Presentation from the IWSM Mensura 2015 conference held October 5-7 in Cracow, Poland

1. Workshop PifPro’15 in Kraków, October 5, 2015
Dr. Thomas Fehlmann October 5, 2015
© Euro Project Office AG, 2015 Page 1

2. Dr. Thomas Fehlmann October 5, 2015
© Euro Project Office AG, 2015 Page 2
1981: Dr. Math. ETHZ
1982-89: Manager Software–Development
1990-95: Senior Consultant – Digital Equipment Corp.
1996-99: Sales Support Manager – Proposal Center
1999ff: Euro Project Office AG, Zürich
Akao Price 2001 for original contributions to QFD
Member of the Board of QFD Institute Germany – QFD Architect
Chair of SwiSMA, the Swiss Software Metrics Association
2000ff: Six Sigma Black Belt for GMC Software AG
Lean Six Sigma for Software Development
Quality Function Deployment (QFD) and New Lanchester Theory
Net Promoter Score Associate
2012: Member of the DASMA Board
2013: Vice-President ISBSG

3. Eberhard Kranich studied Mathematics and Computer Science, with an emphasis on
Mathematical Statistics, Mathematical Optimization, and on Theory of Polynomial
Complexity of Algorithms. He worked at T-Systems International GmbH in Bonn until
2013, Germany, as a Six Sigma Black Belt and Quality Assurance Manager, mainly in
the context of software development.
Dr. Thomas Fehlmann October 5, 2015
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4. We will start explaining AHP, apply it for the sample Project X, and then show how
this profile can be used for estimating cost based on history of similar projects.
Dr. Thomas Fehlmann October 5, 2015
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5. The AHP matrix is also re-formatted in order to accommodate the topic names on
both dimensions. You only can entre comparison values on the lower left matrix half;
the upper right half is completed automatically.
The method used for calculating they Eigenvector is the so-called “Annihilator”
approximation method – same as used in Glenn’s templates – and has been explained
in depth at the ISQFD 2011 in Stuttgart.
The most popular method is called “Annihilator” :
Step 1: Normalize the components 𝑎𝑖,𝑗 by their total component sum column-wise;
Step 2: Calculate sum of rows 𝑟𝑗; this yields a vector 𝒓 = 𝑟1, 𝑟2, … , 𝑟𝑛 ;
Step 3: Again normalize the vector 𝒓 by its total component sum;
Step 4: Normalize the vector 𝒓 by the maximum component; this yields the solution
candidate 𝒙′.
Step 5: Calculate 𝒙 = 𝑨𝒙′;
Step 6: The distance 𝒙 − 𝒙′ indicates how near to the principal Eigenvector the
solution is; i.e., whether the approximation is sufficient.
Dr. Thomas Fehlmann October 5, 2015
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6. This view only shows the relevant part for the decision making.
Dr. Thomas Fehlmann October 5, 2015
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7. There is also an AHP Hierarchy template, allowing to do model a two-level hierarchy
such as the famous one showed earlier.
It requires defining the top level topics that each becomes a 2 × 2 AHP decision
matrix or greater. The topics on the second level can then be compared by mapping
their local profile weight onto a global profile.
Dr. Thomas Fehlmann October 5, 2015
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8. When adding topics on the second level, you simply insert or delete rows in the AHP
Summary table. You cannot add or delete the first or last row in a top level group; the
restriction applies throughout the QFD tools.
Dr. Thomas Fehlmann October 5, 2015
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9. In the AHP Hierarchy sheet, insert and delete functionality are borrowed from
standard Excel functionality.
The top level topics are numbered A, B, C,… and the corresponding local AHP decision
matrices will be allocated on separate sheets in the AHP Hierarchy workbook.
Dr. Thomas Fehlmann October 5, 2015
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10. The results on the second level allows identifying top priorities even for large sets of
topic categories, thus identifying the critical few out of large sets of topics.
Dr. Thomas Fehlmann October 5, 2015
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11. Top requirements can be selected by limiting the number to be added in further QFD
deployments, here for instance to only three out of five topics.
The functionality clearly becomes more important when selecting the seven most
important topics out of a few hundred, without having to do hundred times hundred
comparisons.
AHP is often used to extract the 72 most important criteria form large customer
voice data.
Dr. Thomas Fehlmann October 5, 2015
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12. Santillo uses AHP for early and quick counts based on the ISO/IEC 19761 COSMIC
standard (Santillo, 2011); thus, it seems very reasonable to use AHP also for
measuring PIF. The result of measurement will not be absolute numbers, rather a
profile as usual used when working with transfer functions. Beni et al. (Beni, et al.,
2011) define four key groups or classes of PIF. These groups define the hierarchy; the
resulting profile describes the impact on productivity for each of the PIF.
Each of the fours group splits into a number of subgroups; each subgroup is detailed
