3.1 Engineering change

Engineering change can be defined as ‘an alteration made to parts, drawings or software that have already been released during the product design process. The change can be of any size or type; the change can involve any number of people and take any length of time.’ (Jarrett et al. Reference Jarratt, Eckert, Caldwell and Clarkson2011). Understanding change plays a fundamental role in limiting the risk and effort involved in evolving a complex product, which is usually based on a previous design. Meeting new requirements or introducing innovation requires engineering change just as much as dealing with problems emerging in the product development process. Change to a product are only required, if the product has no margins to absorb the required alteration to the requirements.

Any modification, in the iteration or a formal engineering change, can have a knock-on effect onto other parts of the design if margins are exhausted (Eckert et al. Reference Eckert, Clarkson and Zanker2004), which in turn have to be modified making planning difficult. Each proposed change or modification can be expressed as changes to one or more parameters of a component or system. Each parameter might become critical if small, required changes (or combinations of changes) initiate significant changes in other components. Components can act as (i) change absorbers, receiving change without passing it on, (ii) change carriers passing the same degree of change on as they have received, or (iii) change multipliers, which pass more changes on to other parts of the system, which will in turn need to be redesigned (Eckert et al. Reference Eckert, Clarkson and Zanker2004). Change becomes problematic when change absorbers turn into change carriers or change multipliers and becomes critical when changes avalanche. In this context designers talk of the change absorbers as margins that are being used up.

Consequently, there is a clear link between margins and processes, as changes in such critical situations may be brought under control given sufficient design resources and time, but companies might need to abandon these projects. From the perspective of an individual change, there might be several knock-on effects; some that can be dealt with directly and others that require detailed analysis, which leads to ripple effects with the magnitude of changes decreasing over time. Griffin et al. (Reference Giffin, de Weck, Bounova, Keller, Eckert and Clarkson2009) identify typical patterns of change propagation while Pasqual & de Weck (Reference Pasqual and de Weck2012) observed ripple patterns, with the number of change requests increasing towards deadlines.

Designers need to do several things in managing change. These include; making explicit decisions about how to implement a change (Earl et al. Reference Earl, Eckert and Clarkson2005), understanding product connectivity, and analysing the ability of the component to absorb a specific change (Keller et al., Reference Keller, Eckert and Clarkson2009). Various tools have been developed to predict the impact of change, through probabilistic links (Clarkson et al. Reference Clarkson, Simons and Eckert2004) anticipating changes (Cohen et al. Reference Cohen, Navathe and Fulton2000) or underlying functional models (Grantham et al. Reference Grantham Lough, Stone and Tumer2006; Hamraz et al. Reference Hamraz, Caldwell, Ridgman and Clarkson2015). More recently change prediction has been integrated with visualizing other aspects of the product (Kocar & Akgunduz Reference Kocar and Akgunduz2010) and integrating product based change prediction with process models (Ahmad et al. Reference Ahmad, Wynn and Clarkson2013). It is evident that there is a need to understand design margins as a link between engineering change and iteration, to be able to manage and plan iterations efficiently.

3.2 Iteration

Iteration has mainly been considered from the perspective of how design processes can be planned so that products can be delivered with the least number of iteration cycles (see Wynn & Eckert Reference Wynn and Eckert2017 for a comprehensive review). This perspective focuses on information dependency between tasks, which have to be repeated if the correct information is not available, rather than on the properties of the product that lead to iteration. Iteration is recognized as a fundamental part of most design processes and reflected in many models of design processes (Wynn & Clarkson Reference Wynn and Clarkson2018), either as part of exploration and convergence or as feedback loops in a stage-based process. Iteration is often seen as something to be minimized or avoided by defining design problems more clearly or confining iteration to the early phases of design processes (Suh Reference Suh1990) in what Safoutin (Reference Safoutin2003) calls the ‘zero-iteration ideal’. Unger & Eppinger (Reference Unger and Eppinger2011) argue for risk management through ‘controlled, feedback-based redesign’ governed by design reviews. Iteration can be characterized in terms of cyclic dependencies among overlapping, sequential and parallel tasks. Iteration occurs because new information becomes available or new goals need to be addressed (Chusilp & Jin Reference Chusilp and Jin2006; Jun & Suh Reference Jun and Suh2008). Parallel iteration is the mechanism through which convergent iteration takes place (Cho & Eppinger Reference Cho and Eppinger2005). However, the predominant mode of iteration is overlapping, as many tasks are not carried out completely in parallel. Thus decisions about planning when to start a specific task are critical (Krishan et al. Reference Krishnan, Eppinger and Whitney1997). Unplanned iteration occurs because of ‘unexpected failure of a design to meet specifications’ (Yassine & Braha Reference Yassine and Braha2003) and when new requirements come up (Ahmad et al. Reference Ahmad, Wynn and Clarkson2013). It can lead to significant delays in process duration, increases in cost due to additional work and unproductive periods for engineers. This can lead to churn, where the design is not advanced, but the designers still cannot be redeployed to other tasks.

