1. ATLAS: Analytics for Total Life-cycle
Management of Automotive Systems
WHITEPAPER
2. CONTENTS
Ÿ ABSTRACT 03
Ÿ THE MODERN CAR: NOT JUST A SMARTPHONE ON WHEELS 04
Ÿ A 360 DEGREEVIEW OF DATA 05
Ÿ ATLAS MODELING STRUCTURE 06
Ÿ ROLLING UP THE CORE MODELS BY AUGMENTATION 07
Ÿ FROM ROLLED UP MODELS TO FULL LIFE CYCLE MODELING 09
Ÿ ATLAS USE CASES ACROSS THE LIFECYCLE 10
Ÿ AN OPTIMAL ANALYTICAL SOLUTION STRUCTURE THAT ENABLES
TOTAL LIFECYCLE MANAGEMENT. 12
Ÿ CONCLUSION 14
3. ABSTRACT
The automotive industry is undergoing a significant transformation.
Owing to the massive emergence of automotive technology and the rapidly increasing expectations of customers, cars have
already become “running computers” with dozens of ECUs generating tons of data in real time.
This paper describes an innovative approach by HARMAN,ATLAS:Analytics for Total Life-Cycle for
Automotive Systems.This framework embodies the total life-cycle based problem solving methodology for the Automotive
companies and service providers.
Harnessing the data, not just from the car, but from the eco-system on the whole is a capability companies are looking to
harness.The ATLAS approach tries to solve this problem of being “Data Rich and Insight poor”.
The approach takes a total view of the customer, manufacturers, dealers and suppliers along with the entire eco-system of
the automotive industry and provides these organizations the basis to set up a Multi-Criteria-Decision-Support-System
(MCDSS) for the entire automotive eco-system.
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4. THE MODERN CAR: NOT JUST A SMARTPHONE ON
WHEELS
The car is no longer just a means of transport. It is ranked fourth in terms of where an individual spends the majority of
his or her time.The automotive industry has responded by making a significant shift with regards to technology and data.
The emergence of advanced and complex automotive technology, along with the increasing expectations of customers, has
transformed the car into a "running computer" containing dozens of ECUs and generating tons of data.The ecosystem of
the average vehicle is more "data-rich" than ever and getting richer by the day.
This rich source of data, including MES, ERP, and Warranty cost modeling, can and should be harnessed as a resource for
business insights.The ATLAS approach is an attempt to solve the current lack of insight and transform a "data rich but
insight poor" present into a "data rich and insight rich" future.
04
Figure 1:A car in its entire eco-system of manuacturer, dealer, user, supply chain, factory
Inspection
Process
Design
Factory
Supply
Chain
Design
Produce
Quality
Conceptualize
Sale
Service
Dealer
Manufacturer
Users
Passengers
Driver
5. A 360 DEGREE VIEW OF DATA
A consumer invests in a car as a total product—not as a set of separate parts or systems.As such, total customer
satisfaction is complex, and it cannot be gauged or inferred by assessing data in isolation and for separate systems.
Achieving total customer satisfaction has to involve a coordinated effort across every function, department, and person and
each area's associated data.
The ATLAS modeling approach takes data from these multiple sources and uses multi-dimensional analysis to generate
models that cater to needs across the life cycle and ecosystem of the vehicle.
The problem may appear complex, but the ATLAS modeling approach utilizes a very structured approach to streamline and
simplify the problem.To understand this better, let's take a look at what the ATLAS structure looks like.
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6. ATLAS MODELING STRUCTURE
The key to ATLAS is the CAR-Model. Modeling the car has to be the key theme; the car model is created at an appropriate
level of abstraction by a process known as Analytical Hierarchy Process.This process is primarily about flowing down the
importance or severity for a given criteria through the hierarchy of the car.
Car Model or the Core Model
The key to ATLAS lies in the CAR-Model, which is driven by a process known as the Analytical Hierarchy Process.This
process focuses on flowing down the significance or severity of any criteria through the hierarchy of the car, addressing:
Ÿ Component failure
Ÿ Time to next service
Ÿ Component performance
All attributes are prioritized by occurrence and severity.
