Category Archives: Managing Uncertainty

Strategies To Manage Uncertainty In R&D Projects

Canadian firms spend very little on R&D. Risk adversity is a leading reason for business leader’s preference for investing profits in M&A activities rather than growing the core through R&D, innovation, and new product development. R&D projects or projects with development work almost always suffer from schedule and cost overruns so business leaders avoid the trouble and invest profits in ways that they understand and feel are more predictable. The problem is that no new value is created and is quite often destroyed with M&A. Firms that can effectively manage uncertainty in R&D projects can achieve higher profits, growth, and improved competitiveness.

How can firms better manage uncertainty in R&D, new product development, and innovation?  The project constraint triangle is a helpful tool for R&D project managers to develop proactive strategies to manage uncertainty in R&D allowing business leaders to make wise investment decisions with effective risk mitigation and achieve their strategic business goals.

Product Development Project Constraint Triangle

The product development project constraint triangle helps to understand how to manage the impact of uncertainty in R&D projects, new product development, and innovation. The project constraint triangle is illustrated below:

Product Development Project Constraint Triangle

Most project managers understand this constraint triangle very well – project outcomes are constrained by scope, resources, and schedule.  Project outcomes are viewed simultaneously both externally, from the market’s perspective, and internally, from the firm’s perspective. The market wants value (performance, quality), at a good price, and when they need it. The firm wants profitable projects (efficient expenses) at acceptable risk leveraging their resources (core competencies in people, process, tools, and intellectual property).  Project managers continually trade-off cost and schedule to achieve project outcomes in normal projects with low to moderate risk in the application of normal project &risk management methods with schedule buffers and budget risk contingencies.

In the case of R&D projects, new product development, and innovation uncertainty and the resulting risk is much higher.  Development uncertainty occurs in scope with effects impacting schedule (schedule overruns) and resources (cost overruns). Firms with low risk tolerance usually stop here. The project constraint triangle though helps us to clarify management approaches for lowering uncertainty in R&D projects and insight into how firms can better manage uncertainty.

Uncertainty Management Strategies

The project constraint triangle define the trade-off space for project managers and reveals several strategies for managing uncertainty in R&D projects. The strategies are:

  1. Fix Resources + Fix Scope -> Vary Schedule
  2. Fix Scope + Fix Schedule -> Vary Resources
  3. Fix Schedule + Fix Resources -> Vary Scope
  4. If Able Contractual Relief Valves: Scope Relief, Schedule Relief, Resource Relief.

We often implicitly understand these alternatives but don’t explicitly state them nor proactively exploit them to their full potential for their improved business outcomes through better mitigation. Our business assumptions also can impede how we might exploit them to their full potential. We also need to consider the market context for how we might exploit these strategies in business-to-consumer, business-to-business, and business-to-government markets.

These strategies ultimately decide where the impact of uncertainty is absorbed in mitigation. This is the key to proactively managing the impact of uncertainty rather than just reacting too late.

Strategy #1: Fix Resources + Fix Scope -> Vary Schedule

This is the default strategy for most firms where uncertainty in R&D projects is absorbed when schedule buffers are exceeded by extending the schedule (ie. schedule overrun). The schedule overrun may then cause cost overruns from the continued involvement from the ‘standing army’ assigned to the project who must deliver the fixed scope beyond the budget risk contingency. Inexperienced firms fall into this trap and further reinforcing their risk adversity.

Resources are fixed in R&D projects because R&D staff are often constrained by the finite and limited number of internal staff with unique knowledge, skills, and experience and labour market constraints from engineering or specialist shortages. Cost savings by reducing R&D staff levels further constrains R&D project managers during difficult times.

Scope is fixed by the market requirements process leading to a product specification and customer needs definition.  Project planning processes require a precise scope definition to permit solution definition, estimation, and scheduling resources.

Implicit assumptions in this strategy are that R&D teams can’t find additional productive resources when needed to deliver the project and the project scope is sacred. When uncertainty arises we need to wait for our fixed resources to become available and if their work is on the project critical path a schedule delay results. R&D project managers often become the ‘scape goat’ when all available project buffers are gone.

