Quantitative Approach to Structure Teams

1. Quantitative Approach to Structure Teams Presented by Team 4 Jim Kile Don Little Samir Shah

2. Current State of Software • 60% of U.S. software projects are failing in some manner either through outright failure to deliver or abandonment • It is estimated that software failures have likely cost the U. S. economy at least $25 billion and perhaps as much as $75 billion • A typical cell phone, which contains 2 million lines of software code now, is estimated to contain 20 million lines of code by 2010 • The General Motors Corporation estimates that its cars will run software having almost 100 million lines of software code by 2015

3. A Team Approach • Use of a team approach helps bring new products to market faster • Projects completed more quickly and with a lower risk of failure • Research suggests that team output is superior to that of an individual • Higher return on assets • Reduction in Changes

4. Team Challenges (General) • Communication • Lack of clear communications is a common root cause for failed projects! • Coordination of team activities • Relationships • Individual team members • Team values

5. Team Challenges (Formation) Human Resources are your “most valuable resource” • People cannot simply be interchanged as each have: • Multiple different skills • Different levels of proficiency • Different work references • Selection • Individuals should be selected based on their skills • Individuals should be selected based upon their ability to function within a particular team environment

6. Team Challenges in Software Development Projects • Quantification of a team member’s skills • Matching team members to tasks which make them most effective • Quantification of skill and productivity variations • The productivity of one programmer compared to another may vary by as much as a factor of eight to one [RAKOS] • Assignment is typically not automated • Project management software lacking • No current ability to facilitate team building and handle team problems automatically

7. Characteristics of Effective Teams • Projects are completed faster • Project risk is lowered and the likelihood of failure diminished • Work preferences are maximized based upon the skills of the team member • Work efforts are cost-effective

8. Two Papers • Paper #1: “A Quantitative Approach To The Formation of Workgroups” • Classification and clustering to generate recommendations for the composition of a software development team • A simulated annealing technique is used • Paper #2: “Utilizing Cluster Analysis to Structure Concurrent Engineering Teams” • Clustering and classification to generate concurrent product development teams • Clustering techniques used: • Single Linkage, Complete Linkage, Average Linkage, the Centroid Method, and the Ward’s Method

9. Paper # 1 A Quantitative Approach to the Formation of Workgroups by Greg Burdett and Raymond K-Y Li

10. Contribution • Proposes a quantitative approach to solving the team building problem • Defines criteria for skill identification and preference factors • Identifies further criteria to maximize work preferences and skills while maintaining cost-effectiveness • Compares a vast number of possible combinations to identify the “best” team using a simulated annealing technique

11. Problem Space for Team Building • Variables • Total staff pool = n persons • Team of r persons selected for team • Number of possible combinations (unconstrained) • n!/r!(n-r)! • Example #1 • Let n = 100 eligible staff • Let r = 5 (a team of five members) • 75,287,520 possible combinations! • Let r = 10 (a team of ten members) • 17,310,309,456,440 possible combinations!

12. Additional Variables Affecting Selection • If skills and preferences factors are high (favorable) • And salary costs are low (favorable) • Then the overall cost will be “LOW”

13. Theoretical Basis for Variable Selection • “Southern Paper” – based on achievement of certain key results • Does not serve as a measure of any specific skills • “Performance Appraisal System” – a subjective approach to skill measurements • “Performance Dimension Profile” • 360 degree feedback

14. Limitations on the Quantitative Measurement of Skills & Preferences • Quality of input data • Specifically skills and preference measurements • Employee job descriptions need to be precise and well defined • Preference factors need to be determined by a suitable interview or survey process

15. Research Methodology • Creation of various Tables • 5 activities • 5 skill types assessed • Pool of 10 staff assumed • Binary ratings were given • Measurement of the proficiency of a person in a skill • Scale 0 – 9 • Simulated annealing approach used to search for the “near optimal” team

16. Simulated Annealing • Originally Developed by Metroplis (1953) • Intended to find a near-optimal solution within a reasonable time frame • An incremental improvement algorithm • Generates a random initial configuration • Compares that with randomly generated neighbor configurations • If a neighbor configuration offers a cost improvement it is accepted as the current optimal solution • Limitations • No guarantee that the final solution will be optimal • Potential for finding a local optima • No neighbour configuration offers a cost improvement, yet it is not the true optimum • It can accept a worse (higher cost) solution according to a probabilistic acceptance function to reduce the impact of this limitation

