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AIJSTPME (2009) 2(4): 1-13
Designing proactive assembly systems (ProAct) – Criteria and interaction between automation, information, and competence Fasth Å. Chalmers University of Technology, Division of Production Systems, SE-41296 Stahre J. Chalmers University of Technology, Division of Production Systems Bruch J. Chalmers University of Technology, Division of Production Systems and Jönköping University, Department of Industrial Engineering and Management Dencker K. Royal Institute of Technology, Department of Production Engineering and Swerea IVF, Industrial Research & Development Corp Lundholm T. Royal Institute of Technology, Centre for Design and Management of Manufacturing systems Mårtensson L. Royal Institute of Technology, Department of Industrial Economics and Management Abstract Production companies of today face extreme challenge to meet the rapid changes and increased flexibility that mass customization require. More and more customers are requiring the product to suite specific needs such as design, function and sustainability. These requirements results in increasing demands for the developers of the product but also for the personnel who will assemble the products in the final assembling. This paper suggests the need for further development, primarily addressing time parameters in dynamically changing assembly systems. We propose proactivity as a vital characteristic of semi-automated assembly systems, to increase fulfilment of customer demands and decrease non value-adding tasks. In proactive assembly systems, the potential of human operators and technical systems is utilised. Criteria for proactivity are reviewed from automation, information, and competence perspectives. Empirical data have been collected from five production companies in Sweden. Keywords: Assembly systems, Automation, Information and Competence 1 Introduction Current tradition for designing and developing assembly systems may not be adaptable to the needs and future challenges that production companies have to face. Frequent demands and requirements, both internal and external trigger a plan for change in different production areas. Extreme flexibility will be needed in future assembly systems to be able to
handle rapid changes as a result of an increasing number of product variants e.g. mass customisation. This will result in smaller batches and shorter time limits for set-up between products. As a result, companies have to find more flexible methods for assembling their products and become more proactive in the assembly system itself. Indentifying
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Fasth Å. et al. new strategies to reduce time parameters in a system e.g. cycle-time, set-up time, throughput time and non value-adding tasks becomes vital. According to Frese and Fay [1], research in general focuses on reactive concepts [2] e.g. the operators have to perform already given tasks and solve problems which have already occurred. We believe that the operator group within the assembly system should have the ability to work proactively and that the operators‟ job flexibility or action space should increase so that problems can be prevented and thereby decrease the downtime in the system. The work situations an operator will face in the future are unlikely to be exactly identified by work instruction sheets. It is assumed that assembly work settings enabling proactive behaviour of the human operators will be important to handle the increasing uncertainty of work contexts [3]. The objective of the ProAct project [4] is to identify proactive solutions for time minimisation at the operational shop floor level in assembly systems [5]. The approach is based on the concept of proactive behaviour; as the ability of operators to control a situation by taking action and effectuating changes of the work situation in advance in order to create a favourable outcome [6]. A proactive behaviour can be characterised by three main areas [7]; • Anticipation of problems related to change • Initiation of activities that lead to solution of change related problems and improvements in work, and • Resolution of change related problems. Proactive operator work was defined by Griffin et al [3] as “the extent to which the individual takes selfdirected action to anticipate or initiate change in the work system or work roles”, Operators should then have correct situation awareness, longer time horizon and an anticipative perspective on continuous improvements of their work places [8]. This could lead to minimising of predicted and unpredicted disturbances and thereby, the availability of the assembly system will be increased [9]. Proactivity is not used as a competition benefit by companies today, even though proactive behaviour can result in increased organisational effectiveness [10]. Ideas similar to proactivity have been presented. The concept that operators of complex systems can perceive, comprehend, or project system behaviour, to increase control ability and precision, was defined as situation awareness [18]. Our assumption is that proactivity is highly influenced by three areas of
