Uploaded byTamrMarketing
PPTX, PDF89 views

How to Implement a Spend Analytics Program Using Machine Learning

The document outlines the challenges and methodologies for implementing a spend analytics program using machine learning, highlighting issues such as inaccurate and incomplete data leading to inefficiencies in spend analysis. It features a case study from a high-tech OEM demonstrating the impact of machine learning in improving classification accuracy and enabling better supplier negotiations. The speakers from Tamr advocate for the integration of machine learning to enhance data accuracy, streamline supplier management, and ultimately drive significant cost savings.

Embed presentation

Download to read offline
1Confidential 1 How to Implement a Spend Analytics Program Using Machine Learning in 2020 January 15th, 2020
2 Speakers Matt Holzapfel Solutions Lead at Tamr Leslie Bull Head of Western U.S. Sales and Partner Programs at Tamr Sanjay Rajan Strategic Sales Head, Manufacturing/Industry 4.0 at Tamr
3 Agenda 1 Why Accurate Spend is still a Problem What does your Spend Analytics Program look like? 2 Case Study High Tech OEM The ML Difference 4 How to Apply Machine Learning 5 Q&A 63
44 Why is it difficult to achieve? Accurate Direct Spend Analysis
5 What are you spending money on? Who are you spending it with? Lorem ipsum dolor sit amet at nec at adipiscing03 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Lorem ipsum dolor sit amet at nec at adipiscing01 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Who are my top suppliers in any one spend category?03 ● Consolidate suppliers by category ● Savings of $0.02 to $0.09 per $ consolidated What are my top spend categories?01 ● Actively manage your top categories ● Which categories are most impactful to bottom line? Lorem ipsum dolor sit amet at nec at adipiscing03 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Lorem ipsum dolor sit amet at nec at adipiscing02 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Lorem ipsum dolor sit amet at nec at adipiscing01 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Do you have an “always accurate” supplier master?06 ● If you haven’t mastered your suppliers, your foundation for spend analytics is weak Can you prevent creation of duplicate parts?05 ● Within and across business units how much part proliferation exists? ● $10K per duplicate part in your system (ATK) Can you identify maverick purchasing?04 ● Are contracts negotiated for best price and terms always being used? 02 ● Who do I have leverage with? ● Is it changing - $, Geography? Who are my top suppliers? 02 ● Know who you have leverage with to capture the best opportunity for best rates and discounts
6 What makes it difficult to answer the “What and Who”? ERP 1 ERP 2 Acme Business Systems ABS AB Systems Power Parts Acme Speciality Inc. subsidiaries 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible Duplicates dilute your spend across different entities Are you spending twice as much with a supplier than you think? XLS
7 What makes it difficult to answer the “What and Who”? 1. Incomplete Data 2. Duplicate Data 3. Inaccurate Data 4. Not Granular enough 5. Not Accessible Does not belong in category Correctly Classified Missing - scattered in other categories $15M $20M $35M Electrical Equipment 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible Even a small % of misclassification in a Top category means millions in lost savings Accuracy is all or nothing Spend Categories
8 What makes it difficult to answer the “What and Who”? 1. Incomplete Data 2. Duplicate Data 3. Inaccurate Data 4. Not Granular enough 5. Not Accessible 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible Your classification needs to represent items you actually source Vague descriptions or those that are not directly actionable represent wasted savings opportunities Heating and Air Conditioning Humidity Control Heating Equipment Split Exchanger Heat Exchanger Finned Tubular Exchanger One Point Exchange Boilers
9 What makes it difficult to answer the “What and Who”? 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible P-card Non-PO One-time buy Stock Materials Recurring Services Capital ... Source? Visibility into “blank” spend can unlock potential savings opportunities
10 What makes it difficult to answer the “What and Who”? Mergers, Acquisition Business Resource constraint Data is scattered across systems Other IT projects take priority ? 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible Critical to get accurate data into the hands of those who can and will leverage it What is stalling your data unification?
1111 What complicates it further?
12 Make Buy Sell - Supply Chain is complex OEM Supplier Distributors Manufacturer $ Hightech Automotive Industrial Equipment Medical Device Energy Food and Beverage Chemicals ….. 3PL ● Intermediate products ● Sub assemblies ● Turnkey Products ● Raw Materials ● Tiered Mfg. ● Distribution ● Warehousing ● Fulfillment ● Inventory ● After Market ● Services
13 PartManufacturer Supplier Supply Chain Attributes Supply ScheduleDemand Plan Where Used Unit Cost Annual Spend Annual Spend MPN Description ERP PN Description VPN Description Annual SpendAlternate Preferred Substitute Unit Price Discount Schedule Lead Time VMI Consignment Awards Allocations What to Make When to Make How Much Where Materials Resources How to Make Capacity Constraints Priorities Status Monitoring Manage Change Inventory Status Classification Tier 1 Tier 2 Tier 3 SKU/Family Product Assembly Sub-Assy Part Make/Buy Lifecycle CAD BoM Configurations Versions Revisions Change Management ECN ECO PCN Affected Items Disposition New Product Introductions Milestones Requirements Simulations Tests Prototypes Supplier Quality Audits Scorecard On Time DeliveryQuality Cost Variance Product Quality FPY Non Conformances Outgoing Quality Complex Web of Relationships Distributor DPN Description
1414 What do companies typically do?
15 How are organizations coping? Hire 3rd Party Services (last 15+ years) Integrate Applications (last 10 years) Employ Data Scientists (last few years) - Typically overseas resources - Fixed budget project - Fixes done to IT requirements Typically poor ROI, one-time fix - Most prevalent approach - Takes Years before you see value - High Startup Costs - High maintenance costs Does not Scale, and not Agile - Focus on Big Data, Analytics - Write Deterministic “Rules” - Trial and Error, Iterative - Add more Resources Is it on time or under budget?
1616 Case Study: High Tech OEM
17 High Tech OEM with >$5B in Spend High Tech OEM Supplier (Foxconn) Distributors Manufacturer (Intel) ● Parts ● Products ● Turnkey Assemblies ● Sub-Assemblies ○ Mechanical ○ Electrical $ 3PL Savings Opportunity Potential Misclassification of $3B value in 15,000 parts
19 As Is Process - Manual hence Slow - Resource Intensive - Unreliable - Expensive to Govern/Maintain - Not Agile (ex: add new source) Employ Data Engineers and hire 3rd Party Services
20 Examples of the types of misclassification in results ● Potential Misclassification of $3B value in 15,000 parts ● High Value parts in low value categories ● Low Value parts in High Value Categories ● “Tail Spend” category was among the top 3 - knew this was wrong ● …. Labels Plastic Injection Molding Printed Circuit Boards Motherboards Motherboards Network Switch Tape Drive Microprocessors Solid State Drives