17. Why Simulated Annealing Technique? • Well suited for: • Large numbers of permutations • When constraints are present • Has been well tested by researchers and reasonably easy to understand and implement

18. Experiment Execution and the Cooling Schedule • The creation of two Turbo C programs were used as the basis for team skill requirements • Several executions of the method were performed • Randomly generated staff pools of 10, 20, 50, and 100 • A cooling schedule is used to determine how many possible solutions are included in the search and the extent to which higher-cost solutions are accepted to avoid local optima

19. Results • Complete analysis method results • Simulated annealing method results

20. Conclusion • Authors provided a viable automated solution to some fundamental problems in team building • Quantitative representation of personnel skills and preferences • Objective function allows the salary cost, skill and preference components to be combined to determine the relative costs of various combinations of human resources • Simulate Annealing – enables the large number of possible team combinations to be searched and optimal (or near-optimal) solutions obtained in a reasonable time frame

21. Paper # 2 Utilizing Cluster Analysis to Structure Concurrent Engineering Teams by Paul Componation and Jack Byrd

22. Contribution • Proposes a structured methodology for creating concurrent engineering teams through clustering • Hypothesizes faster project completion and lower risk of project failure • Provides a methodology to quantify the risk reduction through use of the method • The most effective clustering technique was average linkage, when communication flow perceived to be critical to project success

23. Problem Space for Team Building • Matrix organization • 23-person product development team • 2-chemists • 3-Material Scientists • 3 – Control Engineers • 4 –Manufacturing Engineers • 5 – Quality Assurance • 6 – Mechanical Engineers • Project work breakdown structure (WBS) identified • 19 design tasks • Overall duration: 17 Weeks • Project Personnel were originally assigned on a part-time basis

24. Why use a Concurrent Engineering (CE) approach? • CE is an integrated approach to the design of products and their related success • CE uses the multidisciplinary teams to address design problems • It is easy to catch issues earlier • Literature shows reported savings through the use of the CE approach • Reduction in product development time • Reduction in engineering changes • Improved time to market • Reduced scrap and rework • Increased service life • Higher return on assets

25. Evaluation of Results (Metrics) • Projects to be completed faster • Projects to be completed with a lower risk of project failure • This was a key metric of the study

26. Research Methodology • Use of various clustering techniques to identify teams • Single linkage • Complete linkage • Average linkage • Centroid method • Ward’s method • Collect diverse data points during product development • Task precedence relationships • Personnel available • Projected communication levels between design tasks • Risk levels

27. Clustering Techniques Primer • Used to analyze data sets related to the grouping of objects, individuals, events, or data points • Single linkage • Uses the minimum distance between objects • Complete linkage • Uses the maximum distance between objects • Average linkage • Uses the average distance between objects • Centroid method • Uses the Euclidean distance between objects • Ward’s Method • Uses the minimum sum of squares

28. Research Design – Set-up (Input) • Team structure identified • Team clusters identified • Design tasks assigned

29. Results – Reduced Project Risk Profile

30. Results – Risk Profile and Duration • In addition to identifying reduced risk, it was found that the different clustering techniques also had an impact…

31. Results • Communication flow became an important factor for assignment and project success • Average linkage • Most effective clustering technique for automated team assignments in this experiment • Consistently produced as team structure that resulted in • A more favorable risk profile • Minimum project duration

32. Conclusion • Communication flow plays a vital role in the success of a team • Enhancing critical communication flow can decrease the probability of a team failure • The most effective clustering technique for team building was average linkage • Ward’s Method performed nearly as well as average linkage

33. Coda What do the results from both papers tell us? Teams can be formed using classification, optimization, and clustering techniques

34. Review of Quantitative Approaches • Paper #1: Simulated annealing • Classification and optimization • Creating teams through • Quantifying skills • Quantifying work preferences • Paper #2: Average linkage • Clustering and optimization • Creating teams through • Quantifying skills • Quantifying risk

35. Weaknesses • Both papers propose mathematical models • No controlled experimentation used as a verification technique – the math verifies itself • No modeled simulation used as a verification technique • The mathematical basis leaves one wondering … • Will these models work in the real world?