Master Data Apache-PwC Controlling Your Master Data Through Data Governance Global Material Master Data Management Project Andre Siaw, PwC Andrew Mullinax, Apache Corporation PwC Disclaimer: 2013 PricewaterhouseCoopers LLP, a Delaware limited liability partnership. All rights reserved. PwC refers to the United States member firm, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details. This content is for general information purposes only, and should not be used as a substitute for consultation with professional advisors
Agenda Introduction Material Master Overview Governance Process Technology Implementation People & Change Management Key Learnings Leading Practices Questions/Contact PwC Disclaimer: 2013 PricewaterhouseCoopers LLP, a Delaware limited liability partnership. All rights reserved. PwC refers to the United States member firm, & may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details. This content is for general information purposes only, & should not be used as a substitute for consultation with professional advisors 2
3 INTRODUCTION
MBOE/D, Net Introduction Founded in 1954, Apache is one of the world s top independent oil & gas exploration & production companies with operations in the United States, Canada, the North Sea, Egypt, Australia & Argentina. 2012 Total Assets: $61 billion 2012 Proven Reserves: 3 billion barrels of oil equivalent (boe) 2012 Production: 779,000 boe per day 2012 Revenue: $17 billion Total Acreage: 41 million Employees: 6,000 Introduction to Apache Apache s portfolio strategy has enabled the company to grow throughout commodity cycles, delivering strong financial results consistently through an unrelenting focus on rates of return & benefiting from a high-margin product mix. Strong culture reinforced by centralized management & incentive systems, decentralized decision-making, cost-control focus & creative application of technology 800 750 700 650 600 550 500 450 400 350 300 250 200 150 100 50 0 4
Apache SAP Background SAP Implementation SAP MM went live in 2008 Limited & late focus on cleansing data Ineffective data cleansing due to lack of data standards 5
Implementation Outcomes Issues after SAP Implementation Poor data quality generated issues around inventory & reporting Procurement users dissatisfied with results of data quality & processes Assessment conducted in 2010 to determine the extent of the issue Assessment recommendations: Establish data governance set-up Re-cleanse material master data to common, global standards Implement an MDM tool to help sustain ongoing data quality & standards 6
Challenges Material master records were found to contain significant quality issues which impaired the ability to locate inventory items, limited spend visibility, increased inventory costs & reduced process efficiencies Materials Data Management Prior State Business Impacts Poor data quality, both incomplete & obsolete data Ineffective search impairs ability to find items No global catalog or standard taxonomy No version of truth No single owner or data governance organization (multiple regional groups) with common processes or standards Multiple siloed technologies, with manual controls & processes, limiting ability to enforce standards Limited capability to monitor & sustain data quality Significant use of free form text results in: Higher costs (off-contract buying) Higher recycle (at buyer & AP levels) Limited ability to analyze & aggregate spend Duplication of data & effort across groups Lack of visibility impacts ability to optimize inventory Inefficient tools lead to backlog in MM maintenance Impacts reliability, supply chain optimization & process automation 7
Project Objective Project Value Drivers Consolidate & standardize materials repository to represent a 'single source of truth' across enterprise Standardize & improve data management processes through automation & integration of common workflows Cleanse data to Apache standards & enhance taxonomy Implement Technology & integrate with SAP to manage & sustain data quality Deploy the MDM tool to Business Units enabling them to create & change standard material masters Strategy Project Scope Strategy is two-fold: Legacy Data Cleansing: Cleanse to common standards through application of governance processes, policies & tools Sustaining Solution: Implement MDM tool for creating new materials Data Governance supported by MDM Tool (sparesfinder) Data Cleansing & Data Quality Management Materials: OCTG, Controllable equipment & MRO/Consumables/Other materials Geographies: Central, Permian, Gulf of Mexico, Canada, North Sea, Argentina, Australia, Apache Egypt, Qarun, Khalda, Beryl 8
9 MATERIAL MASTER OVERVIEW
Processes/transactions where MM s are used Material Master Overview Material Master is a repository of item records with specific attributes that provide critical data on materials & equipment that are purchased & inventoried Material Search ERP Master Data Request Master Material Data Request Master Records General material data Material description Material purchasing data Accounting data Plant specific data Spend Analysis Plant Maintenance Work Order Bill of Materials Outline Agreement Business Warehouse Manual requisition Inventory Management Purchase Requisition Purchase Order (PO) Goods Receipt Invoice Receipt Payables (A/P) 10
