Supply Chain Analytics and Data Management Deloitte Consulting Perspectives. June 4, 2009

Transcription

1 Supply Chain Analytics and Data Management Deloitte Consulting Perspectives June 4, 2009

2 Contents Defining Enterprise KPIs Institutionalizing KPI and Data Management Useful Principles and Tools Select Case Studies

3 Defining Enterprise KPIs

4 Improve Market Strate gy Sales Strategy Channel Development Business Development Marketing Strategy Volume Build & Strengthe n Customer Re la tionships Key Cash / Asset Design Wins Management Custome r Licensing & Growth Royalties Account Portfolio Management New Customer Acquisition Leverage Income Generating Assets Market Position Price Realization Drive Platform Strengthen Leadership Pricing Roadmap / Portfolio Demand & Supply Mgmt Management NPI Process Price Optimization Product Related Servic es Brand Development Pric ing Governance VPAs Improve Customer Interaction Efficiency Marketing & Advertising Sales Customer Service & Eng. Support Order Fulfillment & Billing Selling, General & Administrative (SG&A) Improve Corporate/ Sha red Se rvic e Efficiency IT,Telecom& Networking Facilities Human Resources Procurement & Vendor Mgmt Financial Management Legal Cost of Goods Sold (COGS) Improve Supply Chain Improve Design Efficiency & Cost & Development Efficiency Structure Supply Chain Planning Strategic Sourcing Logistics & Distribution Front End Mfg Back End Mfg R & D Product Development Technology Development Design for X Income Taxes Improve Income Tax Efficiency Tax Advantaged Supply Cha in Property, Plant & Equipment (PP&E) Improve PP&E Efficiency Re alesta te & Infrastructure Equipment & Systems Working Capital Improve Working Ca pital Efficiency Inventory Planning WIP & Inventory Control Channel Mgmt Accounts, Notes & Interest Receivable Accounts, Notes & Interest Payable Company Strengths Improve Managerial & Governance Effectiveness Governance Business Planning Employee Engagement Workforce Development External Factors Improve Execution Capabilities Business Improvement Execution Partnership& Collaboration Product Dev. Program Execution Agility& Flexibility KPIs address prioritized improvement levers Shareholder Value Revenue Growth Operating Margin Asset Efficiency Expectations Revenue Growth Operating Margin (after taxes) Asset Efficiency Expectations

5 Assess how KPIs counterbalance each other to provide true insight into strategy execution Some examples where complementary KPIs provide balanced insight into underlying operational performance: Product Leadership Days of Inventory a On Time Delivery Reducing inventory without improving supply chain processes can adversely affect OTD c b Development Project ROI vs. Expectations b Performance Against Key Dev. Program Milestones Incurring additional development cost simply to meet milestones can degrade ROI Customer Intimacy a Operations Excellence Customer Share of Wallet c Gross Margin Buying share primarily through pricing tactics may hurt achievement of gross margin targets

6 KPI design to provide checks and balances KPI is balanced by KPIs Customer share of wallet On Time Delivery Gross Margin % Demand Forecast Accuracy Development Project ROI vs. Expectations Return On Net Assets (RONA) Days of Inventory % of Employees Motivated to Perform Beyond Job Responsibilities Performance Against Key Product Development Program Milestones Number of Silicon Versions Customer Quality (RMAs) Performance Against Key Strategic Improvement Program Milestones Mktg & Sales Contribution Margin vs. Spend Time to Market for Key Product Launches Win Rate for Key Design Ins