The core modeling techniques representing the behavior or performance of each car component leverage information
from one or more data sources, including:
Structured Data: Diagnostics trouble codes, repair records, warranty records
Semi-structured Data: Call center records, customer complaints, automotive website reviews
Commercial and 3rd Party Data:Automotive rating agencies
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Parts...
Sub
Assemblies
Car
Accessories
CAR-Model
Failure Rate Models
Prioritized Hierarchical
Failure Mode Models
Rolled Up
Models
Dealer Data
Repairs Data
Inspection at Factory
Warranty
ERP Data
Commercial Data
DTC-Diagnostic Trouble Codes
Quality Records
Operations Planning
Models
Consumer Behaviour
Models
Service Parts Demand
Models
Design
Manufacturing
Social Media
Call Center
Unstructured
Data
Semi
Structured
Data
Structured
Data
Rating Agency
Score
Figure 2:ATLAS Modeling Approach, Core model first rolled-up to higher level models to address multiple areas of concern
7. 07
# Cars
@ a location
Car Model
Failure
Forecasts
Inventory
Data
Demand
Models
Demand
Shaping for Spares
Location of Service
Centers and Warehouses
Service Channel
Planning
Miles for
each Car
# Cars
@ a location
Car Model
Failure
Forecasts
Inventory
Data
Demand
Models
Demand
Shaping for Spares
Location of Service
Centers and Warehouses
Service Channel
Planning
Miles for
each Car
# Cars
@ a location
Cost Model
Failure
Forecasts
Operations
Planning
Models
Miles for
each Car
Scheduling of Jobs,
Repairs
Replace versus
Repair Strategies
Strategies Bases on Age
(and/or usage) at Failure
Cost Repair Limit Strategy
Spare Parts Inventory
Car
Model
Figure 3: Illustrating rolled up model- for operations planning
ROLLING UP THE CORE MODELS BY AUGMENTATION
Once this core model is in place, it can be "rolled up" to create specific models that cater to use cases across life cycles.
For example:
Spare Parts Planning: Roll up the failure rate core model for a given part, augment the model with geographical information
of the vehicles in a location and vehicles with a specific mileage.
Rolled-up models for Operational Planning address use cases such as:
Ÿ Scheduling of repair jobs
Ÿ Making decision re: replace vs. repair strategies based on cost-benefit analysis and the rolled-up model
Ÿ Optimal stock of the spare parts inventory
8. Compiling All the Data Under One Roof
Multiple data sources—ATLAS integrates multiple data types and sources into core models and composed models.
The types of data leveraged include:
Ÿ Structured
Ÿ Semi-structured
Ÿ Unstructured
These sources are combined via various inference algorithms that are appropriate for the specific kind of data and the
objective of the model.
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Data Sources Record Types
DTC
Mainly Reveals
Causes Effects
Difficulty in
Inference
Complexity of
Techniques
Warranty
Data
Call Center
Social Media
Parts Replaced
Service Performed
Technician Comment
Customer Reactions
Sentiments
Customer Delight
Problem Reports
Clarification/How-to
Structured
Semi-structured
Unstructured
Height of bars indicate relative distribution
“Type of Data”
Scale
Figure 4: Popular data types in Auto motive the life-cycle with what it mainly reveals, difficulty in inference and complexity of techniques
9. FROM ROLLED UP MODELS TO FULL LIFE-CYCLE
MODELING
The ATLAS approach, described step by step, can be converted into a Multi Criteria Decision Support system that covers
multiple data sources and functions throughout the automotive lifecycle.