11 GOVERNANCE
Why We Need Data Governance Data Quality Data Cleansing alone Data Cleansing alone Data Cleansing with Governance Data Cleansing with Governance Time Data Governance is critical for maintaining data quality Data cleansing alone will improve data quality, but over time data quality deteriorates & additional resources & effort are required to restore data quality to acceptable standards Cleansing without Governance leads to data quality decay over time 12
Governance Data Governance is a framework of capabilities which when executed together, help maintain data that is accurate & consistent to meet Apache s business requirements Governance is fundamental to MDM cleansing data & establishing sustaining solution to keep data clean Key Enablers to Enforce Standards & Sustain Quality Taxonomy & Dictionary: standardization & enhancement of material description Taxonomy & Dictionary Policies, Principles & Standards Policy: guidelines & principles to enforce data governance Processes: Guidelines on how data policies are created & implemented Processes Data Governance Governance Metrics Governance Metrics: measures to monitor performance of data Technology: Scalable tools to enable governance capabilities Technology 13
What is Taxonomy? Taxonomy is a hierarchical structure to organize materials. Taxonomy facilitates search by users, sourcing spend analysis, & data exchange with suppliers. Data Dictionaries like PIDX, SMD, etc. can be leveraged to realize a taxonomy by providing standard material description elements like noun, modifier & attributes. Upper Level Taxonomy Lower Level Taxonomy Level 1 Level 2 / Class Attributes Values BEARING Noun BEARING, BALL Noun, Modifier Attributes TYPE ROW INSIDE DIAMETER OUTSIDE DIAMETER WIDTH SIZE SERIES STYLE RADIAL CLEARANCE CAGE MATERIAL ADDITIONAL DETAIL Attributes Required & Preferred SERIES VALUES EXTRA LIGHT HEAVY LIGHT MEDIUM Attribute Values 14
Outcome of Structured Data Illustrative Value Add Uncleansed Description Cleansed Description Example 1 Structured data created from unstructured data Complete & enriched data created from incomplete data Short Description: VALVE,GATE:WDG,6IN,CL 600,RF,22IN FACE-~ Long Description: VALVE,GATE:WEDGE,6IN,CLASS 600,RF,22IN FACE TO FACE, FULL BORE,BEVELLED GEAR OPERATED,CARBON STEEL BODY,ASTM A105 OR ASTM A216 GR WCB,13CR/STELLITE TRIM,MANUFACTURED TO ANSI/ASME B16.5,,STELITE FACED SEAT,WEDGE,BACKSEAT & BACKSEAT BUSH Short Description: VLV,GATE:WEDGE,6in,600lb,RF FLANGED,CS,> Long Description: VALVE, GATE TYPE: WEDGE VALVE SIZE: 6in PRESSURE RATING: 600lb CONNECTION TYPE: RF FLANGED BODY MATERIAL: CARBON STEEL MATERIAL SPECIFICATION: ASTM A105/ASTM A216 GRADE: WCB SEAT MATERIAL: STELLITE OPERATOR: BEVEL GEAR STANDARD: ASME B16.5 CONSTRUCTION: 22IN FACE FACE CONSTRUCTION: 13CR STELLITE TRIM NMA (Noun Modifier Attribute) Structure fundamental concept for building Taxonomy 15
16 PROCESS
Sustainment Model Establish & control data quality across enterprise through governance & taxonomy Define Standards 1 2 Cleanse & Validate Detect, correct, enrich & validate incorrect data to improve data quality Data Quality Initialization Iterative process Data Quality Sustainment 4 3 Continuously monitor data quality standards & provide metrics for ensuring sustainability Monitor Quality Process Enhancement Maintain business & technical processes to support data quality While data can be effectively cleaned, ongoing sustainment requires tools to enforce standards & monitor quality 17
Governance Council What is Governance Council? Collaboration of subject matter experts to manage & enhance dictionary All taxonomy/dictionary issues will be reviewed before changes are finalized in the system Council will be coordinated & facilitated by Corporate MMDM team Benefits Strategic management of material master data BU engagement on important taxonomy/dictionary issues BU consensus on standardization of materials described differently across various BU s Who are the members? Business Unit Representatives Houston Material Master Data Governance Team Requester other than the BU Representative 18
Legacy Operating Model Business Unit Purchasing Staff Materials Control Staff Houston Master Data Team SAP Creators of requests had least knowledge of materials No 24/7 availability Delays due to time zones between BU s Engineers Slower processing times due to back & forth between Houston & the BU 19
New Operating Model Field users BU 2 Catalogers Field users BU 1 Catalogers Field users BU 3 Catalogers BU 2 Approver BU 1 Approver BU 1 BU 3 Approver Domain expertise is decentralized Field users BU 4 Catalogers BU 4 Approver BU 5 Approver Master Data Management Tool BU 6 BU 6 Approver Houston Master Data Team Creators are field users with the most knowledge of materials More users improve material description with continued deployment Tool is used to enable ongoing Governance Field users BU 5 Catalogers BU 6 Catalogers Field users 20