7 An example: Building out the KPI framework with drill-downs $13 $12 $12 $11 $11 $10 $10 $9 $8 $7 $6 $5 $4 $3 $2 $1 $0 $7 $6 $5 $4 $3 $2 $1 $0 $7 $6 $5 $4 $3 $2 $1 $0 $7 $6 $5 $4 $3 $2 $1 $0 $7 $6 $5 $4 $3 $2 $1 $0 $7 $6 $5 $4 $3 $2 $1 $0 Q3 07 Q4 07 Q1 08 Q2 08 Tigris Q3 07 Q4 07 Q1 08 Q2 08 Tigris Barcelona Q3 07 Q4 07 Q1 08 Q2 08 Barcelona Barcelona Q3 07 Q4 07 Q1 08 Q2 08 Barcelona Barcelona Q3 07 Q4 07 Q1 08 Q2 08 Barcelona Barcelona Q3 07 Q4 07 Q1 08 Q2 08 Barcelona Barcelona Q3 07 Q4 07 Q1 08 Q2 08 Barcelona Drill-down metrics link back to strategies through the value map Value Driver Operating Margin Improvement Lever Improve Design & Development Efficiency Development Projects ROI vs. Expectations Enterprise KPI Project Cash Contribution ($ M Product Development Investment R&D Investment (by platform) Project Cash Contribution ($ M) Margin Pool & Share (by platform) Value Map Activities Supporting Improvement Lever Project Cash Contribution ($ M) Project Cash Contribution ($ M) Project Cash Contribution ($ M) Shared Technology Investments Die size vs. Planned Post-launch Design Revisions Drill-down KPIs Project Cash Contribution ($ M) Mfg Cost vs. Planned Project Cash Contribution ($ M) Others...

8 Institutionalizing KPI and Data Management

9 Data Management Defined it as: The collection of decision rights, processes, standards, policies and technologies required to manage, maintain and exploit information as an enterprise resource. The processes by which you manage the quality, consistency, usability, security, privacy and availability of the organization's data Focused on: Governance Standards Processes Organization Technologies Data Management Strategy

10 Data Management requires a multi-disciplinary approach Governance Standards Process Cross-functional governance structure to set the vision, own how data is used, collected, protected and maintained, and make tradeoffs (i.e., use, design, implementation and risk tradeoffs) Data element, structure, and quality standards, based on functional, business and regulatory requirements, and common across all functions and business units across the enterprise Definition of processes for data maintenance that drive high levels of data quality and conformity to standards and requirements Data Management Addresses: Master data entities (e.g. what is a Customer?) Data attributes (master data, transactional data, derived data) KPI/Metric definitions and use Constraints, Metadata Data quality measures Organization Technology Definition of roles associated with data governance, accountability, maintenance and quality management A data management solution roadmap that supports data integration, data maintenance workflow, data standards enforcement and data quality management Data security and privacy Regulatory compliance Policies (e.g. Customer Naming Standard, Address Completeness)

11 Understanding Data Management Master Data can be defined as reference data that drives the overall integrity of transactions, reporting, and business process information captured in various applications Master Data Management (MDM) provides a foundation and enabling architecture to enable a reliable, consistent Source of Truth for master data across constituencies and applications Enterprise Data Management (EDM) extends MDM to include the management of all relevant business data that supports an enterprise process & information lifecycle, including analytic, operational, as well as master data. Master Data is: Foundational data that defines the key business objects of a corporation A core set of data elements used to model and drive many of the processes and information systems in the company Data that doesn t change at a high frequency Data Type Reporting & Analysis Data Transactional Data Derived Reference Data Master Data Examples Profitability, margin analysis, value measurements Sales orders, quality testing results, inventory movements Customer-specific pricing, part numbers, average selling price Core definitions, attributes, and categorization EDM Solutions should address: Support for multiple use cases Operational Collaborative Analytic Cross-domain reference data dependencies Process, organizational, and system impacts and readiness

12 BI Governance includes KPI and Data BI Strategy and Governance KPI Owners Functional Executive Direct the Business Executive KPIs (10-15) Manage the Business Analytical Reports & KPIs (50-100) Run the Business Operational Reports & KPIs (1000) KPI Performance/ Reporting Event Management Collaboration Data Architecture Data Architecture & Metadata Data Quality (Owners, Stewards) Data Sources IT / Data Custodians

13 Pillars of BI and Data Governance KPI Owners Define KPIs and usage policies Define KPI hierarchy and drilldown relationships Establish KPI calculations and business rules Determine KPI baselines & targets Define data sources Data Council Data Architecture Metadata Data Stewards Prioritize BI and data requirements Assign/delegate data stewards Define data quality targets and standards Data Quality Set data usage policies Data quality monitoring & reporting Manage change control processes Oversee regulatory, security, privacy compliance