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assembly systems design and operation [11] i.e. the Levels of • Automation (LoA) • Competence (LoC) and • Information (LoI) It is vital to find criteria and interaction between these and to combine the right levels of the suggested parameters in order to maximise the systems flexibility and the operators‟ action space. This paper will address the criteria and interaction between the three areas: automation, information, and competence. Further the paper will describe a Meta methodology (the ProAct loop), for design of a proactive system in a structured way with the production goal to increase flexibility and achieve time minimisation. 2 Theoretical framework This section will briefly introduce three concepts: levels of automation, information and competence. Further, different methodologies used will be presented. 2.1 Level of automation (LoA) There is no uncomplicated way to make automation human-oriented that is applicable across all domains and types of work. Different processes and domains may put different emphasis on precision, stability and/or speed of production. Human-centeredness is therefore a reminder of the need to consider how the system can remain in control, rather than a goal in itself. Each case requires its own considerations of which type of automation is the most appropriate, and how control can be enhanced and facilitated via a proper design of the automation involved [12]. Smart automation is defined by Ohno [13] as the human aspect of 'autonomation' whereby automation with a human touch is achieved. Fitts MABA-MABA list (Men-Are-Better-At – Machines-Are-Better-At) [14], from 1951, gives an indication of how to allocate tasks between humans and machines in technical systems, e.g. assembly systems. Sheridan [15] argues that if tasks in which machines are better are automated and operators are still required to monitor such automation and maintain full situation awareness [16] we might lose more than we gain. Sheridan and Verplank (1978) proposed a scale of levels of automation (LoAs) involving automation of decision making and action [17]. However, automation also includes information gathering and analysis. Parasuraman et. al [18] accordingly
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Designing proactive assembly systems (ProAct) – Criteria and interaction between automation, information, and competence proposed an extension of the LoA concept to four information-processing stages: (a) information acquisition, (b) information analysis, (c) decision making, and (d) action, with each stage having its own LoA scale (for similar scales, see [19];[20]). Table 1 provides a historical review of levels of automation research. The levels of automation concept, used in this paper was defined by Frohm [21] as; “The allocation of physical and cognitive tasks between humans and technology, described as a continuum ranging from totally manual to totally automatic” Frohm [21], defines physical tasks as the level of automation for mechanical activities, mechanical LoA, while the level of cognitive tasks is called information LoA. Further it could be explained as; mechanical LoA is WITH WHAT to assemble and cognitive LoA is HOW to assemble on the lower levels (1-3) and situation control on the higher level (4-7). Table 1: Definition of Levels of Automation [22] Year 1958
1962
1980
1981
1985
1997
Definitions of Levels of Automation Divides the levels depending on who is initiating control; the human (1-4), the human together with automation (5-8) or the automation (9-17), (Bright, [23]) The extent to which human energy and control over the production process are replaced by machines (Amber & Amber, [24]) The level of automation incorporates the issue of feedback, as well as relative sharing of functions in ten stages (Sheridan, [25]) Automaticity is defined in six levels from conducting the tasks manually, without any physical support, to fully automated congnition with computer control (March & Manari, [26]) The degree of mechanisation is defined as the technical level in five different dimensions or work function (Kern & Schumann, [27]) The level of automation goes from direct manual control to largely autonomous operation where the human role is minimal (Billings, [28])
1997
1998
2000
2001
2002
2008
The level of automation in the context of expert systems is most applicable to cognitive tasks such as ability to respond to, and make dicisions based on, system information (Endsley, [29]) The level of automation is defined as the sharing between the human and the machines with different degrees of human The interaction and task division between the human and the machine should instead be viewed as a changeable factor which can be called the level of automation (Parasuraman et. al, [18]) Level of mechanisation can be defined as the manning level, with focus on operating of the machines (Groover, [31]) ‘Manual’ tasks as being those in which humans are responsible for conducting the task. „Semi-automatic‟ is a higher level of automation and involves automated alignment and application of epoxy by a robot. Material handling, on the other hand, is still conducted by humans, unlike „automatic‟, where material handling is also automated. (Ducheon, [32]) The allocation of physical and cognitive tasks between humans and technology, described as a continuum ranging from totally manual to totally automation (Frohm, [22])
2.2 Dynamo++ The DYNAMO++ methodology was developed [33] based on previous research [21]. A taxonomy for measuring both cognitive (information) and physical (mechanical) Level of Automation (LoA) in a current stage of a system was defined [22]. The aim was to measure accessible LoA in order to find an appropriate span of levels of automation and, by that, maintain high productivity by reducing production disturbances [21]. The focus of further development is on the analysis and clarifies whether the current systems‟ LoA is too high, too low, or too static in order to fulfil the companies' triggers for change e.g. internal or external demands. Further to redesign that system in a structured way with the most advantageous LoA.
