Microsoft Word - 1murphy.docx CHEMICAL ENGINEERING TRANSACTIONS VOL. 58, 2017 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Remigio Berruto, Pietro Catania, Mariangela Vallone Copyright © 2017, AIDIC Servizi S.r.l. ISBN 978-88-95608-52-5; ISSN 2283-9216 Revision of the Working Time Classification to Optimize Work Processes in Modern Agriculture Stefanie Reith*a, Jürgen Frischa, Brigitte Winklerb a Association for Technology and Structures in Agriculture (KTBL), Bartningstrasse 49, D-64289 Darmstadt b Saxon State Agency for Environment, Agriculture and Geology (LfULG), August-Böckstiegel-Strasse 1, D-01326 Dresden s.reith@ktbl.de Precise knowledge of work processes is necessary to estimate the working time requirements in agriculture. Working time data are key criteria for structuring, organization and economic calculation of work processes. The focus is on times for working persons as well as for equipment and work objects. To derive specific data, transparent and variable models reflecting the reality are needed. The analysis of agricultural processes is based on systematic work classification, whereby the individual sections represent the total work and total working time, respectively. Review of existing time classifications revealed weak points resulting in the need for a revision of the time classification. The requirements include a wide applicability to agricultural work processes, understandable structuring and simultaneous consideration of man power and working equipment. The new time classification also takes actual production methods including automated recording of time data into account. If necessary, the element times can further broken down. In conclusion, recording and analysis of working time data provide the basis for a comprehensive assessment of the status quo. Optimization of work processes reduces required time and have positive effects on the economics of work systems in agriculture. 1. Introduction Data on working time requirement recorded by different methods (Auernhammer, 1976; Schrade et al., 2005) are indispensable for strategic planning of work processes in agriculture (Jäger, 1991). According to Hesse (1925), these data can be used to represent an individual work element, a specific complex of work elements or work performance within a certain unit of time. Effective time management provides the basis for fulfilling precise objectives and requirements. The evaluation of the time is based on the working time classification subdividing the total working time into its element times. An exact definition of the element times is essential for measurement of the working time requirement of specific work elements. Equally important is the identification of influencing variables affecting the individual work elements. These have to be recorded and described in detail, as well (Gindele, 1972). The calculation of standard times for an operation is allocated appropriate attention in various studies (Auernhammer, 1976; Hammer, 1976; Schick, 2006). In general, multistage model calculation methods are used to provide key figures regarding labor organisation for agricultural procedures as well as for combined procedures. By means of logical connection between work elements and their influencing factors, working time requirement can be calculated in model form at the working procedure stage and put togehther to form any desired production process. Apart from helping to plan work, the classification of work processes admits representative statements about the efficiency of working and production procedures. The influence of particular time types can also be seen and understood. With regard to the determination of element times, the number of subdivisions depends on the demanded accuracy. Possibilities of classifying work processes are shown in Table 1. The whole process of conducting working time studies is described by Schick (2006). For individual process steps, an automated data recording is possible (Herzog and Schick, 2014). DOI: 10.3303/CET1758021 Please cite this article as: Reith S., Frisch J., Winkler B., 2017, Revision of the working time classification to optimize work processes in modern agriculture, Chemical Engineering Transactions, 58, 121-126 DOI: 10.3303/CET1758021 121 Table 1: Possibilities of work classification Classification Element Abstract Local Operation time Preparation time Closing time Inherent delay time Functional and technical fault time Working in the field/on the headland Drive on the road/cart track Economic Functional Times with a positive/negative financial balance