strategy for risk management through problem framing in technology acquisition

~ ' I U T T E R W O R T H ~ E I N E M A N N

In~rna~onalJournalofProjectManagement Vol. 13, No. 4, pp. 219-224, 1995 Elsevier Science Ltd

Printed in Great Britain 0263-7863/95 $10 .00+ 0.00

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Strategy for risk management through problem framing in technology acquisition

K T Yeo Centre for Engineering and Technology Management, School of Mechanical and Production Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 2263

Problem solving and decision making, be they technological or business-related in nature, are performed in environments of varying degrees of uncertainty. The uncertainty is, however, not wholly exogenous, but relative to the problem-solving capability. The paper develops a framework for risk management in technology acquisition. It classifies problem situations into three broad types over a certainty-uncertainty spectrum based on the degrees of newness and familiarity of the technology used. The framework helps the planner to determine the appropriate levels of required planning and learning. The learning process helps to create a dynamic shift in problem framing from uncontrolled uncertainty to relative certainty.

Keywords: risk management, problem framing, technology planning

Traditional risk analysis perceives risk as an inevitable phenomenon that is characteristic of all future events as yet unmaterialised. The concept of risk is usually expressed as a function of the uncertainty associated with such events. The terms 'risk' and 'uncertainty' are sometimes used inter- changeably. However, more often, the concept of risk is expressed in terms of the probability of occurrence (fre- quency), and the severity of loss (or gain) that will be a consequence of such an occurrence. The severity of loss is measured by the deviation from the expected value of the event's possible outcomes.

The concept of uncertainty in traditional risk analysis is therefore treated as an independent variable which is objectively present and associated with all future events. This also implies that uncertainty is exogenous, and is independent of the will and capability of a would-be problem solver or a team of such problem solvers.

This paper argues that uncertainty and its resultant risk are not wholly exogenous. A technology acquisition project may be perceived as not risky if the would-be problem solver already possesses an adequate and relevant mental capacity developed from current knowledge and past experience. Conversely, the same project may be considered as highly risky because of the problem solver's ignorance, complacency, or simply lack of ideas.

An effective strategy for risk management depends on appropriate problem framing for a future event or project. This paper develops a generic problem-framing framework

based on the concept of relative uncertainty as perceived by a problem solver; this is the first vital step towards effective risk management. The framework proposes three broad types of problem situation, namely problem situations under uncontrolled uncertainty (type III), problem situations under controlled uncertainty (type II), and problem situations under relative certainty (type I).

The level of uncertainty may be perceived as a continuum or spectrum, from uncontrolled uncertainty under the condition of great ambiguity to that of relative certainty under a highly structured and controlled problem-solving condition. The proposed framework, which arbitrarily delineates the uncertainty-certainty continuum into the three broad types, is designed to help problem solvers, planners and decision makers to formulate appropriate strategies for risk management in technology acquisition projects.

Planning for technology acquisition

The rapid changes in technology have created both difficul- ties and challenges in technology based enterprises. These enterprises, increasingly embedding technology in their products and processes, depend on continual technological innovations and acquisitions to give them the necessary competitive edge. The degree of difficulty in managing technological innovations depends on the company's ability to solve technology related problems. Such capability is

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Strategy for risk management through problem framing in technology acquisition: K T Yeo

linked to the firm's familiarity with the technology involved. As technological innovations and applications evolve, most enterprises are likely to be faced with three broad types or stages of technology, which can be characterised as 'base ' , 'new-familiar ' , and 'new-unfamiliar' , as defined by Roberts and Berry ~. The concepts of 'newness' and 'familiarity' are introduced here to describe the various stages of a technology experience curve within a company. The ideas of newness and familiarity are not absolute; they are perceived as being relative to a firm's core competencies, technological environment, and past experience.

Given the problems of newness and unfamiliarity, the effective management of technology acquisitions depends, to a great extent, on the approach taken by the enterprise to frame and solve its technological and related problems. The proposed strategy for risk management is to link technology planning with purposeful organisational learning. The development of the framework draws lessons and experiences from recent research in systemic thinking and problem solving approaches. Such research brings fresh perspectives and enables a new paradigm for the risk management of technology projects to be developed.

