A Three-Stage Filter for Effective Technology Selection
By Williams, Leon | |
Proquest LLC |
A three-stage technology selection tool provides a framework and a simple process for evaluating and selecting new technologies.
OVERVIEW: Selecting the right technology for a given application is critical to the future of any enterprise. Companies invest billions of dollars each year in new technologies intended to provide competitive advantage. While there have been many attempts to rationalize the technology selection process, most are not sufficiently focused on the technology itself and many processes simply add a layer of complexity to an already difficult process. We have developed a three-stage technology selection tool that provides a framework and a simple risk-managed process for evaluating and selecting new technologies. The tool provides a powerful visual aid to help clarify key attributes of various technologies and the decisions related to their adoption, illustrating for both team members and senior management the decision-making processes engaged by technologists and project managers in considering new technologies. The three-stage filter was tested during a new product development project for a leading multinational company seeking smart material technologies to drive strategic innovation.
KEYWORDS: New product development , Risk management , Technology selection , Smart materials
US companies invest hundreds of billions of dollars in R&D programs each year-$365 billion in 2011 (
In this context, new technologies are frequently the basis for competitive advantage. Choosing the right technologies- and being aware of the options-is critical. Large companies, in particular, are at risk from disruptive innovation if they fail to identify and utilize appropriate emerging technologies to their advantage (
Managing communication and complex decision making requires effective tools. Shehabuddeen and colleagues (2006) argue that a filter tool, structured around a logical framework and grounded in practical experience, is the best method of managing the complex and disparate information needed by the technology decision-making team. There are some popular and well-understood tools for integrating new technologies into new product development (NPD) programs; the most widely used are technology road-mapping ( Phaal et al. 2004 ) and Stage-Gate processes ( Cooper and Kleinschmidt 1994 ). However, there is no comparable tool in widespread use for the evaluation and selection of new or emerging technology for NPD. The three-stage filter introduced in this paper attempts to address this need by providing a simple, effective tool to facilitate this process and manage the risks associated with it.
Background
The idea of a filter tool for technology selection is not new. Several such tools, which help identify appropriate technologies by assessing a set of technologies and eliminating unsuitable ones through a series of discrete stages, have been proposed. All have the objective of providing a methodology or framework that is logical and that captures decisionmaking processes, thus minimizing the risk of poor choices due to a lack of transparency in the process. Most of these tools use a combination of matrices and financial or mathematical approaches. Early work in this area, conducted by Yap and Souder (1993) , proposed a system comprised of a series of screening processes whereby candidate technologies are reviewed from different perspectives, such as strategy, competition, and the technological environment. This system incorporates some useful elements, such as the use of scoring or weighting processes to assign numeric values to subjective criteria and the idea of a barrier mechanism to prevent unsuitable technologies from proceeding further through the filter. Later approaches attempted to resolve all qualitative attributes into numerical data for comparison ( Bhatti 2000 ; Verter 1997 ).
Kirby and Mavris (2000) developed an eight-step Technology Identification, Evaluation and Selection tool (TIES). Arguing that a purely score-based system, although powerful, can be too simplistic, they use instead what they call a "technology frontiers" approach that evaluates technologies at each stage through a series of formulae, the results of which feed into a graphical output. The approach is intended to provide a more accurate and robust account of a technology's capabilities than a score can. However, the sophistication of the process does tend to produce a more fragmented and unwieldy tool.
Taking a broader view of communication and decisionmaking processes, Torkkeli and Tuominen (2002) developed a seven-step Group Decision Support System (GDSS) intended to enable multidisciplinary teams to communicate effectively in the decision-making process, which most observers see as vital for successful technology selection. The GDSS provides a more generalized approach, with less specific focus on the technology selection process itself, than we sought in developing the tool discussed in this paper. However, its overarching focus on maximizing communication within the technology selection team is useful in this context.
In a review of existing tools, Shehabuddeen and colleagues (2006) concluded that most were too narrowly focused on cost and too generic, as well as being poorly aligned to technology selection and not empirically tested in the field. They created their own tool, a two-stage filter in which candidate technologies are first assessed on the basis of defined sets of essential and non-essential requirements, such as technical and financial criteria. Those that pass this first filter move on to the adoption filter, which evaluates characteristics that determine the technologies' suitability for adoption within the organization, such as risk and integratability.