Cost-neutral times Cost-generating times Times of maximum machine efficiency Times of low machine efficiency due to organization Times of low machine efficiency due to operating conditions Machine downtime due to a defect 2. Existing working time classifications in agriculture The origins of the time classification for agricultural work processes are attributed to time studies conducted by Seedorf (1919). With the aim of improving time studies, v. Bismarck and Buchholz (1931) focussed on a permanent optimization of structuring and organization of work processes. They differentiated between preparation time, operation time, non-productive time and fault time. Since the mid 1950s, this time classification has been further developed and applied by integrating additional element times. According to Daelemans (1977), a method (known as CIOSTA-method) used to determine the working time requirement was already reported during the sixth CIOSTA-congress held in Helsinki in 1955. The relevant element times were divided into five main groups: operation time, non-productive time, delay time, preparation time and transit time. This scheme was widely – although differently accentuated – used by various researchers and work groups pursuing different objectives (Figure 1). Figure 1: History of the development of the working time classification in Germany (incomplete) Seedorf, 1919 v. Bismarck and Buchholz, 1931 CIOSTA- method, 1955 Röhner, 1956 Schweizer, 1963 Krause, 1964 Kreher, 1969 KTBL-calculation method, 2000 TGL 80-22289, 1970 TGL 22289, 1974 Schmid, 1977 Hahn, 1984 Hermann, 1997 Sonnen, 2007 122 Auernhammer (1976) made a further contribution to the improvement of agricultural working time analysis by developing an integrated method to break down work processes and to describe their influencing factors. Due to the division of Germany after the Second World War, two different working time classifications developed, among which the TGL 22289 and the calculation method of the KTBL were the most frequently used in the former GDR and in western Germany, respectively. In comparison to the KTBL-calculation method with six element times, the TGL 22289 is characterised by eight element times resulting in a wider range of applications (Winkler and Frisch, 2014). Nevertheless, there is insufficient clarity and inadequate comparability making it difficult to understand the findings obtained from the analysis of agricultural work processes. 3. Requirements for a time classification Already Herrmann (1999) postulated a standardized and optimally structured time classification. The more detailed the subdivision of the total working time, the more precisely the element times will be defined. Thus, relevant information necessary for planning work processes will be sufficiently available to provide an accurate representation of reality. According to Fechner (2014), the expectations as well as the special requirements differ from user to user of the time classification (Table 2). A farmer is interested in obtaining significant information needed for evaluation of work and machine efficiency. Current knowledge of the technically suitable, economical and manageable methods is essential for optimazing production processes and farm management. The accountant benefits from the time classification by evaluating agricultural work performance of employees and cooperatives. Overall, the aim is to achieve a logical and transparent structure for the respective user promoting a high level of acceptance among users. Additionally, experience has shown that the existing working time classifications are no appropriate to consider the methods used in modern agriculture. Mechanized working methods are increasingly important. Technological progress and future trends in automated time recording (e.g. ISOBUS) also demand a revised time classification. Hereof, the following requirements are to be taken into account:  uncomplicated  sufficiently detailed  expandable  suitable for all kinds of work  applicable for automated time recording Table 2: Users of working time classification (Fechner, 2014; modified) User Information Farm management Accounting Effectiveness of the production methods Machine efficiency Weak point analysis Planning of alternative solutions Work performance control (time requirement) of employees Scope of a service (work of agricultural contractors or cooperatives) Research Details on work processes Causes for fault times Performance evaluation of new machines and working methods (simulation) 4. The new time classification for agricultural