Most business managers, whether vendors, vendor clients or implementers, are unaware of the fundamental capabilities that knowledge based decision support can provide to minimize project risk for all sides of technology utilization. Given that over 90% of IT projects fail on first attempt, according to the Standish Group, more thought and research on the evaluation and selection process is needed. Many of these failures - some 30% - are the direct result of poor selections, and represent upward of $30 billion in wasted investment annually.
On the vendor side, the challenge of educating the potential client of their offerings results in long sales cycles, meticulous and numerous RFI responses, and potential for a mismatch, result in projects that go awry. These failed projects do not bode well for the vendor, since the sales cycle costs can only rise, and their reputation can suffer. Consequences can be more severe for the client where it can, in extreme cases, lead to business failure. Implementers (which can be internal IT departments as well as consultants) can also find that decision-maker indecision leads to lengthened sales cycles, missed opportunities, and risk of competitive intrusion. The root cause of this indecision is an inability of the implementer to give confidence to the stakeholders of their choice of solution.
All sides need thought and research to build data and process information in a meaningful context, which takes time and costs money for all participants going through the selection process. But without spending time, thought, research and money there is increased business risk to all.
To cut away from this devil-and-the-deep-blue-sea conundrum means looking under the hood of evaluation and selection practices, to determine if there are better ways of moving through them. There is certainly room to ask the fundamental question of whether the current practice of RFI / RFP processes, among other internal organizational procedures, are adequate to the task of selecting complex systems? The record indicates there is much room for improvement.
In essence, for complex selections, the human-machine combination has to work together to drive the solution. Both have to be understood and complement each other in the process. It is easy for the human to be overwhelmed, or simply run out of time, and the machine interface and engine to be inadequate to the task. However, the results must benefit the process if human and machine can function effectively together to process information and avoid the pitfalls of past selection processes.
In this, the second part of this article, we shall follow a simplified process as an illustration. The method was used by the author to conduct a selection on a Personal Digital Assistant (PDA). In this case, the end result was the purchase of the suggested item! The first part, is an overview of decision support systems and knowledge based selection.
About This Note: This is a two part note where Part 1 is a discussion of the use of an IT Knowledge Based selection tool as part of a Decision Support System selection process. This second part of our two-part article is a tutorial with which illustrates using such a system to select a Personal Digital Assistant (PDA).
Though a PDA is far less complex than, for example, an ERP system, processes and procedures enabling narrowing down of solutions, and avoiding dissatisfaction, while taking on assessed risks, are part of process in Knowledge Based Selection methods.
In this article, we shall follow a simplified Knowledge Based selection process as an illustration.
To follow this process as a tutorial, you should go to the WebTESS 2.0 website, and bring up the PDA knowledge base on a separate browser window.
Note, however, that the author also invoked TESS to explore more sophisticated analysis and tradeoffs. TESS is TEC's full-featured Knowledge Based system designed for major complex selections involving hundreds and even thousands of selection criteria of many different levels of criticality.
Value Trees
Value trees, simply put, provide a measure of value of a solution against your business requirements. They generally consist of criteria arranged into hierarchical trees, much like directories on a hard disk - and not too differently related. Files in a folder are related by implication to the name of the folder, for example. The criteria are prioritized according to the main goal, and alternatives are rated against the criteria at the lowest level of the hierarchy. WebTESS 2.0 Knowledge Bases (KBs) are arranged in this way.
(Author note: The criteria can or should represent variously business objectives, business capabilities, processes, and end criteria which can represent features and functions, or measurable items related to other issues of the vendor-client relationship, including project management and vendor viability issues(for example). In our case the relationship is limited in the KB to availability and warranty concerns only, partly to simplify the issue, and partly because the selection of a single PDA did not depend on how long the company is likely to be around - at least, the significance of this to the decision was not a significant or a major cause for differentiation)
As a noted in part 1, most IT selection tools on the web contain only "features and functions" criteria. For many complex decisions, this can be inadequate.
After selecting the PDA KB, you will be placed in a window entitled "Summary". There are four sections to this window. On the left side is the decision hierarchy - in fact the value tree of criteria. The top three criteria are "Features", "PDA Configuration", and "Other Product and Manufacturing Considerations", and these criteria are what you first see in the tree. You can view deeper levels in the tree by clicking on the [+] boxes on the left of each criterion. Note the initial focus is on the top criterion (or root) of the tree - "Selection of a PDA". The root name is repeated on the right hand side, above the Criteria label. To the right of the tree is a list of criteria, and input boxes under a title "Relative Priorities". A pie graph gives feedback to the relative priority distribution further to the right. This constitutes the second area of the Summary window.
SOURCE:http://www.technologyevaluation.com/research/articles/a-case-study-and-tutorial-in-using-it-knowledge-based-tools-part-2-a-tutorial-16388/
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