Analytics Products & Analytics Products & Solutions for the Solutions for the manufacturing manufacturing

industryindustryPresentation by:

MaruthiMadhuJaganVamsi

Hari

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Product IdeaProduct Idea

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The heart of The heart of manufacturingmanufacturing• 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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A massive opportunityA 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

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• Current solutions focus on monitoring and notification

• High potential for applying predictive analytics for rapid intervention

A viable analytics productA 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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DB Hadoop

CNC Log DataMachine Interface

Scheduler

Driver

MapReduce Module

Model building and

training

Prediction Engine

Log File model

Mahout Analytics Package

Web / DashboardAdmin / Config User Config

Solution DetailsSolution 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 buildingo Using either Naïve-Bayes, Neural Nets or Bayesian Nets to identify

failure

• Outputo 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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High level outputHigh level output

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Alert Ticker: Machine 10345 requires attention (Click to View); 103 machines normal; 3 machines prone; Ma..

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F2 error. 50% likelihood of failure in 8 hours

F3 error. 25% likelihood of failure in 24 hours

F1 error. 90% likelihood of failure in 1 hour

CNC Machine dashboard

CostingCosting• 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 costo Infrastructure costs

o Product maintenance

o Customer support

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Marketing and pricingMarketing 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 modelso 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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Consulting SolutionsConsulting Solutions

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Supply Chain

Operations

Quality Control

• High product dev. cost and

time.

• Poor collaboration across supply

chain partners

• Lack of real time visibility into

supply chain events

• High Inventory

• Flexibility to accommodate

changes in production schedule.

• Adhering to delivery schedules

• Poor Customer experience

• Poor asset efficiency

• Numerous quality problems

Inventory Control

Maintenance

Commercial

Production Line

Industry Pain PointsIndustry Pain Points

Operations

Maintenance

Costs of delays in production lineCosts 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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Consulting FrameworkConsulting Framework

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AssessmentAssessment

Business Analysis

Data Analysis

Iterations

• Client Interviews

• Define metrics & Data analysis (3 weeks)

• Pre-processing and Model building (2 weeks)

• Client presentation

• End-to-End solution delivery (TBD based on requirements)

• Expected outcomes

• Increase in Productivity

• Efficient use of resources

• Cost reduction

Solution DetailsSolution 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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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

Use CasesUse Cases

Increase ProductivityIncrease Productivity

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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

Tim

e

Cost

Quality

constraints

Reduce wait timeReduce wait timeGoal 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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