3.3 Design margins

Austin-Breneman et al. (Reference Austin-Breneman, Yu and Yang2015) discuss the information bias in negotiations between design teams in complex systems design, referring to the buffer or margin that designers add to their own estimated values. They draw on game theory to model complex design processes to run simulations of the effect information bias has on finding an optimal design solution. They argue that engineers have envelopes of parameter values in which they negotiate. The paper identifies three bias strategies, namely, no bias, constant bias and decreasing bias as the design process progresses. They simulate a measure of the number of iterations in a small-scale artificial problem. The results show that a fixed bias to more likely to result in ‘suboptimal’ solutions than no bias or a decreasing bias; however, decreasing bias requires significantly more iterations. A similar theme is picked up in the work of Dawson et al. (Reference Dawson, Fixson and Whitney2012), who look at design safety factors as the amount by which properties, such as strength, exceed their requirements, which they see as a means to mitigate against misalignment between the product architecture and the organizational structure. Through a game theory inspired simulation they propose faster information sharing, and temporary acceptance of suboptimal partial solutions as a way to improve product performance. They conclude that projects in which the all participants are incentivized to manage safety factors, converge much more successfully, whereas wild swings in the safety factors have seriously detrimental effects. Fernandes et al. (Reference Fernandes, Henriques, Silva and Moss2014) make a similar point based on an analysis of a large body data about change requests for jet engine design in order to study the evolution of requirements over the course of a project. They identify the effect of imprecision of the value of key parameters as one of the key causes of design iteration in design processes and propose better analysis of the parameter values and communication of this imprecision more clearly to different design teams.

Canbaz et al. (Reference Canbaz, Yannou and Yvars2014) use an agent based approach to model design convergence through negotiation between design agents in collaborative complex systems design. Collaborative design requires both accommodating the needs of others and the uncertainties that the design process is subject to. Other authors adopt a set-based design approach (Sobek et al. Reference Sobek, Ward and Liker1999; McKenney et al. Reference McKenney, Kemink and Singer2011) where design options are represented through ranges of parameters that are narrowed over the course of a product development process. Within these sets of potential values considering all the constraints the agents have to negotiate feasible solutions which more or less meet the requirements of each agent. More recently they turned their attention to manage rework using Set-Based Design (Kennedy et al. Reference Kennedy, Sobek and Kennedy2014). Canbaz et al. (Reference Canbaz, Yannou and Yvars2014) use Monte Carlo simulation to find the best solution based on a wellbeing indicator for each agent. They express margins as min–max values of ranges of acceptable values.

Margins are also used to provide flexibility, as they contribute to the adaptability of an existing product to particular requirements. Such adaptability is designed into an individual product at the beginning to allow for changes in the course of the product life cycle or during the design of the follow-on product (Ross & Hastings Reference Ross and Hastings2005). Both Ross & Hastings (Reference Ross and Hastings2005) and Qureshi et al. (Reference Qureshi, Murphy, Kuchinsky, Seepersad, Wood and Jensen2006) advocate assessing the flexibility of a product by systematically anticipating and rating the potential future changes to ‘future proof’ the design, which will inevitably introduce a degree of redundancy into the product. In Ross & Hastings (Reference Ross and Hastings2005) this assessment is achieved through mapping out the tradespace, i.e. the range of possible parameter values that provides potential solutions. Where the design sits within this tradespace indicates margins on the product. In the design of a specific product the designers can trade the form, the material and the function off against each other. Only by realizing a specific form in a specific material it is possible to fulfil a particular function. Eckert et al. (Reference Eckert, Isaksson and Earl2012) argue that margins therefore lie in the relationship of these three elements. De Neufville et al. (Reference De Neufville, Scholtes and Wang2006) introduce design options as a form of deliberate planning for a small number of potential changes that will be carried out to the product including calculations of the cost of planning in these options and the savings made in using a design option as opposed to making a change from scratch. Martin & Ishii (Reference Martin and Ishii2002) assess whether a system can meet changing requirements through its lifetime. Guidelines and principles for design of products for future evolution are brought together by Tilstra et al. (Reference Tilstra, Backlund, Seepersad and Wood2015) and extended by recent work of Tan et al. (Reference Tan, Otto and Wood2016) which aims to assess the impact of margins on systems performance and develop a composite margin index.