Newness and familiarity A simple test of newness is that of whether the technology is already embodied in the existing products or processes of the company'. If the answer is positive, then it is a 'base' technology; otherwise, it is a new technology as far as the company is concerned. A company may be familiar with a certain technology but may still consider it new because it has not yet been incorporated into the main line of the company's business. In other words, a particular technology may not be considered new in the industry, but still be considered as new to a particular company, because the company does not have prior experience of using it. The above definition of newness is relative. The definition of 'base' technology in this context is therefore not necessarily the 'technology mastered by all the firms in the business', as normally defined in the literature.

A company's familiarity with a technology is the degree of knowledge of the technology that exists within the company but is not necessarily embodied in the existing products or processes. The familiarity with the technology is influenced internally by the level of technical competence and experience in the firm. For instance, it depends on whether the main features of the new technology relate to or overlap with existing corporate technological skills or knowledge, e.g. the process of coating optical lenses, or precision engineering for miniaturisation. Externally, it also depends on the maturity and accessibility of the technology, e.g. liquid crystal display technology for notebook computers. The familiarity of a company with a particular key technology is a critical variable that at least partially explains much of the success or failure in technology based business development.

In the spectrum of riskiness in technology acquisition management, base technology presents the least risk, while new-familiar technology is in the middle. The other extreme end is new and unfamiliar technology. A new and unfamiliar technology is usually an emerging technology which could open up a window of new opportunity. How- ever, as defined above, the concepts of newness and familiarity are relative and dependent on a firm's, an

industry's, or even a country's internal capability and experience curve. An emerging technology that is already taking root in one firm could be considered new and unfamiliar in another firm, in a different industry, or in, say, a less developed country.

For enterprises to secure sources of future growth and profitability, they must continually move on from base technology to new and familiar technology, and ultimately explore the new and unfamiliar (relatively speaking) technologies. While perceptions of newness and familiarity are relative, enterprises must be aware of the evolution of the development lifecycle of a particular technology, as defined objectively and globally.

The risks of failure increase as firms move into new and unfamiliar territories. However, ultimately, the success of an enterprise depends on its mastery of new and currently unfamiliar technology. The problems of new and unfamiliar technology are problems of high uncertainty which are often uncontrolled. The problem of uncertainty is largely due to the lack of clear problem definition, prior experience, knowledge and information, which contributes to ambiguity and the inability to predict future outcomes. The proposed framework for problem framing creates a link between the concepts of newness and familiarity and the concepts of uncertainty and ambiguity.

Uncertainty and ambiguity The three problem types proposed above are as follows:

• type I: relative certainty; • type II." controlled uncertainty; • type 111: uncontrolled uncertainty or ambiguity.

The three problem types are defined in such a way that they correspond to the three technology types defined above. The application of base technology within an enterprise is framed as a problem situation under relative certainty. For example, product or process enhancement using proven technology and employing internally available expertise, knowledge and facilities belongs to this category. Both the business and technological risks are likely to be low under the type I condition.

On the other hand, business development based on new but familiar technology is framed as a problem situation under controlled uncertainty. The company has a good knowledge of the new technology except that it has not incorporated such technology into its existing products or processes. For example, a manufacturer of conventional machining centres and other machine tools that is currently planning to extend its 'cutting' business to include laser and water-jet cutting technologies could well be in such a problem situation, dealing with new but familiar technology. Moderate risks are involved in acquiring technologies of which the company has no prior experience.

The most difficult problem situation framed is that under uncontrolled uncertainty, where new and unfamiliar technology is contemplated. The problem situation under uncontrolled uncertainty is also termed a problem under ambiguity. The concept of ambiguity is borrowed from Schrader's 2 idea of distinguishing the concept of ambiguity from that of uncertainty. The concept is built on two asser- tions. Firstly, there is a need to create a clear differentiation between uncertainty and ambiguity, which involve different problem solving tasks. Secondly, the degree of uncertainty

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and ambiguity is not exogenous, but is at least partially determined during the problem framing process.