Strategy #2: Fix Scope + Fix Schedule -> Vary Resources

This strategy is based on the assumptions that the scope can’t be changed and the project deliverables must meet a certain date. Uncertainty is absorbed by adding more resources and therefore cost to the project. Market driven firms in highly competitive industries are extremely sensitive to schedule so must fix the schedule so are more likely to adopt this strategy. Firms realize that they can’t go it alone to achieve their strategic goals.

Additional resources can be added by several methods:

  • Subcontracting R&D work packages to access productive resources with specialist knowledge.
  • Partnering with another firm with applicable core competencies.
  • Collaboration with university or R&D institutes to access resources.

The suitability of these approaches is determined by the project profitability (and profit sharing), responsiveness and alignment with other business entities, and understanding the critical path of the R&D project schedule. Internal resistance often impedes outsourcing R&D as does the ‘not designed here’ behaviour driven by the belief that specialist knowledge does not exist in other firms.

Strategy #3: Fix Schedule + Fix Resources -> Vary Scope

This strategy is based on challenging the assumption that scope can’t be changed. The schedule date is fixed and limited R&D resources are fixed so uncertainty is absorbed by backing off of the scope promises tied to where uncertainty is impacting the project critical path.

Scope reduction methods that can absorb the impact of uncertainty are:

  • Minimum viable product approaches.
  • Spiral product development approaches that offer future upgrades based on solutions to uncertain elements of the product concept.
  • Differentiating between must-haves and nice-to-haves.
  • Prepare upfront alternative ‘plan Bs’ for uncertain elements of the product.
  • Specify functions not solutions to provide technology trade-off spaces for design decisions.

Unfortunately R&D projects often get locked into contracts that drive precise scope definition without building in scope reduction mechanisms.  Firms become fixated on certain solutions and become blind to alternatives.  Firms also assume that customers won’t want a partial product even though the customers may not even be aware of the product concept.

Strategy #4: Contractual Relief Valves: Scope Relief, Schedule Relief, Resource Relief

In certain markets, such as government defence markets where novel scope is required, contractual relief valves are used.  Scope is also often added to defence contracts after contract award as security threats change in response to world events resulting in opportunities for schedule and cost relief.  Rarely is scope reduced to meet budget and schedule when uncertainty threatens to use up project buffers.

Contractual relief is also employed in business-to-business markets as customer needs change after contract award.  For consumer markets though contractual relief is not applicable requiring R&D project managers to proactively provide trade-off margins to work within the project constraint triangle long before market launch.

In a rapidly changing world relief valves are becoming increasingly important to build into R&D projects upfront in order to achieve business objectives.

Lessons For Proactive Management of Uncertainty in R&D Projects

How can we use the insight provided by the project constraint triangle to manage uncertainty in R&D projects better? Firms should develop a hybrid application of these strategies appropriate for your firm and your market and consider the following:

  • Draw out and challenge underlying assumptions influencing uncertainty mitigation methods in R&D projects in your firm. This may point to the need for broader cultural change as these may be deeply rooted in your employee’s underlying beliefs.
  • Build mechanisms for scope relief up front in the R&D project plan by recognizing that uncertainty may exceed original plan or the market may have changed since the project was started. Don’t default to strategies that default to absorbing uncertainty by schedule and cost overruns.
  • Adopt project risk management methods for novel projects.
  • Activities with high uncertainty need to be removed from the critical path of the project either through the solution choice or realistic technology road mapping that can underpin a spiral development path.
  • Higher percentage of reuse to achieve the scope. Focus new development areas that limits uncertainty to 10-20% and build in schedule buffers and risk contingency to fit the selected percentage.
  • Investment of time and effort to develop productive subcontractors well ahead of the R&D project because firms can’t go it alone in today’s markets. Invest in familiarizing them in your work processes, building personal connections with R&D staff to understand strengths, and improving communications.
  • Early development of partners with compatible strategies well ahead of the R&D project.
  • Building solution alternatives (plan B and C) to achieve the scope into the R&D project plan.
  • Early collaboration with university and R&D institutes off the critical path of the project.
  • Schedule buffers and budget risk contingency need to fit the level of uncertainty present in the project.

