the internet of things and power bi in...
TRANSCRIPT
Plants and digital
supply chains of the
future
Smart product
innovation
Connected marketing,
sales, and services
IoT enabling rich
connected consumer
experiences
Pervasive connectivity
enabling new offerings
and revenue
R&D,
Engineering
Production,
logistics
Marketing,
Sales & ServiceConsumer Business
CONNECTED BUSINESS NETWORKSTransforming how products are designed,
manufactured and sold
CONNECTED SERVICES AND EXPERIENCESCreating new business models
as a service provider
Predict Costly
Production Issues• Quality assurance across the assembly line is
imperative
• Identify errors, slowdowns and predicting potential
failures before they occur, rather than after they are
detected
• helps companies be proactive and improve
productivity.
And their impact on the business
• Reduced manufacturing cycle time
• Higher cost of wasted materials, time and resources
• Inability to address customers’ critical requirement for speed to market
Product
quality not
acceptable
Challenges Jabil was facing…
• Continuous requirement to increase yield, reduce amount of scrap and re-work
• Traditional inspection techniques for ensuring quality quickly becoming
outdated with more one-off production runs
• Adding more equipment and people to existing manufacturing processes
would not have significant impact on increasing throughput
Inspection steps along the SMT line cannot always detect the quality issues
Source of failure can be introduced at multiple stages but cannot be detected until it ispowered-up for testing at the end
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Results
• Predict machine processes that will slow down or
fail with an 80% accuracy
• Reduced costs of scrap and re-work of 17%
• Delivered energy savings of 10%
Variation: 11% (tolerance ≤ 11%)
Vibration frequency: Too high
Bit wear: High
Result in plant 2: Failed
PREDICTED
FAILURERecommended maintenance
in next 48 hours
PREVENTATIVE
MAINTENANCE
FOR TOMORROW
10 TuesdayOctober
7:00 AM
Strategy
Jabil was able to transform their manufacturing
production lines with advanced analytics solutions
built on Cortana Intelligence Suite
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CRM ERP MES SPC
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Goal
Jabil wanted to better predict errors or failures on
assembly floor before they occur, saving
customers’ time and money
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Other systems
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PREDICTED FAILURE
IN NEXT 48 HOURS
Supplier data
Customer
data
Production
data
Historical data
Azure
Services Our rate of rejection has decreased dramatically now
that we can predict these failures early in the process
Quality Assurance for Manufacturing
IMPROVEProduct Quality
BOOSTCustomer Satisfaction
REDUCECosts and Downtime
DELIVERReal-time Analytics
INCREASESpeed to Market
DIMINISHScrap and Rework
INFORMPossible Equipment
Fault or Failure
Cortana Intelligence Suite
Intelligence
Dashboards &
Visualizations
Information
Management
Big Data Stores Machine Learning
and Analytics
CortanaEvent HubsHDInsight
(Hadoop and
Spark)
Stream
Analytics
Data Intelligence Action
People
Automated Systems
Apps
Web
Mobile
Bots
Bot
FrameworkSQL Data
WarehouseData Catalog
Data Lake
Analytics
Data Factory Machine
LearningData Lake Store
Cognitive
Services
Power BI
Data
Sources
Apps
Sensors
and
devices
Data
Quality Assurance in Manufacturing
ALS 1CM 1
ALS 2 …Final ALS
Functional Test
Enclosure
Assembly
Integrated
TestAudit
Packing/
Shipping
OEM
ALS 1CM 2
ALS 2 …Final ALS
Functional Test
Assembly Line – a sequence of Assembly Line Steps (or Stations).
Each ALS usually ends with a regular, already in place, test.
Quality Assurance for Manufacturing | Concept
ALS 1CM 1
ALS 2 …Final ALS
Functional Test
Enclosure
Assembly
Integrated
TestAudit
Packing/
Shipping
OEM
ALS 1
OEM assembly pipeline
ALS 2 … Final ALS
ALS 1CM 2
ALS 2 …Final ALS
Functional Test
OEM Owned
CM Owned
Hierarchy of pipelines
• OEM assembly process is also a pipeline.
• ALS are separated in time and/or space.
Assembly Line – a sequence of Assembly Line Steps (or Stations).
Each ALS usually ends with a regular, already in place, test.
Quality Assurance for Manufacturing | Concept
ALS 1CM 1
ALS 2 …Final ALS
Functional Test
Enclosure
Assembly
Integrated
TestAudit
Packing/
Shipping
OEM
ALS 1
OEM assembly pipeline
ALS 2 … Final ALS
ALS 1CM 2
ALS 2 …Final ALS
Functional Test
OEM Owned
CM Owned
Hierarchy of pipelines
• OEM assembly process is also a pipeline.
• ALS are separated in time and/or space.
Assembly Line – a sequence of Assembly Line Steps (or Stations).
Each ALS usually ends with a regular, already in place, test.
➢ Failures at CM final functional test passed each individual ALS test.
➢ Post sale/delivery failures passed the integrated test system already in place. Yet they
still happen months or years after delivery and incur repair/warranty costs.