In this process, a problem solver selects a specific uncertainty-ambiguity boundary which demarcates problems into distinct types. Such a demarcation has organisational consequences in terms of choosing the appropriate sup- porting strategies in the problem solving process or in a risk management process.

In the proposed framework, however, unlike in Schrader's, uncertainty is used as a broader generic concept of which ambiguity is a subset used to represent the extreme case of uncontrolled uncertainty. The separation into three states of uncertainty, namely uncontrolled and controlled uncertainty and relative (controlled) certainty, where uncontrolled uncertainty is characterised by great ambiguity and relative certainty has little ambiguity, is arbitrary. The concepts of uncertainty and ambiguity are closely related to the predictability and controllability of outcomes of future events. The following section describes the idea of mental models as a powerful concept for better understanding of the dynamics of the problem-framing process.

Mental models and problem framing The concept of mental models can be comprehended from Mintzberg's 3 following idea. 'Managers (like everyone else) use their information to build models of their world, which are implicit synthesized apprehensions of how their organizations and environments function. Then, whenever an action is contemplated, the manager can simulate the outcome using his implicit model. '

Also, in Checkland's 4 soft systems methodology, a conceptual (mental) model is constructed as an idealised mental map for dealing with a specific problem situation such as the acquisition of an advanced manufacturing technology or the launching of a new marketing initiative. The model is a highly intellectual construct of the 'ideal' type based on the current knowledge and information available. In other words, the ideal type is necessarily bounded, to a certain extent, by the aggregate of past experience or the lack of it. The mental model can also be enriched by knowledge, creative insights, and ideas from other external sources. The soft systemic thinking using the concept of a mental model and its relation to human learning can be used to further enrich the field of project management, as emphasised by Yeo 5.

The value of the mental model lies in its ability to provide a mental frame of reference to assist in the diagnosis and structuring of the perceived real-world problem situation. The gap between one's mental model and reality, and any differences between the multiple models of multiple problem solvers, are issues for debate. A purposeful and constructive debate can be a meaningful learning and enriching process for those who are intimately involved. The focused debating and multilearuing process may lead to the rethinking of past assumptions and conventional wisdom. The outcome of the process is an enhanced mental model enriched by ideas, insights, and information from multiple sources. Such a model will be more relevant and well prepared to deal with a problematic or 'risky' situation.

The characteristics of the mental models are deemed to have an influence on problem solving behaviours in risk management. The richness or poverty of a mental model will determine the scope of the relevant solution space for

a problem situation. To further illustrate the characteristics of mental models and their relations with problem framing under type I, II, and III conditions, three sets of scenarios are examined.

Type I: Problem framing under relative certainty

The following is a scenario of a company which is operating in a mature business. The company uses proven technology in its products and has well established facilities which have been developed over many years. The company has assembled a team of engineers to develop an enhanced version of an existing product using the same process technologies with minor modifications, and drawing support from internal capabilities and resources. In this scenario, the problem solvers, namely the project leader and his team, are able to 'read' the problem situation and quickly understand the project objectives, scope and specific requirements. In other words, they know quite clearly what is to be done, what information to look for, and what the desired outcomes would be.

In this case, the project team appears to already possess the necessary mental model to deal with the problem situation at hand. The shared mental model helps the team to map out all the key variables and important interrelationships among these variables. The problem situation is perceived to be well structured. The conceptual mental model and the perceived reality coincide, or there are few differences between them. Not much learning is required. The values of the variables are usually estimated with deterministic confidence and expressed as single-valued estimates. Similarly, the value of the desired outcome is also predicted with a high degree of confidence. This is a type I problem situation under relative certainty where the existing mental model closely represents the real-world conditions. The potential risk involved is minimal.

The planning process is also relatively well defined with few iterations. The internal planning is based on proven planning methodology and procedures. The planning objective is to achieve rapid convergence to a predetermined solution. Project implementation favours a management by objectives approach.