These approaches speak to the need for a broader and more holistic approach to how R&D, innovation, and new product development support your firm’s business strategy. Failing to develop partnerships and supplier relationships in advance doesn’t position R&D projects for success. Constraining the proportion of development activity to manageable levels while taking a longer term perspective also frees up R&D project managers to make effective trade-offs in the project constraint triangle. Experienced firms tend to understand these trade-offs better and build these strategies into their R&D, new product development, and innovation investments.

How SMEs Can Better Withstand Market Uncertainty

Markets today are experiencing higher levels of volatility, uncertainty, complexity, and ambiguity or VUCA. A market VUCA crisis arises when a threat emerges from the uncertainty that directly impacts a firm’s survival. SMEs (firms less than 500 employees) are more susceptible to a VUCA crisis in the market place than larger firms that possess the ability to absorb more shock. Most literature use case studies of large firms. As SMEs survive and scale management must also still be concerned about how to withstand a VUCA crisis impacting the firm’s business model and value proposition.

A key question then is How can SME management ensure their firms are more resilient in the presence of market VUCA?

Uncertainty Management Assessment

Syrett and Devine recently devised a framework to assess the readiness of a firm to manage uncertainty in their book Managing Uncertainty: Strategies For Surviving and Thriving in Turbulent Times. Six components of uncertainty management are proposed:

  1. Strategic Anticipation – The capability to determine and the ability to implement a strategy that is highly responsive to an unpredictable and potentially volatile environment.
  2. Navigational Leadership – The capability to instill a collective sense of where the organization is and the confidence and optimism to move forward into an uncertain future.
  3. Agility – The capability to move rapidly and flexibly in order to shape or adapt to the threats and opportunities arising from uncertainty.
  4. Resilience – The capability to absorb and positively build on adversity, shocks, and setbacks.
  5. Open Collaboration – The capability to dissolve boundaries, forge links, and reach outside through partnerships and the sharing of ideas and information to gain a broader perspective and maximise innovation.
  6. Predictive Learning – The capability to sense, probe, and analyze previously hidden patterns and trends in order to anticipate sudden or disruptive change.

Assessing the firm against each of the components of the framework enables management to determine the firm’s readiness to prepare for market VUCA crises, mitigate downsides, and exploit upside opportunities. This framework can be integrated with a firm’s strategic planning & execution process and applies some of same uncertainty management techniques that can be applied to projects and innovation.

SME vs Large Firms

Using Syrett & Devine’s uncertainty management framework some of the leading approaches that are more appropriate for SMEs are worth identifying. Of the six components agility and absorption are the core approaches firm’s have traditionally adopted to survive a market VUCA crisis. Large firms possess greater absorption capabilities with less agility due to their size whereas SMEs on the other hand can exploit agility to compensate for their lack of absorption capabilities. The mix of agility and absorption available to management depends on where the firm is positioned on the growth cycle as shown below:

Firm Agility Absorption

Beyond agility and absorption the other four components are useful to facilitate a more proactive approach to prepare for a unpredictable market VUCA crisis giving more safety margin rather than a panic reaction to a sudden surprise.

Some of the inherent characteristics of the SME though should also be considered because they can both help and harm how these uncertainty management approaches can be exploited by SMEs. The most relevant SME characteristics include:

  • Inherent agility arising from the smaller size of the firm (as mentioned already).
  • Closely related to agility is the fact that managers and staff tend to be more geographically close enabling faster communications and rapid response.
  • A culture of driving the business by personal relationships (of the owner/founder) rather than professional management.
  • Cost consciousness arising from the tight resource constraints in SMEs.
  • Higher level of risk taking particularly in the earlier stages but diminishes as firm’s grow.
  • Centralized decision making by the owner with a greater coincidence of power between the owner and manager roles.
  • Owner has a direct impact on day-to-day operations.
  • Owner and small management group are often multi-hatted in their management roles and juggling multiple priorities.
  • Owner’s decision making is based on a commercial orientation although may be influenced by personal preferences, desire for lifestyle, and community reputation.
  • The owner may have a lack of ambition to grow and may just give up if the market VUCA crisis is severe enough – this discussion is more oriented towards SMEs that have the potential to grow much larger as opposed to local businesses.
  • Informality and lack of written procedures.
  • Differences between existing employees and new professional employees as the firm grows.

Approaches available to SME management in each category of the uncertainty management framework are as follows.

