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Digital Transformation Western Digital
The document outlines Western Digital's vision for supply chain digitization by 2030, emphasizing the importance of centralized planning, automation, and data-driven decision-making. It discusses enhancements in planning cycle time, supply-demand optimization, and collaborative multi-enterprise processes while detailing improvements in visibility, responsiveness, and efficiency metrics. Key takeaways highlight the significant benefits of a digitized backbone in supply chain management and the critical role of deeper analytics in harnessing its full potential.
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Digital Transformation Western Digital
- 1. Imagining Supply Chainsin 2030 GLOBAL SUMMIT
- 2. Imagining Supply Chainsin 2030 IMAGINE 2022 2 Agenda 01 Western Digital Overview 02 Digitization: Our Definition 03 Extending Centralized Planning Platform 04 Supply Demand Optimization 05 Key Metrics Improvements 06 Beyond Real Time: Bringing outside world in 07 Multi Enterprise: One WD+ Benefits of End-to-End Digitization. Redesigning Service by Opening the Door
- 3. Western Digital A LeadingData Infrastructure Company TECHNOLOGY ENGINE CUSTOMER VALUE ~14k active patents GLOBAL SCALE PORTFOLIO BREADTH ~65K employees worldwide World’s leading storage solutions provider
- 4. Leading Portfolio Breadth& Depth Solutions to capture, preserve, access and transform data NAND Components Embedded Flash Devices Cards USB Portable Storage Direct-Attached Storage Network Attached Storage & Personal Cloud Client+ HDD Client SSD Enterprise SSD JBOD & JBOF Storage Servers OpenFlex™ NVMe-over-Fabric Storage Memory Extension Drive Capacity Enterprise HDD NVMe-oF ™* Bridge ASIC NVMe-oF Bridge Adapter *NVMe-oF = NVMe over fabric
- 5. Digital Transformation isCritical What is Digitization? Enablingquicker responses withobjective“deeper”capabilitiesand driving therightenterprise decisions • Automation– Removemanualprocesses tospeedup individual responses andchange the typeof work • Analytics– Go deeperand enabledata-baseddecisionmakingtoimprovethecapabilities • TheNetwork–Connect the Dataflowand Decisionmakingtoconsider thebiggerpicture
- 6. Extending Centralized PlanningPlatform Improvement in planning cycle time and responsiveness Metric Metric Supply Chain Response Daily (7x Improvement) Human Intervention <15% - Exceptions only Optimization / Trade-offs Global (End to End visibility) Different Goals for Different teams Drive manufacturing Procurement Components manufacturing External manufacturing Common Goals for all teams Customer forecast and demand Components Production Control Supply Chain Planning Materials “Square Set” Product manufacturing Build request Build plan Drives Demand Via procurement team Supply commit Build commit Material commit Demand planning Corporate demand profile Supply plan In-parallel processes Central Supply Planning (Key constraints maintained centrally) Service Level Demand Supply Buffers Variability Constraints Plan Inventory
- 7. Central Planning Benefits MultipleImprovements with the enablement of Central Planning Engine/Common MRP for drive factories “Removing the ceiling” from planning capabilities Minimal effort in generating weekly plan Cross skill development in managing multiple product segments Single harmonized process and systems Enable exception based planning People System Consolidated applications (CP/MRP/PDH) to improve planning scalability, speed and supportability Harmonized product data hub (PDH) to house parts, BOMs, planning and procurement attributes Sunset of multiple home grown applications. (10+ 2) Processes Resolving Material and Capacity Shortages in Parallel Enable QUICK scenario planning to evaluate multiple capacity/demand etc. scenarios Single plan for Components and products One common planning process across all the legacies
- 8. Simplifying the systemslandscape… Planning System ERP 1 ERP 2 Home Grown/Excel Long Term Planning - MP / BP MRP MRP • Build Plan • Component Plan • BOM Build Demand Planning Send/Receive Supply Commit** Demand Planning Factory Planning Order Mgmt. Allocation Order Fulfillment MRP Capacity Planning MRP Demand Planning Forecasting Home Grown* Order Mgmt. Order Fulfillment Send/Receive Supply Commit** Send/Receive Supply Commit Forecasting : Home Grown* Factory Planning* Capacity Planning* Allocation (By Region) Supply Planning Allocation (By Customer) Order Mgmt. Order Fulfillment Factory Planning * Capacity Planning* Forecasting: Home Grown* SCP NSD/Home Grown Supply Planning Allocation Product Group 2 Product Group 1 Product Group 3 Central Planning , Long Term Planning - MP / BP Send/Receive Supply Commit** Product Group 2 Product Group 1 Product Group 3 Factory Planning Capacity Planning Forecasting Home Grown* Send/Receive Supply Commit** Send/Receive Supply Commit Forecasting : Home Grown* Factory Planning Capacity Planning Factory Planning Capacity Planning Forecasting: Home Grown* Supply Planning Order Mgmt. Supplier Collaboration Common MRP Allocation Order Fulfillment Common Sale Forecast Common Demand Planning. & Real Time S&OP 2017 2022 Sales Forecast System Journey
- 9. © 2021 WesternDigital Corporation or its affiliates. All rights reserved. | WESTERN DIGITAL CONFIDENTIAL Extending Supply Demand Optimization Scale Cloud/OEM Channel Retail Configure to Order VMI Hub Build to Order Build to Targets Demand Constraints 150 K Demand Lines 50EXB Demand Volume Cloud/OEM Channel Retail Build to Forecast Demand Constraints 510 K Demand Lines 330EXB Demand Volume Customer Locations Distributio n 15 FulfillmentsSites 525 K CapacityConstraints 1M Material Constraints 22 Manufacturing Sites 10k-70K ValidPaths to BuildProducts 6K Supplier Material Product Group 1 – 1.5M Constraints Factories Constrain ts BOMs Product Group 2 and 3 – 0.3M Constraints 30 Manufacturing Sites 15 FulfillmentsSites 2K Supplier Material 10k-15K Valid Paths to Build Products 150 K Material Constraints 200K CapacityConstraints Constraints : Lead Time Mfg Capacity Handling 50 Customers 160 Customers
