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22 Using design structure matrices in visualising design processes
Studies in Multidisciplinarity, Volume 2 Editor: G. Malcolm 9 2004 Elsevier B.V. All fights reserved.
22 Using design structure matrices in visualising design processes Elias August, Claudia Eckert and P. John Clarkson Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
The binary design structure matrix (DSM) was introduced as a graphical information tool to visualise engineering design processes. Numerical DSMs, which provide designers with extra information, followed. To capture the design process of complex products as in the aerospace, automobile, and telecommunication industries, it is necessary to deal with very large DSMs. The aim of this chapter is to present different types of DSMs and their alternatives; to discuss their advantages and disadvantages; and to discuss the restrictions of numerical DSMs, when used as visual rather than computational tools. A way of handling large DSMs by zooming and hierarchical structuring is also discussed.
1.
THE P R O B L E M
Complex engineering design projects, such as the design of a new aeroplane or a new car, can involve tens of thousands of tasks and products that comprise hundreds of thousands of parts. Coping with this amount of information is virtually impossible for designers and design managers. In a study on the customisation of helicopters (Eckert et al., 2001), senior designers commented that the most experienced person in the organisation only understood about 50% of the product. Such a lack of overview in engineering can be catastrophic, since for a complex product small changes can have an enormous impact on the product as a whole. In addition, minor 305
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tasks slipping or being ignored can cause expensive delays to the design process. Understanding the connectivity between project tasks and components is the key for supporting and managing design processes efficiently (see Eckert and Clarkson, 2002 for a detailed discussion). While it remains a challenge to understand the connections between two tasks or two components in terms of the parameters that link them, it is extremely difficult to get the whole picture. Consequently, one of the major challenges to the research community is how to display large models of products and processes. Existing techniques are clearly inadequate. At present most engineering companies the authors have visited use Microsoft Project to display their project plans. To cope with the complexity, processes are modelled at a high level of abstraction and tasks are hierarchically grouped. Individual designers or teams use Gantt charts and PERT charts to show their own activities. Typically, project managers showed the authors extremely cumbersome printouts of processes, which were folded several times in a folder or pinned to an office wall. Product models are even more difficult to display. While companies are beginning to construct complete CAD models of products, these are often difficult to use and require understanding both of the product and the system to elicit information from them. The most typical representation of a product is a bill of materials (BOM). This lists all the components of the product, which may be made in-house or purchased externally, and typically groups them into sub-assemblies as required by manufacturing. As the design is developed, the BOM continuously changes. Usually a part does not appear in a BOM before it is completely designed or the final decision regarding a supplier is made. Designers can find BOMs extremely difficult to use and are often not allowed to interact with them. There is clearly a need for an intuitive and effective visualisation of products and design processes. One approach to solving this problem is to use DSMs, which engineers find attractive since they are familiar with matrices. This chapter discusses different types of DSMs and their strengths and limitations when used to display connectivity in design. Section 2 introduces different types of DSMs, discusses their use in the context of engineering design and concludes with issues of current research. In section 3, the difficulties arising when dealing with large DSMs are outlined, and ways of tackling these problems by hierarchical structuring of the underlying data for the use of zooming and related techniques are discussed. Finally, conclusions and recommendations for future research are presented in section 4.
Using design structure matrices in visualising design processes
2.
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DESIGN STRUCTURE MATRICES
The binary DSM was introduced as a graphical information tool to visualise the engineering design process (Steward, 1981), in particular the connectivity or dependency between components, activities (in this chapter we refer to them as tasks), or parameters. In a binary DSM, connections between elements are indicated by a simple cross, see fig. 1. The idea was developed into numerical DSMs (Eppinger, 1991; Eppinger et al., 1994). Numerical DSMs provide one value as an indication of the strength of the dependencies between components, tasks, or parameters (Eppinger et al., 1994 also mention other numerical DSMs). Large numerical DSMs have been used to capture the design process of complex products such as helicopter rotor blades (Clarkson and Hamilton, 2000), cars (Eppinger et al., 1994; Browning, 2001), telecommunication systems (Browning, 2001) or constructions (Austin et al., 1999), to cite a few. A DSM on its own does not provide information on how the connection information must be read, which can confuse the user. A typical
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interpretation could be - as in fig. 1 - A is connected to B, therefore the matrix by definition would be symmetrical and half of the matrix would be redundant. Eppinger et al. (1994) use task-based DSMs, where the connection is read as: task B depends on information from task A, e.g. the axis needs information from the engine. Clarkson et al. (2001) interpret the connection as "the risk of a change from component A propagating to component B". In the latter two cases, the matrix is not necessarily symmetrical. Traditional matrices map elements of the same kind against each other, e.g. components. However, it is also possible to map different elements, e.g. tasks against parameters, as in a conventional table.
2.1.