SME Agility

Agility is the core inherent strength of the SME. Rather than using this strength for sudden short term reaction the goal here is to exploit agility with a longer time horizon to buy time to survive.

Approaches available to SME management to manage uncertainty with SME agility are:

  • Fast opportunity exploitation – the ability to spot fast and shift quickly focus (pivot), resources, cash, and management attention to new opportunities.
  • Ability to say no to new opportunities to enable the firm to focus its constrained resources.
  • Maintain flexible people, organization structure, and processes.
  • Find and seize opportunities to improve efficiency to free up highly constrained cash to fast exploitation.
  • Fast time to market for new products and services – supported by fast experiments and rapid learning.
  • Exploration during seasonal low in annual cycle. Exploit in the rest of the year.
  • Nurture a culture of agility that avoids status symbols, building a hierarchy, transparency of information, and performance over founding employee tenure.
  • Preserving staff rather than cost cutting and emphasize hiring flexible staff.
  • If able to acquire, buy new businesses not mature businesses.

SME Absorption

Although the SME does not have the resources of a large business there are approaches that enable management to buy time until generating cash from fast opportunity exploitation which are:

  • Maintain low fixed costs in core operations to minimize cash drain from a sudden VUCA crisis.
  • Build a war chest even if it is not large to fund fast opportunity exploration.
  • Customer lock-in from high switching costs in profitable core to buy time.
  • Seek protected core markets as much as possible leveraging personal relationships.
  • Seek powerful patron with vested interest in the success of the SME such as a customer or investor.
  • Categorize and differentiate ‘good fat’ and ‘bad fat’ to support modest cost reduction of ‘bad fat’ first. Spend time with staff defining what these two categories mean. Engage staff eliminating ‘bad fat’. Cut ‘good fat’ as a last resort.

Coincidence of Owner Power in SMEs

Before looking at the remaining four components of the uncertainty management model it is important to look at the coincidence of owner power present in many SMEs as this can harm the ability of the firm to withstand a market VUCA crisis.

The higher coincidence of power resting with owner of a SME rather than a corporate board presents the most difficult challenge for managing a market VUCA crisis. The Greiner growth phase crisis model suggests that smaller/younger firms can pass through several crises in the early stages related to crisis of leadership, autonomy, and control as the firm becomes too large for the owner to personally direct. These are internally focused crises and key crucible milestones for the SME.

Assuming the ambition of the owner remains growth as opposed to a lifestyle preference a market VUCA crisis creates a life or death decision point for the owner putting their personal preferences in direct conflict with the long term survival of the firm. Faced with a VUCA threat to the core business the owner themselves may become the problem and must recognize that they need to loosen control of decision making and involve SME management and staff more in decisions impacting the firm. Owners need to consider if their personal preferences are harming the ability of the SME to prepare for a market VUCA crisis.

Navigational Leadership for SMEs

Market VUCA can create strong anxiety and fear in the workforce particularly in SMEs who are more exposed. Although employees working in SMEs are more resilient to survival stress management must still display confidence and optimism moving forward through a clear vision and goals. VUCA can sap morale and energy.

Key methods to enable navigational leadership for SME include:

  • See a VUCA crises as an opportunity.
  • Inspire confidence by making sense of the external environment, explain why action is needed, and articulate a clear vision.
  • Maintain speedy decision making process and avoid centralized control. Owners need to give up decision making power. Engage staff in determining how the firm will move towards clear vision.
  • Make allowances for course corrections in the vision because not everything will be known in the presence of VUCA.
  • Keep staff focused on the new mission.
  • Establish a diverse and collaborative mindset in the growing SME culture.

Strategic Anticipation for SMEs

SME management needs to make thoughtful choices in spite of VUCA, assess alternate scenarios, consider implications, and prepare accordingly.

Key approaches to enable strategic anticipation for SMEs include:

  • Avoid becoming internally focused as the firm grows and more institutionalized – always stay external focused as formal processes are established but ensure continuous improvement embedded in the culture.
  • Just In Time Approach to strategy setting, risk taking, and resource allocation that support agility.
  • Develop the ability to do fast option evaluation with informed go/kill/hold decision making.
  • Need to constantly question the assumptions of the business (Drucker) including: assumptions about the environment in particular (society, structure, market, customer, technology); assumptions about the SME’s mission; and assumptions about the core competencies needed to accomplish the SME’s mission. I would add assumptions about the owner’s preferences as the elephant in the room that needs to be addressed hastening resolution of the crisis of control in Greiner’s model.

