By Navneet Bhushan and Karthikeyan (Karthik) Iyer

Increasing globalization indicates that the world will see an increase in global software development. Studying the global software development landscape, this article identifies six different global software development scenarios. These include, in-house – near-shore; in-house – offshore; outsourced – near-shore; outsourced – offshore; collaborative – near-shore and collaborative – offshore. In each of these scenarios the relative project management challenges and priorities are different. The followingis Part Two of the three-part series. Part One discussed project management and global software development scenarios.

Methodology

The question that has been explored in this study is whether there are any variations in terms of software project management in different global software development scenarios. To answer this question, input from experts such as project managers who have worked in the six scenarios were invited to collaborate. Their results were collected and consolidated using the well-established methodology of the analytic hierarchy process (AHP).

Analytic Hierarchy Process

The AHP provides a means of decomposing a problem into a hierarchy of sub-problems, which can more easily be comprehended and subjectively evaluated. The subjective evaluations are converted into numerical values and processed to evaluate each alternative on a numerical scale. The detailed methodology of the AHP is as follows:^1,2

1. Problem is decomposed into a hierarchy of categories and parameters.
2. Data is collected from experts corresponding to the hierarchical structure, in comparison (pair-wise) of alternatives on a qualitative scale. Experts can rate the comparison as equal, marginally strong, strong, very strong or extremely strong. The comparisons are made for each criterion and converted into quantitative numbers on a nine-point scale.
3. The pair-wise comparisons of various criteria generated are organized into a square matrix. The diagonal elements of the matrix are: The criteria in the i^th row is better than criteria in the j^th column if the value of element (i,j) is more than one; otherwise criteria in j^th column is better than criteria in i^th row. The (j,i) element of the matrix is reciprocal of (i,j) element.
4. The principal eigenvalue and the corresponding right eigenvector of the comparison matrix gives the relative importance of various compared criteria. The elements of the normalized eigenvector are termed weights with respect to the criteria or sub-criteria.
5. The consistency of the matrix is then evaluated. Comparisons made by this method are subjective and the AHP tolerates inconsistency through the amount of redundancy in the approach. If this consistency index fails to reach a required level, the answers to the comparisons may be re-examined. The consistency index (CI) is calculated as: CI = (lmax – n) / (n– 1) (1) Where, lmax is the maximum eigenvalue of the judgment matrix and “n” is the order of the matrix. This CI is compared to that of a random matrix (RI). The ratio derived, (CI / RI) is termed the consistency ratio (CR). It is suggested that the value of the CR should be less than 0.1.^1,2
6. The ratings of each alternative are multiplied by the weights of the sub-criteria and aggregated to get local ratings with respect to each criterion. The local ratings are then multiplied by weights of the criteria and aggregated to get global ratings.

Project Management Prioritization in Global Software Development Scenarios

For each of the six global software development scenarios, various experts (project managers, program managers, etc.) were invited to give input into a pair-wise comparison format shown in Figure 1.

The relative priorities are derived from a process of consensus creation based on inputs from multiple experts. These experts have been involved in the successful delivery of global software development projects. There are, however, variations in their views on relative importance of the parameters.

In the initial setting, the experts were given the background of the model. The parameters of the category and sub-category were explained to them in a workshop setting where they debated on the parameters and their value in general terms. After the workshop these experts were asked to fill in the relative importance of each parameter in pair-wise qualitative comparisons for each of the six scenarios as shown in Figure 1.

Figure 1: Pair-wise Comparison from Experts

The AHP produces a relative rating or weight of each of the alternatives. In this case each project management arm gets a relative importance or weight with respect to each of the global software development scenarios. This is obtained from each expert. The consistency of each expert in giving these ratings is evaluated based on the consistency ratio. To create a consensus with various experts, geometric mean (G.M.) of all ratings corresponding to a particular project management arm in a particular scenario are computed. The G.M. as shown in Table 1 uses a normalized geometric mean (N.G.M.). The N.G.M. gives relative importance or weight of each arm in each global software development scenario.

Results

Outsourced Software Development Projects

In the near-shore outsourced software development projects, scope, quality, time and cost, are the most important areas (a relative weight of 18 percent each). Risk management is also important (a relative weight of 12 percent). In the offshore outsourcing scenario, however, most important management issues are related to risk management (a relative importance of 26 percent as compared to 12 percent) and communication management (a relative importance of 26 percent versus four percent in the near-shore scenario). Time management, integration management and quality management are three important areas to focus on in the offshore scenario with a relative importance of 13 percent, ten percent and nine percent, respectively. Figure 2 and Figure 3 shows the Kiviat charts for outsourced software development projects – near-shore and offshore. Combining these two Kiviat charts as shown in Figure 4, shows a shift in relative importance of project management arms based on whether the outsourced software development projects are near-shore or offshore.

Figure 2:Outsourced Near-shore Scenario

Figure 3: Outsourced Offshore Scenario

Figure 4: Outsourced Scenarios – Near-shore and Offshore

In-house Software Development Projects

In near-shore – in-house software development projects, the most important aspect of software project management is quality (a relative weight of 23 percent). This is followed by time and cost management (a relative weight of 17 percent). This is not surprising as classical project management talks about balancing the three constraints of time, cost and quality. It is worth noting, however, that scope and human resource management are also quite important (relative weight of 13 percent). When the in-house projects are developed in multiple geographic locations such as in-house offshore software development projects, there is a shift in human resource management issues and communication management issues (a relative weight of 25 percent). The greatest jump is in relative importance of communication management (from three percent to 25 percent). Time, integration and risk management comes close to each other (at around 12 percent relative weight). An individual can infer that as the in-house software development projects are executed with offshore compared to near-shore, people and communication issues become more relevant and important. Figure 4 and Figure 5 shows the Kiviat charts for in-house software development projects – near-shore and offshore. Combining these two Kiviat charts as shown in Figure 7, shows a shift in relative importance of project management arms based on whether the in-house software development projects are executed as near-shore or offshore.

Figure 5: In-house Near-shore Scenario

Figure 6: In-house Offshore Scenario

Figure 7: In-house Scenarios – Near-shore and Offshore

Next up: Part Three will explore collaborative software development projects, more near-shore and offshore scenarios and the conclusion in the last of this three-part series.

References

1. Bhushan N. and Rai K., Strategic Decision Making – Applying the Analytic Hierarchy Process, Decision Engineering Series, Springer, UK, Jan. 2004.
2. T.L. Saaty, Analytic Hierarchy Process, McGraw-Hill, New York, 1980.

About the Authors:

Navneet Bhushan is the founder / director of an innovation co-creating firm, Crafitti Consulting Pvt Ltd. He has worked close to two decades in managing and developing IT, innovation and productivity solutions and has worked in large commercial and government organizations. He is the principal author of Strategic Decision Making – Applying the Analytic Hierarchy Process published by Springer, UK, 2004. His current research interests include complexity, open innovation and globalization. He is a visiting faculty member at Welingkar School of Business Management. Contact Navneet Bhushan at navneet.bhushan (at) crafitti.com or visit http://www.crafitti.com.

Karthikeyan (Karthik) Iyer is a Founder / Director of an innovation co-creating firm, Crafitti Consulting Pvt Ltd. He has more than nine years of experience in the IT industry and has consulted on innovation in processes, product strategy, technology, intellectual property and organizational innovation capability. His areas of interest include innovation culture and chaos theory, open innovation, inventive principles and technology evolution trends. Contact Karthikeyan (Karthik) Iyer at karthikeyan.iyer (at) crafitti.com or visit http://www.crafitti.com.