction for deriving and defining several target-conditions for a value stream [19,20,21,22,23].

Hempen provides a modern approach for defining target-conditions, which is a difficult issue in practice. Here, they are defined by parameters which are categorised as follows: (C1) calculated indicators, (C2) general process information, (C3) process pattern & process indicator and (C4) performance indicator. Typical examples of these categories are customer tact time as a calculated indicator, defined inventory size as general process information, work method as process pattern and basic time as process indicator, productivity as performance indicator [24]. The parameters for defining target-conditions are based on performance-enhancing and learn-enhancing target setting characteristics. On the one hand, performance-enhancing characteristics are e.g. challenging, realistic and oriented to superior objectives. On the other hand, learn-enhancing characteristics are e.g. solution-open, clearly appraisable as well as influenceable on a daily base [24,25,26,27,28].

Following Rother´s approach the target-condition parameters get achieved by continuous (short-cyclic, incremental) improvements that are supported by the improvement and coaching kata. In addition to this approach, the parameters of a target-condition are also accomplishable by discontinuous improvements (innovation leaps) [18,29,30,31].

The following challenges – especially in practical application – arise from deriving and striving for target-conditions.

1.2 Identification of challenges in improvement work

The definition of performance-enhancing and learn-enhancing target-conditions resp. their parameters is a great challenge in the improvement work in a specific value stream. Furthermore, the characteristics of the ideal-state seem very abstract to the affected operational workers and a derived resp. defined target-condition appears incomprehensible. A possible result may be lack of understanding and low acceptance for the next target-condition of the value stream to strive for.

To close this gap in practical improvement work with target-conditions, the so-called "specific design principles" are introduced. The specific design principles support deriving, defining and striving for performance-enhancing and learn-enhancing target-conditions. This connecting link between ideal-state and target-conditions is only formulated for a specific case of application. Thus, the characteristics of the ideal-state get illustrated clearly and their relationship to relevant parameters of the target-condition reveals obviously. As a result, the specific design principles support and deepen the understanding of affected operational workers when striving for target-conditions.

This paper shows how specific design principles are derived from the (theoretical) ideal-state. Subsequently, performance-enhancing and learn-enhancing target-conditions defined by several parameters are derived from specific design principles (see table 1).

2. EXTENDED APPROACH FOR DEFINING TARGET-CONDITIONS

The specific design principles are applicable for several practical challenges like: (a) breaking down the overall (company´s) objectives to value stream level, (b) support affected (operational) workers for understanding the characteristics of the ideal-state, (c) particularly when deriving resp. defining performance-enhancing and learn-enhancing target-conditions and their parameters.

Management of Technology – Step to Sustainable Production, 11-13 June 2014, Bol, Island Brač, Croatia

Specific design principles refer to a unique case of application such as the packing value stream. Hence, in contrast to the characteristics of the ideal-state, the once formulated specific design principles cannot be transferred from one situation to another.

Figure 1 shows the steps of formulating specific design principles based on a comprehensive analysis of the current-condition (step 1) and orientation to the characteristics of the ideal-state (step 2). They are the basis for deriving resp. defining performance-enhancing and learn-enhancing target-conditions for practical improvement work. By support of the specific design principles the target-condition 1 – based on the current-condition – can be derived resp. defined (step 3a). Hereby, point of time t0 represents the current-condition and t1 the first/next target-condition to be strived for.

After accomplishing or implementing the parameters of the target-condition 1 by discontinuous and/or continuous improvements, the target-condition 1 is the next current-condition. A new target-condition 2 is derived resp. defined by applying the specific design principles to continue systematic improvement work (step 3b).

Thus, the specific design principles support both executive personnel and operational workers when deriving, defining and striving for target-conditions. The approach for deriving resp. defining target-conditions by using specific design principles is presented in a practical case of application. Hereby, the value stream of packing original spare parts out of a package (e.g. metal box) into a cartonage is considered.