Different types of D S M s
In principle, any kind of connectivity could be displayed in a matrix. However, certain kinds of DSMs are commonly in use. The car in fig. 1 illustrates how different components are physically connected to each other in a component-based DSM (Browning, 2001) and a network graph. In this example, the DSM takes up more space than the simple, carefully laid-out graph. But a matrix with more connections would result in a very confusing graph. A team-based DSM (Browning, 2001), showing the interaction of teams in a project, can be constructed similarly. A component-based DSM can also be used to predict change propagation (Clarkson et al., 2001) in order to capture a probabilistic measure of the risk of a change to one component affecting another component (cf fig. 2). This allows a
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s y s t e m - l e v e l a n a l y s i s o f the i m p a c t a n d l i k e l i h o o d o f b o t h direct a n d i n d i r e c t changes. T h e u s e o f t a s k - b a s e d D S M s c a n be illustrated w i t h the i n s t r u c t i o n s for b a k i n g a c a k e (fig. 3). E a c h task is w r i t t e n o n a s e p a r a t e p i e c e o f p a p e r w h e r e their c o r r e c t o r d e r is n o t k n o w n . B y r e a d i n g the i n s t r u c t i o n s c a r e f u l l y , it is p o s s i b l e to o b t a i n an initial t a s k - b a s e d D S M r e p r e s e n t a t i o n ( B r o w n i n g , 2 0 0 1 ) , p r o v i d i n g us w i t h an o r d e r in w h i c h the o p e r a t i o n c a n b e u n d e r t a k e n . W e c a n n o t start c o o k i n g b e f o r e all the o p e r a t i o n s are o r d e r e d into a s e n s i b l e s e q u e n c e , since s o m e tasks d e p e n d on the o u t p u t o f others. F o r e x a m p l e , w e c a n n o t cut u p the d o u g h b e f o r e w e h a v e m a d e it. B y r e o r d e r i n g a D S M into a lower triangular form w e will find a task s e q u e n c e , w h e r e tasks d o n o t d e p e n d o n the i n p u t f r o m later tasks, see s e c t i o n 2.2. H o w e v e r , the l o w e r t r i a n g u l a r f o r m is n o t u n i q u e (see fig. 4), it is o n l y o n e o f m a n y p o s s i b l e s e q u e n c e s a n d the D S M will n o t tell us w h i c h o n e to c h o o s e . T h i s is a c l e a r l i m i t a t i o n o f D S M s . A p o s s i b l e g u i d e l i n e is d e f i n i n g a
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cost function associated with the DSM. In this case the minimum cost route will be the most desirable. However, this requires additional information and often depends on making tacit knowledge explicit. In the cooking example, it might be of benefit to find the shortest sequence time. For instance, there is apparently no reason why we should not perform tasks 9 and 1 at the end (note that the DSM shows only the initial sequence of the baking process). But if we assume that task 1 implies that we should leave the paste untouched for a while, leaving these two tasks until the end will be a waste of time. For the same reason, performing task 3 before 5 (as opposed to the sequence shown by the DSM) will save time if we assume that the electric mixer does not require the cook's attention. A DSM carries implicit information about tasks that could be carried out in parallel. Once the input information is available, any number of tasks can get started as long as they are finished before their information is required. However, those tasks that are carried out in parallel cannot be displayed. The baking example shows another limitation of DSMs. In real-life planning, we apply many heuristics to planning activities thus reducing the number of possible routes through a search space. For example, everybody who bakes cakes would know that whisked eggs collapse again and task 3 would be left to be performed as late as possible, not contemplating any other order of tasks. Finally, the process presented by the task-based DSM can be also visualised as a tree graph (an outline of the graph is shown in fig. 4). The parameter-based DSM (Clarkson et al., 2000; Browning, 2001) in fig. 5 shows the four parameters to be determined in order to design a simple mechanical tool such as a spanner. A common problem in design is parameter interdependence. No matter how the rows and columns are
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2.2.
Operations on DSMs
In task-based DSMs, a lower triangular form gives a process with no mutual dependencies. By contrast, the distance to crosses above the diagonal shows the amount of rework required in a dependency loop. The shorter the distance, the less problematic the dependency is. Parameter-based DSMs show similar information dependencies. For component DSMs the concept of a lower triangular form is meaningless. Reordering or partitioning techniques have been developed to obtain (block) lower triangular DSMs (Steward, 1981; Kusiak et al., 1993). In the case of loops, it is desirable to keep the feedback loop as small as possible (fig. 5) by reordering the DSM appropriately (Eppinger et al., 1994). This means avoiding the sequencing of tasks such that they might fail in the final stage of the design, requiting the whole process to be repeated instead of only the last part. Feedback loops illustrate a limitation to the utility of planning software and highlight a demand for human interaction. Several methods are available to force a DSM into a lower triangular form. This simplification can be achieved by either defining the block with coupled tasks as one task, however, information quality is lost (Eppinger et al., 1994; Rogers, 1997); by breaking the tasks apart and defining new tasks, which present an initial guess for the parameter, a more confident value, and the final value (Clarkson and Hamilton, 2000); or by using a technique called tearing (Steward, 1981). In a parameter-based DSM, tearing would mean that the user decides which parameters can be estimated to initiate the process in the loop, thus removing the corresponding crosses in the upper triangular part of the DSM.
