1. Analytics Products &
Solutions for the
manufacturing industry
Presentation by:
Maruthi
Madhu
Jagan
Vamsi
Hari
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2. Product Idea
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3. The heart of manufacturing
• Computer Numerical Controlled machines
• Used across various sectors of the manufacturing
industry
• $120 bn industry
• 4 million units in China alone!
• High impact on productivity
• Downtime is expensive
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4. A massive opportunity
• Complex machines, with ~300 parts
• Prone to failure – average MTBF of ~3000 hours,
with average repair time of 1 hour
• Holds up assembly line costing ~$5000 an hour
• ~$40 bn of annual loss due to machine downtime
• Current solutions focus on
monitoring and notification
• High potential for applying
predictive analytics for rapid
intervention
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5. A viable analytics product
• Process CNC machine logs
• Aggregate logs from multiple machines and
industries
• Build failure prediction models
• Notify at pre-determined thresholds of confidence
• SaaS-based, to aggregate data across users
• On-premise version for data-sensitive users
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6. Admin / Config Web / Dashboard User Config
Log File model MapReduce Module
Model
Prediction
building and
Mahout Analytics Package Engine
training
Driver
DB Hadoop
CNC Log Data Machine Interface Scheduler

7. Solution Details
• Data inputs
o Log files (standard CNC log
o Failure types and data – reasons and actions taken
o CNC machine list and details
o Maintenance schedules
• Model building
o Using either Naïve-Bayes, Neural Nets or Bayesian Nets to identify failure
• Output
o Multiple, escalating states for each type of failure, identified by events
o Each state would denote an increasing likelihood of eventual failure
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8. High level output
CNC Machine dashboard
1264 1264 1264 1264 1264 1264 1264 1264 1264
1264 1264 1264 1264 1264 1264 1264 1264 1264
F2 error. 50%
likelihood of failure
1264 1264 1264 1264 1264 1264 1264 1264 1264
in 8 hours
F3 error. 25%
likelihood of failure
1264 1264 1264 1264 1264 in 1264
24 hours 1264 1264 1264
1264 1264 1264 1264 1264 1264 1264 1264 1264
F1 error. 90%
likelihood of failure
in 1 hour
Alert Ticker: Machine 10345 requires attention ( Click to View); 103 machines normal; 3
machines prone; Ma..
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9. Costing
• Product development cost
o Building failure prediction models
o Building the SaaS infrastructure
o Building the web dashboard and notifications
• Product installation cost:
o Setting up log feeds and adapters from the customer’s machines
o Building and configuring list of machines
• Product operational cost
o Infrastructure costs
o Product maintenance
o Customer support
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10. Marketing and pricing
• Ecosystem:
o Consumers: Manufacturing industries operating CNC machines
o Partners: CNC machine manufacturers
• Marketing Approach:
o Option 1: Sell the product to CNC machine manufacturers
o Option 2: Partner with manufacturers and sell the product directly to
consumers
• Pricing models
o Value based: Capturing 50% of savings: $1250 per machine per year
o Market based: At 10% of maintenance cost $1000 per machine per year
o For a customer like Tata Motors that operate around 5000 machines,
pricing would range from $5 mn to $7.5 mn per year
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11. Consulting Solutions
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12. Industry Pain Points
• High product dev. cost and time.
Supply Chain • Poor collaboration across supply
chain partners
Operations
Operations
• Lack of real time visibility into
Quality Control supply chain events
• High Inventory
Inventory Control
• Flexibility to accommodate
Maintenance
Maintenance changes in production schedule.
• Adhering to delivery schedules
Commercial
• Poor Customer experience
Production Line
• Poor asset efficiency
• Numerous quality problems

13. Costs of delays in production line
• Boeing has incurred a massive $2.5 billion write-off in the single
quarter of 2009.
• The development cost of propulsion system for F35 (Joint Strike
Fighter), built by Pratt & Whitney, has increased costs from
$4.8 billion to $8.4 billion
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14. Consulting Framework
• Client Interviews
Assessment
Assessment Iterations
• Define metrics &
Data analysis (3
weeks) Business Data
Analysis Analysis
• Pre-processing and
Model building (2
weeks)
• Client presentation
• End-to-End solution
• Expected outcomes
delivery (TBD based
on requirements) • Increase in Productivity
• Efficient use of resources
• Cost reduction
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15. Solution Details
• Data inputs (last 6months to 5 years data)
o Plant Shift Schedules, Person utilization details
o Machines Utilization Details, Maintenance Schedules,
o Resources & Skills Matrix
o Purchase orders
o Storage of raw materials in warehouse
Output
o A cloud based solution with Web GUI,
visualization and reporting features
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16. Use Cases
Manufacturing in shipping
Company Profile:
•Revenues for company : 2 billion per year
•Profit : 180 million
•Cost of production: 1.2 bill
•Ship components : 400,000
•No of ships made per year- 18 ships per year
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17. Increase Productivity
Goal is to make optimal use of People, Machines and Time resulting in
high productivity
Models used: Goal Programming, Markov chain Monte carlo
Benefits of optimization is reduction of costs up to 2% resulting in 24
million profit
Co
e
ts
Tim
constraints
Quality
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18. Reduce wait time
Goal is to reduce the delays and wait time in
production line
Methods used : Markov chain Monte carlo, Neural
Networks
Benefits of optimization is increase of productivity by
10% equivalent to1.8 ships (200 mil revenue)
Total Benefit: Increase of profits from 180 to 240 million
10 % of the increased profits.
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