Figure 1 – Specific design principles support the striving for ideal-state

3. USE CASE

Practical improvement work at the packing value stream is presented based on selected parameters of the current-condition and the target-condition 1: (1) “work system”, (2) “work method” with (3) “basic time” and (4) “productivity”. But before deriving resp. defining these parameter applying specific design principles, an introducing description of the analysed and defined packing value stream in current-condition is necessary.

3.1 Describing the considered packing value stream

The original spare parts in packages (here: metal boxes) arrive from two different sources in the considered value stream; an automated pallet transport system and an internal milk-run system. Forklift drivers take the metal boxes to different allocation areas with respect to customer assignment. From there, the forklift drivers take the metal boxes to the packing area when specific customers need to be processed. The packing area is divided into several packing groups – the actual work systems for packing. Here, packers move the picked original spare parts from the metal boxes into the cartonages and fulfill the value-adding operations in this value stream.


The basis for all calculations and concepts for defining target-conditions is the so-called representative cartonage. Thereby, all quantities and varieties of the product spectrum of original spare parts (dimensions, bulkiness, weight, etc.) are considered proportional for packing a single cartonage.

The supply of metal boxes with parts to be packed in the work systems is a push supply. Service level relevant packing issues may default, so rush packing jobs are necessary which detract continuous packing of incoming metal boxes from the two sources negatively. In addition to the value-adding activities in the work systems, the packers do also have to perform support activities like preparing or completing a cartonage and handling packing material. After completing cartonages in the work systems, the forklift drivers take them to a tying machine. Thereafter, the customer ready cartonages are taken by the forklift drivers to an allocation area for loading containers.

3.2 Formulating specific design principles

Based on comprehensive analysis of the current-condition and the orientation to ideal-state (see Figure 1, step 1), the following selected specific design principles are formulated for the packing value stream (see Figure 1, step 2): (1) “immediate packing of parts from incoming packages into cartonages” and (2) “the packer is packing!”

Table 1 presents characteristics of ideal-state in first column. In second column, specific design principles clarify each of the characteristics applied to the packing value stream. Parameters which define performance-enhancing and learn-enhancing target-conditions derived from specific design principles (see Figure 1, step 3a) are listed in third column.

Table 1 – Defining target-conditions based on specific design principles derived from ideal-state (selection)

Selected characteristics of ideal-state

Selected specific design principles

Selected parameters defining performance-enhancing and learn-enhancing target-conditions

Continuous one-piece-flow

Immediate packing of parts from incoming packages into cartonages

Value stream Work system (C2) Layout and material flow Productivity (C4) Area utilisation Shift model Lead time

100% added value The packer is packing! - Minimise walking - Perform only value adding

packing activities - No setting-up by the packer - Seperate packing from

logistics activities

Work method (C3) via “MTM-method description” and its result “basic time” Number of workers Productivity (C4) Value stream Work system Layout and material flow

To point out the improvements between current-condition and target-condition, the following two underlined parameters of Table 1 are selected: “work system” as a general process information and “work method” as a process pattern. Thereby, the practical realisation of the defined performance-enhancing and learn-enhancing target-condition 1 is documented as well as the link between ideal-state, specific design principles and a target-condition. Finally, the effects of the continuous and discontinuous improvement measures at the packing value stream are reported by the third parameter “productivity” (performance indicator).

3.3 Parameter “work system”

The parameter „work system“ is derived from the specific design principle (1) „immediate packing of parts from incoming packages into cartonages” (see Table 1), which is related to the characteristics “continuous one-piece-flow” of the ideal-state. This parameter is categorised as (2) “general process information” for defining performance-enhancing and learn-enhancing target-conditions.