work processes The time classification is relevant to the recording of time of working persons as well as of machine running. Automated working time measurement is allowed by different machines statuses indicating activity or machine downtime. A precise definition is possible to specifically assign the statuses to element times. The new definition introduces three main categories (operation time, process time, total time) which can be subdivided into further levels (Figure 2). The notation to describe the different element times uses t with corresponding numeric identifiers. The sum of operation time, fault time and non-productive time (level 1) represents the total working time. Overall, the following definitions apply to the revised time classification:  tH operation time = t1  tP process time = t1 + t2  tG total time = t1 + t2 + t3 123    Figure 2: Working time classification for agricultural works tG total time tp process time level 1 level 2 level 3 level 4 t1 operation time t11 execution time t111 execution time t112 loaded drive t1121 at work site t1122 on the road t113 unloaded drive t1131 at work site t1132 on the road t114 checking t12 turning time t13 loading and unloading time t131 loading with shunting t1311 shunting t1312 loading t132 unloading with shunting t1321 shunting t1322 unloading t14 inherent delay time t15 adjustment time t16 relaxation allowance t2 fault time t21 functional and technical fault time t211 functional fault time t212 technical fault time t22 fault time due to work organization t23 fault time due to weather t24 contingency allowance t3 non- productive time t31 supply time t311 at starting point t312 at work site t32 job preparation time t321 set-up time t3211 at starting point t3212 at work sitet322 operation briefing t33 job closing time t331 shut- down time t3311 at starting point t3312 at work sitet332 operation briefing t34 transit time t341 drive t3411 farm to field t3412 farm to storage t3413 field to storage t3414 field to field t35 servicing time t351 at starting point t352 at work site aggregation level 124 4.1 Operation time (t1) The operation time comprises execution time which consists of – divided into two additional levels – execution time in a narrower sense including travelling time for transports (loaded or unloaded drive) and checking the resut of the operation. Returning to the second level, the time for loading and uploading means of production (e.g. fertilizer, seeds), the adjustment of the working equipment as well as further adjustment of working processes and the turning time belong to the operation time. The turning time describes the required time for changing the working direction of working equipment (e.g. at the headlands). Additionally, a distinction is made between the inherent delay time and the relaxation allowance due to work load. Interruptions due to personal reasons are not included. 4.2 Fault time (t2) The fault time describes unplanned faults. These times refer to functional and technical faults. Technical faults represent the need to repair agricultural machinery faults or to replace defective elements, devices or machines. In contrast, functional faults are defined as times for putting work equipment into operation again without modifying elements of the equipment and without material consumption. Besides, fault times are caused by work organization, contingency allowance or the weather. 4.3 Non-productive time (t3) The non-productive time is defined as planned activities which are essential to maintain sustainability of the work system and to restore the initial system. The supply time includes filling or unloading means of production and further additives. There is a precise differentiation between starting point and work site. This also applies – within the job preparation time and the job closing time – to the machine set-up time (needed e.g. for setting the machinery for the required working width, working height or working depth) as well as to the corresponding shut-down time. Set-up times involve set-up times at the farm and set-up times in the field. In this context, the operation briefing contributes to the success of the various work processes. Additionally, the transmit time and the serving time for preventive maintenance of working equipment are part of the non- productive time. 5. Conclusions The recording of working time data is the fundamental prerequisite for the calculation of standard times needed for the modelling of work processes. It is very important to detail the influencing factors affecting the individual work