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Existing Management Systems
Product Lifecycle
& Engineering
New Product
Development
Product Design
Improvement
Demand
Forecasting
Accuracy
Improvement
Integrated
Capacity
Managemant
Low-Cost
Sourcing
Spend Analysis
and Supplier
Consolidation
Transportation
& Logistics
Optimization
Core
Models
Channel
Consolidation
Marketing
and Sales
Promotion
Warranty Cost
Reduction
Customer
Loyalty
ProgramIn-house Quality
Improvement
Inventory
Reduction
Plant
Productivity
Improvement
Material Cost
Reduction
Product Data
Management
Internal Data External Data
PLM-databases
SCM-databases
Quality-databases
ERP/MRP/MES
Inspection Records
Call Center
Parts Replacement
Social Media/Media
Dealer Records
Warranty Claims
OBJECTIVES
Supply Chain
Planning
Manufacturing
Operations
Sales &
Distribution
After sales &
Customer
Service
Supplier
Management
& Procurement
Decisions Decisions Decisions Decisions Decisions Decisions
Figure 5:ATLAS life cycle process covering all phases of automotive lite cycle
Inference/Rule
Engine(s)
Composed Models
Models
10. 10
ATLAS USE CASES ACROSS THE LIFECYCLE
NPI Stage and Product Strategy
At the New Product Integration (NPI) stage,ATLAS manages to contribute viable answers to the most crucial strategy
questions.The task of automotive product strategy is nothing less than to give the best possible prediction of which
cars consumers will buy in the future, which features consumers will covet, and which features will become obsolete.
As such, product strategy is the most important task in vehicle development and the driving force beyond the larger
corporate strategy at a company. Product strategy must answer:
Ÿ Which cars will consumers by, and how many?
Ÿ What should the complete product portfolio look like with regards to brand, model lines, and variants?
Ÿ Is it enough to continue and redesign or is it necessary to develop new models and lines?
Ÿ What cars are our competitors going to offer?
Product Design Improvement
Product design and design improvement decisions are aided by ATLAS models.
The component reliability and field failure rates that result from existing component databases directly affect the
design process. Since the ATLAS process is based on core models that in turn are based on component reliability
models, it provides direct insights into component engineering and quality performance.
Product Data Management
The ATLAS model uses product data to create model improvement processes based directly on performance of car
models
Supply Chain Planning
ATLAS models help guide decision making with regards to:
Ÿ Demand Forecasting, and
Ÿ Integrated Capacity Management
Parts that demand forecasting would be derived from a model composed of part failure rates.ATLAS models could
also help drive decisions regarding supplier selection, QC, inspections, and tune-ups.
Supplier Management and Procurement
The composed models in the ATLAS process help guide decision making with regards to:
Ÿ Material cost reduction
Ÿ Spend analysis
Manufacturing Operations
Decision makers within the manufacturing and operations space can leverage ATLAS to:
Ÿ Improve the manufacturing process
Ÿ Enhance and streamline the QC and Inspection processes
ATLAS achieves this by culling feedback from the early warning system based on warranty data, call center data, and DTC
codes so that manufacturing and QC processes can be tuned and refined on an ongoing basis after the release of a vehicle.
11. 11
Sales and Distribution
Voice of Customer and CRM data that has been integrated into the ATLAS models, along with the geographical data
culled from sales and customer satisfaction across regions, model types, and vehicle segments, help decision makers
act effectively with regards to:
Ÿ Sales strategy
Ÿ Distribution and logistics optimization
After Sales and Warranty
The most important and precise outputs of the ATLAS framework have a direct impact on warranty costs, since a
good part of the structure data comes from failure rates and reliability modeling of the components along with Text
Analytics on technician and repair shop comments.
12. AN OPTIMAL ANALYTICAL SOLUTION STRUCTURE
THAT ENABLES TOTAL LIFECYCLE MANAGEMENT.
This representation forms the basis of:
Ÿ A structured process for dealing with analytics projects.
Ÿ An approach where we can place different expertise doing different work in a practical project
Ÿ Business analyst and customer service staff are mainly responsible for the top 2 layers, viz.
Ÿ Decision
Ÿ Scenario Work-flow layer
Ÿ The senior modeler is responsible for the composed model layer
Ÿ The statistical modeler is responsible for the core modeling layer
Ÿ The data quality and data modeler is responsible for the data layer- here we can have the whole of DW personnel
also based on the size and scope of the DW or database.
Ÿ This structure also draws lines/boundaries between the scope of a platform like M2M/MS-Azure, a statistical tool/library
like R and the enterprise platforms.
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Supply Chain End-User
Real World
Decisions
What could be done?
What should be done?
Scenarios/Work-flow
What is happening?
How it is happening?
How is the data stored,
retrieved and made
available?