312 2.3.
E. August, C. Eckert and P. John Clarkson Additional information in D S M s
The DSMs discussed in section 2.1 are binary DSMs. Eppinger et al. (1994) present numerical DSMs and discuss how they can be used to store extra information, such as the strength of connectivity between the different components; time to be spent on performing the task; or parameter sensitivity. A further method to store additional information in a DSM, without losing much on simplicity and visibility, is by means of colour coding. In Clarkson et al. (2001), the boxes in the DSM were filled with the colours red, amber and green to indicate high, middle and low change propagation risk, respectively. Figure 2 shows a black and white version of this matrix. In this case, the area of the boxes varies according to the risk of change propagation, making this graphical product risk matrix readable by the visually impaired.
2.4.
Summary
In visualising a design process, a DSM can provide an easy means of displaying serial tasks and their coupling. Parallel tasks can easily be derived from a DSM, but the DSM does not allow the display of alternative tasks, which may be relevant in the planning of complex processes. A traditional DSM can also only hold binary connection information or information about one kind of connection. For example, in the case of a component change, the change often propagates only through dependencies of a certain kind. To cope with the problem of having different kinds of dependency links, Jarratt et al. (2002) propose a third dimension to show the kinds of links between the components (fig. 6).
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Using design structure matrices in visualising design processes
3.
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LARGE DSMs
Realistic DSM models often consist of large data sets featuring thousands of components and loops. For example, Boeing has built DSMs with over 10,000 different tasks, but kept them fairly sparse. Eppinger et al. have observed that the companies they work with often comment that the process of building large matrices has been of more use that the matrix itself. With different visualisation techniques, they might be able to make more use of them during the design process. Existing techniques, which simplify the process of extracting information from large DSMs, are discussed in the following sections.
3.1.
Zooming
Overview matrices, such as that in fig. 7, provide a one-glance overview of the design problem and can be used to navigate a large matrix. However, they do not carry detailed information and have to be used in conjunction with a zooming technique. Zooming in will cause the loss of the overall picture, which is the main disadvantage of this visualisation technique. On the other hand, the nonrelevant parts of the process are out of sight and do not distract the user who can concentrate on the important parts. A simple approach to counter the problem of seeing only one region when zooming is using multiple views or a spreadsheet approach (Chi et al., 1997; Baldonado et al., 2000). It allows direct comparison of different sections of the DSM with each other by having two or more enlarged sections displayed simultaneously (fig. 7). The more complex, but also more elegant techniques of Focus and Context (Kreusler and Schumann, 1999) and Fisheye (Ware, 2000), are sensible alternatives.
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314 3.2.
E. August, C. Eckert and P. John Clarkson Hierarchical sets
Structuring the DSM hierarchically enhances the ability to handle a large DSM. If subtasks are clustered into blocks of higher order tasks, it is possible to shrink the size of the matrix and the DSM structure becomes clearer. If each iterative sub-cycle is transformed into a block (Rogers, 1997), a lower triangular higher-order DSM is obtained (fig. 8). This requires the definition of task groups, which themselves can be presented as DSMs. In this iterative process, a larger block could for example represent a testing activity, where engineers, technicians, designers, etc., are working together on a joint problem. The group's output will be forwarded to the next task group, for example manufacturing, which is also a combined block. In a component matrix, a sub-cycle could represent a car engine, which itself can be modelled as a DSM, since although the engine' s components are highly connected with each other there will be far fewer connections to other parts of the car than within the engine itself. There are many ways to structure a hierarchical set, by form, function, teams in the company, etc. These sub-structures can be decomposed until the smallest set, which cannot be decomposed further (for example, a screw or a single engineer), is reached. The conflict between natural hierarchies and the sub-cycles' hierarchy given by the DSM is the challenge design managers have to master. It means they have to manipulate the components' or teams' structure so that these natural structures coincide with the iterative subcycles in order to avoid extensive feedback loops between different naturally unconnected parts. Hence, the greatest challenge of hierarchical structuring is that for each product or process many different structures are possible. A typical product component structure representation is the BOM, which groups the components that are used to build sub-assemblies. However, this is not the structure that would be chosen either to define assembly orders or to design products. Hence, each complex product could be modelled using not only component hierarchies, but also system hierarchies. For example, a car could be broken down into body, engine, power train, etc. In addition,
Fig. 8. Obtaininga lower triangular higher-order DSM by iterative grouping.