The ideas of margins enabling redesign is also picked up by Tackett et al. (Reference Tackett, Mattson and Ferguson2014), who address margins in terms of excess, as ‘the quantity of surplus in a system once the necessities of the system are met’ and capacity as ‘the ability of a system to meet future performance objectives using existing system excess’. Their study on refurbishment of ships concentrates on system level properties as excess factors such as volume and show how these values can be calculated to understand how new functionality could be added without prohibitively expensive changes to the structure of the ship. This is based on a functional basis approach (Hirtz et al. Reference Hirtz, Stone, McAdams, Szykman and Wood2002), which offers a set of general functions through which systems can be described. It differentiates between excess in these functions such as excess flows, and excess in the emergent physical properties of the system, like volume, with which the functions are realized.

4.1 Formal definition of margins

This definition is now refined using the following related concepts, where both requirements and constraints come with a ‘must exceed’ and ‘must not exceed’ view.

1. (i) Requirements ( R ): the values parameters need to reach. These are usually external to the problem coming from the customer or business units, although many requirements are also generated internally. Requirements can be of ‘must exceed’ or ‘must not exceed’ type.

2. (ii) Constraints ( Const ): values a parameter must reach or not exceed, which can be intrinsic or extrinsic to the problem.

3. (iii) Capability ( Cap ): the values a parameter could reach regardless of specific constraints or requirements.

4. (iv) Buffer ( B ): The portion of parameter values that compensates for uncertainties.

5. (v) Excess ( E ): The value over, and above, any allowances for uncertainties.

Requirements can be stated independently of solutions, whereas constraints are associated with how specific solutions, and surroundings, actually are defined. Constraints can arise from the problem, from the emerging solution, for example carry-over components, or from the process by which it is designed, such as frozen components (see Eckert & Stacey Reference Eckert and Stacey2014).

For example, we want to add weight to a shelf consisting of a board and a bracket, see Figure 1. Initially, the shelf was required to carry 50 N (R), but the board of the shelf has the capability of carry 90 N before failure (Cap) (assuming that the cantilevered board is rigidly fixed at both ends).

Margins can be thought about from different but interrelated perspectives:

1. (i) Margins on a simple parameter, e.g. for the board only Cap _board.

2. (ii) Margins on a performance parameter, that may be based on the combined effect of multiple parameters, i.e. the margin of the entire shelf, Cap _shelf.

Figure 1. Shelf with capabilities, constraint and requirement.

Margins can refer to single parameters p or vectors of parameters $\boldsymbol{P}=\langle p_{1},p_{2},\ldots ,p_{n}\rangle$ . Figure 2 shows the margin M ( P ) (Eq. (1)) as the difference between the capability Cap ( P ) of the component with parameters $\boldsymbol{P}$ and requirements R(P) and constraints Const(P) . The margin is therefore the componentwise difference between the capability for a parameter and the requirement for the parameter. For the purpose of the paper it is assumed that margins can be expressed as numerical values. A margin is the useable difference between capabilities, requirements and constraints.

If there is no constraint the margin is the difference between the requirement and the capability.

In a must exceed case Eq. (1) applies,

(1)

for a must not exceed case Eq. (2) applies,

(2)

In the general case, margins are the minimal difference between the capability and either the requirements or the constraints. The general notion of margins can thus be stated as in (3) covering the four situations as shown in Figure 2.

(3)

Figure 2. Margin (light blue bar) between requirements and capabilities for a component or system.

In the shelf example, we are in the top right corner in Figure 2. If we only look at the components in isolation, the board has a margin of M_board = Cap(board) - R(board) , i.e.

M_board = 90 N - 50 N = 40 N. If we look at the entire shelf, we need to include the constraint of the bracket which has a ‘must not exceed’ constraint. M_bracket = R(bracket) - Cap(bracket ), i.e. M_bracket = 60 N - 50 N = 10 N.

