In this thesis, a transferable concept is developed to identify possible automation potentials in existing processes in small and medium-sized enterprises, taking into account the following research questions: How does the existing process of machine labeling work? What are the weak points of the current process of machine labeling? How can possible weaknesses be eliminated and, in the best case, automated? Can the developed concept be transferred to other processes? This in turn raises the central research question of the scientific paper: How can a structured procedure for the identification of optimization potentials as a basis for process automation look like and what has to be considered when applying it?
To answer these research questions, existing concepts in the relevant literature are examined with regard to their advantages and disadvantages for the planned application. From this, a procedure divided into three main steps is developed, which offers different methodologies for the steps to be worked on. The process to be investigated and the desired primary goal of automation determine which of the methods should be used. Subsequently, the practical suitability of the developed concept is tested on the given example process of machine labeling of a medium-sized mechanical engineering company. The results of the exemplary application show that it is possible to develop a clearly structured and easy to handle concept for a medium-sized company, which recognizes the weak points and optimization potentials.
Table of Contents
Abstract
Table of Contents
List of Figures
List of Tables
List of Abbreviations
1. Introduction
1.1 Problem Definition
1.2 Literature Review
1.2.1 Existing concepts
1.2.2 Process
1.2.3 Automation
1.2.4 Small and medium-sized enterprises
1.3 Research Question
1.4 Hypotheses
2. Method
2.1 Object
2.2 Procedure and methods
2.2.1 Process analysis
2.2.1.1 Process identification
2.2.1.2 Process recording
2.2.1.3 Process documentation
2.2.1.4 Process evaluation
2.2.2 Target Concept Creation
2.2.2.1 Conception of target processes
2.2.2.2 Comparison to the current situation
3. Results
3.1 Process analysis
3.1.1 Process identification
3.1.2 Process recording
3.1.3 Process documentation
3.1.4 Process evaluation
3.1.5 Summary of the process analysis
3.2 Target Concept Creation
3.2.1 Conception of target process
3.2.2 Comparison to the current situation
4. Discussion
4.1 Confirmation / Disconfirmation Hypotheses
4.1.1 I. Hypothesis
4.1.2 II. Hypothesis
4.1.3 III. Hypothesis
4.2 Limitation
4.3 Implication
4.4 Conclusion
References
Annex
Abstract
As a result of the constantly increasing competitive pressure, small and medium-sized companies, which operate under worse conditions than large companies, are particularly challenged to improve their efficiency. In order to be able to play a role in the future as well, a progressive optimisation of current processes and procedures through automation solutions is essential.
In this thesis, a transferable concept for the determination of possible automation potentials in existing processes in small and medium-sized companies is developed, taking into account the following research questions: How does the existing process of machine labelling work? What are the weaknesses of the current machine labelling process? How can possible weak points be eliminated and, in the best case, automated? Can the developed concept be transferred to other processes? This in turn raises the central research question of the scientific work: How can a structured procedure for identifying optimisation potential as a basis for process automation look like and what should be considered when applying it?
In order to answer these research questions, the existing concepts of the relevant literature are examined with regard to their advantages and disadvantages for the planned application. From this, a procedure structured in three main stages is developed, which offers different methodologies for the steps to be processed. Here, the process to be analysed and the desired primary goal of automation decide which of the methods are to be used. Subsequently, the practicability of the developed concept is tested on the given example process of machine labelling of the medium-sized F Maschinenbau GmbH.
The results of the exemplary application show that it is possible to develop a clearly structured and easy-to-use concept for a medium-sized company, which reveals and enables the weak points and optimisation potentials of an existing process with regard to automation and eliminates the disadvantages of previous concepts. In order to eliminate the weak points, possible remedial measures are defined in a target-performance comparison, which must subsequently be examined for their economic efficiency. In this way, the client hopes to increase the degree of automation and process reliability as well as to reduce costs.
Due to the better readability only the masculine form is used in this thesis. The female and neutral form is of course always included.
