Exploring Ford’s New Skill Team :
Global Data Insight & Analytics
Rajeev Kalamdani
Senior Analytics Scientist/MBB
REINVENTING FORD: AUTOMOTIVE AND MOBILITY
Our strategy has one foot in today and one foot in tomorrow – encompassing our core
business as an automaker and new opportunities in mobility.
FULLY REALIZING THE BENEFITS REQUIRES A SYSTEMATIC APPROACH
Data and Analytics Capabilities
Operational Efficiency and
Effectiveness
• Manufacturing
• Purchasing• Corporate Strategy• Finance• Quality• etc.
Transform
the Customer Experience
• Marketing & Sales• Customer Experience• Dealer Assistance
Enable New Mobility
Products and Services
• Autonomous Vehicle Technology
• Ford Smart Mobility • Ford Pass
Global Data Insights & Analytics
4
Data Supply Chain
5
Ford Production System
6
Manufacturing Analytics
7
Scheduling
• Vehicle sequencing
• Labor optimization
• Order bundling
• Economic order quantities
Plant floor
• Bottleneck analysis
• Preventive maintenance
• Plant floor data visualization
• Quality tie to stations
Freight and customs
• Complexity / batching
• Route optimization
• Material flow
• Customs, duties, tariffs
Material logistics Plant floor Scheduling and sequencing
MOS Analytics
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• Fault Pattern Identification
• Machine Learning
• Spare Parts Inventory Optimization
• Correlation of Interventions to PM Actions
• Schedule Optimization
• Theory of Constraints
Constraint Management
Planning & Scheduling
Predictive Maintenance
Reaction Plan
IMPLEMENTING ENTERPRISE DATA AND ANALYTICS STRATEGY
Advanced Data Management
Build a World-Class Infrastructure
Invest in Talent
Exceptional Analytic Capabilities
▪ Standardization▪ Data Quality
▪ Curation
▪ R&D
▪ New Areas of Application
▪ Collaboration
▪ Technical Governance
▪ Data Storage
▪ Processing
▪ Integration
▪ Recruiting▪ Developing▪ Retaining
• Develop Interactive Dashboards to Provide Insights from
Existing Data
• Include Analytics
• Avoid prettier reports
Descriptive - Dashboards
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• Identify Critical Assets
• Mine Production Data to Identify Trends in Frequencies of
Faults
• Determine Control Limits to Initiate Maintenance Actions
Predictive – Data Mining and Pattern Identification
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Case Study : Machine Health Monitoring Using ML
Data – Features and Labels
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Machine Learning Models (Supervised)
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• Supervised Classification using:
• K Nearest Neighbors
• Logistic Regression
• Support Vector Machine
ොµ =1
𝑛
𝑗=1
𝑛
𝑥𝑗
ො𝜎 =1
𝑛
𝑗=1
𝑛
(𝑥𝑗 − ොµ)2
• Feature values appear to be normally
distributed with a few outliers
• This can be verified by a normality test
• Using Maximum Likelihood Estimators for the
mean and standard deviation:
• The models were fit using simulated normal
data for the training data set to generate
classifiers
• Classifiers used to predict from the test data
• Novelty Detection
• Elliptic Envelope
• For the purpose of this project the limits were
set at ±3σ in keeping with the conventional
practice for control charting
• Limits can be tuned to improve performance
of the classifier if some “truth” data is
available
Machine Learning Models (Semi-Supervised)
Results: 3D Plot & ROC Curve
• To Centralize Analytics or Not, That is the Question (Forbes 2013)
• Why IT Fumbles Analytics (Harvard Business Review 2013)
• The Value of Business Analytics (Analytics Magazine 2017)
• Ten Ways Big Data is Revolutionizing Manufacturing (Forbes 2014)
• Industrial Analytics Based on Internet of Things will Revolutionize Manufacturing (Forbes 2016)
Wrap Up
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