These tools, and the studies that produced them, identify useful concepts for technology selection, but they do so by increasing the amount of information the decision-making team must collect and analyze. As a result, they tend to complicate rather than simplify the process. The lack of widespread use of such tools would seem to suggest that they do not meet the needs of working NPD teams, possibly because they are too complex and not sufficiently grounded in practical application.
What is needed is a simple but comprehensive technology selection tool that can be used in the early stages of an NPD program to identify technologies that merit further investigation. Based on our experience working with NPD teams, such a tool would need some key attributes if it were to be effective in facilitating technology selection:
* It would need to be rapidly populated using only nondirect, publicly available data such as technical papers and journals.
* It would have to offer tools for eliminating unsuitable technologies through logical assessment and decision making.
* It would be required to illustrate the entire decisionmaking journey in a single tool readily accessible to managers and technologists alike.
* It would benefit from a strong emphasis on visual clarity, through careful management of data and the use of color coding.
Combined, these attributes should provide a means of managing the decision-making process to minimize risk by clearly presenting core information to the decision-making team and preserving a record of the decision-making process. The tool should help teams improve the speed and accuracy of their choices while recording the decision-making journey and capturing the reasons for those choices.
Our Research
Our work to develop the three-stage filter focused on the first phase of a larger NPD program to develop radical technologies in support of strategic innovation at a multinational consumer goods company. This first phase of the NPD program ( Figure 1 ), which was concerned with selecting suitable technologies, comprised five steps leading to the selected technologies being further developed within the larger innovation program. These steps, undertaken by a project team made up of technologists and managers from the research team and the company, located across four separate sites, followed a conventional technology selection process, beginning with acquiring and testing candidate technologies and moving through increasingly elaborate prototypes, leading to a recommendation of the most appropriate technologies for the intended purpose.
In actual use, the three-stage filter would follow the scoping phase of the NPD process. In Step 1, the data acquired in the initial phase would be used to populate the tool. The technologies recommended by the filter process could then provide guidance for the selection process that makes up the second step of the larger process.
In our research, however, the three-stage filter was implemented in Step 2, in parallel with the company's conventional investigation process, and refined based on feedback from the project team. The tool was developed to fill a need for rapid assessment of a wide array of potentially suitable technologies to which hands-on access for early assessment might be too costly or simply impossible. The filter tool had to provide guidance on the most likely suitable technology using only technical literature and logical decision-making stages to inform the choices made. The objective of the filter was to do this in a visually driven, easy to follow way that made visible the full progress of assessment and decision making. Elements of earlier models, such as scoring against relevant criteria and assessment of technologies through stages ( Yap and Souder 1993 ; Shehabuddeen at al. 2006 ), provided an efficient way of condensing complex information into simplified numeric data that could be easily conveyed; color coding added an important visual element to support the presentation of the numeric data. The risk of oversimplification identified by Kirby and Mavris (2000) was noted, but some simplification was considered a fair trade-offfor simplicity and ease of populating and operating the tool-its purpose, after all, was to act as an early indicator for technology selection choices ahead of a more protracted and sophisticated investigation within the conventional NPD program. Keeping the filter to three stages streamlined the tool and encouraged the efficient use of available data while providing sufficient review points. Data were gathered and input by the technical team and reviewed by the whole team at the completion of each stage; the entire process is visible at a glance ( Figure 2 ).
The newly developed tool was tested by comparing its implementation and output with a conventional technology assessment approach. The project team found that the tool, which relied on information available in technical papers and journals, could be populated with data much more quickly than the conventional process, which required physical samples for analysis. Both processes identified the same technologies as the most suitable, with the exception of an additional technology suggested by the filter but rejected by the practical assessment, only to be adopted later in the NPD program when a way to use it was found. The technology choices made by the conventional process were validated through the development of increasingly sophisticated prototypes, which demonstrated the practical viability of the investigated technologies in steps 2 and 4 of the NPD technology selection phase. The three-stage filter identified these same technologies without requiring the resources demanded by the hands-on investigation, and in a fraction of the time the practical investigation required.
Using the Three-Stage Filter
The three-stage filter establishes a framework for gathering and presenting information for selecting appropriate technologies. It offers a window into the process for nontechnical team members, allowing them to gain insight into what is likely new knowledge for them, and provides a straightforward mechanism for managing risk by making decisions understandable and traceable within the tool. The filter uses a color-coded scoring system to allow instant visual recognition of leading or failing technologies. Each color is tied to a numeric value (red=1, amber=2, green=3).