Figure 2a and Figure 2b show the work system in current-condition where packers are packing. Here, they have to choose between 16 customer specific metal boxes in each work system to choose for packing parts into cartonages. The sizes of areas are: 16.0m x 6.0m for each work system; 1.2m x 0.8m for a metal box; up to 2.2m x 1.4m for cartonages. In this configuration of metal boxes and cartonage in the work systems, FIFO-packing (first-in-first-out) is not applicable because packers cannot reproduce the sequence of incoming metal boxes. As a result, the packers choose the metal boxes for packing with respect to subjective criteria. Hence, the above mentioned rush packing jobs are necessary and this leads to low service level.

Figure 2 – (a) Layout and (b) picture of a work system in current-condition

The improvement of the parameter „work system“ in target-condition 1 is an innovation leap, because redesign of the work systems is done within a small period of time. Here, the new “4:2-packing principle” is introduced. This implies, that all parts from four incoming metal boxes are packed into one but not more than two cartonages available in the work system at the same time (see Figure 3a and Figure 3b).

Figure 3 – (a) Layout and (b) picture of a work system in target-condition 1

Reasons for that are based on different filling levels of metal boxes and the attributes of versatility of the original spare parts. The packers get a replicable FIFO-principle, because the parts of the four delivered metal boxes must be packed into cartonages in order to get new metal boxes delivered into the work system again. Hence, the specific design principle (1), that incoming parts from packages (metal boxes) must be packed into cartonages immediately, is obeyed. To sum up, we have at least equal throughput of spare parts in a smaller area needed.

3.4 Parameter „work method”

In the underlying packing value stream, the work method represents all necessary activities for packing parts into cartonages; the activities are based on type, frequency, weights, bulkiness as well as on handling distances of the original spare parts. The process pattern (C3) is represented by the MTM-method description (MTM: Methods-Time Measurement, see Figure 4) and its result, the basic time (process indicator, C3). Thus, the work method describes all necessary activities for packing the so-called representative cartonage with original spare parts and with respect to the packing adjustment. This parameter is influenced by the parameter work system and derived from specific design principle (2). The specific design principle (2) „the packer is packing!“ is oriented to the characteristics “100% added value” of the ideal-state. This leads to improvements of value-adding activities, reductions of support activities and elimination of waste. Subsequently, the influences of applying this specific design principle on the MTM-method description and its result (basic time) are presented.


In current-condition, the applied work method is evaluated by MTM-UAS (UAS: Universal Analysing System) and the basic time for packing of the representative cartonage is determined (see Figure 4). In target-condition 1, a new MTM-method description related to the improved work system is created. Here, the influencing factors on value-adding and support activities are improved based on a new layout of metal boxes and cartonages in the work systems. Waste (e.g. walking) is eliminated as much as possible; here due to a reduced size of the area of the work systems. This leads to a reduced basic time for packing the representative cartonage. The implementation of the new work method is an innovation leap, but learning and applying the new process pattern is done by continuous improvement.

Figure 4 – MTM-method description for packing in target-condition 1 (available only in German)

3.5 Parameter „productivity”

Productivity is the main indicator of performance when considering the underlying packing value stream. Amongst others, it is dependent on both parameters “work system” and “work method”. The parameter productivity is categorised as a “performance indicator” (C4) and derived from both presented specific design principles (1) and (2).

When calculating productivity for packing the representative cartonage, the main contributions to the input factors are the following: work system, work method and especially the basic time. The output is represented by the total number of incoming metal boxes in the work systems to be packed into cartonages. Here, workforce productivity is calculated as follows: number of packed metal boxes divided by total number of workers per shift. This calculation obeys both presented specific design principles. In current-condition at point of time t0, each worker is packing parts of 5.55 metal boxes into cartonages per shift in average. In target-condition 1 at point of time t1, a rise by 20% of incoming metal boxes is expected. With respect to the improved parameters, the decreased factor input leads to an average of 7.94 packed metal boxes into cartonages. To sum up, the workforce productivity rises by +43% compared to current-condition.