In a design situation we can therefore accept a 10 N change to shelf without a change. For a change of <40 N we need to only change the bracket, but for >40 N we need to rework the board and the bracket.

Many parameters in a product are practically constrained by their physical and functional location in a product. Research on engineering change propagation has shown, that engineers often overlook the constraints arising from other parts of the system, that are not functionally or mechanically linked to them (Jarratt et al. Reference Jarratt, Eckert and Clarkson2004). For example, the size of a component is constrained by the space between the neighbouring components. Another example is the temperature that a component needs to withstand is constrained by the operating temperature of neighbouring components. Adjacent components might heat up and therefore impact the allowable space due to thermal expansion.

In practice it is rarely enough to look at the margin on a single parameter and groups of margins have to be looked at together. The way different parameters combine depends on the nature of their relationship, most of which will be governed by the underlying physics in the product. However, recurring patterns of margins can be detected:

1. (i) Min/max margins, where the overall relationship is governed by the smallest or largest parameter. This applies for example to clearances, where one would intuitively think of the clearance of a component or system as the smallest values. We could also look at the shelf as a Min/Max relationship and argue that 10N as the smallest margin is the margin on the system.

2. (ii) Additive relationships, where all the parameters accumulate and only the total is interesting. This applies, in particular to weight, but also to the stack-up of geometrical tolerances.

3. (iii) Key equations, where while many parameters influence the final design, relatively simple equations express relationships among the small number of key parameters. For example, the fuel consumption in a car mainly depends on the engine and the weight of the car, but is also affected by tyres, aerodynamics etc., which might be neglected in some calculations. In our shelf example we have ignored the link between the bracket and wall, which is assumed to be sufficiently strong.

In any system the margins are interrelated, as the parameters that define the system are dependent on each other. Component or system margins, as the result of components and systems being put together, in turn have their own margins defined on a systems level. This is the case for the shelf, bracket and board example where the components have their margins, and the shelf has a system margin. However, the margins do not decompose or aggregate in a linear fashion within or across a system. Components with large margins can be part of a system that itself has no margins or a subsystem can have margins while many individual components do not. In particular, as margins are hierarchically composed or decomposed, margins can be gained or lost through the way they are combined. As the shelf example illustrates it can be a matter of interpretation whether a part of the system is seen as constraint or an element of the system; this usually depends on the sphere of influence of the designers.

4.2 Margins and uncertainties

At the beginning of a design process many requirements, constraints and capabilities of components or systems are still uncertain. This could be conceptualized as ranges of parameter values; however, in practice designers tend to pick a value that they know from experience would cover most of the range and try to use analysis and testing to firm up the value. For example, for a reused component the capabilities are known from the last version, whereas the requirements and constraints are only emerging, so designers might start off with working on the worst case scenario of the last design episode. For a new development, the requirements and the capability might be given from the outset, while the constraints are emerging as other components are developed.

4.3 Margins as buffer and excess

As illustrated in Figure 3 margins can be conceptualized of as having two elements,

1. (i) a buffer B , which is used to cater for uncertainties; and

2. (ii) an excess E , which is the surplus versus the known requirements.

Since the parameter value $p_{j}$ of a component or system is seldom exactly known, the range of uncertainty needs to be accounted for in design. The precise capability is not known, and therefore the buffer is a range to cater for the expected variation of the component or systems capability. In the same way, the constraints are not known precisely either. As an example, designing a circular tube that needs to have a diameter of 50 mm, may in practice vary in diameter once produced. The required diameter may also need to be changed due to new or refined knowledge in the requirements and loads. Both contribute to an uncertainty justifying a buffer to be assigned, say 2 mm. Assuming standard tube diameters are preferred, a designer may already pick a diameter of 55 mm. This leaves the designer with a buffer of 2 mm and an excess of 3 mm and a combined margin to the nominal design of 5 mm. This situation is shown in Figure 3 and is defined in Eq. (4). As the requirements may either increase or decrease they are drawn as a distribution around the nominal value the requirement. The margins are the surplus above the requirements, which is part of the buffer. In the case of reduction of requirements, this results in an increased excess.

(4)

Figure 3. Margins as the sum of the buffer and the excess, accounting for a range of uncertainty.

