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

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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

10

• 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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