CONTROLLING LEVELS OF AUTOMATION – A MODEL FOR IDENTIFYING MANUFACTURING PARAMETERS

CONTROLLING LEVELS OF AUTOMATION A MODEL FOR IDENTIFYING MANUFACTURING PARAMETERS Veronica Granell1, Jörgen Frohm2, Mats Winroth1 1

Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Sweden 2

Division of Production Systems, Chalmers University of Technology, Gothenburg, Sweden

Abstract: The term Levels of Automation is defined in this paper as the interaction and task division between the human and the machine within a manufacturing system. This paper presents a model for identifying manufacturing parameters to control automation levels. The results show that different parameters are identified as capabilities that affect the level of automation and the output of the system. Conclusion is that the model presented can serve as a way to control and choose the right level of automation by adjusting either input parameters or the level of automation which together with performance measures form a continuous system. Copyright © 2006 IFAC Keywords: Manufacturing systems, model, performance measures, manufacturing levers, parameters 1. INTRODUCTION that this research is operative. The amount of automation is included in an element called process technology (Miltenburg, 1995). The process technology element may be adjusted with issues such as standardization, statistical process control and setup time reduction. Process technology can be defined in terms of a set of capabilities and limitations (Hayes and Wheelwright, 1984). A manufacturing structure framework is presented by Kotha and Orne (1989) where the process structure complexity dimension is viewed and a synthesized framework concept bridging process structure, product line and organizational scope is provided. This framework suggests that there are critical elements to consider for further research considering both content of fit and the process of fit between structure, strategy, technology and performance. In line with their research, this paper discusses parts of the process technology complexity presented in the conceptual synthesis by Kotha and Orne (1989). The purpose of this paper is to present a model for identifying input parameters (capabilities) and output parameters (performance measures) of a manufacturing system related to the levels of automation (included in process structure) of the system at an operative level. Earlier research has

Controlling levels of automation in manufacturing processes may enhance value creation and eliminate waste. According to the Lean Production philosophy (Liker, 2004), waste should be eliminated in the manufacturing processes, such as waiting time, over production, etc. The interaction and task division between the human and the machine is called Level of Automation (LoA) (Parasuraman et al, 2000). Hence, finding and implementing the right Level of Automation in a controlled way could be a way to maintain the effectiveness of the system (Bellgran and Säfsten, 2005; Frohm et al, 2005). The performance of a manufacturing system is decided by a number of subsystems (Miltenburg, 1995). Manufacturing levers control the output of the system and define its requirements. Despite a large amount of literature on manufacturing systems (e.g. Groover, 2001; Miltenburg, 1995) and manufacturing strategy (Hayes and Wheelwright, 1984; Hill, 2000; Miltenburg, 1995; Säfsten and Winroth, 2002) where classifications of manufacturing systems as well as its components and subsystems are described, research on controlling levels of automation and its effect on other parameters is limited. The reason could be

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tested different taxonomies for measurement of automation levels in production flows (Lindström et al, 2005). How measurement of LoA was applied to manufacturing is described in the next section.

LoA10

Performed by the technical system

Computerized Tasks

Mechanized Tasks

Cognitive Tasks

Manual Tasks

LoA10

1.1. Levels of Automation measurement Measurement of static automation levels has been made prior to the research of this paper in three sequential case studies. The aim of the sequential approach was to develop a methodology for measuring LoA in manufacturing processes. The model for quantifying work functions was developed from an original LoA scale presented by Sheridan (2002) and ranges from LoA 1 (totally manual work) to LoA 10 (totally automated work) and was separated into the two basic classes of activities mechanized tasks and computerized tasks, see figure 1. In all three case studies, the Value Stream Mapping method (Rother and Shook, 2003) was used as a basic tool for visualizing the production flow and the operations performed in the system. Values of work station times, value added amount, stock size, and levels of automation for mechanized and computerized tasks were captured. In the first case study, the value stream mapping was done prior to measuring LoA (Lagoma, 2004). Conclusions of the case studies were that the Value Stream Mapping method can be used when measuring levels of automation in production flows, material handling processes should be separated from the manufacturing process and measuring automation levels may be used as a foundation for improvement discussions at both operative and strategic level in a company. A methodology for measuring LoA was also presented (Lindström et al, 2005). This LoA measurement methodology contains three basic steps: 1. 2. 3.