The process begins with as broad a capture of technologies as possible, keeping practical constraints to a minimum. Technologies are then evaluated against specific requirements, with those not possessing the required capabilities eliminated. Those technologies with the required capabilities are then reviewed for their suitability for the context in which they must function. These three stages provide an efficient yet broadbased method for evaluating technology for any application.
Stage 1: Key Functional Attributes
Key functional attributes are the principal capabilities being sought in the new technology, as determined at the outset of the project. As the maximum number of potential technologies should be included at this stage, to minimize the risk of overlooking a potentially disruptive technology, the number of key attributes should be kept to a minimum. In the test case, a large multinational consumer products manufacturer wished to develop a new product incorporating multiple moving elements with a high degree of reliability and control. The ability of some smart materials to perform as solid-state actuators was seen as a potential solution to achieve a high density of mechanical capability with a minimum of mechanical complexity (see "Smart Materials," right). The three-stage filter was developed to support and guide the team, who had no prior experience of working with smart materials, through the process of evaluating and down-selecting smart material technologies with the appropriate capabilities. Two key attributes were sought: the ability to change shape and the ability to have the change initiated by an applied electrical or mechanical input (such as heat).
In the first stage, each of the technologies is given a score of zero to three for each attribute. A zero indicates the technology does not have the attribute at all. (Technologies that score a zero on one or more attributes should be included in the first stage as evidence they have been considered, although they will not advance.) The team must use its expertise to decide how well each of the technologies appears to fulfill the specific criteria based on the information available and award an appropriate score. In some cases, technologies may simply meet or not meet a given criteria, and in these cases, all candidates will have scores of three or zero. The filter still works for these criteria, although at a less sophisticated level, and zero-scoring technologies will still be shown to have been considered.
Overall scores are accrued by multiplying together the values assigned for each key attribute; multiplication eliminates any technology that scores a zero on any attribute. If failure to fulfill one or more categories is tolerable, then a value of one can be substituted for the optional attribute. Numerical scores are then scaled as high, medium, or low, and each technology is assigned a color-red, amber, or green-based on its scaled score. Only technologies with medium or high scores are advanced to stage 2.
Stage 2: Evaluation of Primary Attributes
The second stage provides a more critical examination of the technologies that survive the first level of evaluation. All scores are reset to zero, and candidate technologies are graded on a series of more focused criteria defined by the primary attribute-that is, the main function the technology is expected to deliver. The primary attribute for the technology in our test case was the ability to change shape. The filter allowed the team to evaluate each technology's performance with regard to that attribute in more depth using a range of specific criteria, including the amount of change, the physical displacement and speed of the change, and the force exerted by the material ( Figure 3 ).
This raw data is retained within the table for reference and overlaid with the tool's color-score system.
The second stage of the filter allows the technologies that survived the stage 1 filter to be described in more detail, including such attributes as form factor, typical applications, images, and exemplars. Taken together with the technical performance data, this information comprises a concise overview of the technologies under consideration for nontechnologist team members.
Stage 3: Contextual Evaluation
In stage three, the remaining technologies are assessed for their suitability for the particular context in which they will be used. The importance of such considerations is discussed by Schroder and Sohal (1999) in their work on the adoption of advanced manufacturing technologies (AMTs). Data from indirect sources continues to be used to examine the shortlisted candidates in light of real-world industrial considerations such as cost, availability, and robustness, using the same ranking system as in the first and second stages. While the first two stages gathered quantitative performance data, this final stage focuses on more qualitative factors, such as reliability, capability, or robustness, that cannot be quantified in the same way that technical attributes can be. Consequently, the technology selection team must apply its collective wisdom to derive a formula to represent the logical relationship between the criteria being considered. The basic principles for deriving a formula are fairly straightforward; the key is to avoid any one criterion skewing the result out of proportion to its weighting-this is where the wisdom of a multidisciplinary team is invaluable.
In our case, the formula included typical considerations such as cost and reliability, as well as project-specific criteria such as speed of functioning and capability ( Figure 4 ). Scores for functional requirements that were roughly equal in importance were added and the total divided by the cost of the technology, reflecting the high importance cost plays in selecting a technology. The whole was then multiplied by a score for the technology's ease of availability, with zero representing a technology that cannot be obtained-an obvious showstopper, no matter how high the technology's scores on other attributes.
