BIG DATA ANALYTICS: THE TRANSFORMATIVE POWERHOUSE FOR BIOTECH INDUSTRY ADVANCEMENT. David Wiggin October 8, 2013

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1 BIG DATA ANALYTICS: THE TRANSFORMATIVE POWERHOUSE FOR BIOTECH INDUSTRY ADVANCEMENT David Wiggin October 8, 2013

2 AGENDA Big Data Analytics Four Examples Global Supply Chain Visibility Demand Signal Repository Supplier /Vendor Management Batch Traceability Closing Thoughts

3 What is Big Data Analytics? It s where advanced analytic techniques operate on big data sets It s about two things: big data AND advanced analytics > The two have teamed up to leverage big data > The combo turns big data into an opportunity Big Data isn t new. Advanced Analytics isn t new. > Their successful combination is new > Hundreds of terabytes of data just for analytics is new from Philip Russom, TDWI Research Director, Data Management 3 Teradata Confidential

4 Analytics 1.0 Traditional Analytics from IIA Research Director, Tom Davenport 4 Teradata Confidential

5 Analytics 2.0 The Big Data Era from IIA Research Director, Tom Davenport 5 Teradata Confidential

6 Analytics 3.0 Fast Business Impact for the Data Economy from IIA Research Director, Tom Davenport 6 Teradata Confidential

7 Global Supply Chain Visibility Suppliers Manufacture Consolidation Shipping Customs Distribution Center Shipping Customers Supply Chain Events Lead time events Raw materials availability Yield Equipment Status Delivery status Inventory location Forecast consumption Order fluctuations Integrated Detailed View Of Supply Chain Data Predictive Analytics and Impact Modeling Reporting Reporting Reporting Reporting Steps to Global Supply Chain Visibility Create a detail, integrated view of the supply chain event data Enable event management and alerts Invest in predictive analytics; identify event impacts, triggers & resolutions 7 Teradata Confidential

8 Business Value of Global Supply Chain Visibility Ensure consistent, safe supply Establish cycle time specifications Create more accurate forecasts based on up-to-date data Establish better production plans Enable process controls to monitor process and reduce variability producing more JIT inventories 8 Teradata Confidential

9 Supply Chain Performance Company Background Headquartered in Austin, Texas Global leader in the design and manufacture of embedded semiconductors for the automotive, consumer, industrial, networking and wireless markets. Design, research and development, manufacturing or sales operations in more than 30 countries. Business Challenges Due to a globally distributed supply chain, inability to consolidate manufacturing operations and engineering data across the entire supply chain was limiting manufacturing performance and engineering s ability to resolve product yield and quality issues As a leading electronics supplier to the automotive industry, the need for product traceability throughout the distributed supply chain was critical for quality and reliability management in order to remain competitive and sustain market share. Solution Overview Global consolidation of ERP, WIP transactions, metrology, equipment and test data into a relational data model and warehouse. Transform raw data into operational metric information with in-database statistical workflows and groupings. Provide drill-thru access from metrics to raw data for detailed analysis. Results and Benefits Decreasing cycle time and increased yield 1% Gross Margin improvement (~$100M / year) 1 percentage point of yield improvement Reduced Customer Quality Incident (CQI) response time by 50% Enabled Zero Defect Methodology to achieve < 1 defective PPM shipped 90% of new products shipped with zero defects Product Visibility & Traceability, Customer Returns Analysis, Equipment Performance Optimization, Product/Site Cost Analysis, Yield Acceleration, Advanced Process Control and Integrated Production Scheduling 9 Teradata Confidential 9 >

10 The Appeal of the Demand Signal Repository By Simon Ellis IDC Manufacturing Insights Sep 2013 forward-looking companies have made dramatic advances on the demand side. Where these advances have been most dramatic, and potentially transformational, has been when companies have begun with a data first approach, both in terms of what, where and how, but also in terms of accuracy and accessibility. focusing on data accuracy and governance, both in terms of ensuring that the data they use is qualitatively and quantitatively correct companies have been looking at ways to better manage large amounts of demand data Considered in isolation, the value proposition for the DSR is compelling Reduction in the latency of demand, where demand data is available in real or near-real time to enable demand-sensing, while facilitating business decisions that better respond to changing demand Teradata Confidential

11 Demand Signal Repository (DSR) 11 Teradata Confidential

12 DSR: Decrease Costs, Streamline Efficiencies Decreased costs > Spend less to deliver the right product > Improve forecast accuracy Reducing out-of-stocks can contribute as much as 4% to the bottom line Increased efficiency > Become more responsive > Streamline business processes > Improve the results of each individual initiative Most manufacturers can expect a productivity improvement of 10% to 15% from the strategic use of POS data 12 Teradata Confidential

