Графические вероятностные модели для принятия решений в проектном управлении Ольга Татаринцева (Data Scientist at Eleks) Как часто вам приходится принимать решения, используя знания в определенной предметной области? На сколько хороши такие решения? А теперь представьте, что вы собрали знания лучших экспертов в предметной области. Похоже, что ваши решения, основанные на этих знаниях, будут куда более взвешенными, не так ли? Мы будем говорить о системе ProjectHealth, которая была построена на основе опыта лучших экспертов в проектном управлении в компании Eleks. Для реализации поставленной задачи была использована графовая вероятностная модель, а именно байесовская сеть, имплементированная на Python. За время работы над проектом мы прошли шаги от извлечения требований, поиска данных и построения модели с нуля до реализации BI дашборда с возможностью углубиться в детали, доходя до сырых данных. Сейчас ProjectHealth экономит большое количество времени для топ менеджмента и ресурсов компании, так как мониторит состояние бизнеса в малейших деталях ежедневно и делает это как настоящий эксперт. Все материалы: http://datascience.in.ua/report2017

1. REASONING WITH PROBABILISTIC
GRAPHICAL MODELS IN A PROJECT
BASED BUSINESS
BY OLGA TATARYNTSEVA, DATA SCIENTIST

2. • Neural networks
• Deep learning
• Natural language processing
• Image processing
• Big data
• Bioinformatics
Today we are NOT about

3. • Neural networks
• Deep learning
• Natural language processing
• Image processing
• Big data
• Bioinformatics
• Project based business
• Company organization
• Project management and
project success
• Applying the DS methods to
business problems
• Small example
• Results we gained
Today we are NOT about We are about

4. Chief Technical officer
... Delivery Director
... PM
... Architects
Business
Analysts
Developers QA
Data
scientists
...
...
...
Simplified structure of delivery organization

5. Chief Technical officer
... Delivery Director
... PM
... Architects
Business
Analysts
Developers QA
Data
scientists
...
...
...
Simplified structure of delivery organization
PROJECT

6. Scope
Resources
Quality
Time
Project management triangle

7. ProjectTeam
Direct contacts with the
customer
Requirements
management
Solution design
Product development
Project budget planning
Working on improvements
and up-sale opportunities
Management of internal
activities
DeliveryDirector
Summarized projects’
budget and cash flow
results
Calculated CSAT
Calculated ESAT
General view of project
activities
Information from reports
built by project team
CTO
Budget flow for vertical
Tendencies of overall CSAT
Tendencies of overall ESAT
Information from reports
built by DDs
Information transfer

8. Chief Technical officer
Delivery director
PM PM …
Delivery Director
PM PM …
...
PM ...
1000+ employees
Simplified structure of delivery organization

9. Don’t panic
We have PGM

10. CTO at ELEKS has a BI Dashboard

11. • Declarative representation of our understanding of the world
• Identifies the variables and their interaction with each other
• Sources:
• experts’ knowledge
• historical data
• Representation, inference, and learning
• Handles uncertainty
Probabilistic graphical models

12. «As far as the laws of mathematics refer to reality, they are not certain,
as far as they are certain, they do not refer to reality»
Albert Einstein, 1921
Uncertainty

13. • Causal inference
• Information extraction
• Message decoding
• Speech recognition
• Computer vision
• Gene finding
• Diagnosis of diseases
• Traffic analysis
• Fault diagnosis
PGM applications
Daphne Koller

14. Observations:
None
Project health:
success = 68.91%

15. Project
health
Time: In time Time: Exceed
Budget:
Match
Budget:
Exceed
Budget:
Match
Budget:
Exceed
Success 95 50 40 10
Fail 5 50 60 90
Success = 𝑃 𝑆 𝑖𝑛_𝑡𝑖𝑚𝑒, 𝑚𝑎𝑡𝑐ℎ + 𝑃 𝑆 𝑖𝑛_𝑡𝑖𝑚𝑒, 𝑒𝑥𝑐𝑒𝑒𝑑 + 𝑃 𝑆 𝑒𝑥𝑐𝑒𝑒𝑑, 𝑚𝑎𝑡𝑐ℎ + 𝑆 𝑒𝑥𝑐𝑒𝑒𝑑, 𝑒𝑥𝑐𝑒𝑒𝑑 =
= 0.95*0.6659*0.8071 + 0.5*0.6659*0.1929 + 0.4*0.3341*0.8071 + 0.1*0.3341*0.1929 = 68.91%
F𝐚𝐢𝐥 = 𝑃 𝐹 𝑖𝑛_𝑡𝑖𝑚𝑒, 𝑚𝑎𝑡𝑐ℎ + 𝑃 𝐹 𝑖𝑛_𝑡𝑖𝑚𝑒, 𝑒𝑥𝑐𝑒𝑒𝑑 + 𝑃 𝐹 𝑒𝑥𝑐𝑒𝑒𝑑, 𝑚𝑎𝑡𝑐ℎ + 𝐹 𝑒𝑥𝑐𝑒𝑒𝑑, 𝑒𝑥𝑐𝑒𝑒𝑑 = 31.09%
Node name:
Project health
States
success
fail
Depends on
Time, Project Budget
Conditional probability distribution