List of Figures
Figure 1. Model for Profitability analysis for industry 4.0-basic technologies (following Obermaier, 2019, p. 194)
Figure 2. Procedure Process analysis (following Glitsch, 2020, p. 4)
Figure 3. Development plan of the project management (following Glitsch, 2020, p. 9)
Figure 4. Simplified process illustration (following Best & Weth, 2009, p. 63)
Figure 5. Dimensions of Industry 4.0 (following Obermaier, 2019, p. 84)
Figure 6. Development of F Maschinenbau GmbH company site (F Maschinenbau GmbH, 2020c)
Figure 7. Structured concept for identifying optimisation potential as the basis for process automation (following (Best & Weth, 2009, p. 62; Glitsch, 2020, 9; Obermaier, 2019, p. 194)
Figure 8. Process analysis steps (following (Best & Weth, 2009, p. 62; Glitsch, 2020, p. 4)
Figure 9. Process description after the demarcation (following Kadensky , 2014, p. 4)
Figure 10. Exemplary Value Stream Symbols (Roland Schnurr, 2017)
Figure 11. Exemplary Current State Value Stream Map (Ross & Associates Environmental Consulting Ltd., 2007, p. 28)
Figure 12. Example of a Swim Lane Diagram or Cross functional flowchart (Chirag Kansal, 2018)
Figure 13. Example of a process FMEA worksheet (following Melzer, 2019, pp. 155-158; Meran et al., 2014, pp. 170-176)
Figure 14. F Maschinenbau GmbH Type plate (F Maschinenbau GmbH, 2020a)
Figure 15. Pictogram “Do not put your hands inside” (Deutsches Institut für Normung e.V., 2012, p. 81)
Figure 16. Extract of the machine and product type plate production process description
Figure 17. Standard labelling for product Compact Filter KF (F Maschinenbau GmbH, 2011, p. 438)
Figure 18. Top 10 quantity of sales in 2019 from SAP
Figure 19. Standard signage best-selling products from the combination of the F Standard-ABC with the data of the SAP system
Figure 20. Extract from exemplary guide
Figure 21. Legend for the symbols of the process documentation (following Standard flowchart shapes of Microsoft Visio Professional 2013)
Figure 22. Swim Lane Diagram - Machine labelling
Figure 23. Flow chart for sub-process of the Swim Lane diagram - Machine and product type plate production
Figure 24. Excerpt from Microsoft SharePoint list for type plate production (F Maschinenbau GmbH, 2020a)
Figure 25. Standard machine type plate blank (F Maschinenbau GmbH, 2011)
Figure 26. Standard product type plate blank (F Maschinenbau GmbH, 2011)
Figure 27. Manual input mask laser programme
Figure 28. Standard electric type plate (F Maschinenbau GmbH, 2011)
Figure 29. Flow chart for sub-process of the Swim Lane diagram - Electric type plate production
Figure 30. Example of an electrical type plate template from CorrelDraw
Figure 31. Example sign Caution (F Maschinenbau GmbH, 2011)
Figure 32. Example sign Notice (F Maschinenbau GmbH, 2011)
Figure 33. Example sign Danger (F Maschinenbau GmbH, 2011)
Figure 34. Flow chart for sub-process of the Swim Lane Diagram - Warning and information sign production
Figure 35. Columns AFA (following Wiegand & Franck, 2017, pp. 101-102)
Figure 36. Extract from the AFA of the engravers in the main warehouse
Figure 37. Process FMEA template (following Melzer, 2019, pp. 155-158; Meran et al., 2014, pp. 170-176)
Figure 38. Legend for the illustration of the process evaluation
Figure 39. Evaluated process of the machine labelling
Figure 40. Evaluated process of the machine and type plate production
Figure 41. Evaluated process of the electric type plate production
Figure 42. Evaluated process of the warning and information sign production
Figure 43. Target process machine and product type plate production
Figure 44. Numbered target process for comparison with the current process
List of Tables
Table 1. Procedure steps of the process analysis according to Best and Weth (Best & Weth, 2009, p. 62)
Table 2. Comparison of existing concepts regarding applicability (following Best and Weth (2009), Obermaier (2019) & Glitsch (2020))
Table 3. Definition of SMEs according to EFAM (following Becker et al., 2020, p. 18)
Table 4. Possible definitions of the level of detail (following Best & Weth, 2009, p. 67)
Table 5. Comparison of VSM and Swim Lane Diagram regarding suitability
Table 6. Example of activity focus analysis (following (Wiegand & Franck, 2017, p. 103) . 28 Table 7. Reasons for an FMEA (Deutsche Gesellschaft für Qualität DGQ, 2015, p. 113)
Table 8. Example for the evaluation criteria of an FMEA (following i-Q Schacht & Kollegen Qualitätskonstruktion GmbH, 2020; Meran et al., 2014, pp. 170-176)
Table 9. Possible template for the comparison between target and current situation
Table 10. Adapted evaluation criteria (following i-Q Schacht & Kollegen Qualitätskonstruktion GmbH, 2020; Meran et al., 2014, pp. 170-176)
Table 11. Recommended further steps depending on colour combination
Table 12. Comparison of target process to current process
List of Abbreviations
Activity Focus Analysis
American National Standards Institute
German/English
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1. Introduction
1.1 Problem Definition
Small and medium-sized enterprises (SMEs) are considered to be a motor for economic growth and employment in Germany. SMEs differ from competing large companies in that they have fewer resources available in relative terms. This may be due to insufficiently qualified personnel or to major financial bottlenecks. Nevertheless, they are exposed to the same competitive conditions (Becker, Ulrich, Schmid, & Feichtinger, 2020, p. 17). To be able to survive in this competition, the need for Industry 4.0 and automation solutions is growing. But here too, equal opportunities do not exist.
Previous Industry 4.0 concepts mainly refer to full automation in large manufacturing or service companies. However, this perspective cannot be transferred to small and medium-sized enterprises, as their economic success does not depend on scales and economies of scale but on the knowledge and skills of their own employees. (Becker et al., 2020, p. 22)
In addition to the lack of resources, SMEs have other structural deficits with regard to the possible automation of internal processes and the hoped-for increase in competitiveness. Many of the SMEs were founded as one-man businesses and have grown into medium-sized companies within just one generation. This is one of the reasons why many of the in-house processes have grown disproportionately fast with the increasing number of units and product variations. Due to a lack of time and knowledge, attention was seldom paid to whether the added activities would create added value or instead lead to the creation of error potentials (through human work steps) or an increase in costs (through waste) (Baszenski, 2015, pp. 13-14; Becker et al., 2020, p. 17; Bosse & Zink, 2019, p. 144). The only premise was a functioning process that produces the desired result.
As these companies were often pioneers for a technology or a new product, the competitive situation was manageable at the beginning. Today, this luxurious market situation can hardly be found anymore, especially in the industrial sector. Rather, according to Obermaier (2019, p. 43), an “increase in process efficiency [...] to maintain competitive cost structures is considered necessary” and a concrete need for action in the direction of Industry 4.0 can be identified (Obermaier, 2019, p. 92).