4. CONCLUSION AND OUTLOOK

This paper presents an approach for deriving and defining the characteristics of the ideal-state for supporting practical improvement work at value streams. The specific design principles, which are formulated for a unique case of application, are the connecting link between ideal-state and target-conditions. They support both executive personnel and operational workers for deriving resp. defining as well as striving for performance-enhancing and learn-enhancing target-conditions. The formulation of specific design principles leads to an understanding and acceptance for the next target-condition of the value stream. On the one hand,


the parameters work system and work method are performance-enhancing, as new standards and performance indicators affect the packers and guide them to target-orientated action. Furthermore, the packers must also engage and work in new processes and in new work systems; they are encouraged to make further improvements. On the other hand, both parameters are learn-enhancing, as they are modifiable when deriving resp. defining and striving for target-conditions. As a result, these two parameters are (daily) influenceable and especially solution-open. When defining target-conditions, the parameter productivity is both performance-enhancing and learn-enhancing. Productivity is a challenging, realistic and clearly appraisable performance indicator. As a result, future improvement actions are measureable too and give feedback to executive personnel and operative worker (packers) about the level of fulfillment when accomplishing a target-condition. This specific case of application showed, that increasing productivity is the result of combining continuous improvements and innovation leaps. Either way, practical improvement work at a value stream with target-conditions and specific design principles can be used at logistics problems.

In a following publication, the various continuous and discontinuous improvement actions to strive for the target-condition 1 and its parameters for increasing productivity will be more particularly. Furthermore, in terms of performance-enhancement and learn-enhancement (target-conditions) the next target-condition 2 of the packing value stream will be set forth. The experiences gained in this practical improvement work are currently transferred to other areas and plants/sites of the original equipment manufacturer.

Further research has to address the necessary generalisation, transferability and specification of possible ideal-state characteristics. The following questions will be discussed: (a) how can the characteristics be associated with the product development process?; (b) can appropriate "neutral" design principles be formulated to derive resp. define performance-enhancing and learn-enhancing target-conditions?

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[2] Zeilhofer-Ficker, I.: Ersatzteillogistik – After-Sales-Service als lukratives Verkaufsargument, GBI-Genios, Nr. 04, Munich, 2007.

[3] Schuh, G.; Stich, V., Wienholdt, H.: Logistikmanagement, Springer-Verlag, Berlin Heidelberg, 2013. [4] Klug, F.: Logistikmanagement in der Automobilindustrie, Springer-Verlag, Berlin Heidelberg, 2010. [5] Chan, F. T. S., Chan, H. K., Choy, K. L.: A systematic approach to manufacturing packaging logistics,

International Journal of Advanced Manufacturing Technology, 29(2006)9-10, 1088-1101. [6] Gudehus, T.: Logistik - Grundlagen, Strategien, Anwendung, Springer-Verlag, Berlin Heidelberg, 2005. [7] Lee, S. G., Lye S. W.: Design for manual packaging, International Journal of Physical Distribution &

Logistics Management, 33(2002)2, 163-189. [8] de Koster, R., Le-Duc, T., Roodbergen, K. J.: Design and control of warehouse order picking: A

literature review, European Journal of Operational Research, 182(2007)2, 481-501. [9] Lange, V., Mahlstedt, K.: Vielfältige Potenziale, Fraunhofer-Institut für Materialfluss und Logistik,

Logistik Heute, (2007)9, 58-59. [10] Jodin, D., ten Hompel, M.: Sortier- und Verteilsysteme - Grundlagen, Aufbau, Berechnung und

Realisierung, Springer Verlag, Berlin Heidelberg, 2005. [11] Kuhlang, P.: Industrial Engineering - Systematische Gestaltung produktiver, industrieller Wertströme,

Habilitationsschrift, Vienna University of Technology, 2012. [12] Nebl, T.: Produktionswirtschaft, 6. vollständig überarbeitete und erweiterte Auflage, Oldenbourg

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