Applying Industry 4.0 principles To Electronics Manufacturing with Valor

Transcription

1 Applying Industry 4.0 principles To Electronics Manufacturing with Valor Oren Manor Unrestricted Siemens AG 2017 Realize innovation.

2 VALOR: Digitalizing Electronics Manufacturing ELECTRONIC IDEATION REALIZATION MECHANICAL 2 Unrestricted Siemens AG

3 VALOR: Digitalizing Electronics Manufacturing ELECTRONIC Leverage Digital Twins of the Product, Process and Production to ensure continuous, optimized, high-quality process MECHANICAL Unrestricted Siemens AG

4 Modern manufacturing introduces new challenges LOT SIZE ONE OPTIMIZING MATERIAL USAGE ADVANCED ANALYTICS 4 Unrestricted Siemens AG

5 Industry 4.0 aims to provide a framework for addressing the challenges of modern manufacturing 5 Unrestricted Siemens AG

6 LOT SIZE ONE

7 Lot Size One Accurate Finite Planning for high-mix produce an optimal work plan and maximize material usage

8 Lot Size One Accurate Finite Planning for high-mix produce an optimal work plan and maximize material usage Setup time One-time setup per group Production complete earlier

9 Lot Size One Accurate Finite Planning for high-mix produce an optimal work plan and maximize material usage Design-agnostic tool running 950 Fabrication, Assembly, Test and Reliability checks Analyzing the Digital Twin of the Product for issues affecting performance, yield and quality Lean NPI flow optimize transition from Design to Manufacturing with DFM

10 Lot Size One Accurate Finite Planning for high-mix produce an optimal work plan and maximize material usage Lean NPI flow optimize transition from Design to Manufacturing with DFM One tool for all manufacturing engineering tasks Process Preparation - One tool covers all manufacturing engineering tasks Creating a Digital Twin of the Process and production system Pick & Place Stencil Reflow Visual Inspection Automated Inspection Manual Assembly ICT & Repair Final Assembly & Configuration Functional Test / Repair Production Preparation Packaging

11 Display $45.00 Memory $15.00 OPTIMIZING MATERIAL USAGE Communications $37.50 Cameras $11.00 Processor $20.00 Mechanical $30.00 Others $37.60 Labor $4.00 Total $200.10

12 Optimizing Material Usage Deliver materials to the line when needed eliminate excess WIP, improve inventory turnover REMOTE ERP SITE MASTER FLOOR / AREA LINE / CLUSTER C O N S U M P T I O N

13 Optimizing Material Usage Deliver materials to the line when needed eliminate excess WIP, improve inventory turnover Prioritize material selection at warehouse (open/older reels) Automatic communication with storage towers Group setup minimize change-over of materials / feeders BEFORE AFTER

14 Optimizing Material Usage Deliver materials to the line when needed eliminate excess WIP, improve inventory turnover Real-time management of material flow between warehouse and shop-floor Prioritize material selection at warehouse (open/older reels) Automatic communication with storage towers Group setup minimize change-over of materials / feeders BEFORE AFTER 2M excess inventory reduction / 5.6 months ROI

15 Data Records per work-order ANALYTICS 10,000,000, ,000, , Lot-level (material) DATA COLLECTION Component-level (material, quality) PCB-level (material) Component-level (material, quality, process metrics) Year

16 Analytics Plug & Play data acquisition and control Create a Digital Twin of the Production

17 Analytics Plug & Play data acquisition and control Create a Digital Twin of the Production Performance, utilization, quality, OEE KPIs available to MES & other applications Descriptive analytics identify bottlenecks, root-cause

18 Analytics Predictive Analytics as a competitive advantage Yield / quality / cost feedback to Design and Engineering drive product and process improvement Optimize inventory turnover $2,000 per each 100K units $3.93 $3.91 $0.05 $0.09 $3.98 $4.00 Average site (5 lines) places 50 Million components per month

19 Analytics Predictive Analytics as a competitive advantage Yield / quality / cost feedback to Design and Engineering drive product and process improvement Optimize inventory turnover Deep learning technologies and regression techniques to identify data correlation