Roles & Responsibilities In order to sustain the material master data management initiative, the following organization structure has been established: Role Responsibilities Houston Master Data Team (Cataloger) Domain expertise for ongoing Data Governance, Taxonomy & Dictionary Data Quality & QC Processes Manage Governance tool Technical errors & Helpdesk BU Approver Approve standard material master creation requests submitted by BU requesters Approve non-standard material master & taxonomy requests to be sent to Houston Master Data team for processing Participate in Governance council discussion Participate in QC processes & rectifications BU Cataloger Create standard material master creation requests submitted by fields users Update local plant & warehouse level changes in the tool 21
22 TECHNOLOGY
Technology Defined Effective master data management requires both MDM tool & other supporting components working together as part of a comprehensive solution Component Description MDM Tool Manages master data through its lifecycle Provides a single version of truth for accurate & efficient decision making Facilitates maintenance & syndication of an enterprise taxonomy Provides enhanced front-end data validation & search capability ERP Data Quality Management Tool Workflow Management Tool Integration Monitoring & Reporting Tool Contains full material master data to enable transactions Serves as source of truth for local data attributes Provides capability to identify & correct errors & inconsistencies in data by applying pre-defined business rules & data standards Enables processes such as monitoring, profiling, cleansing, mapping & enrichment Enables the passage of information, documents (e.g. requests), & tasks (e.g. approvals) between users & systems to facilitate accuracy, accountability & process automation Facilitates automation & enhances user experience Provides reporting across ERPs for spend analysis, inventory analysis, etc. 23
Tool Enables Process & Controls SparesFinder MDM Tool MDM tool MDM tool supported by Governance & Processes is the sustaining solution Entry Point to create, maintain & search all material masters Benefits User Access Enterprise-wide solution to sustain data quality Maintains global dictionary single source of truth Single common approach standard rules apply to all 24/7 availability Advanced search capability Prevents creation of duplicates Helps leverage existing inventory Phased deployment to field users with multiple levels of approval All users have role based access in the tool Users focused among Supply Chain, Plant Maintenance & IT functions Automated workflow can be configured to Business Unit requirements What is created? Two fundamentally different records can be created in the tool Attributed description can be created by business unit users Catalog (OEM) description can only be created by Houston Features Menu driven; pre-populated list of NMA Manages taxonomy NMA Manages Global & Local level ERP data Controls Units of Measure Controls Attributes values 24
25 IMPLEMENTATION
Project Streams Processes that are executed concurrently as part of project implementation Robust, repeatable & scalable processes were built & tested in Pilot Phase with a small number of critical materials Repeatable in cases of new acquisitions Governance Cleansing & Cutover Cleansing of materials by MDM vendor Apache review & acceptance Cleansed data upload from MDM tool to SAP Integration & Tool Deployment Seamless availability in SAP of data created in MDM tool Houston Deployment Pilot rollout to first Business Unit Deployment to other BU s 26
Cleansing & Cutover Process Cleanse legacy data to global standards through application of governance processes & tools Iterative process Regional review & Apache acceptance of data cleansed by MDM vendor Raw Legacy Data Cleansing by MDM Vendor Review by Apache Regions Houston Acceptance SAP 27
Deployment Plan Time Design, Build & Test Pilot Deployment (Houston) Pilot Deployment to Business Unit 1 Deployment to BU 2 Deployment to BU 3 Deployment to BU 4 Deployment to BU 5 Deployment to BU 6 Concurrent streams Legend: Design, Build & Test Pilot Deployment to Houston Pilot BU Deployment Tool Deployment to Global BU s Phased Deployment Train the trainer strategy Pilot implementation of MDM tool in Houston before rollout to BU's Pilot rollout to first BU implement lessons learned Deployment includes training, access to MDM tool & support 28
29 PEOPLE & CHANGE MANAGEMENT