Marbler and Bley (2018, p. 11) showed in their 2017 study “ Industry 4.0 in German medium-sized businesses” that SMEs have recognised this necessity. With the help of an independent market research institute, they surveyed 1,157 medium-sized industrial companies with 30 to 2,000 employees in Germany. Of the industrial companies surveyed, 36% stated that their main aim in investing in digital technologies was to reduce production costs or increase efficiency (Becker et al., 2020, pp. 30-31; Marbler & Bley, 2018, p. 11). This coincides with the statements of Bosse and Zink (2019, p. 13), according to which the majority of SMEs have now recognised the relevance of digitisation and automation. Nevertheless, “most of them are still in the early stages of implementation” (Bosse & Zink, 2019, p. 13). “The reasons for this are the various challenges, such as the lack of employee skills or the lack of a strategic orientation of digitisation, [...] as well as the overarching question of how digital change should be approached” (Bosse & Zink, 2019, p. 13). SMEs lack experience “where and how best to start” (Bosse & Zink, 2019, p. 154). In addition, activities to increase efficiency are often associated with large investments and development efforts, which SMEs find difficult to implement (Bosse & Zink, 2019, p. 154).
The basic challenges for SMEs are therefore the systematic identification of automation potentials, a structured approach and the formulation of a suitable corporate strategy, not a lack of knowledge of the necessity (Bosse & Zink, 2019, pp. 14-15). The intention of this master's thesis is therefore to show small and medium-sized companies a structured and generally valid concept for the approach to developing automation solutions for a process. This will be tested on the example of the process of machine labelling at F Maschinenbau GmbH.
1.2 Literature Review
In order to be able to create a transferable concept for the structured approach in SMEs to the planned process automation, existing concepts are reviewed and examined with regard to their structure. In addition, for a uniform understanding it is necessary to establish valid definitions for the terms process, automation and SMEs in the context of the work.
1.2.1 Existing concepts.
In the course of the literature search, three different recommendations or procedures were selected for the structured identification of possible optimisation potentials:
- Process analysis and problem diagnosis, within the framework of potential analysis, for the optimisation of business processes (Best & Weth, 2009)
- The process and potential analysis to identify economic potential for industry 4.0 investments (Obermaier, 2019)
- Process analysis in four steps with subsequent optimisation (Glitsch, 2020)
These recommendations are briefly presented and serve as a basis for the approach to be developed.
Best and Weth (2009)
In their book on business process optimisation Best and Weth present process analysis as “one of the most complex activities [...] - and also one of the most critical” (Best & Weth, 2009, p. 62). If the analysis is not carried out properly, the entire optimisation project is built on a “shaky foundation” (Best & Weth, 2009, p. 62). They recommend the following steps as a basis for the “systematic diagnosis of the causes of the problem” and identification of the optimisation potential (Best & Weth, 2009, p. 62):
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Once these steps have been taken, the information gathered and the weaknesses must be defined into clear problems. Best and Weth (2009) use specific checklists to categorise these problems (Best & Weth, 2009, pp. 86-93). Obermaier (2019)
Obermaier (2019) defines the current processes of production as the starting point for estimating potential profitability (Obermaier, 2019, p. 193). Therefore Obermaier proposes a process and potential analysis in five steps (Obermaier, 2019, p. 193). Compared to Best and Weth (2009), this model, which is shown in Figure 1 is coarser in its level of detail.
As-is process modelling:
Identification of weak points and optimisation potentials
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Identification of economic potentials through target-actual comparison
Potential analysis:
Quantitative, monetary and qualitative evaluation of the potential for economic efficiency
Figure 1. Model for Profitability analysis for industry 4.0-basic technologies (following Obermaier, 2019, p. 194)
Obermaier refers in his explanations of the individual steps to the example of a Manufacturing Execution System (MES) (Obermaier, 2019, pp. 194-201). This impairs the general validity of the approach towards a qualitative and quantitative evaluation of the profitability potential of Industry 4.0 investments (Obermaier, 2019, p. 201).
Glitsch (2020)
Glitsch (2020, p. 2) clearly states that “every process project must begin with process analysis”, because this is the first time that processes are made visible (Glitsch, 2020, p. 2). She names four reasons and occasions for this:
- Introduction of a quality management system
- Tracing the process flow to configure software
- Survey of staffing needs in order to adapt them
- Process digitisation / automation (Glitsch, 2020, pp. 8-9)
The approach it proposes consists of the four steps shown in Figure 2.
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Glitsch sets as a goal uniformly, correctly and fully documented processes with clearly identified optimisation potential (Glitsch, 2020, p. 4). The following methods, among others, are conceivable on the way to this goal:
- Interviews
- Workshops
- Questionnaire
- Job analyses (Glitsch, 2020, p. 5)
After the successful process analysis, the project moves on to process optimisation, in which the target state is designed (Glitsch, 2020, p. 9).
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Figure 3. Development plan of the project management (following Glitsch, 2020, p. 9)
Comparison of existing concepts
In order to classify the existing concepts in terms of their usability, the comparison shown in Table 2 was made. The concepts were evaluated in four categories with regard to their general applicability, their reference to methods, the demonstration of a systematic approach and the level of detail of the steps.
Table 2.