21 Machine Learning Proof of Concept Results Accuracy after just a few iterations of the model85% Accuracy within the training data set95% Records categorized by Tamr90% Weeks to ingest data, train experts, and tune the ML model1.5
22 Actionable results in less than a week Before Tamr 80 distinct categories After Tamr 66 distinct categories ➔ 3.7X increase in parts classified to Microprocessors and SSDs ➔ 6X decrease to parts classified under Computer Accessory Kits “We buy a lot more of Microprocessors and SSDs. This does not make sense” “Computer Accessory Kits is not granular enough”
25 Result - Unable to achieve Goals Incorrect Info, Uncertainty and Gaps hinders accurate Analytics Where Used (by Qty): ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● ? ● ? ● ? Award History Year Type Limit Unit $ 2015 RFQ? 15% ? 2019 SS 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Spend Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 ? Alternate Murata Elec... ? Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 ? Alternate ? Supplier: Digikey Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Mfg. Part Description: Supplier PN: GRM0335C1H100JA01J Category: Passives >> ……….. >>................ ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO ... ERP Fields: MG, PL Price History: Supplier ? Category Spend Supplier: $310M ? Annual Part Spend: ? Category Spend Mfg.: ? Annual Mfg. Spend: ? Annual Supplier Spend: $17M ? Category Spend: Murata vs Digi-Key ? Spend Ranking: Digikey 10?
26 Price History: Supplier Result of Accurate Data Unification at Scale Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Category Spend Mfg.: $42M Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207
27 Capture bottom line savings Based on other clients and assuming $1B direct spend 80% decrease in manual data prep & technical support $0.6M 40% improvement in classification accuracy drives better negotiations $3.5M 20% increase in spend under management $1.1M 100% increase in analytic adoption$2.0M 10% improvement in RFP timing with better supplier intelligence $0.6M 25% increase in suppliers included in each RFP $1.1M $8.9M Total Benefits per $1bn spend $500M+ Savings From Sourcing Analytics Across Siloed Businesses Testimonial
2828 The ML Difference
29 Example: Rules-only approach Supplier Transaction description Classification rule GRAINGER 1-3/4" Steel Crowfoot Socket Wrench If Supplier = Grainger then Category = Tools
30 Example: Rules-only approach Supplier Transaction description Classification rule If Supplier = Grainger AND Description = Tape then Category = Adhesives GRAINGER 3M DOUBLE SIDED TAPE GRAINGER 1-3/4" Steel Crowfoot Socket Wrench If Supplier = Grainger then Category = Tools
31 Example: Rules-only approach Supplier Transaction description Classification rule If Supplier = Grainger AND Description = Tape Measure then Category = Tools GRAINGER TAPE MEASURE If Supplier = Grainger AND Description = Tape then Category = Adhesives GRAINGER 1-3/4" Steel Crowfoot Socket Wrench If Supplier = Grainger then Category = Tools GRAINGER 3M DOUBLE SIDED TAPE
32 How many technical resources can you afford? If Supplier = Grainger AND Description = Tape then Category = Adhesives Supplier Transaction description Classification rule GRAINGER 1-3/4" Steel Crowfoot Socket Wrench If Supplier = Grainger then Category = Tools GRAINGER 3M DOUBLE SIDED TAPE If Supplier = Grainger AND Description = Tape Measure then Category = Tools GRAINGER TAPE MEASURE Book of classification rules 2020 edition
33 ML augments rules-systems to provide greater speed & accuracy Supplier GRAINGER Item Name PLIERS PO Subject TOOLS FOR MOTORMAN WORKSHOP User category Tools A user manually categorizes a subset of transactions ML learns the underlying classification patterns Pliers are classified as Tools 100% of the time Grainger is usually, but not always, mechanical hardware The word workshop has no significance All other items, including new items, are automatically classified by ML’s knowledge base 2018 POs (Human classified) ... 2018 Invoices (ML classified) 2020 Invoices (ML classified)
34 When is ML needed? High category variety (any tier) Many sources (systems, users) Lean organization Need for speed!
3535 How to apply Machine Learning?
36 Typical Rules-based Process - Manual = Slow - Resource Intensive - Unreliable - Expensive to Govern/Maintain - Not Agile (ex: add new source) Employ Data Engineers and hire 3rd Party Services
37 Human-Guided Machine Learning Leverage Human-Guided ML (now) - Probabilistic Model-based - Expert-Guided, Less Resources - Continuously Improves Results - Fast time to value - Scalability and Agility
38 Agile approach starts with the business question, creates value incrementally Analytic Outcome BUSINESS VALUE Catalog Prepare Analyze Visualize How many unique suppliers do we have? Analytic Outcome BUSINESS VALUE Catalog Prepare Analyze Visualize How much are we spending on “Mechanical”?Sprint 1 Sprint 2 ● Each sprint limits the scope of data to only what is required to answer the analytic question ● Going end-to-end enables you to understand bottlenecks (resources & technology) limiting analytic velocity, and design strategies to enable data operations (DataOps) to scale, with minimal upfront investment
3939 Exclusive Offer Spend Analytics Sprint
40 Exclusive Offer for Webinar Attendees: January 2020 Tamr Spend Analytics Sprint A two week sprint to answer business- critical questions about your spend Identify gaps in your current spend analytics processes and benchmark your performance Possible sprints: ● How many suppliers do we have? ● How much are we spending on a specific category? ● How many parts do we have? What’s included ● Discovery session to define high impact questions you can answer with your data ● Tamr-led analytic sprint that combines Tamr software, domain expertise, and analytics ● Analytic reports ● Custom insights & recommendations Request a demo or email growth.team@tamr.com by the end of January to get started
4141 Q&A
42 ● Visit Tamr.com to learn more ● Get a Demo to talk to an expert and see Tamr in action ● Read the Tamr Blog to gain further insights ● Speak to Sanjay/Matt directly, email sanjay.rajan@tamr.com or matthew.holzapfel@tamr.com Thank You For Joining Us!
43 Questions 1. We already have a MDM solution. How does ML work with our legacy MDM? 1. What kind of organizational and data prep do we need to do before you can leverage ML? 1. What level of expertise do you need in an organization to tune/maintain a ML model? a. Fear that they may not have the expertise in the organization 1. What does a successful ML project look like from a timeline and resource standpoint? Part 2 - What could delay a ML Project or worse what makes one fail ? 1. We have been building our own ML Model over the past year or so, what is the advantage of using Tamr? a. It is a product - low maintenance, ease of use b. ML may be “free” but productizing it is expensive - operationalizing it is difficult c. Scaling d. Agility e. Extending it to other entities/use cases across your organization for Data Unification
4444 Appendix
4545 Beyond Spend Classification?