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Fig. 9. A hierarchical structure (note the difference to the graph in fig. 1). systems such as the fuel system or electronics, which go through the entire product, would require a totally different description hierarchy (fig. 9). A hierarchical structure implies different levels in a DSM, where these levels may take many forms. A sequence of levelling can be defined by starting with big components and end with the smallest, or by the command structure of a company: the group manager, project manager, engineer and technician, where the latter two will be of same (or parallel) rank. Such levels provide a logical structure for the different levels of zooming. Hence, not all of the stored information has to be displayed on any level. This is of particular benefit when dealing with large DSMs in terms of visibility and computer capacity management. Finally, note that in the case of a parameter-based DSM hierarchical structuring is not straightforward. The clustering of various parameters into a parameter-conglomerate requires a value to be defined that is the output of such a conglomerate. This is a topic of future research.
4.
CONCLUSIONS
Different binary DSMs have been presented, along with their advantages and disadvantages. The limitations of numerical DSMs as a visualisation tool were mentioned, and colour-coding introduced as a possible solution. Change-propagation matrices using a 3D DSM, intelligent zooming techniques using a hierarchical structure, and the utility of a spreadsheet approach were also highlighted. In summary, a carefully constructed DSM can be a useful aid in visualising the characteristics of a product and its associated design process. However, DSMs remain limited by their lack of ability to visualise parallel task sequences (they are only implied) and hence their indifference to possible task sequences. This makes them at times less intuitive to interpret and begs the question of how more restrictions could be applied to the DSM or how other visualisation techniques could be used. For example, if a cost
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relation among the tasks were to be established, a cost-minimising task sequence could be the one to choose. Possible alternative visualisation techniques could include directed graphs. They are strongly related to DSMs, and therefore do not provide any extra information, but are more intuitive to interpret. In particular, such graphs are useful for highlighting component connectivity, where a DSM provides a rather poor visualisation. They are also useful for hierarchical structuring. One such graphical technique, the Petri net (McMahon et al., 1993), is able to show parallel task sequences implicitly and is also dynamical and allows interaction. Future research is likely to include the development of a hybrid approach coupling a number of visualisation methods or allowing transformation from one to another, providing guidelines for hierarchical structuring needs to be performed. In addition, structuring, and additionally a Virtual Reality approach (English and Bloebaum, 1999; Kirner and Martins, 2000) to aid visualisation of DSMs is a topic for future research.
REFERENCES Austin, S.A., Baldwin, A.N., Li, B., Waskett, P.R., 1999. Analytical Design Planning Technique (ADEPT): a dependency structure matrix tool to schedule the building design process. Construction Manage. Econ. 18, 173-182. Baldonado, M.Q.W., Woodruff, A., Kuchinsky, A., 2000. Guidelines for using multiple views in information visualization, AVI 2000, Palermo, Italy. Browning, T., 2001. Applying the DSM to system decomposition and integration problems: a review and new directions. IEEE Trans. Engng. Manage. 48, 292-306. Chi, E.H., Barry, P., Reidl, J., Konstan, J., 1997. A spreadsheet approach to information visualization, ACM Symposium UIST, Banff, Alberta, Canada. Clarkson, P.J., Hamilton, J., 2000. Signposting: a parameter-driven task-based model of the design process. Res. Engng. Des. 12, 18-38. Clarkson, P.J., Melo, A., Connor, A., 2000. Signposting for design process improvement, Artificial Intelligence in Design '00, Worcester, Massachusetts, USA. Clarkson, P.J., Simons, C.S., Eckert, C.M., 2001. Predicting change propagation in complex design, 13th International Conference on Design Theory and Methodology, Pittsburgh, Pennsylvania, USA. Eckert, C.M., Clarkson, P.J., Zanker, W., 2001. Change and customisation in complex engineering domains, aspects for a better understanding of changes, 13th International Conference on Engineering Design, 147-154, Glasgow, Scotland, UK. Eckert, C.M., Clarkson, P.J., 2002. Connectivity as a key to supporting design, Artificial Intelligence in Design '02, Cambridge, UK. English, K., Bloebaum, C.L., 1999. Complex system solution management using visualization, Proceedings of 3rd World Congress of Structural and Multidisciplinary Optimization, Niagara Falls, NY, USA. Eppinger, S.D., 1991. Model-based approaches to managing concurrent engineering. J. Engng. Des. 2, 4, 283-290.