Comparison of existing concepts regarding applicability (following Best and Weth (2009), Obermaier (2019) & Glitsch (2020))
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Obermaier's approach (2019) cannot demonstrate universal validity due to the specific focus on the economic potential of introducing an MES. In the second category, the reference to methods for the individual steps, the recommendation of Glitsch (2020) has deficits, as only four methods are briefly indicated. The approach of Best and Weth (2009), with eleven individual steps on one level, is too detailed and thus has a red X in the fourth category. All three approaches have a systematic and structured approach, which is why no X is given in the third category.
None of the existing concepts offers a flawless structure for the criteria on which the work is based. In order to compensate for the weaknesses (red X) of the individual approaches, a separate concept will be developed (see 2.2, p. 16), which is based on the advantages (green tick) of the respective existing recommendations.
1.2.2 Process.
A process is a sequence of logically connected activities, so-called process steps. These provide a service or transfer an object from a defined initial state to a desired final state (Best & Weth, 2009, p. 63). Each process follows the same laws:
- Processes can be repeated and sometimes run parallel to other processes.
- Processes are always characterised by a clearly defined start and end point.
- There is an input-output relationship between successive processes.
- The output of one process flows as input into at least one subsequent process (see Figure 4) (Best & Weth, 2009, p. 63).
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Figure 4. Simplified process illustration (following Best & Weth, 2009, p. 63)
1.2.3 Automation.
If something happens automatically in everyday life, it is assumed that there is no need to intervene in this process. The same applies in the technical environment. Appropriately, the international electrotechnical vocabulary (under entry 351-42-30) defines the term automatically as “a process or device that runs or operates under specified conditions without human intervention” (DKE Deutsche Kommission Elektrotechnik Elektronik Informationstechnik in DIN and VDE, 2017). This, together with the following comments from Plenk 2019, describes what is meant by the term automation in this paper.
Automation technology is a field of engineering science that includes parts of mechanical engineering, electrical engineering and information technology and deals with measures to be able to operate machines or plants without human involvement. The better this goal is achieved, the higher the degree of automation. In a highly automated factory, people usually take over the monitoring of the machines [...] while the present productive processes run automatically. (Plenk, 2019, p. 1)
Automation solutions basically have the same structure. They consist of the “actual machine, an information-processing component that generates the automatic sequence, and sensors and actuators that connect the two components” (Plenk, 2019, p. 2). The development is pursuing the objectives here:
- Increase in productivity,
- Reduction of production times,
- F acilitation of human work,
- Reduction of costs,
- Increase in quality. (Heinrich, Linke, & Glöckler, 2020, p. 6)
As the terms automation, industry 4.0 and digitisation are often used synonymously in linguistic usage, a brief definition of the terms is provided. The term Industry 4.0 originally stands for a future project of the German Federal Government (Obermaier, 2019, p. 6) and is better known internationally as “Industrial Internet” (USA) or “Factories of the Future” (EU) (Obermaier, 2019, p. V). In the literature, Industry 4.0 is described as “a form of industrial value creation characterised by (extensive) digitisation, automation and networking of all actors involved in value creation” (Obermaier, 2019, p. 7). The aim is thus to achieve closer integration of the digital and physical worlds by “digitising [and automating] processes, reducing interfaces and harmonising IT systems” (Bosse & Zink, 2019, p. 154). Accordingly, automation is only one of the three dimensions of the holistic approach Industry 4.0 (see Figure 5).
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Figure 5. Dimensions of Industry 4.0 (following Obermaier, 2019, p. 84)
As shown in Figure 5 digitisation is also a sub-sector of Industry 4.0, and the synonymous use is probably due to the fact that in most cases automation is not possible today without digitisation. This becomes clear when the general structure of an automation solution described on page 9 is recalled. There it speaks of an “information processing component” (Plenk, 2019, p. 2), which must have the information available in digitalised form. The term “digital automation” also appears in the literature (Wischmann & Hartmann, 2018, p. 177). This is underlined by the initial use of the keyword digitisation in the 1960s, when Automatically Programmed Tools were first used to automate manufacturing processes through electronic control commands (Garrel, 2019, p. 11). Digitisation thus enables automation, but it is not the same as automation because it also takes a more holistic approach (Garrel, 2019, pp. 11-12).
That digitisation goes beyond automation is also shown by the generally recognised stages of the industrial revolutions. There, mechanisation is followed first by automation and then digitisation (Becker et al., 2020, p. 9; Obermaier, 2019, p. 10). This makes it clear that the subject areas build on each other, but that synonymous use of terms should be avoided. The use of Internet technologies since the 1990s and the so-called Internet of Things has enabled networking between physical objects and digital information, leading to new application scenarios (Garrel, 2019, p. 12). Examples include driverless transport systems or Internet- enabled machines that communicate with each other. It is therefore becoming increasingly difficult to draw a clear line between digitisation and automation, and the topics are thus becoming more closely interlinked.
Despite numerous and long-standing examples of success, automation is often viewed critically even today (Plenk, 2019, p. 1). However, the use of these technologies is not primarily intended to rationalise jobs away, but rather to improve working conditions and increase the attractiveness of work and the sustainability of a company. After all, “processes and procedures can be made more efficient, for example to create resources for new projects, orders and activities” (Bosse & Zink, 2019, p. 27).
1.2.4 Small and medium-sized enterprises.
SMEs are considered “as a motor for economic growth and employment” (Becker et al., 2020, p. 17). At the same time it is undisputed that automation is also essential for SMEs and that there is a clear need for action (Obermaier, 2019, p. 92). However, opinions on what is meant by the term SMEs are diverse (Becker et al., 2020, p. 18). Three common definitions have emerged in Germany.