46 Result - Unable to achieve Goals Incorrect Info, Uncertainty and Gaps hinders accurate Analytics Where Used (by Qty): ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● ? ● ? ● ? Award History Year Type Limit Unit $ 2015 RFQ? 15% ? 2019 SS 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Spend Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 ? Alternate Murata Elec... ? Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 ? Alternate ? Supplier: Digikey Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Mfg. Part Description: Supplier PN: GRM0335C1H100JA01J Category: Passives >> ……….. >>................ ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO ... ERP Fields: MG, PL Price History: Supplier ? Category Spend Supplier: $310M ? Annual Part Spend: ? Category Spend Mfg.: ? Annual Mfg. Spend: ? Annual Supplier Spend: $17M ? Category Spend: Murata vs Digi-Key ? Spend Ranking: Digikey 10?
47 Category Spend Mfg.: $42M Price History: Supplier Example - Implement as a sequence of ML Projects Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207 BoM Mastering Master part descriptions Identify Duplicates across all data Match with Exploded BoM Data Part Classification Accurate Categories Spend Classification Master part descriptions Match with AVL and Demand forecasts Supplier Classification Match with RFQ and Supplier Awards Analytics Part Mastering (Golden Records) “Single Source of Truth” of Parts
48 Price History: Supplier Result of Accurate Data Unification at Scale Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Category Spend Mfg.: $42M Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207
50 Agenda 1 Why Accurate Spend is still a Problem - Lifecycle of a Part - Complex supply chain relationships - Entity complexity What does your Spend Analytics Program look like? - How are you coping today? - What we hear when we talk to our customers - Make versus Buy ! 2 Case Study High Tech OEM - HPE use case but talk about auto, industrial examples - Challenges, impact of the problem - Setup the problem with meaningful examples (parts, products, bom etc.) The ML Difference - Why ML? Make the case - Contrast with traditional MDM - Point out how ML solves specific problems that traditional methods fail at 4 How to Apply Machine Learning - Sequence of projects - User workflow - Production implementation 5 What else is around the corner? - Steward - ? 63
52 Challenges Suppliers Parts BoM Duplication Misclassification Impact Cost Inaccurate classification of products Billions in mis- classified products = Lost leverage Long lead time, and error prone process introduces noise into the system Hundreds of person- hours to integrate/curate Struggle to support standards-based classification Lost efficiency from leveraging standards- based info Data unavailable to support high multi- million dollar negotiations Millions lost in supply negotiations, potential rebates, and economies of scale
53 Price History: Supplier Result of Accurate Data Unification at Scale Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Category Spend Mfg.: $42M Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207
54 Result - Unable to achieve Goals Incorrect Info, Uncertainty and Gaps hinders accurate Analytics Where Used (by Qty): ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● ? ● ? ● ? Award History Year Type Limit Unit $ 2015 RFQ? 15% ? 2019 SS 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Spend Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 ? Alternate Murata Elec... ? Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 ? Alternate ? Supplier: Digikey Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Mfg. Part Description: Supplier PN: GRM0335C1H100JA01J Category: Passives >> ……….. >>................ ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO ... ERP Fields: MG, PL Price History: Supplier ? Category Spend Supplier: $310M ? Annual Part Spend: ? Category Spend Mfg.: ? Annual Mfg. Spend: ? Annual Supplier Spend: $17M ? Category Spend: Murata vs Digi-Key ? Spend Ranking: Digikey 10?
57 Machine Learning Proof of Concept Results Goal: Categorize parts into USPSC taxonomy with at least 80% accuracy using source data “as-is” - P/N, Description, Commodity Group, Product Line Demonstrated a reduction in manual effort and time required to classify parts ✓ 1.5 weeks to build and train the model, which can be easily generalized to new data ✓ 87.2% of the data was categorized by Tamr’s machine learning capabilities ✓ 85% accuracy after just a few iterations ✓ With only a few hours of training by Subject Matter Experts ✓ Demonstrated the ability of Tamr to reliably classify parts with minimal feedback from SMEs ✓ Continued use of the system will yield further improvements in accuracy and reductions in SME effort ✓ Governed categorization workflow can be generalized to other use cases including BOM parts for improved down stream predictive spend analytics
73Confidential 73 Executive Initiatives that Tamr Accelerates ● Consolidated View of Spend as a Negotiation Lever ● Improve Performance of Supply Chain with better Convergence between Forecast, Plan and Schedule ● Accelerate Data Unification post M&A ● Innovate and Improve Design with Closed Loop Quality
74 Implementation Strategy Parts Classification Down Stream Analytics BOM Parts Mastering Supplier Classification
75 Day in the Life: Data Pipeline Workflow High confidence Low confidence Notification: Manual Review Push to MDM Does the model need to be retrained? Yes No 300 - 400 new SKUs per month Down stream analytics Data analysts report data quality issues Additional: Exploded BOMs, new parts, new product lines
76 Case Study: General Electric Agile, multi-domain entity mastering drives $500M+ in Value “We’ve seen firsthand the transformative results that Tamr’s technology has on an enterprise of GE’s scale. When the cost and complexity of bringing together enterprise datasets is massively reduced, the resulting analytic breakthroughs create opportunities that were previously inaccessible.” Lisa Coca Managing Director, GE Ventures ● < 6 months from pilot to globally deployed data pipeline; 2M records consolidated to 700k ● 25M reduced to 6.4M unique parts; 5- tier analytic-ready classification ● Data from 3 acquisitions integrated with GE’s in <2 weeks Technical Outcome ● Suppliers: Build an integrated view from 75+ ERP systems and 2M supplier records ● Parts: 25M non-unique parts in purchasing systems across 8 BUs ● M&A: Integrate data from acquired entities with existing master views Business Outcome “The supplier data integration was a big win.” Bill Ruh CEO GE Digital & Chief Digital Officer at GE ● $80M savings in Year 1 from Tamr- mastered unified supplier view ● $300M in annual savings identified (0.5% reduction of direct spend) ● Supplier, purchasing, and customer base opportunities quickly identified ● Suppliers: Get GE’s best terms with a given supplier in every negotiation ● Parts: Optimize sourcing strategies to most cost-effective suppliers ● M&A: Increase the velocity of realizing synergies post-acquisition Technical Challenge Business Challenge
77 Price History: Supplier Result of Accurate Data Unification at Scale Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Category Spend Mfg.: $42M Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207
78 Tamr’s Focus - Power your Analytics and Applications through Data Unification BI / Data Viz / Analytics Data Wrangling Sources UsesPeople, Process, Tools, Services, Data Enterprise Data Apps: APS, CRM, ERP, SCM, Quality Supply Chain Data Logical Models – Tamr Automation, Logging Provenance Movement Feedback – Steward Governance Raw Source Catalog Unified Data Hub Multiple ERP Multiple Excel: RFQ, AVL, Allocations Multiple Applications Supplier ERP Extracts Inventory, Quality, Logistics…. Data
7979 Thank you!