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Fasth Å. et al. The stakeholders for this methodology are firstly production engineers, who will have a structure to work with when choosing type and amount of automation for a specific part of a production system. The DYNAMO++ methodology is divided into three different states; current, future and new current stage, as illustrated in Figure 1. These stages contain four different phases; 1) Pre study, 2) Measurement, 3) Analysis and
4) Implementation [33, 34]. These phases or methods are used to answer different questions in order to redesign the system in a structured way and avoid over or under automated systems. Focus in this paper is within the current stage and the future stage. A brief description of the methods used is given in the sections below.
Figure 1: The DYNAMO++ methodology [34] 2.1.1 Current stage Pre study phase • “Go see”, Genchi Genbutsu, e.g. observe the production floor without preconceptions and with a blank mind [13, 35], and chose a part of the system where must improvements are needed. • Get an accurate overview of the current system (number of operations) and the different flows (information, material and product) in the chosen area by using for example VSM (Value Stream Mapping [36]). Measurement phase • Divide the chosen operations into main and sub tasks by using Hierarchical Task Analysis (HTA). The HTA is a method for description of activities under analysis in terms of a hierarchy of goals, sub-goals, operations, and plans [37]. • Measure the current mechanical and cognitive LoA in the subtasks, based on the reference matrix presented in Figure 2.
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Figure 2: The LoA matrix 2.1.2 Future stage Analysis phase The measured LoA value and relative minimum and maximum values for each task are illustrated in a matrix. This results in a Square of Possible Improvements (SoPI), with both physical and cognitive automation possibilities illustrated in Figure 3.
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Designing proactive assembly systems (ProAct) – Criteria and interaction between automation, information, and competence
Figure 3: The LoA matrix with a SoPI [33] The span of different automation solutions contribute to a larger span of possible solutions for replacement and in real time optimisation [5, 38]. Equipment for automation for mechanical tasks, cognitive tasks and information tools can be evaluated with the criteria anticipation, initiation of activities, and resolution of change-related problems [11]. 2.3 Level of competence (LoC) and the operator role in the assembly system According to ISO SS 62 40 70 [39] competence is defined as “the ability and willingness to carry out a task by applying knowledge and skills”. When defining competence in the ProAct project the following implications were made: Ability – experience, comprehension and judgment to use knowledge and skills in practice, where willingness is the attitude, commitment, courage and responsibility; knowledge means facts and methods – to know and skills is to carry out in practice – to do [5]. A model frequently used for human supervisory control of highly automated workplaces was developed by Sheridan [40], primarily for application in process and aerospace industry. The model was further developed and adapted for manufacturing industry by Stahre [41]. The model includes five main operator roles i.e. plan, teach (programming), perform, intervene and learn. Plan means that the operator decides what to do in the system. The teaching step describes how to transfer the proposed plan to the technical system through programming but also to assure that the correct tools and materials are available to fulfil the plan. Monitoring means that the operator starts the process and controls the products when deviation occurs. The operator has to intervene when different disturbances occurs. The operator learns from every new task, product or disturbance and can use this when planning the next batch. A further development of Sheridan’s five operator roles with a combination