The first is the European Commission's recommendation, which sets the ceiling for companies “employing fewer than 250 persons and having an annual turnover not exceeding EUR 50 million” (Europäische Kommission, 2003, p. 39). The Institute for SME Research (IfM) in Bonn, on the other hand, has raised the limit for the number of employees to 499 in order to take account of the structural particularities of the German economy in a European comparison (Institut für Mittelstandsforschung (ifm) Bonn, 2016). As a third definition, the classification of the European Research Area for Applied SME Research (EFAM) has become established. This is also used in the relevant literature (Becker et al., 2020). The EFAM definition takes into account not only quantitative but also qualitative characteristics, which is why it is most suitable for the present work and forms the basis of the study. Accordingly, the following are considered as SMEs:
- all family businesses and owner-managed companies
- management-run companies with up to 3,000 employees and/or a turnover of EUR 600 million
- Enterprises with both previous characteristics (Becker et al., 2020, p. 18).
This “combined, qualitative-quantitative view serves as a depiction [...] of the operational reality of small and medium-sized enterprises” (Becker et al., 2020, p. 18) and is presented in a simplified form in the Table 3 for overview purposes.
Table 3. Definition of SMEs according to EFAM (following Becker et al., 2020, p. 18)
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1.3 Research Question
As briefly described in the problem definition, many SMEs ask themselves how they should approach the automation of an existing process. In this thesis, a concrete example is used to show a way to uncover the possible optimisation potential and to derive suitable measures for automation. In order to achieve this, the following questions are dealt with in the scientific work:
- How does the existing process of machine labelling work so far?
- What are the weaknesses of the current machine labelling process?
- How can possible weak points be eliminated and, in the best case, automated?
- Can the developed concept be transferred to other processes?
The central research question therefore focuses on what a structured approach to identifying optimisation potential as a basis for process automation looks like and what should be considered when applying it.
1.4 Hypotheses
The main objective of the work is to develop a structured and generally valid concept for the approach to developing automation solutions for a process. This will then be applied to an example process of F Maschinenbau GmbH, in order to enable the automation of this process after the thesis.
The following hypotheses were derived from the research question:
I. In the relevant literature there is more than one recommended approach to the automation of an existing process. However, it is possible to unify the existing concepts into a unified approach in the form of a general recommendation on how to automate an existing process.
II. In order to automate an existing process, complete transparency is required regarding the previous process.
III. The developed concept uncovers the weak points of a process and reduces the error rate by at least 10%.
2. Method
In the second part, the developed way to identify optimisation potentials and the subsequent derivation of possible measures is shown. This includes not only the theoretical explanation of the methods to be considered but also the description of the object to be examined.
2.1 Object
F Maschinenbau GmbH was founded in 1970 in southern Germany. The first product of the mechanical engineering company was a hydro cyclone filter system, with which cooling lubricants used in metal processing could be cleaned (F Maschinenbau GmbH, 2020b). Since 2004 the sons of the founder, L. and S. F., have been managing the company and developed it (see Figure 6) into one of the leading suppliers of conveying and filtering systems for metal chips and cooling lubricants in the metalworking industry (F Maschinenbau GmbH, 2020c).
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Figure 6. Development of F Maschinenbau GmbH company site (F Maschinenbau GmbH, 2020c)
Currently F Maschinenbau GmbH employs 1,093 people (as of August 2020). Of these, 591 are production employees (54 %), 382 administrative staff (35 %) and 120 trainees (11 %). In addition to the main factory in Germany, F Maschinenbau GmbH also has two further production sites in Poland and China as well as numerous sales offices worldwide (F Maschinenbau GmbH, 2020c).
Within the company, the Quality Control department, under the generic term Smart Factory, is responsible for the effectiveness and efficiency of the company, aligned with the corporate strategy and the goals derived from it. For this purpose, classical methods from quality management, such as Failure Mode and Effect Analysis (FMEA) and Measurement System Analysis (MSA), are applied with the aim of producing a given result permanently and repeatedly. If these prerequisites are given, lean management methods can be used to concentrate on the efficiency of the achievement of objectives.
The client F Maschinenbau GmbH specified the process of machine labelling / signage as the object to be examined. The reason for this is a high error and failure rate, which according to the client can be attributed to a high manual effort and the expert knowledge of individual employees. Accordingly, a high automation potential can be expected. Since the client wants to use the developed concept to uncover optimisation potential of further processes in the future and thus achieve an increase in efficiency, the final thesis is located in the Smart Factory area.
2.2 Procedure and methods
This chapter outlines the planned procedure in the main part of the study and the methods to be applied. For this purpose, a structured concept was created (see Figure 7) in order to identify the optimisation potentials as a basis for automation. This combines the advantages of the comparison in Table 2 (p. 7) of the concepts described in 1.2.1 (p. 3).
According to Best and Weth (2009, p. 62), process optimisation can also take place without analysing the current status. However, in the event of a lack of process knowledge, important detailed information is lost and the necessary transparency for diagnosing the causes of the problem, which in turn form the basis for improvements, would be missing (Best & Weth, 2009, p. 62).
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Figure 7. Structured concept for identifying optimisation potential as the basis for process automation (following (Best & Weth, 2009, p. 62; Glitsch, 2020, 9; Obermaier, 2019, p. 194)
2.2.1 Process analysis.
The objective of an economically thinking company is to improve quality significantly while reducing production time and costs (Glitsch, 2020, pp. 8-9). In order to achieve this, it must be clear where the weak points lie and any potential is hidden, within the company. For this purpose, it is questioned which processes exist, how they work and at which point problems arise. Since none of the concepts considered in the Literature Review (p. 3) offers a perfect approach (see Table 2, p. 7), the first section of the concept created (see Figure 7) is used as shown in Figure 8 as answer to these questions and thus as a starting point for the subsequent optimisation of the processes.