More Related Content

PPTX
Data Quality
byVijaya K
 
PDF
3 Strategies to drive more data driven outcomes in financial services
byTamrMarketing
 
PDF
Analytics - Trends and Prospects
byDr. Umesh Rao.Hodeghatta
 
PDF
Applications of AI in Supply Chain Management: Hype versus Reality
byGanes Kesari
 
PPTX
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...
byPrecisely
 
PDF
How to Monetize Your Data Assets and Gain a Competitive Advantage
byCCG
 
PDF
Death of the Dashboard
byDATAVERSITY
 
PPTX
Virtual Governance in a Time of Crisis Workshop
byCCG
 
Data Quality
byVijaya K
 
3 Strategies to drive more data driven outcomes in financial services
byTamrMarketing
 
Analytics - Trends and Prospects
byDr. Umesh Rao.Hodeghatta
 
Applications of AI in Supply Chain Management: Hype versus Reality
byGanes Kesari
 
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...
byPrecisely
 
How to Monetize Your Data Assets and Gain a Competitive Advantage
byCCG
 
Death of the Dashboard
byDATAVERSITY
 
Virtual Governance in a Time of Crisis Workshop
byCCG
 

What's hot

PDF
Governing and Preparing Data for Analytics and Business
byMark Smith
 
PPTX
MLOps - Getting Machine Learning Into Production
byMichael Pearce
 
PPTX
A better business case for big data with Hadoop
byAptitude Software
 
PDF
Big Data Management: Work Smarter Not Harder
byJennifer Walker
 
PDF
The CDO Agenda: Competing with Data - Strategy and Organization
byDATAVERSITY
 
PDF
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...
byDATAVERSITY
 
PDF
Trends 2011 and_beyond_business_intelligence
bydivjeev
 
PDF
Data strategy in a Big Data world
byCraig Milroy
 
PPTX
Machine learning - What they don't teach you on Coursera ODSC London 2016
byHarvinder Atwal
 
PDF
data-to-insight-to-action-taking-a-business-process-view-for-analytics-to-del...
bySokho TRINH
 
PPTX
Data leaders summit 2019
byHarvinder Atwal
 
PDF
Hadoop: Data Storage Locker or Agile Analytics Platform? It’s Up to You.
byJennifer Walker
 
PPTX
Building an Intelligent Supply Chain Frankfurt Supply Chain Interests Group 2002
byMichael Cairns
 
PDF
Move It Don't Lose It: Is Your Big Data Collecting Dust?
byJennifer Walker
 
PDF
Teleran Briefing July 2014
byHoward Meadow
 
PDF
Data-Ed: Data-centric Strategy & Roadmap
byData Blueprint
 
PDF
How to Create and Manage a Successful Analytics Organization
byDATAVERSITY
 
PPTX
Chief Data Officer Challenges - Be Data Driven - Deloitte conference - 20161110
byAbed Ajraou
 
PPTX
Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards
byTechSoup Canada
 
PDF
Smart Data Slides: Leverage the IOT to Build a Smart Data Ecosystem
byDATAVERSITY
 
Governing and Preparing Data for Analytics and Business
byMark Smith
 
MLOps - Getting Machine Learning Into Production
byMichael Pearce
 
A better business case for big data with Hadoop
byAptitude Software
 
Big Data Management: Work Smarter Not Harder
byJennifer Walker
 
The CDO Agenda: Competing with Data - Strategy and Organization
byDATAVERSITY
 
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...
byDATAVERSITY
 
Trends 2011 and_beyond_business_intelligence
bydivjeev
 
Data strategy in a Big Data world
byCraig Milroy
 
Machine learning - What they don't teach you on Coursera ODSC London 2016
byHarvinder Atwal
 
data-to-insight-to-action-taking-a-business-process-view-for-analytics-to-del...
bySokho TRINH
 
Data leaders summit 2019
byHarvinder Atwal
 
Hadoop: Data Storage Locker or Agile Analytics Platform? It’s Up to You.
byJennifer Walker
 
Building an Intelligent Supply Chain Frankfurt Supply Chain Interests Group 2002
byMichael Cairns
 
Move It Don't Lose It: Is Your Big Data Collecting Dust?
byJennifer Walker
 
Teleran Briefing July 2014
byHoward Meadow
 
Data-Ed: Data-centric Strategy & Roadmap
byData Blueprint
 
How to Create and Manage a Successful Analytics Organization
byDATAVERSITY
 
Chief Data Officer Challenges - Be Data Driven - Deloitte conference - 20161110
byAbed Ajraou
 
Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards
byTechSoup Canada
 
Smart Data Slides: Leverage the IOT to Build a Smart Data Ecosystem
byDATAVERSITY
 

Similar to How to Implement a Spend Analytics Program Using Machine Learning

PDF
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...
byElemica
 
PPTX
Master Data Mastery – Strategies to improve procurement performance
byVerdantis Inc.
 
PPTX
Pervasive analytics through data & analytic centricity
byCloudera, Inc.
 
