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6th International Scientific Conference

Management of Technology – Step to Sustainable Production

11-13 June 2014, Bol, Island Brac, Croatia




Predrag Ćosić

Executive editors:

Gordana Barić

Goran Đukić


Gordana Barić

Published by:

Croatian Association for PLM


Predrag Ćosić (Chairman) Gordana Barić Goran Đukić Tihomir Opetuk Davor Donevski


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)


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. (A

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All papers are presented in the form which is delivered by authors. The Organizer is not responsible for statements advanced in papers or spelling and grammar irregularities.

Technical Editor:

Mario Lesar ISSN 1848-5022

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Session1: Production Management Stefan Scheifele, Jens Friedrich, Alexander Verl, Modular Production System for Flexible and Localized Production Armin Lechler

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Ngoc Anh Tran, Tobias Teich, Holger Dürr, An Ninh Knowledge-Based Planning Assistant for Technology Optimization Using Process Standards Duong, Ulrich Trommler (WATOP)

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Rafał Prusak, Zbigniew Skuza, Cezary Kolmasiak The Use of Network Methods and Control Charts in Planning and Control of Production Wolfgang Unzeitig, Marlene Schafler, Alexander An Instrument for Reducing Uncertainty in the Early Phase of Production Planning Stocker, Franz Weghofer, Markus Flasch

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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, Eberhard Quadrature Phase-Shifted Fabry-Perot Interferometer Utilized for Nanopositioning Manske, Chung-Ping Chang Florian Frick, Armin Lechler, Alexander Verl Adaptable Control Systems Through User-Reconfigurable SOPC Architectures Gerald Humenberger, Gerhard Liedl, Stefan Measurement of the Emitted Thermal Radiation During CO2-Laser Cutting Heidlmayr František Petrík, Andrej Červeňan, Marian Králik Improving the Maintenance System of the Manufacturing Enterprise Alexander Sunk, Tanja Nemeth, Thomas Edtmayr, Increasing Productivity Systematically by Applying Target-Conditions in Logistical Value Peter Kuhlang, Wilfried Sihn Streams

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Davor Donevski, Diana Milčić, Dubravko Banić Influence of 1D Curves on ICC Profile Accuracy Grzegorz Budzik, Bogdan Kozik, Jacek Bernaczek, Analysis Of The Geometric Accuracy Of Gears RP Casting Moulds Tadeusz Markowski, Tomasz Dziubek, Małgorzata Zaborniak Tomaz Kostanjevec, Matej Vogrinčič Improved Product Development Approach with Multi-Criteria Analysis

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Session4: Sustainability Anita Štrkalj, Zoran Glavaš, Krešimir Maldini, Damir Kinetic Studies of Adsorption System of the Waste Steel Shot / Cr (VI) Ions Hršak, Ivica Šipuš Ivan Dakov, Viktor Hristov, Svetoslav Novkov Innovative Outsourcing Approach for Ensuring IT Clusters’ Sustainable Development

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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 Management of Technology – Step to Sustainable Production, 11-13 June 2014, Bol, Island Brač, Croatia (=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, performanceenhancing 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 targetconditions.

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


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 idealstate 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).


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.

Measurement, Quality, Control, Logistics, Maintenance 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 currentcondition – 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).

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