elements. The resulting simulation of working time requirement is useful to combine single work processes as well as to analyze complex systems. Among other aspects, an enhanced acceptance of the revised time classification is ensured by taking the automated time recording into consideration. The presence of modern communication interfaces at agricultural machinery allows a comfortable data collection with comprehensive parameter information of the machine status and production process. The presented time classification has been proven effective in practice and is suitable for providing data for farm planning and process optimization. Acknowledgments The authors would like to thank the following persons for the joint work in the KTBL working group “Arbeitswirtschaftliche Grundlagen” and constructive cooperation in revising the time classification: Mathias Funk, Bernhard Haidn, Matthias Schick, Johannes Sonnen, Thilo Steckel and Elisabeth Quendler. Reference Auernhammer H., 1976, Eine integrierte Methode zur Arbeitszeitanalyse, Planzeiterstellung und Modellkalkulation landwirtschaftlicher Arbeiten, dargestellt an verschiedenen Arbeitsverfahren der Bullenmast, PhD thesis, Technical University of Munich, Munich, Germany. Bismarck von L., Buchholz H., 1931, Methodik und Technik der Arbeitsbeobachtungen in der Landwirtschaft, Paul Parey, Berlin, Germany. Daelemans J., 1977, Arbeitsorganisation und Arbeitszeitbedarf mechanisierter Feld- und Transportarbeiten, PhD thesis, Justus Liebig University Giessen, Giessen, Germany. Fechner W., 2014, Anforderungen an ein Zeitgliederungsschema in der Landwirtschaft, Proceedings of the 19. Arbeitswissenschaftliches Kolloquium des VDI-MEG Arbeitskreises Arbeitswissenschaften im Landbau, 11-12 March 2014, Dresden, Germany, 7-13. Gindele E. H., 1972, Die Bedeutung agrarstruktureller Elemente für eine rationelle Arbeitserledigung in der Feldwirtschaft, KTBL-Schrift 202, Landwirtschaftsverlag, Münster-Hiltrup, Germany. 125 Hahn J., 1984, Die Zeit in der Analyse und Synthese technologischer Prozesse, habilitation thesis, Humboldt University of Berlin, Berlin, Germany. Hammer W., 1976, Arbeitszeit- und Beanspruchungsfunktionen, KTBL-Schrift 202, Münster-Hiltrup, Germany. Herrmann A., 1999, Modellierung verfahrenstechnischer Bewertungskriterien bei unterschiedlicher Verknüpfung von Ernte- und Transportarbeitsgängen, PhD thesis, Martin Luther University Halle- Wittenberg, Halle, Germany. Herzog C., Schick M., 2014, Methoden zur automatisierten Messung von Arbeitszeit in Gebäuden, Proceedings of the 19. Arbeitswissenschaftliches Kolloquium des VDI-MEG Arbeitskreises Arbeitswissenschaften im Landbau, 11-12 March 2014, Dresden, Germany, 123-127. Hesse P., 1925, Die Bestimmung landwirtschaftlicher Arbeitsleistungen mit Hilfe von Arbeitsstudien, Paul Parey, Berlin, Germany. Jäger P., 1991, Zeitbedarf von Feldarbeiten. Berechnung der Teilzeiten für die Arbeit am Feld – Teil 2, Landtechnik 46, 123-128. Krause V., 1964, Anleitung für Zeitstudien in der Landwirtschaft, Landarbeit und Technik 34, 45-84. Kreher G., 1969, Arbeitsvoranschlag; die Kalkulation der Arbeitszeit für Arbeits- und Zugkräfte im landwirtschaftlichen Betrieb, KTBL-Kalkulationsunterlagen für Betriebswirtschaft 1, Wolfratshausen, Germany. Röhner J., 1956, Zur Methodik der Zeitstudie in der Landwirtschaft, Paul Parey, Berlin, Germany. Schick M., 2006, Dynamische Modellierung landwirtschaftlicher Arbeit unter besonderer Berücksichtigung der Arbeitsplanung, Ergonomia, Stuttgart, Germany. Seedorf W., 1919, Die Vervollkommung der Landarbeit und die bessere Ausbildung der Landarbeiter unter Berücksichtigung des Taylorsystems, Deutsche Landbuchhandlung, Berlin, Germany. Schmid H., 1977, Zeitgliederung für Transport und Umschlag in der Landwirtschaft, Agrartechnik 27, 297-300. Schrade S., Keck M., Schick M., 2005, Determination of Working-Time Requirement in Suckler Cattle Farming Using a Combination of Recording Methods, proceedings of the CIOSTA-CIGR V Congress, 19-21 Septembre 2005, Hohenheim, Germany. Schweizer H., 1963, Beiträge zur Methodik der Arbeitszeiterhebung und Arbeitskalkulation in der Landwirtschaft, PhD thesis, ETH Zurich, Zurich, Switzerland. Sonnen J., 2007, Simulation von Ernteprozessketten für Siliergüter, PhD thesis, Humboldt University of Berlin, Berlin, Germany. Winkler B., Frisch J., 2014, Weiterentwicklung der Zeitgliederung für landwirtschaftliche Arbeiten, Proceedings of the 19. Arbeitswissenschaftliches Kolloquium des VDI-MEG Arbeitskreises Arbeitswissenschaften im Landbau, 11-12 March 2014, Dresden, Germany, 14-21. 126