PDF
B2B Transformation in the Electronics Industry
byPaul Brody
 
PPT
Fact Based Negotiation (JerryTalk audio version)
byJerry Nye
 
PPTX
Are Shared Services Ready for Digital Transformation
byITESOFT
 
PPTX
Final PPT
byRAJAT GARG
 
PDF
Optimize supply chains using machine learning superpowers webinar deck
byTamrMarketing
 
PDF
How to make data actionable for business
byRavi Padaki
 
PDF
20131202 Value of supply chain analytics for the electronics industry - a st...
byThorsten Schroeer
 
PDF
Strategically manage data quality in an erp rollout
byVerdantis Inc.
 
PPTX
Strategically Manage Data Quality in an ERP Rollout
byVipul Aroh
 
PPTX
Tips & tricks to drive effective Master Data Management & ERP harmonization
byVerdantis
 
PPTX
Business process modeling and analysis for data warehouse design
bySlava Kokaev
 
PPTX
Dallas Digital Summit: 6 Steps to Big Data Success
bySameer Khan
 
PPTX
DMA 2014: 6 Steps to Integrate Your Big Data
bySameer Khan
 
PDF
[Webinar] - How to Future-proof Your ERP Applications with Intelligent Automa...
byJK Tech
 
PPT
Sears mdm lunch and learn attribute section final
byMarco Arratia
 
PPTX
Tips & tricks to drive effective mdm & erp harmonization
byVerdantis Inc.
 
PPTX
Pactera Big Data Solutions for Retail
byPactera_US
 
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...
byElemica
 
Master Data Mastery – Strategies to improve procurement performance
byVerdantis Inc.
 
Pervasive analytics through data & analytic centricity
byCloudera, Inc.
 
B2B Transformation in the Electronics Industry
byPaul Brody
 
Fact Based Negotiation (JerryTalk audio version)
byJerry Nye
 
Are Shared Services Ready for Digital Transformation
byITESOFT
 
Final PPT
byRAJAT GARG
 
Optimize supply chains using machine learning superpowers webinar deck
byTamrMarketing
 
How to make data actionable for business
byRavi Padaki
 
20131202 Value of supply chain analytics for the electronics industry - a st...
byThorsten Schroeer
 
Strategically manage data quality in an erp rollout
byVerdantis Inc.
 
Strategically Manage Data Quality in an ERP Rollout
byVipul Aroh
 
Tips & tricks to drive effective Master Data Management & ERP harmonization
byVerdantis
 
Business process modeling and analysis for data warehouse design
bySlava Kokaev
 
Dallas Digital Summit: 6 Steps to Big Data Success
bySameer Khan
 
DMA 2014: 6 Steps to Integrate Your Big Data
bySameer Khan
 
[Webinar] - How to Future-proof Your ERP Applications with Intelligent Automa...
byJK Tech
 
Sears mdm lunch and learn attribute section final
byMarco Arratia
 
Tips & tricks to drive effective mdm & erp harmonization
byVerdantis Inc.
 
Pactera Big Data Solutions for Retail
byPactera_US
 

More from TamrMarketing

PPTX
Data Mastering at Scale with Michael Stonebraker
byTamrMarketing
 
PPTX
Data as a Strategic Asset
byTamrMarketing
 
PDF
7 Steps for Boosting R&D Outcomes
byTamrMarketing
 
PPTX
How Santander UK Accelerates Digital Initiatives by Mastering Customer Data
byTamrMarketing
 
PPTX
DataOps @ Scale: A Modern Framework for Data Management in the Public Sector
byTamrMarketing
 
PPTX
Sailing Toward Global Data Alignment with Carnival Corporation
byTamrMarketing
 
PPTX
Agile Leadership: Guiding DataOps Teams Through Rapid Change and Uncertainty
byTamrMarketing
 
PPTX
Michael Stonebraker: Big Data, Disruption, and the 800 Pound Gorilla in the ...
byTamrMarketing
 
Data Mastering at Scale with Michael Stonebraker
byTamrMarketing
 
Data as a Strategic Asset
byTamrMarketing
 
7 Steps for Boosting R&D Outcomes
byTamrMarketing
 
How Santander UK Accelerates Digital Initiatives by Mastering Customer Data
byTamrMarketing
 
DataOps @ Scale: A Modern Framework for Data Management in the Public Sector
byTamrMarketing
 
Sailing Toward Global Data Alignment with Carnival Corporation
byTamrMarketing
 
Agile Leadership: Guiding DataOps Teams Through Rapid Change and Uncertainty
byTamrMarketing
 
Michael Stonebraker: Big Data, Disruption, and the 800 Pound Gorilla in the ...
byTamrMarketing
 

Recently uploaded

PDF
Maricar Payaoan Belarma Portfolio :)))))
bymaricarbelarmava
 
PDF
Plainte française dans le cadre de l'affaire Epstein
bySociété Tripalio
 
PDF
제 23회 보아즈(BOAZ) 빅데이터 컨퍼런스 - [MBOAX] : ABSA를 활용한 소비자 반응 분석 기반 운영 효율화 대시보드 설계
byBOAZ Bigdata
 
PDF
AI + Data Science: How Artificial Intelligence is Transforming Modern Analytics
byalansanu8
 
PPTX
Unit III SPC.pptx- SECURITY AND PRIVACY IN CLOUD
by23b129
 
PDF
Baltic Startup Funding Report 2025 by FIRSTPICK and Practica Capital
byFIRSTPICK VC
 
PPTX
Hacking-Uncovered-How-People-Get-Hacked-and-How-to-Stay-Safe[1].pptx
bylegendgaminghari
 
PDF
Machine Learning Lectures -- Outliers in Data
byZahra Sadeghi
 
PPTX
AI-Transformation-Executive-Halo-conversation-deck.pptx
byAdelaide Leitão
 
PPTX
SQL FOR DATA ANALYSTS - MSSQLServer for Analysts
bysirtwumasi77
 
PDF
Top 10 Excel Tips for Efficiency | Learn Advanced Excel Techniques CA SUVIDHA...
byCA Suvidha Chaplot
 
PDF
Macro Energy Trust Shock - current developments in the Venezuelan energy sector
byAurélie Dupassieux
 