Context description Value Stream mapping and measurement Development of a future state

LoA1

Performed by the human

LoA1

Fig. 1: Separation of work functions (Frohm et al, 2005) 2. RESEARCH METHOD Mainly two research methods are used within this study; literature review and survey. 2.1 Literature review Academic journal papers, conference papers and books have been used within this study in the areas of manufacturing systems, manufacturing strategy, lean production and performance measurement. 2.2 Survey Collection of empirical data in this study was made in a survey. The main purpose of a descriptive survey is to describe a particular phenomenon, for example its current situation (Williamson, 2002) and to describe the distribution of certain characteristics of a population (Leedy and Ormrod, 2001). In the survey described in this paper, the purpose was partly to capture the perception of production managers and manufacturing engineers working in the industry on terms such as levels of automation and automation strategy, and partly collect the current status on manufacturing and automation issues. The survey technique used was a web-based questionnaire where either an open answer or a statement on a four-graded scale (also the option “don’t know” was available) was required. The sample was derived from the Association of Swedish Engineering Industry in Sweden. 85 respondents, mainly production managers in Swedish industrial companies were asked to answer the questionnaire in June to August 2005. 62 respondents out of 85 answered the questionnaire and the data was then analyzed, see section 4 for results. Background data included respondent business category (fig. 2), professional experience, and experience of automation strategy, education, line of business, no. of employees in the company, manufacturing employees and no. of employees working with manufacturing strategy. In the next section parameters and factors describing the manufacturing system and its performance measures are described.

LoA

Hence, the research on levels of automation so far has given empirical data of mechanized and computerized content in production flows, an understanding of the concept and which types of operations that have a low, medium or high LoA. However, there are many parameters that influence the level of automation and it is necessary to understand more about the linkages between LoA and different parameters within the system, and how the combination of different elements will influence the output. For making research on this, a literature review and a survey are chosen as a starting point.

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3. PARAMETERS IN THE LITERATURE

and defines manufacturing systems as machine tools, material handling system, computer systems and human workers. In this paper, we choose the manufacturing approach similar to the LoA measurements made earlier.

The research question asked is: Which parameters should be identified in a manufacturing system, controlling the level of automation? The idea behind this question is that a greater process quality may be reached within the manufacturing system in a given environment of dynamic customer expectations by controlling the level of mechanized and computerized efforts. Theory of technical systems implies that there is a transformation process going on in the system, and that the technical system is linked to the environment as well as to the human system (Hubka and Eder, 1988). Manufacturing system and –strategy literature often include both the technical and the human system as components of the total system and the environmental influences are expected to be dealt with by adjusting parts of the manufacturing system providing an output to its customers. The next section will describe parts and factors, presented in the literature, that together form the manufacturing system and make it possible to quantify, analyze, and change parameters.

Classifications of manufacturing systems include: • Types of operations performed, number of workstations and system layout, level of automation, and part or product variety In this classification of manufacturing systems, Groover (2001) defines three possible levels of automation (manual, hybrid, and automated) and three different types of number of workstations and layout of the stations. Learning curves are also mentioned as one aspect of the manufacturing system where the learning rate for various types of work is plotted. Human workers are seen as an important element, but are not mentioned as a measurable factor in the classification. 3.2 Performance measures related principles and manufacturing systems

to

lean

Measuring performance of manufacturing systems is closely related to strategic decision making of the intended capability (Groover, 2001). In 1969, Wickham Skinner proposed a top-down approach to manufacturing which starts with the strategy and prevents introduction of sub-optimizing measures. In the beginning of the nineties, Kaplan and Norton (1992) developed the balanced scorecard with different perspectives. Likewise, lean manufacturing literature takes at least four different perspectives into consideration when measuring and controlling the production (Blücher and Öjmertz, 2004): • Internal worker perspective • Customer perspective • Shareholder perspective • Society perspective

3.1 Factors related to levels of automation By designing and changing manufacturing systems, adjustments should preferably be made in a controlled and a structured manner for the best result. Adjustment of a certain factor can affect other parts and factors of the system (Lindström et al, 2005), but these linkages and interactions are however unclear. Nevertheless, several authors have described factors that constitute a manufacturing system (Hayes and Wheelwright, 1984; Miltenburg, 1995; Kotha and Orne, 1989; Groover, 2001) and decisions that have to be made on a strategic level. In order to understand the operative level in the manufacturing system, i.e. automation and the linkages to it, the manufacturing strategy literature was examined regarding the process technology part. According to the manufacturing strategy literature, factors within process technology are: • automation, equipment and linkages (Hayes and Wheelwright, 1984) • the nature of the manufacturing processes, the type of equipment, the amount of automation, and the linkages between the parts of the manufacturing process (Miltenburg, 1995) • mechanization level, systemization level, and interconnection level (Kotha and Orne, 1989)

Combining these perspectives with the lean philosophy and Toyota Production System (Liker, 2004), some measures can be identified, see table 1. Applied to a manufacturing system; how could these measures be translated? Coming back to Kaplan and Norton (1992), it starts with a vision, i.e. every company must have its own measures. The perspectives in table 1 could be used as a guideline for setting up specific measures. Measures related to the customer perspective could then be answered by the question: Which internal capabilities shall we develop and measure in order to satisfy our customers? This question would also be applicable for the shareholder and society perspectives. The internal worker perspective is closely related to the leadership capability.