- 10. On Time Delivery Improvement Fromover 225% Increased Order Promising Window 5x Increased Supply Visibility 3 Qtrs Global Operational Excellence Accelerated Metrics Improvements
- 11. Predictive COVID-19 Peak Real Time Linear Optimization PlanningEngine Predictive Logistics Rapid Planning & Forecasting Maximizing Financial Opportunities Bringing the Outside World In… Thinking beyond real time
- 12. Vision: Collaborative Multi-Enterprise WDC WDCDigital Planning Platform Where are we on the Journey? Multi Enterprise: One WD+
- 13. Lesson Learned • EffectiveChangeManagement •Data– A Strong Foundationis critical • Vision – ThinkBIGGER
- 14. Key Takeaways • TheBenefitsof a Digitized Backbone are “Real” withAccelerated Benefits • Harnessing the true power is only realized by enhancingthecapabilities with deeper analytics applied across the supplychain • The old “End to End” should only be viewed as a foundationfor our futurein Multi- Enterprise Digitizationand Optimization
- 15. Thank you Any Questions? WesternDigital, the Western Digital design, the Western Digital logo, the SanDisk logo, the SanDisk Professional logo, the WD logo, the WD_Black logo, and OpenFlex are registered trademarks or trademarks of Western Digital Corporation or its affiliates in the US and/or other countries. The NVMe and NVMe-oF word marks are trademarks of NVM Express, Inc. All other marks are the property of their respective owners.
Editor's Notes
- #10 Adjusts the Fonts and Sequences.
- #11 Global optimization of supply chain network Over 1000 optimized constraints Product and customer segment Leverages AI, Deep ML, SIR (Susceptible-Infected-Recovered) and NLP models Based on ~10 years of known impacts to suppliers from disruptive events Predictive shipment arrival & delays Alerts Predictive capacity on transport bidding Machine Learning to continuously set Transit Times and Routes Resliency Dynamics Risk vs. Cost Optimization Defines pathways for Supply Chain and Inventory management Predicative Business Outcomes Statistical model for predicting expected business outcomes Suppliers and customers adjacent markets analysis
- #12 Global optimization of supply chain network Over 1000 optimized constraints Product and customer segment Leverages AI, Deep ML, SIR (Susceptible-Infected-Recovered) and NLP models Based on ~10 years of known impacts to suppliers from disruptive events Predictive shipment arrival & delays Alerts Predictive capacity on transport bidding Machine Learning to continuously set Transit Times and Routes Resliency Dynamics Risk vs. Cost Optimization Defines pathways for Supply Chain and Inventory management Predicative Business Outcomes Statistical model for predicting expected business outcomes Suppliers and customers adjacent markets analysis
- #13 Global optimization of supply chain network Over 1000 optimized constraints Product and customer segment Leverages AI, Deep ML, SIR (Susceptible-Infected-Recovered) and NLP models Based on ~10 years of known impacts to suppliers from disruptive events Predictive shipment arrival & delays Alerts Predictive capacity on transport bidding Machine Learning to continuously set Transit Times and Routes Resliency Dynamics Risk vs. Cost Optimization Defines pathways for Supply Chain and Inventory management Predicative Business Outcomes Statistical model for predicting expected business outcomes Suppliers and customers adjacent markets analysis
- #14 Global optimization of supply chain network Over 1000 optimized constraints Product and customer segment Leverages AI, Deep ML, SIR (Susceptible-Infected-Recovered) and NLP models Based on ~10 years of known impacts to suppliers from disruptive events Predictive shipment arrival & delays Alerts Predictive capacity on transport bidding Machine Learning to continuously set Transit Times and Routes Resliency Dynamics Risk vs. Cost Optimization Defines pathways for Supply Chain and Inventory management Predicative Business Outcomes Statistical model for predicting expected business outcomes Suppliers and customers adjacent markets analysis
- #15 Global optimization of supply chain network Over 1000 optimized constraints Product and customer segment Leverages AI, Deep ML, SIR (Susceptible-Infected-Recovered) and NLP models Based on ~10 years of known impacts to suppliers from disruptive events Predictive shipment arrival & delays Alerts Predictive capacity on transport bidding Machine Learning to continuously set Transit Times and Routes Resliency Dynamics Risk vs. Cost Optimization Defines pathways for Supply Chain and Inventory management Predicative Business Outcomes Statistical model for predicting expected business outcomes Suppliers and customers adjacent markets analysis
- #16 Global optimization of supply chain network Over 1000 optimized constraints Product and customer segment Leverages AI, Deep ML, SIR (Susceptible-Infected-Recovered) and NLP models Based on ~10 years of known impacts to suppliers from disruptive events Predictive shipment arrival & delays Alerts Predictive capacity on transport bidding Machine Learning to continuously set Transit Times and Routes Resliency Dynamics Risk vs. Cost Optimization Defines pathways for Supply Chain and Inventory management Predicative Business Outcomes Statistical model for predicting expected business outcomes Suppliers and customers adjacent markets analysis