PPTX
detector for xrays is for detecting with ai
bydishujeemain
 
PDF
Content Strategy Presentation.pdfjjjjhhhhh
byCerineAmmarkhodja1
 
PDF
Real Estate Data Analytics: Unlock Smarter Investment Decisions with Leni
byLeni Analyst
 
PPTX
Toxicity symptoms in plants due to higher concentration
byranveer02yadav
 
PPTX
Autism types, forms and everything we need to know
byfuzertamas
 
PDF
Whitepaper / No code end-user app development (2026)
byOptymyze
 
PDF
"Essential Excel Tips for Beginners & Professionals – 50+ Techniques to Maste...
byCA Suvidha Chaplot
 
PPTX
04-Strengthening collaboration between IEA-ETSAP and IEA-Wind TCP
byIEA-ETSAP
 
Maricar Payaoan Belarma Portfolio :)))))
bymaricarbelarmava
 
Plainte française dans le cadre de l'affaire Epstein
bySociété Tripalio
 
제 23회 보아즈(BOAZ) 빅데이터 컨퍼런스 - [MBOAX] : ABSA를 활용한 소비자 반응 분석 기반 운영 효율화 대시보드 설계
byBOAZ Bigdata
 
AI + Data Science: How Artificial Intelligence is Transforming Modern Analytics
byalansanu8
 
Unit III SPC.pptx- SECURITY AND PRIVACY IN CLOUD
by23b129
 
Baltic Startup Funding Report 2025 by FIRSTPICK and Practica Capital
byFIRSTPICK VC
 
Hacking-Uncovered-How-People-Get-Hacked-and-How-to-Stay-Safe[1].pptx
bylegendgaminghari
 
Machine Learning Lectures -- Outliers in Data
byZahra Sadeghi
 
AI-Transformation-Executive-Halo-conversation-deck.pptx
byAdelaide Leitão
 
SQL FOR DATA ANALYSTS - MSSQLServer for Analysts
bysirtwumasi77
 
Top 10 Excel Tips for Efficiency | Learn Advanced Excel Techniques CA SUVIDHA...
byCA Suvidha Chaplot
 
Macro Energy Trust Shock - current developments in the Venezuelan energy sector
byAurélie Dupassieux
 
detector for xrays is for detecting with ai
bydishujeemain
 
Content Strategy Presentation.pdfjjjjhhhhh
byCerineAmmarkhodja1
 
Real Estate Data Analytics: Unlock Smarter Investment Decisions with Leni
byLeni Analyst
 
Toxicity symptoms in plants due to higher concentration
byranveer02yadav
 
Autism types, forms and everything we need to know
byfuzertamas
 
Whitepaper / No code end-user app development (2026)
byOptymyze
 
"Essential Excel Tips for Beginners & Professionals – 50+ Techniques to Maste...
byCA Suvidha Chaplot
 