Quality Assurance for Manufacturing | Concept
Quality Assurance for Manufacturing | Concept
17
ALS 1CM 1
ALS 2 …Final ALS
Functional Test
Enclosure
Assembly
Integrated
TestAudit
Packing/
Shipping
OEM
ALS 1CM 2
ALS 2 …Final ALS
Functional Test
ID Fail
001 No
002 Yes
003 No
…
099 No
100 No
ID Fail
001 No
002 Yes
003 Yes
…
099 No
100 No
ID Fail Return
001 No No
002 -
003 -
…
099 Yes Yes
100 Yes Yes
Key components:
1. Past failures data
Quality Assurance for Manufacturing | Concept
18
ALS 1CM 1
ALS 2 …Final ALS
Functional Test
Enclosure
Assembly
Integrated
TestAudit
Packing/
Shipping
OEM
ALS 1CM 2
ALS 2 …Final ALS
Functional Test
ID Fail
001 No
002 Yes
003 No
…
099 No
100 No
ID Fail
001 No
002 Yes
003 Yes
…
099 No
100 No
ID Fail Return
001 No No
002 -
003 -
…
099 Yes Yes
100 Yes Yes
Key components:
1. Past failures data
2. Domain knowledge
Quality Assurance for Manufacturing | Concept
19
ALS 1CM 1
ALS 2 …Final ALS
Functional Test
Enclosure
Assembly
Integrated
TestAudit
Packing/
Shipping
OEM
ALS 1CM 2
ALS 2 …Final ALS
Functional Test
ID Fail
001 No
002 Yes
003 No
…
099 No
100 No
ID Fail
001 No
002 Yes
003 Yes
…
099 No
100 No
ID Fail Return
001 No No
002 -
003 -
…
099 Yes Yes
100 Yes Yes
Key components:
1. Past failures data
2. Domain knowledge
3. Existing test systems measurements
Quality Assurance for Manufacturing | Concept
20
ALS 1CM 1
ALS 2 …Final ALS
Functional Test
Enclosure
Assembly
Integrated
TestAudit
Packing/
Shipping
OEM
ALS 1CM 2
ALS 2 …Final ALS
Functional Test
ID Fail
001 No
002 Yes
003 No
…
099 No
100 No
ID Fail
001 No
002 Yes
003 NoYes
…
099 No
100 No
ID Fail Return
001 No No
002 -
003 -
…
099 Yes Yes
100 Yes Yes
Key components:
1. Past failures data
2. Domain knowledge
3. Existing test systems measurements
4. Use machine learning (ML) to put everything together
ML
model
Quality Assurance for Manufacturing | Concept
21
ALS 1CM 1
ALS 2 …Final ALS
Functional Test
Enclosure
Assembly
Integrated
TestAudit
Packing/
Shipping
OEM
ALS 1CM 2
ALS 2 …Final ALS
Functional Test
ID Fail
001 No
002 Yes
003 No
…
099 No
100 No
ID Fail
001 No
002 NoYes
003 NoYes
…
099 No
100 No
ID Fail Return
001 No No
002 -
003 -
…
099 No NoYes
100 No NoYes
Key components:
1. Past failures data
2. Domain knowledge
3. Existing test systems measurements
4. Use machine learning (ML) to put everything together
5. Cascade of models.
ML
model
ML
model
ML
model
1. Assembly line: Manufacturing data is characterized by Assembly Line Steps (ALS)
2. Failure Data: list of failures collected at final ALS becomes prediction target.
3. Domain Knowledge: failures can be subset using SME to extract the failures likely related to manufacturing processes
performed at an earlier ALS, even if the product passed the regular QA test at that earlier stage.
4. Existing Test Systems: Build a training dataset by pairing each ALS specific subset of failures with test measures
collected at it's corresponding step and earlier (or let ML figure it out)
5. Machine Learning: Use the above described datasets to train/build machine learning (ML) models that predict
failures that would happen in later steps.
Prediction is done before the failures happen, at an early ALS when correcting or even scrapping the
product is much cheaper than dealing with a failure at a later or final stage.
Quality Assurance for Manufacturing | Solution Design
ML Process
Define Objective
Access and
Understand the
data
Pre-processing
See Microsoft’s team data science process at aka.ms/TDSP
Other Important Considerations
• Imbalanced Data• Cost-sensitive learning
• Sampling methodologies
• How to split into training and validation sets• Be careful of “leakage”
• Best practice to consider time in split
Quality Assurance in Manufacturing
Batch Layer
distributed processing
complex queries on very large datasets
cold path
historical data
“Data at Rest”
Speed Layer
real time processing
data that action is taken on
hot path
“Data in Motion”
Quality Assurance for Manufacturing | Dashboard Example
Azure Event Hub
Azure Stream Analytics
Azure Machine Learning
PowerBI
SQL Data Warehouse
PowerBI
Blob Storage
Takeaways for CIS Solution - QA in Manufacturing
• Fundamental info comes from client: failure data and domain knowledge. Customer pain
point is actually the gold mine.
• Use existing test systems. Often no need to add additional hardware.
• Uses machine learning to build models that predict failures before they happen. Early prediction of future failures allows for less expensive repairs or even discarding, which are usually more cost efficient than going through recall and warranty cost.
• Azure platform decouples infrastructure components (data ingestion, storage, data movement, visualization) from the analytics engine that supports modern DS languages like R and Python. The solution modeling component can thus be retrained as needed and be implemented using
high performance Azure Machine Learning algorithms, or open source (R/Python) libraries, or models from a third-party solution vendor.