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Eppinger, S.D., Whitney, D.E., Gebala, D.A., 1994. A model-based method for organizing tasks in product development. Res. Engng. Des. 6, 1-13. Jarratt, T., Eckert, C.M., Clarkson, P.J., 2002. Use of a 3-D DSM to improve understanding of product connectivity, Engineering Design Conference, London, UK. Kirner, T.G., Martins, V.F., 2000. Development of an information visualisation toll using virtual reality, SAC'00, Como, Italy. Kreusler, M., Schumann, H., 1999. Information visualization using a new focus + context technique in combination with dynamic clustering on information space, Eighth ACM International Conference on Information and Knowledge Management, Kansas City, Missouri, USA. Kusiak, A., Wang, J., 1993. Efficient organising of design activities. Int. J. Prod. Res. 31, 4, 753-769. McMahon, C.A., Sims Williams, J.H., Brown, K.N., 1993. A transformation model for the integration of design computing, International Conference on Engineering Design, 15861593, The Hague, Netherlands. Rogers, J.L., 1997. Reducing design cycle time and cost through process resequencing, International Conference on Engineering Design, Tampere, Finland. Steward, D., 1981. The design structure system: a method for managing the design of complex systems. IEEE Trans. Engng. Manage. EM-28, 3. Ware, C., 2000. Information Visualization: Perception for Design. Morgan Kaufmann Publishers, San Francisco, CA.
22 Using design structure matrices in visualising design processes Elias August, Claudia Eckert and P. John Clarkson Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
The binary design structure matrix (DSM) was introduced as a graphical information tool to visualise engineering design processes. Numerical DSMs, which provide designers with extra information, followed. To capture the design process of complex products as in the aerospace, automobile, and telecommunication industries, it is necessary to deal with very large DSMs. The aim of this chapter is to present different types of DSMs and their alternatives; to discuss their advantages and disadvantages; and to discuss the restrictions of numerical DSMs, when used as visual rather than computational tools. A way of handling large DSMs by zooming and hierarchical structuring is also discussed.
1.
THE P R O B L E M
Complex engineering design projects, such as the design of a new aeroplane or a new car, can involve tens of thousands of tasks and products that comprise hundreds of thousands of parts. Coping with this amount of information is virtually impossible for designers and design managers. In a study on the customisation of helicopters (Eckert et al., 2001), senior designers commented that the most experienced person in the organisation only understood about 50% of the product. Such a lack of overview in engineering can be catastrophic, since for a complex product small changes can have an enormous impact on the product as a whole. In addition, minor 305
306
E. August, C. Eckert and P. John Clarkson
tasks slipping or being ignored can cause expensive delays to the design process. Understanding the connectivity between project tasks and components is the key for supporting and managing design processes efficiently (see Eckert and Clarkson, 2002 for a detailed discussion). While it remains a challenge to understand the connections between two tasks or two components in terms of the parameters that link them, it is extremely difficult to get the whole picture. Consequently, one of the major challenges to the research community is how to display large models of products and processes. Existing techniques are clearly inadequate. At present most engineering companies the authors have visited use Microsoft Project to display their project plans. To cope with the complexity, processes are modelled at a high level of abstraction and tasks are hierarchically grouped. Individual designers or teams use Gantt charts and PERT charts to show their own activities. Typically, project managers showed the authors extremely cumbersome printouts of processes, which were folded several times in a folder or pinned to an office wall. Product models are even more difficult to display. While companies are beginning to construct complete CAD models of products, these are often difficult to use and require understanding both of the product and the system to elicit information from them. The most typical representation of a product is a bill of materials (BOM). This lists all the components of the product, which may be made in-house or purchased externally, and typically groups them into sub-assemblies as required by manufacturing. As the design is developed, the BOM continuously changes. Usually a part does not appear in a BOM before it is completely designed or the final decision regarding a supplier is made. Designers can find BOMs extremely difficult to use and are often not allowed to interact with them. There is clearly a need for an intuitive and effective visualisation of products and design processes. One approach to solving this problem is to use DSMs, which engineers find attractive since they are familiar with matrices. This chapter discusses different types of DSMs and their strengths and limitations when used to display connectivity in design. Section 2 introduces different types of DSMs, discusses their use in the context of engineering design and concludes with issues of current research. In section 3, the difficulties arising when dealing with large DSMs are outlined, and ways of tackling these problems by hierarchical structuring of the underlying data for the use of zooming and related techniques are discussed. Finally, conclusions and recommendations for future research are presented in section 4.
Using design structure matrices in visualising design processes
2.
307
DESIGN STRUCTURE MATRICES
The binary DSM was introduced as a graphical information tool to visualise the engineering design process (Steward, 1981), in particular the connectivity or dependency between components, activities (in this chapter we refer to them as tasks), or parameters. In a binary DSM, connections between elements are indicated by a simple cross, see fig. 1. The idea was developed into numerical DSMs (Eppinger, 1991; Eppinger et al., 1994). Numerical DSMs provide one value as an indication of the strength of the dependencies between components, tasks, or parameters (Eppinger et al., 1994 also mention other numerical DSMs). Large numerical DSMs have been used to capture the design process of complex products such as helicopter rotor blades (Clarkson and Hamilton, 2000), cars (Eppinger et al., 1994; Browning, 2001), telecommunication systems (Browning, 2001) or constructions (Austin et al., 1999), to cite a few. A DSM on its own does not provide information on how the connection information must be read, which can confuse the user. A typical
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E. August, C. Eckert and P. John Clarkson
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interpretation could be - as in fig. 1 - A is connected to B, therefore the matrix by definition would be symmetrical and half of the matrix would be redundant. Eppinger et al. (1994) use task-based DSMs, where the connection is read as: task B depends on information from task A, e.g. the axis needs information from the engine. Clarkson et al. (2001) interpret the connection as "the risk of a change from component A propagating to component B". In the latter two cases, the matrix is not necessarily symmetrical. Traditional matrices map elements of the same kind against each other, e.g. components. However, it is also possible to map different elements, e.g. tasks against parameters, as in a conventional table.
