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When Machines Decide Improving Manufacturing Productivity with Data Analytics Vasu Netrakanti

When Machines Decide Improving Manufacturing Productivity with Data Analytics Vasu Netrakanti Optessa Inc. Third Industrial Revolution: Digitisation of Manufacturing ( The Economist ). Perspective of optimization solutions provider Predictive analytics  Decision support

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When Machines Decide Improving Manufacturing Productivity with Data Analytics Vasu Netrakanti

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  1. When Machines Decide Improving Manufacturing Productivity with Data Analytics Vasu Netrakanti Optessa Inc.

  2. Third Industrial Revolution: Digitisation of Manufacturing (The Economist) • Perspective of optimization solutions provider • Predictive analytics  Decision support • Integrated with plant infrastructure • Real-time decisions: routing (what next), selection (which order) … In an automated manufacturing environment, computers can help machines decide.

  3. VEHICLE ASSEMBLY PLANT 1 VEHICLE ASSEMBLY PLANT 2 METEOR COMET 400K 500K 300K 2 DOOR 4 DOOR 2 DOOR 4 DOOR 2 DOOR SUNROOF NON SUNROOF SUNROOF NON SUNROOF SUNROOF NON SUNROOF SUNROOF NON SUNROOF SUNROOF NON SUNROOF RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER RED WHITE BLACK BLUE SILVER PRESS LINE MA1 PRESS LINE MA2 Case: Tier 1 On-demand Scheduling OEM Optessa SUPPLIER ROOF LINER DIVISION High complexity – 50 variants High volume for a small manufacturer – 25K parts/month “Broadcast” response

  4. 1St Set of Inputs: Demand

  5. 2nd Set of Inputs: Production Capacity and Rules

  6. Results: Current and Optimized Scenarios Optimized Smoothed OT: 2 hours/day Minimize changeovers Current Follow OEM sequence • Total Orders: 22,798 • On Time: 5,653 • Late: 13,324 • Unscheduled: 3,821 • Total Orders: 22,798 • On Time: 22,798 • Late: 0 • Unscheduled: 0

  7. Case: SC Fab Real-time Yield Management

  8. Case: Auto OEM ASRS Real-time Decisions The flow of painted bodies from the Paint Shop to the ASRS, and then to TCF can be represented through a flow diagram indicating the key vertices and arcs. R30/31: R30/31 is the set of readers on the two lane buffer at the exit of paint (post polish stage). After painted bodies cross the R30/R31 readers, there is no divergence until they reach the next node BT1100. All orders between R30/31 and BT1100 are the look-ahead available for RTS. BT1100: BT1100 is the skid exchange station where the painted body is transferred from Paint skid to Logistics skid. At this point, the decision of storing/bypassing/wax/set out of a painted body is conveyed to the further PLCs. PTT01: PTT01 is the turntable at the entrance of the ASRS. The orders diverge into Lane 1 or Lane 2 of the ASRS after PTT01. PTT02: PTT02 is the turntable at the exit of the ASRS. Orders retrieved from the ASRS converge at this turntable before being passed to the TCF. TT02: This is the turntable which routes LTVs to the LTV TCF buffer. The U202 and W201 bodies continue past TT02. TT03: This is the turntable which routes U202s to the U202 TCF buffer. The W201 bodies continue past TT03. TT04: Turntable routing W201 to W201 TCF buffer. At 60 jph, 3 decisions required every minute Routing: which ASRS cell to store painted body Selection: sequence outgoing job Assignment: which sales order for painted body

  9. Challenges • Reliable, accessible, timely data • Complement or supplement existing business processes?

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