14 BI and Data Management Maturity Optimization & Event Management Collaboration Fully Collaborative End-to-End Supply Chain Collaboration Maturity Level (By Process Area) Data Definition KPIs and supporting data elements defined for insight across the supply chain Common KPI and data definitions KPI and Data owners Metadata centralized Intra-gration Data Capture & Internal Consumption Consistent BI processes and tools across functions, regions and BUs SSOT for enterprise master data (enterprise BOM, etc) Data management process and quality monitoring processes Centralized data management organization Integration Make Data Visible to External Network Common product nomenclature Data lifecycle stage gates and controls Automated workflow and business rules Standardized internal and external data exchange Data synchronization and harmonization to downstream systems Role-based security and access control KPI reporting capabilities Incorporate Data into Planning & Operations Dynamic access KPIs to measure quality and performance Incorporation of predictive analytics Common master data harmonized across all demand and supply planning environments Extended data integration to downstream supply chain systems Real-time data availability across all business units and geographies Real-time inventory visibility and monitoring Supply chain participants leverage BI and data tools to allow mfg schedules and capacities to be shared Timely reporting and analytics that effect change to business process outcomes Performance metrics for trading partners periodically reviewed to reflect business conditions KPI performance aligned across the total supply chain Internal Visibility Connectivity External SC Visibility Ability to Execute

15 Useful Principles and Tools

16 Key Tenets and Lessons Learned Focus on End-to-End solutions understand the entirety of the data lifecycle and define processes that streamline data management activities across business units and functions Establish Guiding Principles obtain executive buy-in and enforcement of key tenets for MDM design and execution Build for the Common Good optimize for global, regional and local success design for the common good and strive to eliminate shadow IT MDM capabilities Business driven, IT enabled establishing business ownership is critical to MDM design, implementation and operations Upfront Design Governance establish design governance model upfront that clarifies stakeholders and process for MDM design decisions Think big, Start small, Deliver pilot enterprise MDM with limited data domains that serve a strategic business need Understand Impacts incorporate business and technical readiness into phased implementation strategy Communicate Early and Often build stakeholder communication and engagement model that promotes value and reinforces key principles

17 KPI Development Approach KPIs represent metrics defined from business priorities, and are used to define the key information requirements, conceptual architecture and information model Business Requirements Gathering Enterprise Value Map HCSC Business 5 Pillar Strategy Strategy Super Business Process Value Business Drivers Drivers Top Down, Metrics Driven Industry Models Existing Assets Functional Improvement Levers Critical Business Questions Priorities Key Metrics & Defining Criteria Leading Practice Architectures Executive Priorities Information Assets (Conceptual Model) Subject Area Logical Data Models Enterprise Data Model Business Information Model Technical Modeling Dimensional Data Models

18 Sample PAIDO Diagram A holistic view of the end-to-end data lifecycle provides insights into root causes and priorities for streamlining data management Process Business process improvement opportunities Owner Gaps, redundancies, and inconsistencies in core data Data Apps / Tools System & Integration Challenges Organizational inefficiencies and failure points *For detail on PAIDO see appendix

19 Select Case Studies

20 Case Study: Enterprise Product Data Management Situation A large semiconductor company was facing critical supply chain issues due to: Inconsistent product data structure, BOM and nomenclature creating problems with supply execution, demand-supply matching, customer fulfillment and inventory control Significant productivity loss due to efforts spent on daily basis on data reconciliation across organization and proliferation of shadow IT Approach Developed data management strategy through solution deployment: Creation of a single trusted source of product information Design and implementation of data management & governance organization Integration of product lifecycle, data management processes, organization & governance, and product data harmonization Results - Gained significant business benefits in visibility, consistency and accuracy, including: Reduced SKU sets by over 40%, which reduced distribution reconfigurations Improved productivity and quality of data across multiple systems Post-merger cross-company and cross-bu data integration and centralization

21 Case Study: Enterprise KPI Definition Situation A large semiconductor company sought to implement a Key Performance Indicator (KPI) program supporting management s monitoring of its strategy execution and business objectives : Approach Definition and deployment of KPI framework: Define critical set of KPIs and align executive team on critical business drivers Establish first level KPIs, and agree on baseline, targets, and ownership by KPI Identify and prioritize key improvement levers to help drive and monitor actions against KPIs Develop data-driven tools for KPI calculation, tracking, and analysis Define a KPI program governance framework to ensure continued validity, insight, and relevance Results - Initial KPI set will greatly improve the ability of management to observe the progress of their strategies and the success of the company s transition to an asset smart business model KPI set provides unity and focus to company performance measurement efforts in general as it extends deeper and wider in the organization Comprehensive set of KPIs identified using a structured approach, clear definitions, and clearly identified targets Pilot KPI deployment leveraged live enterprise data including complete documentation with prototype workbooks, KPI calculations, data sources (systems and individuals), availability, etc.