2.1.
Different types of D S M s
In principle, any kind of connectivity could be displayed in a matrix. However, certain kinds of DSMs are commonly in use. The car in fig. 1 illustrates how different components are physically connected to each other in a component-based DSM (Browning, 2001) and a network graph. In this example, the DSM takes up more space than the simple, carefully laid-out graph. But a matrix with more connections would result in a very confusing graph. A team-based DSM (Browning, 2001), showing the interaction of teams in a project, can be constructed similarly. A component-based DSM can also be used to predict change propagation (Clarkson et al., 2001) in order to capture a probabilistic measure of the risk of a change to one component affecting another component (cf fig. 2). This allows a
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s y s t e m - l e v e l a n a l y s i s o f the i m p a c t a n d l i k e l i h o o d o f b o t h direct a n d i n d i r e c t changes. T h e u s e o f t a s k - b a s e d D S M s c a n be illustrated w i t h the i n s t r u c t i o n s for b a k i n g a c a k e (fig. 3). E a c h task is w r i t t e n o n a s e p a r a t e p i e c e o f p a p e r w h e r e their c o r r e c t o r d e r is n o t k n o w n . B y r e a d i n g the i n s t r u c t i o n s c a r e f u l l y , it is p o s s i b l e to o b t a i n an initial t a s k - b a s e d D S M r e p r e s e n t a t i o n ( B r o w n i n g , 2 0 0 1 ) , p r o v i d i n g us w i t h an o r d e r in w h i c h the o p e r a t i o n c a n b e u n d e r t a k e n . W e c a n n o t start c o o k i n g b e f o r e all the o p e r a t i o n s are o r d e r e d into a s e n s i b l e s e q u e n c e , since s o m e tasks d e p e n d on the o u t p u t o f others. F o r e x a m p l e , w e c a n n o t cut u p the d o u g h b e f o r e w e h a v e m a d e it. B y r e o r d e r i n g a D S M into a lower triangular form w e will find a task s e q u e n c e , w h e r e tasks d o n o t d e p e n d o n the i n p u t f r o m later tasks, see s e c t i o n 2.2. H o w e v e r , the l o w e r t r i a n g u l a r f o r m is n o t u n i q u e (see fig. 4), it is o n l y o n e o f m a n y p o s s i b l e s e q u e n c e s a n d the D S M will n o t tell us w h i c h o n e to c h o o s e . T h i s is a c l e a r l i m i t a t i o n o f D S M s . A p o s s i b l e g u i d e l i n e is d e f i n i n g a
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cost function associated with the DSM. In this case the minimum cost route will be the most desirable. However, this requires additional information and often depends on making tacit knowledge explicit. In the cooking example, it might be of benefit to find the shortest sequence time. For instance, there is apparently no reason why we should not perform tasks 9 and 1 at the end (note that the DSM shows only the initial sequence of the baking process). But if we assume that task 1 implies that we should leave the paste untouched for a while, leaving these two tasks until the end will be a waste of time. For the same reason, performing task 3 before 5 (as opposed to the sequence shown by the DSM) will save time if we assume that the electric mixer does not require the cook's attention. A DSM carries implicit information about tasks that could be carried out in parallel. Once the input information is available, any number of tasks can get started as long as they are finished before their information is required. However, those tasks that are carried out in parallel cannot be displayed. The baking example shows another limitation of DSMs. In real-life planning, we apply many heuristics to planning activities thus reducing the number of possible routes through a search space. For example, everybody who bakes cakes would know that whisked eggs collapse again and task 3 would be left to be performed as late as possible, not contemplating any other order of tasks. Finally, the process presented by the task-based DSM can be also visualised as a tree graph (an outline of the graph is shown in fig. 4). The parameter-based DSM (Clarkson et al., 2000; Browning, 2001) in fig. 5 shows the four parameters to be determined in order to design a simple mechanical tool such as a spanner. A common problem in design is parameter interdependence. No matter how the rows and columns are
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interchanged it is not possible to obtain a lower triangular DSM (as in the case of the reordered DSM in fig. 3), which would indicate that the parameters are uncoupled from each other and have not to be determined by iteration. Rather, the result is a block lower triangular DSM.