Type II: Problem framing under controlled uncertainty

In the second scenario, a company is developing a radically improved product by using new but familiar technology. (An example would be the development and manufacture of potentially lucrative high definition television (HDTV) sets.) In this scenario, the new technology standards are still evolving, and none of these standards have been embodied within the existing products or processes. However, the company does possess the technological skills and knowledge, which are probably derived from the company's ongoing research and development programme. It is also likely that the technology has been systematically monitored from within the corporation in anticipation of potential future applications. The engineering team assembled to develop the product similarly has no great difficulty in defining the project objectives, scope and requirements after initial conceptual and feasibility studies. These conditions provide the team with a reasonable degree of controllability over the problem situation, except that the outcome cannot be predicted with great confidence.

Here the problem solvers seem able to develop the

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necessary mental model(s) that are able to guide them in structuring the problem situation. They are able to identify all the key problem variables and their interrelationships and incorporate them into reconstructed but coherent mental model(s). The reconstruction process continues as long as the current mental model(s) conflict with or deviate from the real-world conditions. There will be a certain amount of debating and learning.

The uncertainty is therefore not due to the problem solvers not knowing the problem variables and their interrelationships, but rather their inability to assign precise values to these variables, and their difficulty in predicting the value of the desired outcome precisely. The uncertainty could well be due to the lack of information and the changeability of an evolving new technology. There could also be volatility in terms of other externally imposed and relatively noncontrollable factors surrounding the business environment.

The problem is somewhat fuzzy and stochastic. The values of the problem variables and the outcome cannot be estimated with a high degree of confidence. The use of range estimates and the inclusion of contingency plans or buffers may be necessary. The above situation is a type II problem situation under controlled uncertainty. The uncertainty is due to the potential dispersion of outcomes, but it is controlled because the problem conditions can be defined or modelled. Since the mental model(s) thus constructed may not yet coincide very closely with the 'reality', the actual final outcome may still deviate from the predicted or desired one.

In the type II situation, the emphasis is on careful preproject planning and fact finding in order to arrive at a broad but firm project objective and definition. Adequate preproject planning is important to ensure smooth implementation at a later stage. Planning efforts can be supported by obtaining feedback from other information sources, particularly the major suppliers, lead customers, and external consultants. On the basis of appropriate risk analysis and correct problem framing, contingency plans may be developed to counter any unforeseen events. Certain replanning iterations may be required. Learning and sharing of information and knowledge is vital at this stage.

Type III: Problem framing under uncontrolled uncertainty The following is a worst case scenario, a type III problem situation of a company contemplating entering a new business involving new and unfamiliar technology. The problem situation in planning for HDTV a decade ago could be a case in point. The development of a radically different new product using unfamiliar processes involving undefined and competing standards is a situation of uncontrolled uncertainty.

An extreme scenario is where the company, to date, has not used the new or pacing technology in any of its existing products or processes, and does not possess the necessary inhouse skills and knowledge. The problem solvers in such a situation may feel that they still do not have a good grasp of the problem. This might imply that the problem solvers were not able to clearly define the project objectives and scope, to specify the requirements, to define the relevant problem variables and tasks involved, or to determine what the desired outcomes would be. The problem situation appears to be highly fuzzy and ill defined.

This is a case where the problem solvers do not yet

possess the necessary and relevant mental model to struc- ture and diagnose the problem situation with any degree of confidence. Consequently, they may not even know how to start the problem solving process. Any attempt at problem solving in the absence of an adequate mental model will most likely lead to failure. This is a situation of problem solving under high 'ambiguity'. In the proposed framework, the problem of ambiguity is seen as a contributor to an extreme case of uncertainty. The uncertainty appears to be uncontrollable, mainly because there is a lack of any mental map to provide the necessary frame of reference for control purposes.