of human needs, phrased by Maslow (1954) and the socio-technical school [42, 43] was done by Mårtensson [44]. The six requirements and related criteria are general and applicable to all kinds of work; • A versatile work content (e.g., the individual should plan, perform, and monitor the production task) • Responsibility and participation (e.g., responsibility for the complete work task, participation in the design process) • Information processing (e.g., planning one‟s work, cognitive activity in new situations, problem solving at disturbances followed by decision-making) • Influence on physical work performance (e.g., choosing an automatic or manual method if possible, physical mobility, temporarily leaving the work place for a short while) • Contact and cooperation with colleagues (e.g., verbal and visual contact with at least one person, contacts with programmers or assembly department, cooperation in teams) • Competence development (e.g., to the individual acceptable skill level, competence being used in more qualified tasks, continuous training) [45]. The list could be used as a checklist when designing the work organisation in any human – machine system and when designing decision support for system operators. The need for cognitive support can be divided into three separate levels, skill-, rule- and knowledgebased, described by Rasmussen as behaviour levels [46]. At the skill-based level activities are carried out with very low need for conscious attention or control. Many tasks are skill- based; they are performed more or less “automatically” by the operator with minimal thinking and can be seen as LoA cognitive= 1 e.g. it can be connected to previous physical practice and experience. If a problem or variation between anticipated and real outcome in the performed task occurs, the operator must increase the mental effort to the rule-based level. If tasks are standardised, e.g. by having tools or techniques [47, 48] to give decision or teaching the operator how to perform a task in the assembling, it becomes rule based and could be transformed into LoAcognitive= 2-4. If an unforeseen situation occurs, or if no rules are applicable to the specific task, the operator's mental effort is raised to the knowledgebased level. Arguments are then made from detailed analyses and there is a need for conscious and
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Fasth Å. et al. focused operator attention. A combination between Sheridan’s supervisory control and Rasmussen’s human behaviour levels was developed by Stahre [41] as an evaluation matrix, used for task analysis, illustrated in Figure 4.
The list is used as a reference when gathering empirical data in case studies in order to investigate and map the operators‟ action space e.g. involvement in these areas which will be brought up in section 3.1. 2.4 Level of information (LoI)
Figure 4: Evaluation matrix, combining supervisory control roles and human behaviour levels [41]
Information could be described as a collection of facts organised in such a way that they have additional value beyond the value of the facts themselves. An information system is a set of interrelated components that collect, manipulate and disseminate data and information and provide a feedback mechanism to meet an objective [49]. A well functioning information flow throughout the chain is a powerful and necessary prerequisite [50]. A modern information system constitutes a network of multi-media work-stations, designed to present an operator with an information environment. This enables the user, within the constraints posed by the work requirements to create actively a work practice that suits the individual users‟ cognitive resources and subjective preferences [51]. Frequent exchange of information between different company levels is crucial to create quick “decisionloops” which are correctly connected and updated with real manufacturing conditions [8]. To achieve efficiency in assembly systems, the information flow has to fulfil several qualitative criteria defined by [52] as; • Relevance - Users benefit in their decision or action because of it • Timeliness - information is available in time • Accuracy - information is free from error • Accessibility - information is readily available • Comprehensiveness - information is free from omissions and redundant data • Format - effectiveness with which information is perceived In order to perform a specific task within the operator group and to make the information flow more efficient, the operator will need information on a specific information level, as shown in Figure 6.
Figure 5: Operator roles and work tasks in an assembly system The operators’ roles in this paper are a mix between the evaluation matrix and the work tasks in the automatic assembly system. Together they form a 15 point list, shown in Figure 5.
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Designing proactive assembly systems (ProAct) – Criteria and interaction between automation, information, and competence Level of Automation (LoA). The technical system, which could be connected to mechanical LoA and in some cases LoI in terms of technical solutions (information carriers).