Open Collaboration for SMEs

The connection of the SME with the outside world is highly influenced by the personal relationships of the owner with customers. Open collaboration in large firms is about breaking down the walls of the organization and connecting with the external market. In the case of an SME open collaboration is about moving beyond the personal relationships of the owner. Collaboration in this case is a means to adapt for the firm to survive in the long term. The goal is spot shifts sooner in core markets and avoid owner’s blind spots. Owners again need to self reflect and decide if they are a barrier to open collaboration and the firm moving towards thinking as a “collective brain”.

Key approaches to enable open collaboration for SMEs include:

  • Develop a collaborative mindset throughout the organization – open to new ways of thinking, new ways of drawing in knowledge and insight.
  • Developing sources of new ideas by crossing industry borders, government organizations, strategic suppliers, and partners and getting beyond geographical borders.
  • Considering ideas from other industries is particularly important for SMEs.
  • Share information freely throughout the firm and avoid using information as a weapon.
  • Collaborate with customers through social media, co-creation, and crowd sourcing if appropriate.
  • Look for opportunities to make temporary alliances for shared resources (keep fixed costs down), joint activity, technology transfer, R&D consortia, industry standards groups, and innovation networks – reach out to universities for new ideas.

Predictive Learning for SMEs

Predictive learning provides the outside view to spot new opportunities to enable fast time to market. Being the first to “connect the dots” in a new way.

Key approaches to enable predictive learning for SMEs include:

  • Exploring customer problems more deeply looking for opportunities for new value creation.
  • Track trends in the market place.
  • Apply double loop learning which is to not just solve the problem but also open up thinking to modifying the goals of the firm to unlock new value creation.
  • Facilitate intrapreneurship within the firm – align intrapreneurship with navigational leadership.
  • Apply basic analytics and data mining of any data the SME has collected to date. Consider if data should be collected and tracked that is not at the moment.
  • Open the team’s mind with new ideas and foster discussions that challenge everyone’s mental models of the way the market is that may be constraining thinking.
  • Learn to act quickly on incomplete and ambiguous information.

SME management should consider how some of these uncertainty management approaches could be adopted by their firm. SME owners should reflect on how their personal preferences influence their decision making that may harm how a firm responds to a market VUCA crisis.

Project Risk Management and High Uncertainty

Risk management for novel development, engineering development, high complexity, new product development, new ventures, and large capital projects demands new and more robust methods to effectively deal with high uncertainty. Prior posts explored varieties of uncertainty and design strategies, facing innovation uncertainty, and technical risk assessments in new product development.

Since most development and innovation work are managed as projects or as a subproject within a large project new methods to manage high uncertainty that integrate within existing project management and project risk management approaches would be useful. This post reviews a book Managing The Unknown: A New Approach to Managing Uncertainty and Risk in Projects written by Christoph Loch, Arnoud DeMeyer, and Michael Pich in 2006.

Project Risk Management Framework

The authors present an integrated project risk management framework that assists project management to select project risk management methods based on the sources of uncertainty and complexity as shown here.

Project Uncertainty Framework

This framework is useful because it integrates existing and well understood project risk management methods with new methods for managing uncertainty like learning and selectionism. The source of uncertainty axis maps well into the uncertainty continuum concept of a previous post. The authors use an uncertainty continuum breaking uncertainty into foreseeable and unforeseeable uncertainty.  Foreseeable uncertainty includes variation and foreseeable events while unforeseeable uncertainty includes unknown unknowns. These distinctions are enough to provide a decision tool to select appropriate risk management methods.

Measuring Project Complexity

The project risk management framework requires project management to determine project complexity. Another useful approach described in the book is a method to measure the project complexity. The authors base their approach on the number of interactions between major parts of the project in three domains: system domain; project task domain; and organizational domain. The authors apply the Design Structure Matrix (DSM) tool to model and visualize how major project pieces interact. Methods are proposed to simplify the measurement process to give quick / high level results for large projects. Complexity is then computed based on the sum of all project elements multiplied by the sum of all interactions. No comparative complexity measure results are presented for project types, industries, etc leaving each firm to develop their own benchmarks from amongst their own project portfolio.