The type III situation is highly fuzzy or messy. The objectives and requirements of technology acquisition and development are ill defined. Organisationally, the managers are ill prepared, and the necessary skills and human resources are not available. Much organisational learning is required to build up the necessary competence. The planning emphasis in such a situation should be on educational acquisition to build up knowledge of the emerging new technology and its related problems, and no major investment commitments should be rushed into.

Some of the related problems can be political and competitive, and these problems are often beyond the power and influence of a single company. Broad plans and general guidelines can be developed at this stage. Experimentations based on prototyping and simulation/modelling can be useful. It is unlikely that any definitive plans and contingencies can be developed, and it is too early for this. The organisation may strengthen its internal human resources through education and training to prepare for the acquisition and commercial exploitation of the new technology when the time is right.

The relations between the problem framing types, the state of technology in an organisation, the mental models, and the recommended technology planning strategy and focus are summarised in Table 1.

Strategy for risk management The development of a viable strategy for risk management in technology acquisition hinges on the correct problem framing of a particular project situation. This is an essential step during the planning phase. The objective of the problem framing is to help the project manager to determine what an appropriate level of planning would be. Planning in turn provides a valuable opportunity for purposive individual and organisational learning 6. The desired effect of organisational learning is a dynamic shift in the problem framing. A continual and dynamic shift from a type III to type II and then type I problem frame causes the level of risk to reduce progressively.

Dynamic shift in problem framing The proposed conceptual framework that demarcates problem situations into three types can be used to guide the technology planning process. Problem framing is both arbitrary and dynamic in nature. The problem boundary shifts as the process of problem learning and solving evolves. A problem situation that is initially under uncontrolled uncertainty may shift to a state of controlled uncertainty as soon as a preliminary but adequate mental model has been constructed. This represents a dynamic shift from a type III to a type II condition. As the situation

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Table 1 Problem framing, mental models, and planning strategy

Problem framing State of technology in organisation State of mental models Planning strategy

Type I relative certainty

Type II controlled uncertainty

Type III uncontrolled uncertainty (ambiguity)

Base or proven technology already in use Well esiablished facilities Well informed on related technology trend and potential

Well constructed and relevant mental models available Clear images of the problem situation High predictability of problem variables and outcomes Confidence with single value deterministic estimate

Technology has not been embodied into existing products and processes Internal technological skills and knowledge available from R&D Required facilities not yet established Informed about the potential of new technology

Adequate mental models available Broad images of problem situation Defined broad objectives, scope and requirements Problem variables and outcomes changeable Use range estimates and contingency

New and emerging technology Absence of an adequate mental model dissimilar to the current corporate base Fragmented images of the problem technology situation Lack of skills and knowledge and Fuzzy and ill structured internal facilities to support the new Problem variables and outcomes not technology yet determinable Informed about the potential and risk involved for new technology

Internal planning effort with proven planning methodology Develop clear objectives, scope and requirements in early stage Planning to achieve rapid convergence to results Management by objectives and use of systems engineering approach Low risk of planning obsolete

Supplement internal planning efforts by using vendors, lead customers and consultant's inputs Purposeful or focused information and data collection and analysis Aims at developing broad objectives and requirements Develop contingency plans and built-in buffers according to risk perception Moderate planning risk

Emphasis on educational acquisitions to build up knowledge of the emerging new technology Emphasis on creative learning and experimentations Develop only broad plans and guidelines Use of self organising autonomous research and development teams High planning risk

develops further into a certain maturity over time, and when the problem variables and outcomes become highly predictable owing to the building up o f an experience curve, there will be a further shift from controlled uncertainty to relative certainty, a shift from the type II to the type I condition. The shift in problem framing is made possible through a highly dynamic planning and learning process.

The intensity o f problem learning will be greatest during the type III stage. The type III stage is a stage of high ambiguity, information gathering and intense debates, and the discussions among the stakeholders and the problem solving team are at their most critical. The focus is on high level strategic planning and systematic institutionalised learning. The focus for the type II stage is on preproject planning. Systematic learning continues, but more emphasis is given to the parameters related to the project proper . A contingency may be developed on the basis of a few base case scenarios or alternatives. When a problem frame arrives at a type I condition, this signifies that the project is ready for implementation when the project objectives, scope and requirements are well defined. The foci of the three problem frames are as follows:

Type I ~ --- Type II ~ Type III

Implementat ion Planning Learning

The dynamic shift o f the problem f lame and the focus is from right to left.