The manufacturing strategy literature takes a topmanagement perspective whereas Groover (2001) approaches manufacturing systems in a more operative way, from a manufacturing perspective,

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Table 1: Performance measures for different perspectives Perspective Internal worker Customer Shareholder Society

Table 2: Question about automation decision Parameter

Measure Safety, skills, capability of solving problems, teamwork, morale Product quality, delivery precision and time Costs, waste reduction Continuous improvements, value adding

Quality Work environment Rationalization Financial Production capacity Risk analysis Volume Time perspective Available workforce

Quality may be one output parameter of a manufacturing system and could serve as a performance measure. Other performance measures are suggested in the literature such as cost, flexibility, and time (Neely et al, 1995), which fit the perspectives in table 1. Dependability is another performance measure that is mentioned in the literature (Slack et al, 2001).

Figure 2 shows the percentage of respondent categories in the survey described in section 2.2. The sample is 62. Given that strategic decision-making influence operative measures, questions to production managers were asked about underlying factors for automation and for formulating a manufacturing strategy. Empirical results are presented in table 2 and 3.

15%

14,5 % 16,1 % 21,0 %

85,5 % 83,8 % 79,1 %

24,2 % 30,6 % 30,7 %

74,2 % 69,3 % 66,1 %

58,1 %

42 %

Don’t know

3,2 %

Table 3: Question about manufacturing strategy Factor

8%

34%

4,8 % 11,3 %

Percentage “high degree and very high degree” 95,2 % 88,7 %

One question in the survey was asked about considerations by formulating a manufacturing strategy, question B: “By formulation of a manufacturing strategy one should consider…” (see table 3). The results in table 3 show that the overall goal of the company is the most important factor, which indicates that the vision controls the strategy formulation.

4. SURVEY RESULTS

5%

Percentage “very low and low degree”

Industrial management

The overall goal of the company The human and work organization Policies for choice of man. process Technical systems Policies for product design Support functions for operative personnel Accessible information systems Layout

Production manager Production development Production technician Other

38%

Fig. 2: Distribution of respondents in survey Question A in the survey was “Decisions about automation should be made on…” The results in table 2 show that the “top three” parameters, which are the most important factors when making decisions about automation are quality, work environment and rationalization. Quality is related to the customer perspective, work environment to the internal perspective and rationalization to the shareholder perspective (table 1).

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Percent. “very low and low degree” 8,1 %

Percentage “high degree and very high degree” 92 %

12,9 %

87,1 %

11,3 %

85,5 %

17,7 %

79 %

3,2 %

14,5 %

74, 2 %

11,3 %

25,8 %

72,6 %

1,6 %

33,8 %

64,5 %

1,6 %

40,3 %

59,6 %

Don’t know

The human and work organization and policies for choice of manufacturing processes are also important for formulating a manufacturing strategy. A discussion will follow in the next section with the purpose to make a synthesis of theory and the empirical results.

There are many different capabilities presented within all levers. As the purpose of this paper is to identify parameters that relate to the levels of automation and present a model, a simplification of the most important parameters is appropriate. Parameters like product variants, volume, layout, and material flow are highlighted in the literature as parameters influencing manufacturing outputs and forming different kinds of production systems (Hayes and Wheelwright, 1984; Miltenburg, 1995). Other parameters closely related to the level of automation are types of operations performed and number of workstations (Groover, 2001). Table 3 suggests that the overall goal of the company is important by formulating a manufacturing strategy, and this is confirmed in the literature (Kaplan and Norton, 1992). The factors in table 2 and 3 fit partly into the four views of performance measures. It is difficult, however, to sort out if factors mentioned in the answers of questions A and B are capabilities or performance measures, or both. From the findings above, a model was developed to show the linkages between capabilities of the system, levels of automation and performance measures, see figure 3. As figure 3 implies, the level of automation is possible to vary and adjust on a scale from 1 to 10 (see also figure 1). It may be wise to first measure the existing level of automation, before adjusting it. A controllable and adjustable level of automation could possibly form an individual production system, which is partly formed by the capabilities (which also are adjustable) and partly by the chosen level of automation. The effect of an adjustment may be possible to measure and visualize in different views as suggested in figure 3.

5. SYNTHESIS OF THEORY AND REALITY Building on the empirical data in question A about automation decisions, over 95 % of the respondents have answered that quality is the most important aspect to consider when automating. The quality aspect in automation that relates to the quality of products and processes is called quality engineering, and can be divided in off-line and online quality control (Groover, 2001). The off-line quality control is concerned with design issues and is applicable prior to manufacturing, whereas online quality control is concerned with ongoing manufacturing operations and after shipment issues (Groover, 2001). The second most important issue to regard by automation with almost 89 % of the answers answering “high or very high degree” is the work environment. Work environment can be connected to the safety of workers, which is one performance measure in the internal perspective in table 1. The third and fourth most important factors for automation decisions in table 2 are rationalization and financial parameters, which can be related to measures in the shareholder perspective, see table 1. The theory suggests that different manufacturing levers, or capabilities of the manufacturing system, together will provide a manufacturing output. An adjustment to one lever will affect the manufacturing outputs (Miltenburg, 1995).