By reducing the uncertainties on a component, for example by conducting tests and understanding the capabilities better or by understanding the use profiles better, designers can reduce the uncertainty a component is subject to and therefore increase the excess on the component. Note that the reduction of uncertainty can come from firming up requirements, or by testing the precision of the manufacturing method, unless the new requirement consumes the entire buffer. The excess is the part of the margin that designers can make use of, for example to deal with future requirements, product upgrades or subsequent optimization. For a case having both ‘must exceed’ constraints and requirements (top left in Figure 2) combining Eqs. (4) and (1) gives

(5)

For other cases, how the excess can be calculated depend on the nature of the uncertainty.

This view of margins relates it to the concept of excess presented in Tackett et al. (Reference Tackett, Mattson and Ferguson2014), but acknowledges that designs are subject to uncertainties by using the concept of a buffer. Product parameters may have buffers, which are not immediately usable in design but offer the potential that they can be reduced and thus release tangible and usable margins. Reducing uncertainty and releasing buffer offers additional options for meeting higher levels of functional requirement or optimizing product parameters against existing functional requirements. However, the reality lies in between. Functional requirements can ‘increase’ taking up some of the parameter margins released but at the same time some of the newly available margins are retained allowing for further future evolution of functional requirements.

Another scenario occurs when one parameter is ‘frozen’ and the design objective is to maximize performance by varying other parameters within their limits. For example, maximizing performance from a newly developed material whilst geometric parameters remain unaltered. Product performance is the measure of function and behaviour, i.e. how well the product performs what it is designed to do. Margins fall into two categories, margins on performance parameters and margins on physical properties of a product. The latter are typically geometric and material property margins, which describe how much the form or material properties of a product or component can be changed before the complete satisfaction of requirements of specific component or sub-system is affected. Note that cliff edge effects come into play, where small changes can lead to the redesign of an entire system. An important type of geometric parameter margin are clearances surrounding a component. These allow the component to grow or to be moved and can therefore absorb potential changes to the product. Clearances can be difficult to handle, because they are not explicitly represented with a particular component but rather deduced from relationships between the components.

If requirements unexpectedly increase, the designer can choose between reducing the excess or by finding means to reduce uncertainty and thereby moving buffer into excess. This is an important insight for industry, as a change can be absorbed by the existing design if its capabilities are better understood by improved testing or improved mathematical modelling.

In many cases it is not possible to determine exactly the division between buffers and excess, since the uncertainty buffers are seldom fully quantifiable because people consider different uncertainties to be relevant. As Van Bossuyt et al. (Reference Van Bossuyt, Dong, Tumer and Carvalho2013) point out the attitude to risk varies. However, some industry sectors, like the aerospace industry, have well developed processes to capture the risks. What was found in interviews with the truck company, is that in practice engineers use ‘worst case scenarios’ that state maximum values rather that ranges. In particular as the demarcation between buffer and excess might be subjective, the concept of buffer and excess provide a useful vocabulary to discuss how much of a component can be repurposed.

5.1 Different terms for margins across the design process

In the truck company different groups of engineers used different words to talk about the same underlying phenomenon of margin, so that designers in one group are often not aware of margins being added by another group. Figure 4 illustrates how the different margins can be accumulated. The findings were also presented to engineers in the jet engine company during validation interviews, who instantly related to the fact that margins are hidden under divergent terminology.

Figure 4. Cumulative effect of margin concepts.

Design teams may add safety margins to the requirements of the components to cater for uncertainties in use include serious misuse of the product as well as emerging requirements and uncertainties during the development process. As trucks have large safety margins on the requirements, it is possible to push the worst case scenarios applications to their very limits.

Companies are however, not just considering the current product, but often have future generations of the product in mind. They aim to redesign major systems every few generations and therefore need to plan in margins against the predicted requirements of future product. For example, if the latest generation system needs 12 electronic ports, but the company expects that each subsequent generation will add 2 ports and therefore designs a system with 16 ports, so that they do not have to redesign the ports for another two generations. Incorporating this room for growth arising from product planning comes at a cost, because the current system is overdesigned. However, it is not only allowing for future generations of the product that might lead to overdesign, but also developing versions of the same product. Therefore, there is a tension between the optimization of an individual product and the flexibility which margins can give an organization (see Isaksson et al. Reference Isaksson, Lindroth and Eckert2014).

