Opportunity Analysis


Opportunity analysis consists of three interrelated activities:

  • Opportunity identification
  • Opportunity-organization matching
  • Opportunity evaluation

Opportunity arise from identifying new types or classes of buyers, uncovering unsatisfied needs of buyers, or creating new ways or means for satisfying buyer needs. Opportunity analysis focus on finding markets that an organization can profitably serve.

Opportunity-organization matching determines whether an identified market opportunity is consistent with the definition of the organization’s business, mission statement, and distinctive competencies. This determination usually involves an assessment of organization’s strengths and weaknesses and an identification of the success requirements for operating profitably in a market. A SWOT analysis is often employed to assess the match between identified market opportunities and the organization.

For some companies, market opportunities that promise sizable sales and profit gains are not pursued because they do not conform to an organization’s character.

Opportunity evaluation typically has two distinct phases—qualitative and quantitative. The qualitative phase focuses on matching the attractiveness of an opportunity with the potential for uncovering a market niche. Attractiveness is dependent on 1) competitive activity; 2) buyer requirements; 3) market demand and supplier sources; 4) social, political, economic, and technological forces; and 5) organizational capabilities. Each of these factors in turn must be tied to its impact on the types of buyers sought, the needs of buyers, and the means for satisfying these needs.

Opportunity identification, matching, and evaluation are challenging assignment because subjective factors play a larger role and managerial insight and foresight are necessary. These activities are even more difficult in the global arena, where social and political forces and uncertainties related to organizational capabilities in unfamiliar economic environments assume a significant role.

My Consultancy–Asif J. Mir – Management Consultant–transforms organizations where people have the freedom to be creative, a place that brings out the best in everybody–an open, fair place where people have a sense that what they do matters. For details please visit www.asifjmir.com, and my Lectures.

Knowledge Engineering


In the traditional approach to systems design, a system analyst, together with the ultimate end-users, or clients, for the project, will complete a functional specification of the system. At that point, the project is essentially in the hands of professional project management and programming staff, because that group possesses the knowledge and skill required to deliver the agreed upon features and functions. In the development of knowledge systems, this is simply not the case. Following the specification of function, a new problem arises. This is because it is not an algorithm that is being developed but knowledge that is being encoded for machine use.

 

The immediate problem is that traditional applications developers do not have sufficient knowledge of the applications area to complete the project from the starting point of a functional specification. This information generally exists in a variety of forms, depending on the application area. In some cases an individual or group of individuals may uniquely possess the relevant knowledge. In other cases, the knowledge may exist in the form of published materials like manuals or textbooks. In still other cases, the knowledge does not presently exist at all, and must be created and developed along with the system itself. This is an extremely difficult circumstance. Further compounding this problem is a critical factor: Regardless of the form in which the knowledge currently exists, it is not in a form that is ready for use by a knowledge system. Someone must decide what knowledge is relevant and desirable for inclusion, acquire the knowledge, and represent it in a form suitable for a knowledge system to apply. In all but trivial applications the task of representing the knowledge requires not only coding individual “chunks” of knowledge, but also organizing and structuring these individual components.

 

Historically, owing to the remoteness and enigmatic quality of artificial intelligence technologies, the person doing the actual systems development and the “expert,” or source of knowledge, were not the same. The availability of tools, in place of enigmatic technologies, has had an impact on reducing this problem. Even if one can imagine the case in which the “expert” whose knowledge is to be modeled is also an “expert” with the use of artificial intelligence development tools, there still remains a sizable problem.

 

In case where knowledge resides with some practitioner or expert, it does not exist explicitly as a series of IF …THEN rules, ready to be encoded. Most practitioners and experts find it difficult to explain explicitly what they are doing while solving problems. They are not cognizant of the underlying rules they are applying. Their expertise has been developed from numerous experiences and involves highly developed pattern recognition skills and heuristics.

 

In the case where the knowledge to be included is contained in text material like manuals, regulations, procedures, and the like, the information is still not in a form ready for inclusion in an expert system. It must be remembered that one of the most often cited advantages of expert systems is that they make explicit the knowledge that is most often implicit and unavailable for review, evaluation, dissemination, and modification. The task of making knowledge both explicit and available for systems application is that of knowledge engineering. Most literature on the development and application of knowledge systems has identified the need for individuals skilled in knowledge engineering as a critical factor to widespread use of technology.

 

Knowledge engineering involves acquiring, representing, and coding knowledge. The representation and coding aspects of systems development have been greatly impacted by these newly available tools. The speed with which prototyping can be accomplished has also helped reduce some of the difficulty in acquiring or refining knowledge. The knowledge engineer now finds it much less costly in time and effort to represent, code, and test early approaches to systems development, providing a more efficient feedback loop. This feedback loop is critical in the development of knowledge systems. The end-user/client for the project is, by nature, going to be much more involved in the systems design process. The “programmer” often is incapable of deciding if the system is behaving properly, owing to a lack of fundamental knowledge about the application area. This is simply not as strong a factor, where the programmer is capable of evaluating the accuracy and efficiency of algorithms. When the product is actionable knowledge rather than algorithms, the ability to evaluate project progress shifts to the end-user/client. This creates the increased emphasis on the feedback loop.

 

My Consultancy–Asif J. Mir – Management Consultant–transforms organizations where people have the freedom to be creative, a place that brings out the best in everybody–an open, fair place where people have a sense that what they do matters. For details please visit www.asifjmir.com, Line of Sight

Hiring Happy Employees


With all the apptitudes, skills, and traits for which managers can test applicants, there is still one thing that’s usually not tested for but that perhaps should be—at least if some recent research findings are valid. Particularly in companies being rocked by downsizings and competitive pressures, there’s something to be said about hiring people who are inclined to remain happy even in the face of unhappy events.

Basically, happiness seems to be largely determined by the person’s genetic makeup—that, in other words, some people are simply born to somewhat happier than others. The theory, in nutshell, says that people have a sort of “set point” for happiness, a genetically determined happiness level to which the person quickly tends to gravitate, no matter what failures or successes he or she experiences. So confront a high-happiness-set-point person with the prospect of a demotion or an unattractive leteral transfer, and he or she will soon return to being relatively happy once the short blip of disappointment has dissipated. On the other hand, send an inherently low-set-point, unhappy person off on a two-week vacation or give him or her a sizable raise or a new computer, and chances are he or she will soon be as unhappy as before the reward.

Like testing employees for any traits, coming up with a set of tests or interview questions to identify happier, high-set-point people requires careful consideration and probably the help of a qualified psychologist. However, the following might provide some insight into the tendency to be relatively happy:

Indicate how strongly (high, medium, low) you agree with the following statements:

  • “When good things happen to me, it strongly affects me.”
  • “I will often do things for no other reason than they might be fun.”
  • “When I get something I want, I feel excited and energized.”
  • “When I am doing well at something, I love to keep at it.”

Agreeing with more statements and agreeing with them more strongly may correlate with a higher happiness-set-point.

My Consultancy–Asif J. Mir – Management Consultant–transforms organizations where people have the freedom to be creative, a place that brings out the best in everybody–an open, fair place where people have a sense that what they do matters. For details please contact www.asifjmir.com, Line of Sight