The overall goal of the company Levels of Automation Capabilities

Performance measures Internal worker view • Safety • Skills Customer view • Product quality • Process quality • Flexibility Owners view • Investment costs Society view • Value creation • Influence on society

• Type of product • Volume • Type of operations • No. of work stations • Layout • No. of product variants • Competence • Quality • Learning curve

Mechanical

Information & control

Fig. 3: A model for controlling levels of automation.

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6. CONCLUSION

Kotha,

S. and D. Orne (1989) Generic manufacturing strategies: A conceptual synthesis, Strategic Management Journal, Vol. 10, 211-231 Lagoma, M. (2004) Creation of Environmental Friendly Production Systems measuring Levels of Automation in Value Streams, Master thesis, School of Engineering, Jönköping University Leedy P. D. and J. E. Ormrod (2001) Practical Research, Planning and Design, Pearson Liker, J. K. (2004) The Toyota Way, McGraw Hill Lindström, V., J. Frohm, and M. Bellgran (2005) Developing a methodology based on Value Stream Mapping for the Measurement of Automation Levels in Production Systems, 3rd International Conference on Reconfigurable Manufacturing, Uni. of Michigan, USA Miltenburg, J. (1995) Manufacturing Strategy: How to Formulate and Implement a Winning Plan, Productivity Press, Portland, Oregon Neely, A., M. Gregory and K. Platts (1995) Performance measurement system design: A literature review and research agenda, International Journal of Operations & Production Management, Vol. 15, No. 4, 80-116 Parasuraman R., T. B. Sheridan, and C. D. Wickens (2000) A Model for Types and Levels of Human Interaction with Automation, IEEE transactions on systems, man, and cybernetics – part A: Systems and Humans, 30; 286-297 Rother, M. and J. Shook (2003) Learning to See: value-stream mapping to create value and eliminate muda, version 1.3, The Lean Enterprise Institute Inc., USA Sheridan T. B. (2002) Humans and Automation: System Design and Research Issues, Wiley series in System Engineering and Management Skinner, W. (1969) Manufacturing - missing link in corporate strategy, Harvard Business Review, May-June Slack, N., S. Chambers and R. Johnston (2001) Operations Management, 3rd ed., Pearson Education Limited, England Säfsten, K.and M. Winroth (2002) Analysis of the congruence between manufacturing strategy and production system in SMME, Journal of Computers in Industry, Volume 49/1 Williamson, K. (2002) Research methods for students, academics and professionals, Information management and systems, 2nd Ed., Center for Information Studies

Production managers and specialists answer that different aspects and views are important to consider when making decisions about investments in automation. The manufacturing literature suggests several parameters that are closely related to the level of automation. These parameters may be defined as capabilities of a specific manufacturing system. From both empirical findings and theory, a model for controlling levels of automation was developed. The model presents several parameters of the manufacturing system, of which some are capabilities and some performance measures. Identifying capabilities of the system will secure finding the right automation level as the best fit between capabilities and level of automation. The chosen level of automation will affect the output (performance measures), which in turn affect the system elements. In this way a continuous control system is achieved where the level of automation can be adjusted and maintain the output variables at a desired value. Further research is suggested to test the presented model. 7. ACKNOWLEDGEMENT The authors would like to thank the Swedish Foundation for Strategic Research (ProViking) for generously sponsoring the project DYNAMO. REFERENCES Bellgran, M. and K. Säfsten (2005) Produktionsutveckling Utveckling och drift av produktionssystem, Studentlitteratur, Sweden Blücher D. and B. Öjmertz (2004), Utmana dina processer! IVF Industriforskning och Utveckling, Mölndal, Sweden Frohm, J., V. Lindström, and M. Bellgran (2005) A model for parallel levels of Automation within manufacturing, 18th International Conference on Production Research, Italy Groover, M. P. (2001) Automation, Production Systems, and Computer-Integrated Manufacturing, 2nd Ed., Prentice-Hall Hayes, R. H. and S. C. Wheelwright (1984) Restoring our competitive edge: Competing through manufacturing, Wiley Hill, T. (2000) Manufacturing Strategy – Text and cases, 2nd Ed., Palgrave Hubka V. and W. E. Eder (1988) Theory of Technical Systems: A Total concept of technical systems, Springer Verlag, Berlin Kaplan R. S. and D. P. Norton (1992) The balanced scorecard – measures that drive performance, Harvard Business Review, January-February

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