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Figure 8. Process analysis steps (following (Best & Weth, 2009, p. 62; Glitsch, 2020, p. 4)
2.2.1.1 Process identification.
Before starting the actual process analysis, the process to be optimised must be clearly identified and demarcated (Best & Weth, 2009, p. 64). Already available data are also helpful here, which can provide important information within the scope of a secondary data analysis (p. 19).
Process demarcation
To be able to clearly demarcate a process, the starting and end points of the process must be precisely defined first (Best & Weth, 2009, p. 64). This also includes defining the organisational units involved in the process and determining the level of detail (Best & Weth, 2009, p. 69). This is elementary, since the level of detail not only determines the information content of the analysis results, but also the effort required (Best & Weth, 2009, p. 66). This raises the question of the need for information (Best & Weth, 2009, p. 66). It should not even be attempted to capture all the small details of a process (Best & Weth, 2009, p. 66). An excessively detailed approach usually swallows up an unnecessary amount of time and resources without contributing to a better understanding of the process (Best & Weth, 2009, p. 66).
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Process name:
Purpose:
Customers of the process: Organisational units involved:
Outcome:
Input (Trigger):
First Process step:
Last Process step:
Intersections:
Resources required:
Success factors:
Figure 9. Process description after the demarcation (following Kadensky , 2014, p. 4)
Secondary data analysis
In order to obtain a company-related overview of a process, existing data and documents are included. These data and documents are stored, for example, in the Enterprise Resource Planning (ERP) system during process preparation or execution. In the best case because of a necessity for the process flow. In the worst case without being used further. As soon as existing data is used to carry out an evaluation or analysis that does not serve its original, primary purpose, this is referred to as secondary data analysis (Arbeitsgruppe Erhebung und Nutzung von Sekundärdaten [AGENS], 2012, p. 12). “The classification as secondary data is based on the differences between the primary reason for collecting the data and its subsequent use” (AGENS, 2012, p. 12). For example, quantity evaluations can be made from ERP data to determine which process variants can be left out of the process definition. If the data is collected anew and only for the purpose of answering an already existing research question, it is so-called primary data (AGENS, 2012, p. 11).
2.2.1.2 Process recording.
“There are two main classic methods for (as-is) process analysis: structured interviews and workshops” (Best & Weth, 2009, p. 70). Which method is chosen depends on the process to be analysed. A workshop is suitable if the process has a high level of interaction between the process steps, e.g. through loops, and the process knowledge of the participants is approximately the same (Best & Weth, 2009, p. 70). Interviews are particularly interesting when experts from different hierarchical levels and departments with different perspectives on a process are to be interviewed (Best & Weth, 2009, pp. 70-71; Obermaier, 2019, p. 195).
Interviews
In order to carry out structured interviews, two main types of surveys can be distinguished. The so-called narrative interview, which is similar to a narrative, or a guideline interview (Mayer, 2013, p. 37).
If the course of the individual case and the context of experiences are at the forefront of the interview, narrative methods such as the narrative interview are usually preferable. If concrete statements about an object or process are the aim of the data collection, a guideline interview is the more economical way. (Mayer, 2013, p. 37)
A guideline interview is characterised by open and unspecific questions, which are freely answered by the interviewee (Mayer, 2013, p. 44, 2013, p. 37). A guideline developed on the basis of preliminary considerations on the problem area is used as an aid (Lamnek & Krell, 2016, p. 347). It is intended to serve as a rough orientation and memory aid and to prevent the interview from becoming a “cosy chat” (Best & Weth, 2009, p. 72; Lamnek & Krell, 2016, p. 347). Above all, it serves to delete subject areas that the interviewee has already sufficiently addressed on his or her own initiative (Lamnek & Krell, 2016, p. 347). The interviewer should therefore not stick too rigidly to the guidelines, but should decide for himself when to ask detailed questions or return to the guidelines if the interviewee has become excessive (Mayer, 2013, p. 47, 2013, p. 37).
The procedure described by Mayer (2013) as an expert interview and by Lamnek & Krell (2016) as a problem-centred interview should be highlighted as special forms of the guideline interview. In both procedures, the interviewee is not interesting as a person but as an expert for certain fields of action. The interview concerns a clearly defined section of reality. The interviewee is seen here as a representative of a group and the guideline has an even stronger steering function to exclude unproductive topics. The central task is to limit and define the expert to the field of interest (Mayer, 2013, p. 38). In doing so, an existing rough scientific concept may be modified by the respondent. These guided interviews differ from narrative interviews for the simple reason that the interviewer does not enter the survey phase without any prior knowledge (Best & Weth, 2009, p. 72; Lamnek & Krell, 2016, p. 345). Employees involved in the process serve as a source of information (Obermaier, 2019, p. 194). These should have clearly accessible knowledge in the limited field (Mayer, 2013, pp. 41-42). The experts here come from different hierarchical levels and departments. This is because the experts for a process are often not to be found at the top level, but rather further down, where decisions are prepared (Mayer, 2013, pp. 41-42).