Overdesign is also the way the margins are described from a value engineering perspective. For example, the shelf board that can carry 90 N rather than the required 50 N might be heavier or more expensive than one that only carries 50 N. Therefore, the component might be targeted in cost saving activities, for example when a company is thinking of bringing out a cheaper product that is targeting another market. Overdesign is also an issue when companies are trying to synchronize service intervals on a product. For example, jet engines are maintained at regular intervals and any component that is likely to cause problems during the next service intervals is repaired or replaced. Unless the condition of the engine components are known, there needs to be a sufficient margin to failure at any state of operation. Typically, many components are designed to be serviced at scheduled major overhauls only, and individual components may be replaced or repaired pre-maturely with their real capability (Thomsen et al. Reference Thomsen, Kokkolaras, Månsson and Isaksson2017). This is seen as an overdesign issue and a candidate for optimization.

Safety margins cater against risks and are allocated to assure that the product is safe and reliable to use, whereas design margins buffer against various uncertainties such as propagated changes coming from other components. Safety margins (value) or safety factors (percentage) typically denote the margin for safety critical failure, whereas design margins have a more general meaning. Aerospace companies often talk about safety margins because certification requirements include safety margins or safety criteria.

When designers focus on safety rather than a cost, they often speak of system redundancy or back-up solutions in the products that enable the product to deal with unexpected situations. This is typically the case with a redundancy system in case of failure, such as enforced in aerospace by the certification requirement that no single failure must lead to a catastrophic failure of the system.

Margins in the form of tolerances are also added from a manufacturing and assembly point of view to ensure that a product will be safe and reliable for a given manufacturing variability. This is considered in robust design (Chen et al. Reference Chen, Allen, Tsui and Mistree1996). Also geometrical tolerances can be seen as a form of margin, that can impact both functionality of the product (gaps, fittings, etc.) and product quality (noise, vibrations). Geometrical tolerances need to be taken into account during design, since they directly affect the margin that can actually be used in a context. If margins become critical, tolerance may become critical in this respect.

Another concept used in practice, are clearances. Clearances are a special form of margin, were the empty space between components can be treated like a component. Clearances are affected by design configuration. Examples are harnessing that has to fit into a passage or the packaging components in a physical space such as an engine compartment of a car. Clearances are also needed to be considered in the detailed definition of designs such as where tolerances stack up and affect both perceived product quality and manufacturing clearances for assembly (Söderberg et al. Reference Söderberg, Lindkvist, Wärmefjord and Carlsson2016).

5.2 Knowing margins

While a component margin is an intuitive concept, it is in practice difficult to know what the margin on a component actually is. For a finished product, margins can be established through testing or potentially through simulation. However, in current industrial practice most products are tested to establish that they are capable of meeting their requirements in the specified way (Tahera et al. Reference Tahera, Earl and Claudia2014). The actual margins of the component can be revealed through expensive destructive testing. However, many margins can be elicited through computer simulation.

The margins on a component under development are not well defined. Typically designers of a complex product such as the truck in the case study start from an existing design by identifying the excess on major systems against the new requirements. While companies are usually not aware of all component margins, they often know the component margins that have become critical in a previous project. For example, if a clearance has been nearly used up and a real effort was needed to fit everything in when fitting cables through a clearance. The designers now know that it is very unlikely that they can push this component (or clearance) further. Such knowledge of margins when evaluating previous designs gives designers an understanding of high risk areas for the new design.

Designers start with some awareness of critical margins from the previous design. They look for excess and critical margins because they know that margins on components can absorb change, while margins that are exceeded can become change multipliers. At the beginning of a design process the designers therefore evaluate the previous design as to where they can make improvements and which parts of the product might be overdesigned and therefore could absorb higher requirements. However, often different teams look simultaneously at the possibility of change in a product and target the same components or areas. For example, the electronics team identifies a clearance for another cable and the design team is planning to increase the size of an adjacent component and wants to fill up this space. This might be picked up at a formal design review, but between reviews it requires somebody to stop the potential problem before it leads to unintentional rework. This is a typical case where an explicit tracking of margins could avoid problems.

Throughout the design process both the requirements and the capabilities of the product are subject to uncertainties which are reduced throughout the design process. Requirements are set and parameter values are frozen at particular points in the process. This affects both the overall value of the margin and the distribution of the buffer and excess over time in the design process.

As the example in the following section will illustrate, besides mitigating product risk and adding to product cost, margins also play a significant role in managing the process risk through reducing the risk of unplanned iterations. In the following example the company has to decide which material to pick based on the cost of the material and two risk factors: that of delaying the design process by needing to adapt a solution late in the process and therefore potentially risking delays to the product delivery; and the risk to product quality from using a less mature solution.