Data Source-1 Data Source-2 Data Source-3
What-if Simulation Work-flow
Work-flowScenarios
Enterprise Applications
Life Cycle Considerations
Azure ML-Web
Azure ML
Process Dynamics Casual Factors
Regression Decision Trees
Naive Bayesian
Neural NetworksLogisticCHAID
Time Series
Survival Model
Hazard Model
Factory
Figure 6: Solution structure for ATLAS
Modeling
13. Scenarios
Composed Models
Core Models
Math Library
Database/Source/warehouse
Scenario: A representation of a functional area/Business Process/Sub-process structured for a Forecasting need. For
example, a scenario could be a new product release or goal-setting for an existing product for a quarter of a financial year
or a business cycle, or a scenario could be total expected revenue/P&L from a product or a product line. Scenarios could
lead to what-ifs and simulations that the decision makers need for decision support. Scenario elicitation is the core
function of the business/technical analyst/domain expert in an analytics project.There are specialized and structured
techniques that are mainly derived from the Define and Measure (D & M) tool kits of Six Sigma project that one needs to
use here.
Composed Model: A model that is composed of one or more core-models and connects the core models to the scenario,
for e.g. in the quarterly goal setting scenario one needs to use Cumulative Quantity of the expected POS and cumulative
quantity of the previous stock or inventory calculation of cumulative figures from raw data or forecast data is done by
means of a composed model.
Core Model: A model that uses the forecasting algorithm/technique, e.g. of a core model could be ARMA (1,1) or a pure
regression model representing y= mx+c in linear or a more complex relationship like higher order quadratic or cubic
regression. Usually at the level of core model we are dealing with algebraic and difference equation based relationships with
constant or statistically modeled coefficients.
Math Library:This is the core math library i.e. a set of techniques, functions and constructs which compute regression,
correlation or statistical distribution and perform optimization by minimization or maximization etc.
Database/Source/Warehouse :The data from various sources is used for modeling; data types as described above include
structured, semi-structured, or unstructured.
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14. CONCLUSION
The business benefits of the ATLAS approach can be summarized as such:
ATLAS provides a way to combine and structure data across multiple sources and departments in a meaningful and
organized way to provide insights into multiple aspects across the vehicle lifecycle
The integrated modeling approach of ATLAS delivers better insights compared to data usage and modeling within separate,
departmental silos.
The radical shift taking place in the automotive industry demands a way to access all data across all touch points.The
ATLAS model helps companies achieve just that.With a single view of all data, automotive companies can make better and
more effective decisions while keeping the best interests of the consumer in mind.
Talk to HARMAN today to find out how we can help your organization assimilate your data and grow your business.
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15. About the Author
Narendra K Ashar, is a Chief Data Scientist and Machine Learning Architect at HARMAN.
He is a graduate in EE from the Sardar Patel College of Engineering Univ. of Mumbai and Masters in EE from the Indian
Institute of Technology Kharagpur.
He has diverse industry experience spanning 28 years, with active contributions in electronic systems, locomotives and
transportation, power system instrumentation, quality and manufacturing. He has done organizational transformation for
operational excellence and is a well known consultant in Lean and Agile transformation for IT and Product Development
practices.
The technical areas of expertise he owns are, Digital Signal Processing & Pattern recognition,Advanced Neural Networks
and Computational Intelligence methods application for automotive, manufacturing, quality management, statistical process
control, energy and utilities, power systems automation and Power Electronics.Application of large data sets in the above
areas and structured methodology and framework architecture for large real-time data sets in the above areas.
At his present role with HARMAN he is the Chief Data scientist leading a team of 50 high profile data scientists in the area
of data science application across verticals.
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17. Supply Chain End-User
Real World
Decisions
What could be done?
What should be done?
Scenarios/Work-flow
What is happening?
How it is happening?
How is the data stored,
retrieved and made
available?
Data Source-1 Data Source-2 Data Source-3
What-if Simulation Work-flow
Work-flowScenario
Enterprise Applications
Life Cycle Considerations
Azure ML-Web
Azure ML
Process Dynamics Casual Factors
Regression Decision Trees
Naive Bayesian
Neural NetworksLogisticCHAID
Time Series
Survival Model
Hazard Model
Factory
Modeling