Discussion
The three-stage filter was conceived to provide rapid guidance for assessing the suitability of new and emerging technologies for specific applications. It allows judgments to be made using, if necessary, only indirect data such as technical papers and journals. This flexibility makes it easier to use and more practical at very early stages, where it can act as a guide for later, more rigorous work to provide definitive technology selections. The tool frames data in a logical way, organizing analysis into discrete stages so that the selection team can manage the progress and direction of the process. As we discovered in the case study, its focus on the theoretical suitability of technologies, rather than practical assessment, can help widen the field to technologies that initial practical assessment may overlook.
In our work, the tool proved as effective as the traditional, more cumbersome system at identifying suitable technologies. In fact, the three-stage process identified the same technologies as the usual process, with a fraction of the time and effort. Furthermore, it identified one additional technology that the usual process had discarded. The validity of these identifications was supported in the later stages of the NPD process, when prototypes incorporating these new technologies were developed and tested.
The three-stage filter was intended to be an intuitive, high-impact visual tool for use in a context that includes both technical and nontechnical stakeholders. Its design attempts to balance detail with simplicity, offsetting the need to capture as much relevant data as possible with the ability to present the data clearly and effectively. In our study, the tool appeared to be well liked and easily understood. In a survey of 37 managers, engineers, consultants, and researchers who were given a short presentation about the tool, most judged it useful and easy to use, and 70 percent indicated they would use it ( Table 1 ).
Analysis of survey responses in combination with verbal feedback from post-presentation discussions with the survey group yielded a number of interesting insights. The consultants who took the survey universally liked the way the tool conveyed complex technical information and facilitated decision making in a robust way, offering protection to client and consultant alike. The tool was also popular with managers, who were universal in their enthusiasm for such a tool, but expressed more caution about its overall usefulness, with 67 percent responding that they thought the tool was useful. Fewer engineers found the tool easy to use but 77 percent of them supported the view that it would be useful for conveying the complex decision-making process of technological assessment to managers. Overall, the survey supported the idea that the tool is intuitive and useable.
Because identifying evaluation criteria and scoring candidate technologies against them requires expertise and experience, the tool may be vulnerable to errors with inexperienced users. However, in the hands of a knowledgeable project team it has the potential to be a powerful tool to identify suitable technologies ahead of any practical investigation and to draw attention to technologies that conventional investigation may overlook.
Conclusion
Our case study showed that the three-stage filter tool could identify technologies that matched key requirements as effectively as conventional technology investigation processes, and it did so much more efficiently, even identifying a suitable technology that the conventional process failed to recognize. The tool lends itself to customization for diverse technologies and specific applications. Judging technologies based on their theoretical suitability gives the tool a potential advantage over other approaches for identifying technologies, which are often hampered by time and cost constraints. This strength helps to mitigate the acknowledged danger of oversimplification with the use of such a tool.
It is highly unlikely that the three-stage filter, or any tool, can forecast with total accuracy something as unpredictable as new and emerging technologies. However, as further work refines its capabilities, it can become a powerful indicator. In the hands of a knowledgeable team, using the right combination of criteria, the three-stage filter is a valuable tool for helping an organization plan and focus its resources more effectively prior to a more extensive technology investigation.
What is needed is a simple but comprehensive technology selection tool that can be used in the early stages of an NPD program.
Smart Materials
The discovery of new materials has helped to define and advance the technology of our society ( Sass 1998 ). As our fundamental understanding of materials and new applications for them increases so this pattern is likely to continue. Smart materials, with their unique and unusual properties, may emerge as major players in our society of tomorrow.
Smart materials are broadly defined as those that "can sense and respond to the environment around them in a predictable and useful manner" (
The sensing and reactive capabilities of smart materials are being actively researched in a number of industries, from health care to defense. They promise to provide enhanced and more efficient solutions in a variety of contexts; for example, shape-changing smart materials are being explored as solid-state actuators that can replace more complex motor assemblies in automotive and aerospace applications (
However, a 2008 parliamentary study (
The three-stage process identified the same technologies as the usual process, with a fraction of the time and effort.
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DOI: 10.5437/08956308X5702155
Copyright: | (c) 2014 Industrial Research Institute, Inc |
Wordcount: | 4910 |
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