13 Supplier / Vendor Management As the external supply base has grown, however, bio-tech & pharmaceutical companies are increasingly recognizing the true complexities of the process. Industry leaders point to challenges across the key areas of quality, delivery, and cost in managing their external supply relationships. 13 Teradata Confidential

14 Supplier / Vendor (Data) Management Detail performance data must be rationalized and integrated for: Price Volume Quality Reliability Support Data integration Flexibility Documentation Promptness Contract Terms Others Factors must be weighted according to company strategy, relative importance of materials, etc. At its core, this is a detail data management challenge 14 Teradata Confidential

15 Challenges of Supplier / Vendor Management Create vendor scorecard standard metrics driven by effectiveness of vendor forecasting and execution Maintain current safety stock risk levels Make vendor program changes based on experience, historical and normalization of procurement and supply chain data Rationalize global vendor selection - # vendors, quality, etc Multiple SAP instances complicates analysis unless data integrated separately; often SAP doesn t capture all, e.g. material quality? Is xls really the best tech for this function? 15 Teradata Confidential

16 Batch Traceability Full audit trail of drug / biologic production process Batch validation procedures Real-time storage levels How valuable (and easy) is the ability to trace any component to any point in the product lifecycle, from receipt of the component through production? 16 Teradata Confidential

17 Supply Chain Visibility / BoM Track & Trace Company Background Western Digital is the 2 nd largest US disk drive manufacturer. Their rapid growth in the 1990 s outpaced their infrastructure resulting in quality issues that impacted their market share. Business Challenges Lacked the ability to link all of the data sources required to track the genealogy of components and assemblies, along with the thousands of test parameters that are collected at each stage of manufacturing Data generation greatly outpaced data analysis capabilities Inability to rapidly identify, contain and solve problems Solution Overview Western Digital developed their Quality Information System (QIS) on a Teradata platform to centralize and analyze years of detailed product data on every unit s entire lifecycle from manufacture and test, through shipment and customer use for yield, quality and reliability improvement and cost reduction. Results and Benefits QIS tracks quality metrics for hard drives 500,000 drives built per day 90M drives in the field Reduction of problem isolation and resolution cycle time from 2 weeks to less than 24 hours Reduced field return rates by 3% per quarter Market share loss due to quality issues reduced from 12% to 0%. Reduced field service costs by increasing yield by 7% for FY $4,200,000 per year Product Visibility & Traceability, Track and Trace, Improved Quality, Defended against Loss of Share 17 Teradata Confidential 17 >

18 Supply Chain Analytics Pays Off Company Background Annual Revenues $53.3 Billion Employees 105,000 Industry Semiconductors and Other Electronic Components Solution Overview Manufacturing data warehouse Includes integration of product master and customer demand data with manufacturing transactions Process control and product test data from 8 manufacturing locations Proprietary engineering data visualization tools - In-house developed Data management for manufacturing operations reporting & monitoring Virtual factory analysis Ad hoc engineering yield and root cause analysis for 10,000+ users Business Impact After Teradata Lot & Unit Traceability for Assembly Operations Manufacturing Test Results Continuously loaded global data 1TB/week 142GB/day 6GB/hour 500MB in every 5-minute mini-batch We have queries that have never run on Oracle running on Teradata We have queries that run in seconds on Teradata that take hours on Oracle Because of Teradata, some ATM analysis processes that used to take weeks now take minutes Results and Benefits Reduced time to reach new technology maturity and product cost targets ($100M s net profit) Ability to more rapidly detect and prevent manufacturing process variability and yield excursions Reduced data access from 3-5 days to 5 minutes Engineering experts leveraged across global factories to solve problems using centralized data Best practices discovered and deployed using comparative analytics across copy exact factories $100M s in added revenue from optimized product availability, performance & quality 18 > 18 Teradata Confidential

19 Closing thoughts Big data analytics pays off in Supply Chain Management A few themes: > Integrate data internal & external > Keep data detail current, and use it for event triggers / alerts > Other industries may be have gotten there 1 st learn from the leaders! Some advice: > Don t just tinker with big data analytics as IT is sometimes prone to do; work with a technology partner that proves real business value quickly and clearly > Don t stop with these uses; analyze sensor data from the manufacturing process, turbo-charge research & development, and more 19 Teradata Confidential

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