on more of its employees. For example, IT staff can be less tasked with maintaining the server lab, and be more targeted on employee support and creating web applications needed for this unique product experience.
3.2.Challenges of cloud
One of the largest challenges for BMF’s cloud strategy is that the migration process will be complex, both technical- and process-wise. Technically, the migration will involve moving all the codes, data and developers to the cloud model. One of our highest concerns is addressing privacy issues for both company secrets and customer data.
There are also substantial business migration concerns, for example, the BMF’s supply chain processes are executed by non-technical business analysts and stylists. Training (for the new site) is needed for these non-technical users to prepare them for possible differences once the code is migrated. All processes will need to be tested and validated before the new site and supply chain are opened for business. It should be expected that there are difficulties in this process and possibly downtime if the migration process is not tightly managed.
Another concern is the high costs of a cloud solution. The management expects that there will be substantial upfront costs for the technical migration, training and time spent on training their employees. Reoccurring monthly costs will also need to be managed, but that management strategy will largely be dependent upon which ISP is ultimately chosen, as an ISP’s monthly costs can vary due to support tiers, time of day and the amount of data.
Off-premise cloud solutions inherently create a dependence on the ISP. This dependence will be for mission critical systems. In the case of BMF, if their systems go offline, business can suffer. If their systems are hacked, the company’s reputation and trust can be lost. Thus, in the event of such scenarios, businesses have a foundational reliance upon their ISPs. In spite of this, BMF’s management still continues to desire the move due to the obvious benefits.
4.Literature Review
Hierarchical Decision Modeling (HDM) is a technical tool used in project selection, resource allocation and evaluation decisionmaking. Its objective is to assist the user, by a series of pairwise comparisons, to reach quantifiable judgmental value using ratio scales. The underlying assumption is that each decision has a number of perspectives and each perspective has number of criteria to consider [1].
Thus, combining the perspectives—quantifiable or non-quantifiable—and the supporting criteria will help in determining the strategy (decision). The HDM is a process using multi-level decisions and utilizing multiple criteria by separating the overall system into several hierarchical levels.
The HDM is also a process based on reaching out to an independent panel of selected experts, who responds to questions by dividing 100 points between two alternatives at a time. The allocation of the points represents each expert’s independent judgment with respect to a specific criterion. The 100-point scale is from 1 to 99. The zero value is avoided to eliminate mathematical difficulties; however, if such a consideration is given, each expert selects 50 points. This means the judgment is neither important nor unimportant [1].
The HDM is based on a pairwise comparison analysis using linear algebra and matrix analysis. The goal is to find the eigenvalue and the eigenvector for each consideration in the matrix. In other words, pairwise comparison is a method used to determine how to evaluate alternatives by providing an easy and reliable means to rate and rank decision-making criteria. Weights are used and assigned to criteria and the results are normalized. The comparison is implemented in two stages:
1.Determining qualitatively which criteria is more important (i.e., establish a rank order of the criteria), and
2.Assigning a quantitative weight to each criterion, such that the qualitative rank order is satisfied.
The process is based on three steps that differ in their underlying scale. First, the measurement is based on a range from an ordinal perspective (i.e., weighting by ranking). The second step is about constructing an interval by weighted ranking, while the third step calculates the ratio scale—the pairwise comparison value. The three steps are summarized below, based on the document HDM by Dundar Kocaoglu.
•Step 1—Completion of the pairwise comparison matrix: Two considerations are evaluated at a time in terms of their relative importance. Index values from 1 to 99 are used. If criterion A is exactly as important as criterion B, this pair receives an index of 1. If A is much more important than B, the index is 99. All degrees are possible in between when comparing A to B. For a “less important” relationship, the fractions would be closer to 50 points. The values are entered row by row into a cross-matrix. The diagonal of the matrix contains only values of 1. The right upper half of the matrix is filled until each criterion has been compared to every other one [1].
•Step 2—Calculating the criteria weights: The weights of the individual criteria are calculated. First, a normalized comparison matrix is created: each value in the matrix is divided by the sum of its column. To get the weights of the individual criteria, the mean of each row of this second matrix is determined. These weights are already normalized; their sum is 1.
•Step 3—Assessment of the consistency matrix: A statistically reliable estimate of the consistency of the resulting weights is made.
4.1.How objectives and criteria are determined
Theoretically, each level of the hierarchy consists of multi-dimensional alternative choices or decision elements, as noted in Figure 1 as Level 1. Multi-criteria objectives that lead to multiple subcriteria are shown in Level 2. At the bottom of the figure, multiple output results from multiple actions are shown in Level 3.
Figure 1.HDM conceptual framework.
The decision element at a specific level has an impact on several elements at the next nod level in the connecting lines. Let’s say, we are seeking to make an operational level decision to produce a cloud model of technology that contributes to several or maybe all subcriteria at the target level. Consequently, reaching our fulfillment level (i.e., the goal) that contributes to several or all the objectives. Figure 1 depicts how the goal, criteria and alternatives are related.
The process of evaluation between each internal relationship in such a hierarchy requires assigning a numerical value to each branch of the hierarchical network structure, as shown in Figure 1. The values are assigned to represent the relative contribution of one element to the next on different levels. As this process is completed, an evaluation model is developed to obtain the relative measure of effectiveness for each element at the bottom of the decision hierarchy, in terms of the elements at the top. In other words, each of the items (that makes Level 2) has a percentage value if the sum is equal to 1. Also, the sum value of each subcriteria is equal to the respective criteria at Level 2 (i.e., the upper limit for the number of relationships is defined by the product of the number of elements at the sublevels).
4.2.Use of experts and Delphi
The