Uncovering Hidden Unknown Unknowns

The authors propose a method to uncover hidden unknown unknowns at the beginning of a project with the goal of enabling a systematic process for project managers. The process involves asking: “What do I know and what do I not know? and where are the major knowledge gaps?” where the knowledge gaps identify where unknown unknowns may emerge. The process steps summarized here are:

  1. Identify the problem structure.
  2. Break the overall problem into pieces.
  3. For each piece, perform risk identification, identify knowledge gaps by probing assumptions in an iterative way.
  4. Estimate the complexity of each project piece and the overall project.
  5. Manage pieces in parallel pieces according to the project risk management framework above.

To visualize the uncertainty and complexity of each major piece the authors propose a pie chart for each piece with a bar for: variation, foreseeable uncertainty, unforeseeable uncertainty, and complexity. These pie charts can be used to decide where additional attention is required according to the project pieces and can be used to capture trends over the project life.

Project Risk Management Methods

The book also describes a broad set of project risk management methods with an excellent set of examples and case studies from many industries. The project risk management methods are summarized here.

Project Uncertainty Management Methods

Beyond the well-known project risk management methods for managing variation, foreseeable events, and residual risk the book goes into great detail describing learning and selectionism as two main methods to manage unforeseeable uncertainty or unknown unknowns.  The authors propose the following definitions:

  1. Learning – “Learning in projects is the flexible adjustment of the project approach to the changing environment as it occurs; these adjustments are based on new information obtained during the project and on developing new – that is, not previously planned – solutions during the course of the project.”
  2. Selection – “In the face of uncertainty, one launches several solution attempts or sub-projects, each with a different solution strategy to the problem in hand. If the solution strategies are sufficiently different, one would hope that one of them will succeed and lead to a successful outcome. Success depends on generating enough variation so that ex post, we obtain desirable results.”

In the case of a learning approach the authors explore improvisational learning and experimentation.  For selection the authors explore what makes selectionism work and offer set based design as an example of how to implement it in practice. Guidance is provided on how to implement these methods and good case examples are provided.

Overall, this book provides a useful project risk management framework and decision tool that can be applied in practice for a more robust project management and risk management system when high uncertainty is present. Effort would need to be planned for and budgeted for in the project which suggests that the approaches would need to be applied on several projects to collect historical basis of estimates. The authors provide a number of methods to address unforeseeable uncertainty but additional methods could be added to this decision tool such as real options, big data, adaption, and creaction reported in prior posts. The book provides a practical method to measure project complexity which is often difficult to measure and benchmark. Anyone assigned accountability for projects with novelty and complexity should read this book.

Product Design For Uncertainty and Transient Advantage

Managing uncertainty in new product development is difficult in a rapidly changing world. Firms need to adopt strategies for transient advantage in turbulent markets as recently observed by Rita McGrath.  Product developers can’t wait though for all the answers and absolute certainty that risks missing market opportunities. Firms need to capture as much value as possible from new products yet product development cycles can be long and potentially beyond the timeframe required for a short wave of transient advantage.

Product developers need to give their firms maximum flexibility to exploit transient advantages so to mitigate and exploit uncertainty product developers need to design-in a higher degree of reliability (in uncertain conditions), robustness, versatility, flexibility, evolvability, and interoperability in their product platform and product lines. To be successful product developers need to understand uncertainty and clarify design strategies available to them during the ‘fuzzy front end’ of design.

What are the varieties of uncertainty and what design strategies can be used to manage a diversity of uncertainties?

Uncertainty Continuum

The simple model of known-knowns, known-unknowns, and unknown-unknowns is a useful starting point to understand the varieties of uncertainty but it is not detailed enough for product development. Schlensinger, Kiefer, & Brown’s uncertainty continuum provides a deeper look at varieties of uncertainty mapping along a scale of predictability from the known to the unknown. Their uncertainty continuum maps from the known along a scale of increasing unpredictability as follows:

  • Completely Predictable – You can say with certainty what the outcome of a given situation will be such as with physical laws.
  • Predictable Through Probability – The outcome can be defined to a particular confidence level using statistics but extremes may exceed bounds.
  • Predictable Through Other Analytic Methods – The outcome might be predicted through chaos theory, computer modelling, which is less precise.
  • Predictable Through Pattern Recognition, Experience, and The Like – The outcome might also be predicted based on limited prior experience or from patterns. The emerging world of big data.
  • Not Predictable At All But You Can Say What Can’t Happen – The outcome is not predictable but certain cases can be ruled out.
  • Completely Unpredictable – The outcome is completely unpredictable.