2.2.
Operations on DSMs
In task-based DSMs, a lower triangular form gives a process with no mutual dependencies. By contrast, the distance to crosses above the diagonal shows the amount of rework required in a dependency loop. The shorter the distance, the less problematic the dependency is. Parameter-based DSMs show similar information dependencies. For component DSMs the concept of a lower triangular form is meaningless. Reordering or partitioning techniques have been developed to obtain (block) lower triangular DSMs (Steward, 1981; Kusiak et al., 1993). In the case of loops, it is desirable to keep the feedback loop as small as possible (fig. 5) by reordering the DSM appropriately (Eppinger et al., 1994). This means avoiding the sequencing of tasks such that they might fail in the final stage of the design, requiting the whole process to be repeated instead of only the last part. Feedback loops illustrate a limitation to the utility of planning software and highlight a demand for human interaction. Several methods are available to force a DSM into a lower triangular form. This simplification can be achieved by either defining the block with coupled tasks as one task, however, information quality is lost (Eppinger et al., 1994; Rogers, 1997); by breaking the tasks apart and defining new tasks, which present an initial guess for the parameter, a more confident value, and the final value (Clarkson and Hamilton, 2000); or by using a technique called tearing (Steward, 1981). In a parameter-based DSM, tearing would mean that the user decides which parameters can be estimated to initiate the process in the loop, thus removing the corresponding crosses in the upper triangular part of the DSM.
312 2.3.
E. August, C. Eckert and P. John Clarkson Additional information in D S M s
The DSMs discussed in section 2.1 are binary DSMs. Eppinger et al. (1994) present numerical DSMs and discuss how they can be used to store extra information, such as the strength of connectivity between the different components; time to be spent on performing the task; or parameter sensitivity. A further method to store additional information in a DSM, without losing much on simplicity and visibility, is by means of colour coding. In Clarkson et al. (2001), the boxes in the DSM were filled with the colours red, amber and green to indicate high, middle and low change propagation risk, respectively. Figure 2 shows a black and white version of this matrix. In this case, the area of the boxes varies according to the risk of change propagation, making this graphical product risk matrix readable by the visually impaired.
2.4.
Summary
In visualising a design process, a DSM can provide an easy means of displaying serial tasks and their coupling. Parallel tasks can easily be derived from a DSM, but the DSM does not allow the display of alternative tasks, which may be relevant in the planning of complex processes. A traditional DSM can also only hold binary connection information or information about one kind of connection. For example, in the case of a component change, the change often propagates only through dependencies of a certain kind. To cope with the problem of having different kinds of dependency links, Jarratt et al. (2002) propose a third dimension to show the kinds of links between the components (fig. 6).
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Using design structure matrices in visualising design processes
3.
313
LARGE DSMs
Realistic DSM models often consist of large data sets featuring thousands of components and loops. For example, Boeing has built DSMs with over 10,000 different tasks, but kept them fairly sparse. Eppinger et al. have observed that the companies they work with often comment that the process of building large matrices has been of more use that the matrix itself. With different visualisation techniques, they might be able to make more use of them during the design process. Existing techniques, which simplify the process of extracting information from large DSMs, are discussed in the following sections.
3.1.
Zooming
Overview matrices, such as that in fig. 7, provide a one-glance overview of the design problem and can be used to navigate a large matrix. However, they do not carry detailed information and have to be used in conjunction with a zooming technique. Zooming in will cause the loss of the overall picture, which is the main disadvantage of this visualisation technique. On the other hand, the nonrelevant parts of the process are out of sight and do not distract the user who can concentrate on the important parts. A simple approach to counter the problem of seeing only one region when zooming is using multiple views or a spreadsheet approach (Chi et al., 1997; Baldonado et al., 2000). It allows direct comparison of different sections of the DSM with each other by having two or more enlarged sections displayed simultaneously (fig. 7). The more complex, but also more elegant techniques of Focus and Context (Kreusler and Schumann, 1999) and Fisheye (Ware, 2000), are sensible alternatives.
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314 3.2.
