1. 1. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Manufacturing analytics at scale 1 Soundar Srinivasan Bosch Data Mining Services and Solutions, Palo Alto, CA Jeff Thompson, Ruobing Chen, Juergen Heit, Dirk Slama
2. 2. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 2 Outline Bosch overview Core business sectors World class manufacturing Data mining at Bosch Successful applications in manufacturing Unique challenges encountered Need for further research
3. 3. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 3 2014 key figures One of the worlds largest suppliers of automotive technology Industrial Technology Energy and Building Technology Bosch Group 48,9 billion euros in sales R&D investment: 4.9 billion euros 360,000 associates as per April 1.15* Mobility Solutions Leading in drive and control technology, packaging, and process technology Leading manufacturer of security technology Global market leader of energie-efficent heating products and hot-water solutions Consumer Goods Leading supplier of power tools and accessories Leading supplier of household appliances 68% share of sales *including BSH Hausgerte GmbH (formerly BSH Bosch und Siemens Hausgerte GmbH) and Robert Bosch Automotive Steering GmbH (formerly ZF Lenksysteme GmbH). 32% share of sales
4. 4. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 4 Four business sectors: A diverse, and rich field for data science applications Mobility Solutions Industrial Technology Energy and Building Technology Consumer Goods
5. 5. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 5 Freely programmable control units PLC and PC based control units Field bus (ethernet- based) Flexible production systems Digital data storage Usage of internet standards Integrated IP- connection Identifiable and communicating objects Mobile operation Scalable systems (cloud as storage, ..) Self-optimising systems Internet-of-things Advanced manufacturing: The next industrial revolution Industry 1.0 2. industrial revolution 3. industrial revolution 4. industrial revolution Industry 2.0 Industry 3.0 Mech. control (cam disc, cam) Energy: water / steam power Punch cards as program memory Conveyer belts Master shafts Energy: electrical 1. industrial revolution Mechanisation Electrification Digitalization Connectivity and Traceability The transformation of Industry 3.0 to Industry 4.0 (advanced manufacturing) occurs gradually Industry 4.0
6. 6. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 6 Two perspectives of Bosch in advanced manufacturing Technology and solution supplier for OEMs and end users LEAD PROVIDER System manufacturer view / production resource view LEAD OPERATOR Product manufacturer view / product view First mover in the realisation of integrated concepts with equipment providers Big Data Business processesDecentralised intelligence Machine models Software Added value networks Connection Production models
7. 7. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 7 As of 12/2014 200+ Manufacturing facilities 1000s of assembly lines Billions Of parts manufactured each year Bosch manufacturing
8. 8. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 8 Manufacturing use cases Test and Calibration Time Reduction Prediction of test results Prediction of calibration parameters Quality Improvement Descriptive analytics for root-cause analysis Early prediction from process parameters Self-optimizing assembly line Warranty Cost Reduction Prediction of field failures from Test and process data Cross-value stream analysis Yield Improvement Benchmark analysis across lines and plants Pin-point possible root causes for performance bottlenecks (OEE, cycle time) Predictive Maintenance Identify top failure causes Predict component failures to avoid unscheduled machine down-times
9. 9. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 9 Business Objective: Reduce test and calibration time in the production of mobile hydraulic pumps Impact Example: Test time reduction 35% reduction in test and calibration time via accurate prediction of calibration and test results
10. 10. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 10 Example: Test time reduction Problem: Bottleneck Test Benches Approach: 1) Identify candidate tests for removal 2) Identify test groups run in parallel 3) Use feature selection methods (group Lasso) to identify least important test measurements. 4) Remove least important test measurements (saving test time) 5) Train a predictive model to predict test outcome from remaining measurements. Layout of the assembly line
11. 11. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 11 Scalable Group Lasso min 1 =1 log 1 + 0 + =1 , + =1 We used Limited-memory BFGS (L-BFGS) with Block Coordinate Descent (BCD) to solve the optimization problem. L-BFGS is used to obtain a quadratic approximation of the logistic regression. BCD is used to solve the resulting sub-problem, i.e., a quadratic problem with group penalty.
12. 12. 12 G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 Scalable Group Lasso Three parts of the approach can be distributed Gradient computation of the logistic function Storage of L-BFGS history BCD sub-problem solver: minimize each block simultaneously When to and why distribute? Distributing the gradient computation is beneficial when sample size is large Distributing the storage of L-BFGS history is beneficial when there are a lot of features Chen et al., (NIPS 2014) show that this distributed version is advantageous over the original only when the number of feature is larger than 10Mil. Distributing BCD is beneficial only when the number of groups is large
13. 13. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 13 Analytics and production environment Device Management Device Abstraction Event Management Software Provisioning Identity Management Production Env.Analytics Environment Hadoop MongoDB DB Connectors Custom Scripts SAS IBM SPSS Statistica Alpine KNIME Revolution R RapidMiner Extraction, Trans- formation, Loading Aggregate Data Historic Training Data Analytics, Machine Learning Descriptive Analysis Predictive Model Extraction, Transformation Predictive Model Prognosis, Decision (-Support) Sales Data Production Data Warranty Data Device Data
14. 14. Challenges in predicting defects in manufacturing 14 Large, but distributed data E.g. One product variant in one plant ~15 million units, 29 data sources, 17 TB data, 22 billion measurements High dimensional 100s-1000s typical Schema- and dictionary-migration over time Near real-time and resource-constrained deployment G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015
15. 15. 15 G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 Other data science challenges in manufacturing Data is short-term stationary Time and feature correlation Label noise Very low (but costly) incidence rates 0-few ppm typical Unequal costs of false alarms and false negatives High accuracy and quality requirements
16. 16. 16 G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 Need for expanding research in manufacturing IEEE Big Data for Advanced Manufacturing Workshop 2015 IEEE International Conference on Big Data Oct 29 Nov 01 2015 @Santa Clara, USA http://ieeebdam15.stanford.edu/
17. 17. G1/PJ-DM | 7/17/2015 | 2015 Robert Bosch LLC and affiliates. All rights reserved. Bosch@Data Science Summit, 2015 Backup 17 Advanced manufacturing App Store/Digital Services (2) Connected Products (1) 3D Printing Next-Gen. Robots Intelligent Powertools Top floor Shopfloor End-2-End Digital Engineering Sales/Marketing & Business Models Product Customization (5) Product Usage Data (3) Batch-Size One (7) Work Environment IoT-Enabled Manufacturing CPS De-Coupling, Product Memory Servitization (4) (9) (6) (8) IoT Service Implementation Embedded | Cloud (10) IoT Service Operation Adaptive Logistics Aftermarket Services Remote Monitoring, Predictive Maint. (12) Source: www.enterprise-iot.org