22 Boneyard

23 SC Information Management Examples Microsoft / DHL Nissan North America, Inc. Exel Samsung Electronics Co, Ltd. Panasonic Best Buy Situation Microsoft was planning a simultaneous roll-out of a new gaming system across three continents, aware of the fact that transportation delays in the U.S. could negatively impact product availability. To avoid supply chain delays, substantial flexibility had to be created in the product flow. Microsoft elected to partner with DHL one year prior to launch, involving them in every step of the supply chain. After increasing plant production from two models to three, Nissan needed extra space to deal with increased volume and complexity at full production. The manufacturer also needed to better monitor the status of shipments and orders in order to produce at the desired rate and measure supplier performance. Samsung employed both an ERP system and a parallel information exchange system to manage facilities around the world. The two systems were not fully communicative forcing the company to use patchwork communication methods such as and telephone. This created significant time delays throughout the enterprise. Panasonic had excess inventory waiting to be sent to distribution centers. They soon partnered with Best Buy, having the retailer collect and release Panasonic point-of-sale information. The electronics manufacturer then used consultants and I2 to plan store replenishment and production. Results A system was designed to provide all partners visibility to critical status at the manufacturing stage Advanced Ship Notices created to track in-transit inventory allowing DCs to pre-plan capacity DHL collaborated with independent partners to facilitate goods movement from manufacturer to end user Gaming consoles continue to flow smoothly through the supply chain increasing customer satisfaction Implemented a Supply Chain Event Manager (SCEM) system to resolve exceptions and provide visibility of shipments Partnered with Exel to create more space by refurbishing a warehouse near the plant to receive and store materials Created a supplier portal to present supplier performance criteria and metrics Implemented a worldwide collaborative portal system to give all customers, suppliers, and subsidiaries access to real-time status of inventory, orders, shipping, and sales Portal (managed on the back-end by SAP) allowed for a one-stop source for all relevant information Changes led to improved customer satisfaction and more accurate sales projections Multi-enterprise collaboration linking manufacturing to shelf products visibility to inventory on the shelf In-stock position at Best Buy stores improved, boosting sales and revenues for both companies Overall Panasonic inventory decreased, further reducing costs These companies have realized tangible benefits and competitive advantages by focusing attention and efforts on developing SCC and sound data management capabilities

24 Leading Practices for Supply Chain BI Reporting and analytics for supply chain processes require foundational data components such as Data Quality, Data Governance, and Metadata Management in order to be successful Product Design & Development Sourcing & Procurement Inventory Management Logistics & Order Fulfillment Demand Planning & Forecasting BI Leading Practices Automate reports sot that they are shared across product design groups to enable: 1) faster time to market 2) costly design errors 3) Assess the performance of subcontractors and third parties Detect product yields and defects Visibility into tool effectiveness decreases the likelihood for costly production failures Create a stable, accurate BOM structure Extend internal systems to integrate with key vendors and suppliers (monthly vendor reports are often insufficient) Clearly define and agree on performance metrics for quality, delivery, and support Analyze enterprise wide vendor spend for volume discounts and price optimization Develop consistent metrics to evaluate supplier performance BI system must provide alerts and notifications for inventory defects and shortages BOM structure definition should be standardized to expedite accurate orders of parts Reporting and analytics should include metrics for lead times, rate of spoilage, defects, stock outs Provide status and visibility into all in-transit inventory Include metrics for On Time Delivery and % of Perfect Orders Provide ability to monitor shipments and provide alerts to key events and proactive adaptation (trace and trace, rescheduling of delivery times,, etc.) Include reports on the effectiveness of transportation cost reductions (route optimization, shipment consolidation) Provide capability to share demand forecasts with Suppliers Provide near real time updates for replenishment of inventory and parts Include metrics such as Increased product accuracy, % of Reduced Inventory, Reduce days of Open PO s BI Leading Practices Data Quality DQ Assessment Data Profiling Data Cleansing Data Controls Data Monitoring Data Governance Organizational Structure Data Ownership Enterprise Data Stewardship Policies and Procedures Executive Commitment Metadata Management Business Rules Data Lineage Impact Analysis Regular Updates End User Accessibility