A linear scale is useful to model the range of predictability to classify variables according to how well the value can be predicted for design but it does not provide insight into the severity of events that is important for risk mitigation / opportunity exploitation in product development.

Uncertainty Framework

Another excellent framework for broadly understanding uncertainty of complex systems was proposed by McManus and Hastings (based largely from experience in the US space program) and one of the best I have seen at capturing a holistic view for managing uncertainty in product development. This framework links categories of uncertainties through risks and mitigations/exploitations to system outcomes to be more useful to engineers.

The framework provides a top-down model to structure uncertainty and risk taxonomies to illustrate cause and effect through the relationship – <uncertainty> causes <risk/opportunity> handled by <mitigation/exploitation> resulting in <outcome>. See the paper for excellent cases to understand the framework. I particularly like this framework because it does not just frame effects of uncertainty as a downside risk but upside opportunity that firms can exploit for transient advantage. The framework is also general in nature allowing it to be applied/tailored to any application.

Varieties of uncertainty used by McManus and Hastings are:

  • Lack of Knowledge –  Facts that are not known, or are known only imprecisely, that are needed to complete the system architecture in a rational way. Knowledge in this case may just need to be collected (because it exists somewhere already) or created.
  • Lack of Definition – Things about the system in question that have not been decided or specified.
  • Statistically Characterized (random) variables/Phenomena – Things that cannot always be known precisely, but which can be statistically characterized, or at least bounded.
  • Known Unknowns – Things that it is known are not known. Things are at best bounded, and may have entirely unknown values.
  • Unknown Unknowns – Things that are gotchas that we cannot contemplate occurring with our current understanding.

An improvement combines the uncertainty continuum defined by Schlensinger, Kiefer, & Brown with the front end of McManus and Hastings’ uncertainty framework to more clearly understand how uncertainty maps to risks.

Design Strategies For Uncertainty

Both models provide guidance for design strategy to give firms flexibility for transient advantage.  The uncertainty continuum suggests at the extreme of completely predictable proven design heuristics are appropriate. At the higher extreme of unpredictability a short horizon learning experimental approach such as creaction is appropriate.

The McManus and Hastings’ uncertainty framework is more powerful for designers by linking uncertainty to levers of design.  McManus and Hastings provides a useful list of risk mitigation and exploitation strategies for new product developers to consider. These design strategies help to fill in the middle zone of the uncertainty continuum. McManus and Hastings identify nine strategies:

  1. Margins – Designing systems to be more capable, to withstand worse environments, and to last longer than ‘necessary’.
  2. Redundancy – Including multiple copies of subsystems (or multiple copies of entire systems) to assure at least one works.
  3. Design Choices – Choosing design strategies, technologies, and/or subsystems that are not vulnerable to a known risk.
  4. Verification and Testing – Testing after production to drive out known variation, bound known unknowns, and surface unknown unknowns.
  5. Generality – Using multiple-function (sub)systems and interfaces, rather than specialized ones.
  6. Upgradeability – (sub)systems that can be modified to improve or change function.
  7. Modularity, Open Architecture, and Standard Interfaces – Functions grouped into modules and connected by standard interfaces in such a way that they can ‘plug and play’.
  8. Trade Space Exploration – Analyzing or simulating many possible solutions under many possible conditions.
  9. Portfolios and Real Options – Carrying various design options forward and trimming options in a rational way as more information becomes available and/or market conditions change.

Although several of these strategies most engineers would naturally use but the list may provide some suggested approaches that are not often considered. These nine strategies help to realize a new product design with system outcomes for reliability, robustness, versatility, flexibility, evolvability, and interoperability.

Most of these design strategies add cost to the new product development project and the product itself but the benefit is flexibility. Firms need to weigh the cost benefit of product flexibility in an uncertain world to support a strategy of transient advantage.