16. Bayesian Model with pgmpy
c_maturity_cpd =
TabularCPD(variable='Customer maturity', variable_card=2,
values=[[0.4, 0.6]], evidence=[], evidence_card=[])
...
pr_health_cpd =
TabularCPD(variable='Project health', variable_card=2,
values=[[0.95, 0.5, 0.4, 0.1], [0.05, 0.5, 0.6, 0.9]],
evidence=['Project budget', 'Time schedule'], evidence_card=[2, 2])

17. Bayesian Model with pgmpy
pr_health_cpd =
TabularCPD(variable='Project health', variable_card=2,
values=[[0.95, 0.5, 0.4, 0.1], [0.05, 0.5, 0.6, 0.9]],
evidence=['Project budget', 'Time schedule'], evidence_card=[2, 2])
print pr_model.get_cpds('Project health')
+------------------+-----------+------------+------------+-------------+
| Project budget | match | match | exceed | exceed |
+------------------+-----------+------------+------------+-------------+
| Time schedule | in_time | exceed | in_time | exceed |
+------------------+-----------+------------+------------+-------------+
| success | 0.95 | 0.5 | 0.4 | 0.1 |
+------------------+-----------+------------+------------+-------------+
| fail | 0.05 | 0.5 | 0.6 | 0.9 |
+------------------+-----------+------------+------------+-------------+

18. Bayesian Model with pgmpy
pr_model =
BayesianModel([('Customer maturity', 'Requirements elicitation'),
..., ('Time schedule', 'Project health'),
('Project budget', 'Project health')])
pr_model.add_cpds(c_maturity_cpd, pr_complexity_cpd,
time_cpd, req_elicitation_cpd, req_management_cpd,
res_availability_cpd, c_budget_cpd, pr_budget_cpd,
pr_importance_cpd, pr_health_cpd)

19. Bayesian Model with pgmpy
res = BeliefPropagation(pr_model).query(variables=["Project health"])
print res["Project health"]
+------------------+-----------------------+
| Project health | phi(Project health) |
|------------------+-----------------------|
| success | 0.6891 |
| fail | 0.3109 |
+------------------+-----------------------+

20. Observations:
Customer maturity: mature
Customer budget: large
Project health:
success = 73.82% (↑4.91%)

21. Bayesian Model with pgmpy
res = BeliefPropagation(pr_model).query(
variables=["Project health"],
evidence={'Customer budget':1, 'Customer maturity':0})
print res["Project health"]
+------------------+-----------------------+
| Project health | phi(Project health) |
|------------------+-----------------------|
| success | 0.7382 |
| fail | 0.2618 |
+------------------+-----------------------+

22. Observations:
Customer maturity: mature
Customer budget: large
Resource availability: available
Project complexity: small
Project importance: very important
Project health:
success = 85.73% (↑11.91%)

23. Observations:
Customer maturity: mature
Customer budget: large
Resource availability: available
Project complexity: complex
Project importance: very important
Project health:
success = 79.45% (↓ 6.28%)

24. Maturity
level
Lead
time
Team
composition
Staffing time
Story point
time variance
Risk
management
Granularity of
stories
Stakeholders
substitution
Acceptance
criteria rating
RotationsLegal risks

26. Events:
None
Project health:
success = 87.36%

27. Cooperation model: fixed bid
Project stage: stable
Cycle time: decreasing
Number of the opened and reopened
bugs: decreasing
Finance: fit the company’s KPI values
Environment: tools are set and in-use
Soft- and hardware: no blocking requests
Human resources: no open vacancies
Customer satisfaction index: high
Number of tasks in the in-progress state:
increasing
Predicted release date: out of the schedule
Hypothetic Project observations

28. Observations:
Listed on previous slide
Project health:
success = 12.71% (↓ 74.65%)

29. Latest date:
2017-02-14
Project health:
success = 53.18%
Major degradation happened on:
2017-02-10
Reason:
Outage in Quality
Reason:
Descending of Project Effectiveness
Reason:
Cumulating of tasks in Progress
Real project

30. • New communication tool for all levels of the company which is
already in use on every day basis
• For C-level provides understanding:
• Of the department
• Of each project in particular
• For Project Manager it is an instrument:
• For project organization and control
• For the Client:
• Fair presentation of the project work
Benefits

31. • Improve metrics for measuring project performance
• Build even more intuitive dashboard
• Introduce the model to our customers
• Improve the reflection of the domain by fitting the model to data
Future work

32. Inspired by Technology.
Driven by Value.
eleks.com