04-Strengthening collaboration between IEA-ETSAP and IEA-Wind TCP
byIEA-ETSAP
 

How to Implement a Spend Analytics Program Using Machine Learning

  • 1.
    1Confidential 1 How toImplement a Spend Analytics Program Using Machine Learning in 2020 January 15th, 2020
  • 2.
    2 Speakers Matt Holzapfel Solutions Leadat Tamr Leslie Bull Head of Western U.S. Sales and Partner Programs at Tamr Sanjay Rajan Strategic Sales Head, Manufacturing/Industry 4.0 at Tamr
  • 3.
    3 Agenda 1 Why Accurate Spend isstill a Problem What does your Spend Analytics Program look like? 2 Case Study High Tech OEM The ML Difference 4 How to Apply Machine Learning 5 Q&A 63
  • 4.
    44 Why is itdifficult to achieve? Accurate Direct Spend Analysis
  • 5.
    5 What are youspending money on? Who are you spending it with? Lorem ipsum dolor sit amet at nec at adipiscing03 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Lorem ipsum dolor sit amet at nec at adipiscing01 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Who are my top suppliers in any one spend category?03 ● Consolidate suppliers by category ● Savings of $0.02 to $0.09 per $ consolidated What are my top spend categories?01 ● Actively manage your top categories ● Which categories are most impactful to bottom line? Lorem ipsum dolor sit amet at nec at adipiscing03 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Lorem ipsum dolor sit amet at nec at adipiscing02 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Lorem ipsum dolor sit amet at nec at adipiscing01 ● Donec risus dolor porta venenatis ● Pharetra luctus felis ● Proin in tellus felis volutpat Do you have an “always accurate” supplier master?06 ● If you haven’t mastered your suppliers, your foundation for spend analytics is weak Can you prevent creation of duplicate parts?05 ● Within and across business units how much part proliferation exists? ● $10K per duplicate part in your system (ATK) Can you identify maverick purchasing?04 ● Are contracts negotiated for best price and terms always being used? 02 ● Who do I have leverage with? ● Is it changing - $, Geography? Who are my top suppliers? 02 ● Know who you have leverage with to capture the best opportunity for best rates and discounts
  • 6.
    6 What makes itdifficult to answer the “What and Who”? ERP 1 ERP 2 Acme Business Systems ABS AB Systems Power Parts Acme Speciality Inc. subsidiaries 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible Duplicates dilute your spend across different entities Are you spending twice as much with a supplier than you think? XLS
  • 7.
    7 What makes itdifficult to answer the “What and Who”? 1. Incomplete Data 2. Duplicate Data 3. Inaccurate Data 4. Not Granular enough 5. Not Accessible Does not belong in category Correctly Classified Missing - scattered in other categories $15M $20M $35M Electrical Equipment 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible Even a small % of misclassification in a Top category means millions in lost savings Accuracy is all or nothing Spend Categories
  • 8.
    8 What makes itdifficult to answer the “What and Who”? 1. Incomplete Data 2. Duplicate Data 3. Inaccurate Data 4. Not Granular enough 5. Not Accessible 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible Your classification needs to represent items you actually source Vague descriptions or those that are not directly actionable represent wasted savings opportunities Heating and Air Conditioning Humidity Control Heating Equipment Split Exchanger Heat Exchanger Finned Tubular Exchanger One Point Exchange Boilers
  • 9.
    9 What makes itdifficult to answer the “What and Who”? 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible P-card Non-PO One-time buy Stock Materials Recurring Services Capital ... Source? Visibility into “blank” spend can unlock potential savings opportunities
  • 10.
    10 What makes itdifficult to answer the “What and Who”? Mergers, Acquisition Business Resource constraint Data is scattered across systems Other IT projects take priority ? 1. Duplicate Data 2. Inaccurate Data 3. Not Granular enough 4. Incomplete Data 5. Not Accessible Critical to get accurate data into the hands of those who can and will leverage it What is stalling your data unification?
  • 11.
  • 12.
    12 Make Buy Sell- Supply Chain is complex OEM Supplier Distributors Manufacturer $ Hightech Automotive Industrial Equipment Medical Device Energy Food and Beverage Chemicals ….. 3PL ● Intermediate products ● Sub assemblies ● Turnkey Products ● Raw Materials ● Tiered Mfg. ● Distribution ● Warehousing ● Fulfillment ● Inventory ● After Market ● Services
  • 13.
    13 PartManufacturer Supplier Supply Chain Attributes SupplyScheduleDemand Plan Where Used Unit Cost Annual Spend Annual Spend MPN Description ERP PN Description VPN Description Annual SpendAlternate Preferred Substitute Unit Price Discount Schedule Lead Time VMI Consignment Awards Allocations What to Make When to Make How Much Where Materials Resources How to Make Capacity Constraints Priorities Status Monitoring Manage Change Inventory Status Classification Tier 1 Tier 2 Tier 3 SKU/Family Product Assembly Sub-Assy Part Make/Buy Lifecycle CAD BoM Configurations Versions Revisions Change Management ECN ECO PCN Affected Items Disposition New Product Introductions Milestones Requirements Simulations Tests Prototypes Supplier Quality Audits Scorecard On Time DeliveryQuality Cost Variance Product Quality FPY Non Conformances Outgoing Quality Complex Web of Relationships Distributor DPN Description
  • 14.
  • 15.
    15 How are organizationscoping? Hire 3rd Party Services (last 15+ years) Integrate Applications (last 10 years) Employ Data Scientists (last few years) - Typically overseas resources - Fixed budget project - Fixes done to IT requirements Typically poor ROI, one-time fix - Most prevalent approach - Takes Years before you see value - High Startup Costs - High maintenance costs Does not Scale, and not Agile - Focus on Big Data, Analytics - Write Deterministic “Rules” - Trial and Error, Iterative - Add more Resources Is it on time or under budget?
  • 16.
  • 17.
    17 High Tech OEMwith >$5B in Spend High Tech OEM Supplier (Foxconn) Distributors Manufacturer (Intel) ● Parts ● Products ● Turnkey Assemblies ● Sub-Assemblies ○ Mechanical ○ Electrical $ 3PL Savings Opportunity Potential Misclassification of $3B value in 15,000 parts
  • 18.
    19 As Is Process -Manual hence Slow - Resource Intensive - Unreliable - Expensive to Govern/Maintain - Not Agile (ex: add new source) Employ Data Engineers and hire 3rd Party Services
  • 19.
    20 Examples of thetypes of misclassification in results ● Potential Misclassification of $3B value in 15,000 parts ● High Value parts in low value categories ● Low Value parts in High Value Categories ● “Tail Spend” category was among the top 3 - knew this was wrong ● …. Labels Plastic Injection Molding Printed Circuit Boards Motherboards Motherboards Network Switch Tape Drive Microprocessors Solid State Drives
  • 20.
    21 Machine Learning Proofof Concept Results Accuracy after just a few iterations of the model85% Accuracy within the training data set95% Records categorized by Tamr90% Weeks to ingest data, train experts, and tune the ML model1.5
  • 21.
    22 Actionable results inless than a week Before Tamr 80 distinct categories After Tamr 66 distinct categories ➔ 3.7X increase in parts classified to Microprocessors and SSDs ➔ 6X decrease to parts classified under Computer Accessory Kits “We buy a lot more of Microprocessors and SSDs. This does not make sense” “Computer Accessory Kits is not granular enough”
  • 22.
    25 Result - Unableto achieve Goals Incorrect Info, Uncertainty and Gaps hinders accurate Analytics Where Used (by Qty): ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● ? ● ? ● ? Award History Year Type Limit Unit $ 2015 RFQ? 15% ? 2019 SS 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Spend Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 ? Alternate Murata Elec... ? Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 ? Alternate ? Supplier: Digikey Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Mfg. Part Description: Supplier PN: GRM0335C1H100JA01J Category: Passives >> ……….. >>................ ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO ... ERP Fields: MG, PL Price History: Supplier ? Category Spend Supplier: $310M ? Annual Part Spend: ? Category Spend Mfg.: ? Annual Mfg. Spend: ? Annual Supplier Spend: $17M ? Category Spend: Murata vs Digi-Key ? Spend Ranking: Digikey 10?
  • 23.