E. August, C. Eckert and P. John Clarkson Hierarchical sets
Structuring the DSM hierarchically enhances the ability to handle a large DSM. If subtasks are clustered into blocks of higher order tasks, it is possible to shrink the size of the matrix and the DSM structure becomes clearer. If each iterative sub-cycle is transformed into a block (Rogers, 1997), a lower triangular higher-order DSM is obtained (fig. 8). This requires the definition of task groups, which themselves can be presented as DSMs. In this iterative process, a larger block could for example represent a testing activity, where engineers, technicians, designers, etc., are working together on a joint problem. The group's output will be forwarded to the next task group, for example manufacturing, which is also a combined block. In a component matrix, a sub-cycle could represent a car engine, which itself can be modelled as a DSM, since although the engine' s components are highly connected with each other there will be far fewer connections to other parts of the car than within the engine itself. There are many ways to structure a hierarchical set, by form, function, teams in the company, etc. These sub-structures can be decomposed until the smallest set, which cannot be decomposed further (for example, a screw or a single engineer), is reached. The conflict between natural hierarchies and the sub-cycles' hierarchy given by the DSM is the challenge design managers have to master. It means they have to manipulate the components' or teams' structure so that these natural structures coincide with the iterative subcycles in order to avoid extensive feedback loops between different naturally unconnected parts. Hence, the greatest challenge of hierarchical structuring is that for each product or process many different structures are possible. A typical product component structure representation is the BOM, which groups the components that are used to build sub-assemblies. However, this is not the structure that would be chosen either to define assembly orders or to design products. Hence, each complex product could be modelled using not only component hierarchies, but also system hierarchies. For example, a car could be broken down into body, engine, power train, etc. In addition,
Fig. 8. Obtaininga lower triangular higher-order DSM by iterative grouping.
Using design structure matrices in visualising design processes
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Fig. 9. A hierarchical structure (note the difference to the graph in fig. 1). systems such as the fuel system or electronics, which go through the entire product, would require a totally different description hierarchy (fig. 9). A hierarchical structure implies different levels in a DSM, where these levels may take many forms. A sequence of levelling can be defined by starting with big components and end with the smallest, or by the command structure of a company: the group manager, project manager, engineer and technician, where the latter two will be of same (or parallel) rank. Such levels provide a logical structure for the different levels of zooming. Hence, not all of the stored information has to be displayed on any level. This is of particular benefit when dealing with large DSMs in terms of visibility and computer capacity management. Finally, note that in the case of a parameter-based DSM hierarchical structuring is not straightforward. The clustering of various parameters into a parameter-conglomerate requires a value to be defined that is the output of such a conglomerate. This is a topic of future research.
4.
CONCLUSIONS
Different binary DSMs have been presented, along with their advantages and disadvantages. The limitations of numerical DSMs as a visualisation tool were mentioned, and colour-coding introduced as a possible solution. Change-propagation matrices using a 3D DSM, intelligent zooming techniques using a hierarchical structure, and the utility of a spreadsheet approach were also highlighted. In summary, a carefully constructed DSM can be a useful aid in visualising the characteristics of a product and its associated design process. However, DSMs remain limited by their lack of ability to visualise parallel task sequences (they are only implied) and hence their indifference to possible task sequences. This makes them at times less intuitive to interpret and begs the question of how more restrictions could be applied to the DSM or how other visualisation techniques could be used. For example, if a cost
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E. August, C. Eckert and P. John Clarkson
relation among the tasks were to be established, a cost-minimising task sequence could be the one to choose. Possible alternative visualisation techniques could include directed graphs. They are strongly related to DSMs, and therefore do not provide any extra information, but are more intuitive to interpret. In particular, such graphs are useful for highlighting component connectivity, where a DSM provides a rather poor visualisation. They are also useful for hierarchical structuring. One such graphical technique, the Petri net (McMahon et al., 1993), is able to show parallel task sequences implicitly and is also dynamical and allows interaction. Future research is likely to include the development of a hybrid approach coupling a number of visualisation methods or allowing transformation from one to another, providing guidelines for hierarchical structuring needs to be performed. In addition, structuring, and additionally a Virtual Reality approach (English and Bloebaum, 1999; Kirner and Martins, 2000) to aid visualisation of DSMs is a topic for future research.
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Eppinger, S.D., Whitney, D.E., Gebala, D.A., 1994. A model-based method for organizing tasks in product development. Res. Engng. Des. 6, 1-13. Jarratt, T., Eckert, C.M., Clarkson, P.J., 2002. Use of a 3-D DSM to improve understanding of product connectivity, Engineering Design Conference, London, UK. Kirner, T.G., Martins, V.F., 2000. Development of an information visualisation toll using virtual reality, SAC'00, Como, Italy. Kreusler, M., Schumann, H., 1999. Information visualization using a new focus + context technique in combination with dynamic clustering on information space, Eighth ACM International Conference on Information and Knowledge Management, Kansas City, Missouri, USA. Kusiak, A., Wang, J., 1993. Efficient organising of design activities. Int. J. Prod. Res. 31, 4, 753-769. McMahon, C.A., Sims Williams, J.H., Brown, K.N., 1993. A transformation model for the integration of design computing, International Conference on Engineering Design, 15861593, The Hague, Netherlands. Rogers, J.L., 1997. Reducing design cycle time and cost through process resequencing, International Conference on Engineering Design, Tampere, Finland. Steward, D., 1981. The design structure system: a method for managing the design of complex systems. IEEE Trans. Engng. Manage. EM-28, 3. Ware, C., 2000. Information Visualization: Perception for Design. Morgan Kaufmann Publishers, San Francisco, CA.