Integrated Change Readiness Approach Effective Change Management involves an integrated approach across Change, Communications, Training, & Deployment. Four Elements of Change Readiness Change Management Communications Training Deployment Change impact analysis End user analysis Leadership alignment Super user & change agent networks Continuous stakeholder engagement to focus change support Communications strategy & plan Targeted messaging Mix of mediums including in person, phone, web, email, & video Regular meetings with BU s & stakeholders Regular project update communications Publication of Project Advisories to communicate important issues Training strategy & plan Training materials that are clear & simple Reusable content for retraining & for new users Timely training delivery Step by step training manual, training exercises & presentations Role-based curriculums Timely training delivery by mix of MDM experts & super users Tracking & reporting On-site support by project team, as needed Flexible delivery that can be modified to local & region needs Mix of mediums including in person, phone, web, email, & video training to suit regional requirements Capture lessons learned for future initiatives Raise user awareness & engagement while reducing need for alternate or workaround systems Successful MDM Deployment 30
31 KEY LEARNINGS
Key Lessons Learned Governance Dictionary needs to be tailored to what is important to your specific industry Off the Shelf dictionaries require substantial customization Focus on governance & dictionary & ensure that rules are in place before cleansing Allocate substantial time & resources to dictionary, as this is the foundation for data cleansing Cleansing Only cleanse records that are really critical Cleansing is not around industry specific materials - domain experts are needed to enhance cleansing Engage Business Unit resources early Technology Tool selection challenges - most tools are geared towards MRO s & Consumable materials 32
33 LEADING PRACTICES
Leading Practices Ensure critical data taxonomy & governance is finalized early Establish repeatable & scalable processes Establish a Governance Council Strategic management & enhancement of material master data by leveraging subject matter expertise Achieves BU engagement on important taxonomy/dictionary issues Achieves BU consensus on standardization of materials described differently across various BU s Deploy tool to Business Units Data creators in the field have the most knowledge of materials Ensures 24/7 availability Facilitates faster & more accurate creation of standard materials Central MDM team to control non-standard material creations & global changes Establish Quality Control & Governance Processes to ensure data integrity & sustain data quality standards 34
35 QUESTIONS & CONTACTS
Questions Andre H Siaw Advisory Director PwC andre.siaw@us.pwc.com Andrew Mullinax Manager, Supply Chain Technologies Corporate Supply Chain Apache Corporation andrew.mullinax@apachecorp.com 36
sparesfinder Materials MDM suite Masterpiece Data Cleaning & Project Control Gatekeeper Governance & Workflow Common taxonomy ISO 22745 compliant Foundation module User Control, Integration, System Admin 37
38 APPENDIX
Glossary Term Material Master Data Governance Data Quality (DQ) Management Data Cleansing Description A repository of item records with specific attributes that are fundamental to purchasing, inventory management, spend analysis & operations A framework of capabilities, which when executed together, ensure that data is accurate & consistent to meet business needs & objectives. DQ Management is the capability to provide reliable data that satisfies the business functions & technical requirements of the enterprise. The process of detecting & correcting erroneous data & data anomalies both within & across the system. Data cleansing can take place in both real-time as data is entered by automated tools or afterwards as part of a Data Cleansing initiative. Dictionary Taxonomy Dictionary provides standard material description elements like Noun/Modifier, attributes, Synonyms, Language & UoM. A hierarchical structure to organize materials. It facilitates search by users, sourcing spend analysis, & data exchange with suppliers. Data Dictionaries can be leveraged to realize a taxonomy by providing standard material description elements like noun, modifier & attributes. 39
MMDM Implementation Benefits Data Quality Benefits Structured data & enhanced taxonomy/dictionary by implementing Data Cleansing & Governance processes Elimination of duplication reduces duplicated inventory, assisting the corporate goal A single source of truth for material master records Consolidated & standardized material master records across all regions Increased material visibility Controls Benefits Inventory Control & Management Improved reporting & monitoring Global reports Global agreements Spend Analytics Other Analytics - KPIs Efficiency Benefits Increased regional involvement & self-service through deployment of MDM tool to BU s Acceleration of processing times BU s take the same time as before but make updates directly in the system, hence eliminating time spent corresponding with Houston cataloger Corporate staff able to better utilize time taken for data entry tasks for data quality & content management 24/7 availability Process automation Cost/Commercial Benefits Optimize dedicated manpower at Corporate as a result of BU deployment, hence saving client resources Reduced spend by leveraging existing inventory Reduced inventory holding costs SG&A cost reduction More readily integrate with new acquisitions/assets in a controlled environment i.e. cleansed, structured environment 40
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