by some terms and phrases. This structure enables the determination and
assessment of those PIF, which are most relevant to a project under consideration.
Dr. Thomas Fehlmann October 5, 2015
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13. The measurement subject is a project, in our case, an ICT project. Different projects,
especially in different environments, will yield different profiles. The following
example of a PIF measurement assesses on a technology-driven project, named
“Project X”.
On the top level, we distinguish the relative weights of the four PIF groups Personal,
Process, Process, and Technology.
Dr. Thomas Fehlmann October 5, 2015
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14. All pairwise comparisons are subject to expert’s judgment only. They will differ for
other projects. Since knowledge about technology is key to personal characteristics in
the project X, it wins.
Dr. Thomas Fehlmann October 5, 2015
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15. The project process relies to a high degree on requirements completeness –
something that is hard to achieve in most ICT projects; however, for a technology-
driven project it wins in weight against other process characteristics.
Dr. Thomas Fehlmann October 5, 2015
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16. As expected for a technology project, product complexity is most important and has
highest impact on productivity.
Dr. Thomas Fehlmann October 5, 2015
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17. Ongoing change in technology affects productivity in our sample project X – it
constitutes also the major risk.
Dr. Thomas Fehlmann October 5, 2015
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18. Dr. Thomas Fehlmann October 5, 2015
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19. Combining these AHP decision matrices yields a profile for the 27 PIF. It may not
make sense to restrict them to five; all factors might add to cost when the PIF
contribute to project estimation. However, it looks more readable when
concentrating on the top five.
The overall winner is again D04: Technology Change, followed by D03: Technical
Environment and A03: Technology Knowledge, the latter from the group of personal
PIF.
This profile is specific to our technology-driven project X. Other ICT projects likely will
have different PIF profiles and therefore different cost drivers. Doing such an AHP
assessment is almost free when compared to other costs incurred when doing
project estimation.
Dr. Thomas Fehlmann October 5, 2015
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20. The 22 randomly chosen projects constitute an Estimation Stack. Since all projects
contain enough effort types, namely five, the number of estimation items is
22*5=120 in total, giving enough flexibility to align a cost model to this estimation
stack. As before, the input value for the getting the cost profile are the logarithms of
the efforts in days; consequently, here it means 5.44=ln(231 Days).
After calibration, this estimation stack was able to predict its constituent projects
with a convergence gap of only 0.09, based on the mapping from PIF to measured
efforts as the transfer function. The cell matrix actually reflect the 𝑎-parameters
indicating how strong the impact of each PIF is on each project. The numbers are
scaled down to range 0 to 9 using an arbitrary scaling factor.
For more details, consult the authors’ IWSM paper 2012 “Quality of Estimations”.
Dr. Thomas Fehlmann October 5, 2015
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21. A transfer function that has a small convergence gap allows predicting new projects.
For this, simply do the AHP for the PIF, then determine their 𝑎-parameter, and use the
response profile for comparing with existing known projects from your history
database.
The prediction accuracy of the PIF estimation stack is similarly good as indicated by
its convergence gap. In practice, estimation stacks remain quite predictive and stable
for a relatively long time within an organization. The more cost driver parameters are
in use, the better the model fits the measured effort profiles.
It is less clear whether these models work as well across organizations and
technologies, although the known parametrical models always provide better
estimations as experts can, even when used within an organization for the first time.
Dr. Thomas Fehlmann October 5, 2015
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22. Forget about the Project Delivery Rate (PDR) – use the PIF profile instead for
benchmarking and predicting projects.
The performance impact factors are bound to change – in 2011, the current focus on
mobile services was not yet here, and security and privacy was considered less a
threat as it is now.
Dr. Thomas Fehlmann October 5, 2015
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23. The authors have published quite a bit on the subject – e.g., in QFD symposia, at SW
metrics conferences like MetriKon or IWSM / Mensura; also at Lean Six Sigma
Conference Glasgow, Strathclyde and in Zurich.
There is a book in in progress for print: "Managing Complexity“. A preprint is available
from the authors.
Dr. Thomas Fehlmann October 5, 2015
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