20 20 Unrestricted Siemens AG

21 Digitalization is the Key to Industry 4.0 Enablement DIGITAL TWIN OF THE PRODUCT DIGITAL TWIN OF THE MANUFACTURING PROCESS & PRODUCTION SYSTEM DIGITAL TWIN OF THE PRODUCTION 21 Unrestricted Siemens AG

22 Thank You Unrestricted Siemens AG 2017 Realize innovation.

23 Digitalization is the Key to Industry 4.0 Enablement ELECTRONIC TEAMCENTER / TEAMCENTER MANUFACTURING MECHANICAL 23 Unrestricted Siemens AG

24 Introduce Valor: show the flow slide (3) but first, use real life images of the process and then show how we digitalize them Footnotes: we provide digital mfg. slutions to Electronics; Digitalize the entire flow; leverage digital twins to ensure continuous process flow etc. Introducing the problems 1) Lot-size-one high variance of products, very small lot size, infinite consumer configurations 2) Material cost constitutes ~80% of manufacturing costs; high-mix production increases the challenge of optimizing material usage (MSD, surplus materials, inventory turnover) 3) Looking back is easy getting descriptive data; the challenge is Prediction of demand, resource availability (predictive maintenance), optimize supplier management Industry 4.0 aims to provide a framework for addressing the modern challenges Virtualization, decentralization, Connectivity, Now let s see practically how we apply these principles to address the challenges in Electronics mfg. Lot size one: 1) Enable accurate (finite) planning for high mix taking all known resources and constraints into account machines, people, schedule, materials producing an optimal work plan; maximize material usage 2) Lean NPI flow optimize the transition from design to manufacturing 1) Manufacturabiltiy analysis to ensure the process can run smoothly (prevent garbage-in/garbage-out) 2) One tool for all mfg. engineering tasks creating digital twin of the process 3) Execution automatic program change new machines have mechanism for storing multiple programs we tell the machine which program to use for a given PCB 24 Unrestricted Siemens AG

25 Materials: Like I said, material is a major part of the cost, affecting margins we want to ensure that on one hand, the machine will not starve, and on the other hand, we don t want to keep unnecessary stock on the floor (taking floor real estate, expiration, moisture, not being used for another line potential waste later, requiring more feeders, stealing between lines ) We enable just-in-time (or Kanban) material replenishment assume it takes x minutes to prepare the material in the warehouse, then x minutes to deliver to the line, then x minutes to create kitting and load the machine we monitor material consumption rate on the machine, and know to automatically trigger a request from the warehouse so that new material will be loaded just when it s needed Define which materials to take (prefer open reels over new reels, older material vs. newer material, same source/lot/vendor before changing vendor) Automated communication with storage towers the person only needs to take the materials and bring them to the line. In addition MM looks at the next jobs and knows to request material for the next jobs if they use the same material Also, MM tells the operator which feeders to keep between jobs during changeover if the next job needs to use the same materials 25 Unrestricted Siemens AG

26 Analytics: One challenge be able to know the status of the mfg. operation at any given moment, for any given location OEE, DPMO, etc. validate that the process runs in line with the plan Second challenge be able to perform root-cause analysis if actual differs from plan, understand why. Third challenge be able to predict our the process will act and proactively make adjustments in advance course correction (Waze example) Soultion: First of all need to get the data, normalize it, make it easily available Valor IoT creates digital twin of production; instant out-of-the-box KPI dashboards up to factory level Big-Data BI ability to contain these huge amounts of information in efficient columnar database structure Provide more global level dashboards compare sites etc. Self-service create KPIs that are interesting to you Drill-down ability to find root cause, gain additional insights, uncover hidden insights (comparison of materials from different vendors, link between temperature and quality) Prediction:????? Summary Digitalization is the key to everything We create digital twins of the electronic product, process and production - and enable implementation of solutions for lot-size-one, lean material mgmt. and predictive analytics for closed loop feedback In our vision we also look to enable machine-to-machine communication, enhance the closed loop feedback to design cost, yield 26 Unrestricted Siemens AG