Importance of organisational learning

The level o f influence is the highest and most critical during the type III frame, when purposive organisational learning is most intense. Decision making is long-term and strategic

in nature. Many top corporate managers and management writers have increasingly, in recent years, advocated that the key to high performance is an organisat ion 's ability to learn and learn quickly in order to achieve sustained im- provement in performance over a long per iod of time 7.

Senge 7 quoted Ray Stata, CEO of Analog Devices: ' the rate at which individuals can learn may become the only sustainable competi t ive advantage ' . He also said that the cri terion for the survivabili ty of human organisations is their efficiency of learning, especially learning by the system as a whole. Ar ie de Geus 6, formerly of Shell (the Anglo-Dutch oil company), observed that an organisat ion 's ability to survive depends on the following. 'Institutional learning, which is the process whereby management teams change their shared mental models of their company, their markets, and their competitors. For this reason, we think of planning as learning, and of corporate planning as institutional learning. '

The f ramework developed in this paper on problem framing gives an emphasis to technology planning as an unceasing and dynamic organisational learning process that the problem solvers or decision makers collectively learn quickly and creatively in building a set of relevant and comprehensive mental models in order to increase their solution space. The development of the mental models allows them to move from an initially ill structured and ambiguous problem state to a controlled uncertainty state, and further to a relative certainty state, where the planning is very focused and defined to achieve rapid convergence to a desired outcome.

Conclusions

The degrees of uncertainty and the resulting risk are not wholly exogenous. They are relative to the internal ability

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of the firm to f lame and solve complex technical and other related problems. Such an internal capability depends on the availabili ty of adequate and relevant mental model(s) possessed by individuals and the organisation as a whole.

The initial and appropriate framing and diagnosing of a problem is critical, as this greatly enhances the effective- ness of the problem solvers in formulating an appropriate strategy which will cause convergence to a viable solution. The problem framing process is enhanced by the understanding of the concept of mental models. An adequate and relevant mental model is constructed and reconstructed iteratively through an intensive planning and multi learning process. The entire learning, planning and strategy formulating process allows the enterprise to organise necessary internal and external supports, including research and development, education and training, building leadership and commitment in order to enhance the probabil i ty of success in risk management.

References

Roberts, E B and Berry, C A 'Entering new businesses: selecting strategies for success' Sloan Management Review Spring 1985 3-17 Schrader, S, Riggs, W M and Smith, R P 'Problem solving in the management of technology and innovation' Working Paper 3345 Sloan School of Management, Massachusetts Institute of Technology, USA (1991)

3 Mintzberg, H 'Planning on the left side and managing on the right' Harvard Business Review 1976 54 (4) 49-58

4 Checkland, P B and Scholes, J Soft Systems Methodology in Action John Wiley, UK (1990)

5 Yeo, K T 'Systems thinking and project management--time to reunite' Int Project Management 1993 10 (2) 111-117

6 de Geus, A 'Planning as learning' Harvard Business Review Mar/Apr 1988 70-74

7 Senge, P M and Sterman, J 'Systems thinking and organisational learning' in Kochan, T and Useem, M (eds) Transforming Organisations Oxford University Press, USA (1992)

Dr K T Yeo is an associate professor in the School of Mechanical and Production Engineering at Nanyang Technological University, Singapore, where he is the Director of the Centre for Engineering and Technology Management, and was the founding programme director of the MBA in the management of technology. He received a PhD and an MSc from UMIST, UK, an MBA from Strath- clyde University, UK, and a BEng in mechanical engineering from the University of Singapore. He gained industrial experience in the utilities, engineering, construction, oil and gas, and aerospace industries.

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strategy for risk management through problem framing in technology acquisition

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