The aim of evaluating such qualitative surveys is to filter out what they have in common by comparing the interview texts (Mayer, 2013, p. 47). The different points of view of the experts must also be taken into account (Mayer, 2013, pp. 41-42). It should also be noted that one's own half-knowledge of the process is not interpreted into the findings (Best & Weth, 2009, pp. 70-71).
Workshops
The implementation of a process initiation workshop creates transparency and understanding for the entire process among the participants, even though they only participate as experts for one process step (Best & Weth, 2009, p. 70). At the same time, weak points can be identified and possible measures derived (Best & Weth, 2009, p. 70). The disadvantage of a workshop is that the results can be distorted by the presence of other process participants (Best & Weth, 2009, p. 70). Weaknesses and problems are deliberately concealed and dismissed in order to prevent the other participants from attacking them (Best & Weth, 2009, p. 70).
Regardless of which of the two methods is used, the findings should be supplemented by observation under live conditions. On the one hand, it considerably improves the interviewer's understanding of the process and, on the other hand, allows a comparison of the findings with practice (Best & Weth, 2009, p. 71).
2.2.1.3 Process documentation.
The graphical process documentation represents the core result of a process analysis (Best & Weth, 2009, p. 77). Which conversely means that the best interviews or workshops cannot develop their full potential if the information gained is not presented systematically (Best & Weth, 2009, p. 77). For graphical process modelling, both a value stream analysis (VSM) and a Swim Lane diagram are basically suitable. The decisive factor here is whether a complicated process flow is to be tracked across departments (swim lane diagram) or whether a manufacturing process controlled by a production system (VSM) is to be examined for its value added. Both methods are subject to the same basic rules. It must be clearly recognisable which task is to be assigned to which organisational unit and in which order these steps are performed. In this way, the relevant interfaces of the process steps and the associated inputs and outputs become apparent at the same time (Best & Weth, 2009, p. 77).
Value Stream Mapping
A VSM serves to obtain a detailed overview of the process and aims to show how the value is created in the process, i.e. where the added value takes place (Wiegand & Franck, 2017, pp. 67-68, 2017, pp. 67-68). Conversely, this means that it attempts to identify places where value is lost or value creation is prevented, for example, by stock levels or excessively long throughput times due to set-up times. This methodology was developed for process mapping in the manufacturing industry in order to visualise and analyse the sequence of production steps and their input and output (Wiegand & Franck, 2017, pp. 67-68). For this purpose, a catalogue of symbols was created to provide a general understanding (see Figure 10) (Wiegand & Franck, 2017, pp. 74-75).
Abbildung in dieser Leseprobe nicht enthalten
Figure 10. Exemplary Value Stream Symbols (Roland Schnurr, 2017)
It should be noted that the value stream analysis does not yet deal with the individual activities. In the second stage, these are considered in the detailed analysis using methods such as the activity focus analysis (AFA) (Wiegand & Franck, 2017, pp. 67-68).
At the beginning the start and stop events of the process must be defined (see Process demarcation, p. 17) (Meran, John, Staudter, Roenpage, & Lunau, 2014, pp. 191-193). The value stream is then always visualised from the stop event upstream (Meran et al., 2014, pp. 191-193). In the next step, both the material and information flows and associated buffer stores between the individual workstations are recorded (Meran et al., 2014, pp. 191-193; Stoesser, 2019, pp. 63-71). In addition, all process-relevant data, such as processing time, set-up time, reject rate, yield, machine availability, etc., must be captured (Meran et al., 2014, pp. 191-193). This is used to calculate the current time (cycle time), which is compared to the maximum available target time (customer interval time) (Stoesser, 2019, pp. 63-71). Once all data have been collected, they are modelled using the symbols shown in Figure 10 (see Figure 11).
Abbildung in dieser Leseprobe nicht enthalten
After the actual presentation, the value stream should still be validated by the interfaces involved in the process (Meran et al., 2014, pp. 191-193). The final step in the value stream analysis is to identify possible waste (e.g. makeready times, downtime, waste) and potential for improvement (Meran et al., 2014, pp. 191-193, 2014, pp. 191-193; Stoesser, 2019, pp. 63-71). In practice, these are often marked by lightning, so-called kaizen flashes at the relevant location.
At first glance, a value stream appears confusing to the untrained eye. However, it represents a good methodology for presenting and understanding the production process of an individual product or product group. It can be used both to visualise and analyse the present process and to represent the target process (Meran et al., 2014, pp. 191-193). Nevertheless, a detailed value stream is time-consuming to create and requires some practice. This is particularly the case for processes that do not always follow the same pattern and contain a large number of variants.
Swim Lane Diagram
By questioning and observing the employees involved in the process, a good impression of how a process works and which departments are involved in it is created. One method of visualising these findings and thus the process is to create a Swim Lane diagram (Meran et al., 2014, pp. 189-190) or also called process function / process flow diagram (Meran et al., 2014, pp. 189-190; Stoesser, 2019, pp. 72-75). The advantage over similar visualisation methods lies in the clear delineation of the various departments in complex, cross-departmental processes. Each department has its own Swim Lane, which contains the process steps it carries out (see Figure 12). In this way, the relevant information flow of a process can be tracked in one go (Stoesser, 2019, pp. 72-75).
Abbildung in dieser Leseprobe nicht enthalten
The Swim Lane diagram can be created to visualise the current process in the analysis phase as well as to model a possible target process in the improvement phase (Meran et al., 2014, pp. 189-190). In addition to the process visualisation, relevant interfaces can be shown, possible causes of errors can be verified and further potential for improvement can be identified (Meran et al., 2014, pp. 189-190).