Figure 6: Why, What and How in the abstraction hierarchy, from [17], edited by [2] The information must be able to answer three questions; what should be used (work function), why the information is relevant, this can be seen both as a goal for a function at a lower level, and the third question how the task is realized as a means for a function at a higher level [53]. This does not imply a removal of detailed information about the product or material flow; rather information is added on a higher level in terms of describing the co-functioning of the different tasks and information flow between different resources [2, 54]. The information flow to and from the assembly unit as well as within the assembly unit was identified by means of a process map. The process map displayed the way work was currently done including information support. Furthermore, it revealed with whom the operator exchanged information and also the source of information. A process map identifies, in sequence, the steps that a department performs to transform inputs into outputs for a specific process [55]. The process map was the basis for the analysis to identify the information required to enable extension of the decision latitude of the assembly operator in order to facilitate or support proactivity. The information flow was also categorised on operation level based on four levels of information; 1. Insufficient 2. Sufficient for expert operator 3. Sufficient for novice operator 4. Too much 3 Analysis towards proactivity The socio-technical school [42, 43] could be seen as an alternative in order to expand the operator action space, see Figure 7. Further, to find the interaction between the three areas. The social system could be related to the operators’ roles, Level of Competence (LoC), Level of Information (LoI) and the cognitive
Figure 7: Action space in relation to the three areas 3.1 Operator roles in the assembly system To be able to map the current state of the operators action space a mapping of the operation roles and work tasks, as listed in Figure 5, was executed. The involvement was mapped on three different levels; full, partly or no participation in the task. Empirical data, illustrated in Figure 8 show that the operators have the main responsibility for less than 20 % of these tasks in all case companies.
Figure 8: Task allocation between different roles In most of the cases, operators had no participation in planning and maintenance (only in minor disturbance handling). Partly involvement differs from 40 to 65 % between the companies. This means that the information flow between operators and other
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Fasth Å. et al. sources becomes more important in the companies with low operator involvement. Braverman wrote in 1974 [56]; “The divorce of mental and physical work reduces, on every production level, the need of workers that are directly engaged in production because this relieves these workers from time consuming mental functions, these tasks are placed in other departments within the company”. In order to create a proactive assembly system we believe that the involvement of the operators in the work tasks has to increase i.e. a remarriage of the mental and physical work of the operators. 3.2 Meta methodology, the ProAct loop The ProAct-loop, shown in Figure 9, is a Meta methodology connecting the three areas by combining the theory and methods described in previous sections. The loop is used in two iterations, the current stage mapping and the future stage analysis. The methodology was tested and validated in five Swedish production companies in 2007 and 2008. A web based tool has been developed in order to perform the methodology in a structured way, as illustrated in Figure 10.
Figure 9: Methodologies used in the ProAct Loop
3.2.1 1st Loop - current stage mapping In the current state mapping, four different types of operations were identified; pre assemblingassembling-test/control-packing. The operations were analysed in each case study, in accordance with the three areas. Figure 11 shows the average LoA levels identified in the different operations. Generally, most manual work was performed in the packing stations while the control and testing stations were the most automated. The most common reason for highly automated control and test stations where the requirement on precision i.e. adjustments of air/oil pressure or power. This is in line with one of the three areas for automation proposed by Hollnagel [12]. The information analysis contained two different mappings, process mapping of the information carriers and type of information to and from the operations [57]. The second mapping made, illustrated in Figure 12, shows what information and with whom the operator communicated with in his/her daily work.
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Figure 10: Structure of the web based tool, 1st loop
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Designing proactive assembly systems (ProAct) – Criteria and interaction between automation, information, and competence
Figure 11: Current stage LoA in the case studies
Figure 12: Current stage of communication between operator and other personnel [57] © King Mongkut’s University of Technology North Bangkok Press, Bangkok, Thailand
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Fasth Å. et al. The arrows in Figure 12 identify one-way or twoway communication. The filled boxes indicate people who had a supporting role for operator groups [57]. 3.2.2 2nd Loop- future stage analysis The second loop of the methodology contains an analysis of to the current stage and the company‟s goal functions and triggers for change [33]. One of the results from the analysis is the Square of Possible improvements (SoPI), as shown in Figure 13. This square represents the tasks or operations´ action space in which automation solutions for the future assembly system may vary. Depending on how movement in this square is done, the effort to change to other technical solutions, is reflected on both the level of information and the level of competence.
