slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Ron S. Kenett Research Professor, University of Turin Chairman and CEO, KPA Ltd. www.kpa-group.com ron@kpa-group.com PowerPoint Presentation
Download Presentation
Ron S. Kenett Research Professor, University of Turin Chairman and CEO, KPA Ltd. www.kpa-group.com ron@kpa-group.com

Loading in 2 Seconds...

play fullscreen
1 / 22

Ron S. Kenett Research Professor, University of Turin Chairman and CEO, KPA Ltd. www.kpa-group.com ron@kpa-group.com - PowerPoint PPT Presentation


  • 112 Views
  • Uploaded on

http://www.rss.org.uk/site/cms/contentEventViewEvent.asp?chapter=9&e=1543. On Generating High InfoQ with Bayesian Networks. Ron S. Kenett Research Professor, University of Turin Chairman and CEO, KPA Ltd. www.kpa-group.com ron@kpa-group.com. Knowledge. Goals. Examples from:

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Ron S. Kenett Research Professor, University of Turin Chairman and CEO, KPA Ltd. www.kpa-group.com ron@kpa-group.com' - lisle


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

http://www.rss.org.uk/site/cms/contentEventViewEvent.asp?chapter=9&e=1543http://www.rss.org.uk/site/cms/contentEventViewEvent.asp?chapter=9&e=1543

On Generating High InfoQ with Bayesian Networks

Ron S. Kenett

Research Professor, University of Turin

Chairman and CEO, KPA Ltd.

www.kpa-group.com ron@kpa-group.com

slide2

Knowledge

Goals

  • Examples from:
  • Customer surveys
  • Risk management of telecom systems
  • Monitoring of bioreactors
  • Managing healthcare of diabetic patients

Information Quality

Analysis Quality

Data Quality

Primary Data Secondary Data

- Experimental - Experimental

- Observational - Observational

bayesian networks
Bayesian Networks

Learning

Judea Pearl

2011 Turing Medalist

Estimating

  • Causal calculus
  • Counterfactuals
  • Do calculus
  • Transportability
  • Missing data
  • Causal mediation
  • Graph mutilation
  • External validity

Programmer’s nightmare:

1. “If the grass is wet, then it rained”

  • 2. “if the sprinkler is on, the grass will get wet”
  • Output: “If the sprinkler is on, then it rained”
customer surveys
Customer Surveys

Behaviors

  • Recommendation

Loyalty

  • Loyalty Index

Attitudes & Perceptions

  • Overall Satisfaction
  • Perceptions

Experiences & Interactions

  • Equipment and System
  • Sales Support
  • Technical Support
  • Training
  • Supplies and Orders
  • Software Add On Solutions
  • Customer Website
  • Purchasing Support
  • Contracts and Pricing
  • System Installation
customer surveys goals
Customer Surveys Goals

Goal 1. Decide where to launch improvement initiatives

Goal 2. Highlightdrivers of overall satisfaction

Goal 3. Detect positive or negative trends in customer satisfaction

Goal 4. Identify best practices by comparing products or marketing channels

Goal 5.Determine strengths and weaknesses

Goal 6.Set up improvement goals

Goal 7.Design a balanced scorecard with customer inputs

Goal 8.Communicatethe results using graphics

Goal 9.Assess the reliability of the questionnaire

Goal 10.Improve the questionnaire for future use

bayesian network analysis of customer surveys
Bayesian Network Analysis of Customer Surveys

Kenett, R.S. and Salini S. (2009). New Frontiers: Bayesian networks give insight into survey-data analysis, Quality Progress, pp. 31-36, August.

information quality infoq of integrated analysis
Information Quality (InfoQ) of Integrated Analysis

Kenett R.S. and Salini S. (2011). Modern Analysis of Customer Surveys: comparison of models and integrated analysis, with discussion, Applied Stochastic Models in Business and Industry, 27, pp. 465–475

11

slide12

Kenett R.S. and Salini S. (2011). Modern Analysis of Customer Surveys: comparison of models and integrated analysis, with discussion, Applied Stochastic Models in Business and Industry, 27, pp. 465–475

12

slide13

Goal 1: Identify causes of risks that materialized

Goal 2: Design risk mitigation strategies

Goal 3: Provide a risk management dashboard

slide15

Support (A  B) = relative frequency

Confidence(A  B)= conditional frequency

Lift (A  B) = Confidence (A  B) / Support (B)

HyperLift= more robust version of Lift

GeNIe

  • Data structure and data integration
slide16

Probability of risks

by typesand severity

  • Communication and construct operationalization
slide17

Peterson, J. and Kenett, R.S. (2011), Modelling Opportunities for Statisticians Supporting Quality by Design Efforts for Pharmaceutical Development and Manufacturing, Biopharmaceutical Report, ASA Publications, Vol. 18, No. 2, pp. 6-16

Monitoring of bioreactor

  • Kenett, R.S. (2012). Risk Analysis in Drug Manufacturing and Healthcare, in Statistical Methods in Healthcare, Faltin, F., Kenett, R.S. and Ruggeri, F. (editors in chief), John Wiley and Sons.
  • Managing diabetic patients
slide20

Time

Treatment of diabetic patient

Decisions

Diet

Dose

Utility

Quality

Risk

Cost

slide21

Risk

Dose

slide22

InfoQ

and BN

Knowledge

  • Data resolution
  • Data structure
  • Data integration
  • Temporal relevance
  • Chronology of data and goal
  • Generalizability
  • Construct operationalization
  • Communication

Goals

Information Quality

Analysis Quality

Data Quality