Table 5 provides a decision aid as to which of the two methods is best suited to a particular scenario. This compares both applications with regard to their suitability for processes with many variants, the present and target modelling, their level of detail with regard to the individual points and the objective pursued.
Table 5.
Comparison of VSM and Swim Lane Diagram regarding suitability
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The table shows in column two that a VSM is not suitable for processes with many variants (red X). The reason for this is that it has a very high level of detail per production step with regard to the process-relevant data (see p. 23), which means that the effort involved would exceed the benefit for many variants. With a Swim Lane diagram, it is the other way round. Since the focus is on the transparent representation of the process flow, it has a lower level of detail than the VSM (red X in column four). Accordingly, the Swim Lane diagram is again more suitable for complex processes with many variants. In the last column the objectives of both methods are listed, which can be seen as the main selection criterion for an application.
2.2.1.4 Process evaluation.
At the end of the process analysis, the recorded current state must be evaluated. The first step is to identify possible weak points. It is necessary to uncover the process steps which are prone to errors with their current sequence and thus prevent a smooth and optimal process flow. In the best case, the process steps identified as weak points turn out to be pure waste and can be eliminated in the course of subsequent optimisation. In the second step, the identified weak points are evaluated with regard to their optimisation potential in order to find possible improvement measures. This then raises the question, which was put aside during the identification process, whether the weaknesses are symptoms or causes (Best & Weth, 2009, p. 85).
Activity Focus Analysis
During the process recording, the person recording gets an idea of which process steps take place and how long they last approximately. However, he or she does not yet know which unit or which employee carries out which activities within the process, how these are to be categorised and how long the individual activities last (Wiegand & Franck, 2017, p. 99). The activity focus analysis (AFA) provides a remedy. The basis for creating an AFA is the graphical process documentation from methods such as a Swim Lane diagram (Wiegand & Franck, 2017, p. 101). In contrast to these methods, the activities in AFA are not related to the process, but to the job (Wiegand & Franck, 2017, p. 101). In order to set up an AFA, all activities of a function must be listed and supplemented by the business transactions in which they occur (Wiegand & Franck, 2017, p. 101). Wiegand and Franck (2017) propose a limit of 20 activities per function (Wiegand & Franck, 2017, p. 101). As a final preparatory step, the types of activities must be categorised (see Table 6). Since AFA is a kind of a Lean Management methodology, the types of activities are also differentiated according to Lean:
- Key activities (K) derived from the original task of the function.
- Secondary activities (S), are directly related to the purpose of the company, but only add value to a limited extent, e.g. process-related meetings or further training.
- Organisational activities (O) which are wasteful from a process point of view, e.g. general e-mails and telephone calls or general office activities which are not process- related. (Wiegand & Franck, 2017, pp. 101-102)
Table 6. Example of activity focus analysis (following (Wiegand & Franck, 2017, p. 103)
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As AFA records the working times of the workers on the shop floor, it takes place at a sensitive company level. In order not to give the impression of a performance review, the works council and the supervisor must be informed in advance (Wiegand & Franck, 2017, p. 104, 2017, p. 100)This allows the works council to be convinced that data protection is guaranteed and the implementation of AFA is more quickly accepted by the workforce (Wiegand & Franck, 2017, p. 104). AFA should also be carried out anonymously. This makes it clear that it is not about employee control, but about the objective recording of the time spent on individual activities (Wiegand & Franck, 2017, p. 103).
The objective of an AFA is to identify weak points and to uncover optimisation potential and waste. For example, on the basis of activities in which non-productive time is consumed or additional activities that have not yet been included in the job description or process (Wiegand & Franck, 2017, p. 107, 2017, p. 99) Ideally, the methodology ensures clear responsibilities for the tasks, reduced interfaces and process loops, as well as avoiding duplication of work and reducing ancillary and organisational activities (Wiegand & Franck, 2017, p. 99).
Failure Mode and Effect Analysis
FMEA is used for systematic risk assessment and is an important quality tool for increasing the reliability of systems (Deutsche Gesellschaft für Qualität DGQ, 2015, p. 112; Melzer, 2019, pp. 155-158). FMEA is usually applied in the development of new or modified products and processes (Deutsche Gesellschaft für Qualität DGQ, 2015, p. 112). Its aim is to avoid or at least detect errors before they can lead to negative impacts in the output (Melzer, 2019, pp. 155-158).
FMEA can basically be used for all types of systems (hardware, software, processes) and combinations thereof. Depending on the application, a distinction is made between product / design FMEA and process FMEA (Deutsche Gesellschaft für Qualität DGQ, 2015, p. 112). As shown in the Table 7, the occasion determines which FMEA is used.
Table 7. Reasons for an FMEA (Deutsche Gesellschaft für Qualität DGQ, 2015, p. 113)
Abbildung in dieser Leseprobe nicht enthalten
An FMEA can be used both to uncover potential weaknesses in the current process and to identify risks and error potentials in the target process (Meran et al., 2014, pp. 170-176). To perform an FMEA, a template similar to Figure 13 should be created.
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Figure 13. Example of a process FMEA worksheet (following Melzer, 2019, pp. 155-158; Meran et al., 2014, pp. 170-176)
[...]
- Arbeit zitieren
- Felix Metzger (Autor:in), 2020, Process Automation in a Medium-Sized Mechanical Engineering Company. Development of a Systematic Approach for the Identification of Potentials, München, GRIN Verlag, https://www.grin.com/document/1183172
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