Figure 13: Square of Possible improvements and future stage analysis In proactive assembly systems, operators are encouraged to take more responsibility, far beyond managing operation and disturbances. Requirements on frequent reconfiguration, either initiated by explicit demands, or by changes due to proactively foreseeable problems or requirements, are normal. Correct amount, quality, and accessibility of information is required to provide the assembly system and its operators with abilities to handle changes within the assembly systems local layout and disturbances during operation. This requires a large amount of information to be exchanged in a short period of time. Moreover, the purpose is to ensure that the operator‟s perspective is taken into account with respect to applicable information and future decision support systems in assembly. This means that in the future there is a need to take operator abilities and limitations into consideration, as well as the operator‟s various ways of using information and
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making decisions in different working situations [58]. Continuous iterative development of the system design increases usability from physical and a cognitive perspective. Through cyclic development of physical and cognitive user interfaces operators may refocus their energy from comprehension of the process to problem-solving, projection of system behaviour, and refining of the system. One criterion is to support correct situation awareness for the operator team. This puts requirements on the information to have ability both to provide correct information about the system, the resources (operators and machines) available in the system and the system as a whole e.g. from higher abstraction levels, as shown in Figure 14.
Figure 14: Time perspective in the Why, What, How matrix [57] The goal for proactivity is to be on the middle diagonal. • Below the diagonal - less information about intentional features • Above the diagonal - less information about functional and material features Information also needs to be presented in a way that supports the operator team to achieve the high situation awareness in a short time. The competence of the operator team is crucial because it realises the system’s built in proactivity. Competence is not entirely replaceable with information but it could be reinforced with the right Level of Automation. Without correct competence it is not possible to achieve appropriate situation awareness in a short time. Competence includes the ability to take the right action but also to predict the consequences of the action taken. The operator has to have competence within the group to be “aware of the environment within a
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Designing proactive assembly systems (ProAct) – Criteria and interaction between automation, information, and competence volume of time and space, and to understand their meaning and the assembly systems status in near time and in the future” [19]. 4 Discussion and conclusion The paper presents the criteria and interaction between three areas; automation, information, and competence in order to design a proactive assembly system. Through a deep understanding of this interaction and optimising the separate areas in a structured way using the proposed Meta-method (the ProAct-loop), the companies can avoid over- or under-automated systems. Also, excessive information and incorrect competence levels can be avoided. Further, companies will be able to balance the three areas and optimise where it is most needed. For example, low competence level can to some extent be compensated by higher levels in the information and/or automation area and vice versa. Furthermore, the system operators' ability to participate and take part of information within the work tasks, as illustrated in figure 4 and the different abstract levels in figure 9 may be radically increased. The operators' situation awareness will improve, thus increasing their ability to act proactively. Acknowledgement The authors want to express their deep gratitude to the Swedish Governmental Agency for Innovation Systems (VINNOVA) for funding the ProAct project. We also want to express our gratitude to our ProAct project colleagues. Further, our thanks go to the participating companies: Parker Hannifin, Global Garden Products, Bosch Rexroth, Siemens Building Technologies, Stoneridge Electronics and Electrolux Home Products Reference [1] Frese M. and Fay D., 2001. Personal Initiative: An Active Performance Concept for Work in the 21st Century, in Research in Organizational Behavior, Edited by Staw,B.M., Sutton, E.L., Elsevier Science, 133-187. [2] Bruch J., Karltun J., Johansson C. and Stahre J., 2008. Towards a Methodology for the Assessment of Information Requirements in a Proactive Assembly Work Setting, in Proceedings of 2nd Swedish Production Symposium (SPS) Stockholm, Sweden.
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