    26 Price History: Supplier Result ofAccurate Data Unification at Scale Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Category Spend Mfg.: $42M Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207
  • 24.
    27 Capture bottom linesavings Based on other clients and assuming $1B direct spend 80% decrease in manual data prep & technical support $0.6M 40% improvement in classification accuracy drives better negotiations $3.5M 20% increase in spend under management $1.1M 100% increase in analytic adoption$2.0M 10% improvement in RFP timing with better supplier intelligence $0.6M 25% increase in suppliers included in each RFP $1.1M $8.9M Total Benefits per $1bn spend $500M+ Savings From Sourcing Analytics Across Siloed Businesses Testimonial
  • 25.
  • 26.
    29 Example: Rules-only approach Supplier Transaction description Classification rule GRAINGER 1-3/4"Steel Crowfoot Socket Wrench If Supplier = Grainger then Category = Tools
  • 27.
    30 Example: Rules-only approach Supplier Transaction description Classification rule IfSupplier = Grainger AND Description = Tape then Category = Adhesives GRAINGER 3M DOUBLE SIDED TAPE GRAINGER 1-3/4" Steel Crowfoot Socket Wrench If Supplier = Grainger then Category = Tools
  • 28.
    31 Example: Rules-only approach Supplier Transaction description Classification rule IfSupplier = Grainger AND Description = Tape Measure then Category = Tools GRAINGER TAPE MEASURE If Supplier = Grainger AND Description = Tape then Category = Adhesives GRAINGER 1-3/4" Steel Crowfoot Socket Wrench If Supplier = Grainger then Category = Tools GRAINGER 3M DOUBLE SIDED TAPE
  • 29.
    32 How many technicalresources can you afford? If Supplier = Grainger AND Description = Tape then Category = Adhesives Supplier Transaction description Classification rule GRAINGER 1-3/4" Steel Crowfoot Socket Wrench If Supplier = Grainger then Category = Tools GRAINGER 3M DOUBLE SIDED TAPE If Supplier = Grainger AND Description = Tape Measure then Category = Tools GRAINGER TAPE MEASURE Book of classification rules 2020 edition
  • 30.
    33 ML augments rules-systemsto provide greater speed & accuracy Supplier GRAINGER Item Name PLIERS PO Subject TOOLS FOR MOTORMAN WORKSHOP User category Tools A user manually categorizes a subset of transactions ML learns the underlying classification patterns Pliers are classified as Tools 100% of the time Grainger is usually, but not always, mechanical hardware The word workshop has no significance All other items, including new items, are automatically classified by ML’s knowledge base 2018 POs (Human classified) ... 2018 Invoices (ML classified) 2020 Invoices (ML classified)
  • 31.
    34 When is MLneeded? High category variety (any tier) Many sources (systems, users) Lean organization Need for speed!
  • 32.
    3535 How to applyMachine Learning?
  • 33.
    36 Typical Rules-based Process - Manual= Slow - Resource Intensive - Unreliable - Expensive to Govern/Maintain - Not Agile (ex: add new source) Employ Data Engineers and hire 3rd Party Services
  • 34.
    37 Human-Guided Machine Learning Leverage Human-GuidedML (now) - Probabilistic Model-based - Expert-Guided, Less Resources - Continuously Improves Results - Fast time to value - Scalability and Agility
  • 35.
    38 Agile approach startswith the business question, creates value incrementally Analytic Outcome BUSINESS VALUE Catalog Prepare Analyze Visualize How many unique suppliers do we have? Analytic Outcome BUSINESS VALUE Catalog Prepare Analyze Visualize How much are we spending on “Mechanical”?Sprint 1 Sprint 2 ● Each sprint limits the scope of data to only what is required to answer the analytic question ● Going end-to-end enables you to understand bottlenecks (resources & technology) limiting analytic velocity, and design strategies to enable data operations (DataOps) to scale, with minimal upfront investment
  • 36.
  • 37.
    40 Exclusive Offer forWebinar Attendees: January 2020 Tamr Spend Analytics Sprint A two week sprint to answer business- critical questions about your spend Identify gaps in your current spend analytics processes and benchmark your performance Possible sprints: ● How many suppliers do we have? ● How much are we spending on a specific category? ● How many parts do we have? What’s included ● Discovery session to define high impact questions you can answer with your data ● Tamr-led analytic sprint that combines Tamr software, domain expertise, and analytics ● Analytic reports ● Custom insights & recommendations Request a demo or email growth.team@tamr.com by the end of January to get started
  • 38.
  • 39.
    42 ● Visit Tamr.comto learn more ● Get a Demo to talk to an expert and see Tamr in action ● Read the Tamr Blog to gain further insights ● Speak to Sanjay/Matt directly, email sanjay.rajan@tamr.com or matthew.holzapfel@tamr.com Thank You For Joining Us!
  • 40.
    43 Questions 1. We alreadyhave a MDM solution. How does ML work with our legacy MDM? 1. What kind of organizational and data prep do we need to do before you can leverage ML? 1. What level of expertise do you need in an organization to tune/maintain a ML model? a. Fear that they may not have the expertise in the organization 1. What does a successful ML project look like from a timeline and resource standpoint? Part 2 - What could delay a ML Project or worse what makes one fail ? 1. We have been building our own ML Model over the past year or so, what is the advantage of using Tamr? a. It is a product - low maintenance, ease of use b. ML may be “free” but productizing it is expensive - operationalizing it is difficult c. Scaling d. Agility e. Extending it to other entities/use cases across your organization for Data Unification
  • 41.
  • 42.
  • 43.
    46 Result - Unableto achieve Goals Incorrect Info, Uncertainty and Gaps hinders accurate Analytics Where Used (by Qty): ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● ? ● ? ● ? Award History Year Type Limit Unit $ 2015 RFQ? 15% ? 2019 SS 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Spend Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 ? Alternate Murata Elec... ? Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 ? Alternate ? Supplier: Digikey Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Mfg. Part Description: Supplier PN: GRM0335C1H100JA01J Category: Passives >> ……….. >>................ ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO ... ERP Fields: MG, PL Price History: Supplier ? Category Spend Supplier: $310M ? Annual Part Spend: ? Category Spend Mfg.: ? Annual Mfg. Spend: ? Annual Supplier Spend: $17M ? Category Spend: Murata vs Digi-Key ? Spend Ranking: Digikey 10?
  • 44.
    47 Category Spend Mfg.: $42M PriceHistory: Supplier Example - Implement as a sequence of ML Projects Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207 BoM Mastering Master part descriptions Identify Duplicates across all data Match with Exploded BoM Data Part Classification Accurate Categories Spend Classification Master part descriptions Match with AVL and Demand forecasts Supplier Classification Match with RFQ and Supplier Awards Analytics Part Mastering (Golden Records) “Single Source of Truth” of Parts
  • 45.
    48 Price History: Supplier Result ofAccurate Data Unification at Scale Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Category Spend Mfg.: $42M Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207
  • 46.
    50 Agenda 1 Why Accurate Spend isstill a Problem - Lifecycle of a Part - Complex supply chain relationships - Entity complexity What does your Spend Analytics Program look like? - How are you coping today? - What we hear when we talk to our customers - Make versus Buy ! 2 Case Study High Tech OEM - HPE use case but talk about auto, industrial examples - Challenges, impact of the problem - Setup the problem with meaningful examples (parts, products, bom etc.) The ML Difference - Why ML? Make the case - Contrast with traditional MDM - Point out how ML solves specific problems that traditional methods fail at 4 How to Apply Machine Learning - Sequence of projects - User workflow - Production implementation 5 What else is around the corner? - Steward - ? 63
  • 47.
    52 Challenges Suppliers Parts BoM Duplication Misclassification ImpactCost Inaccurate classification of products Billions in mis- classified products = Lost leverage Long lead time, and error prone process introduces noise into the system Hundreds of person- hours to integrate/curate Struggle to support standards-based classification Lost efficiency from leveraging standards- based info Data unavailable to support high multi- million dollar negotiations Millions lost in supply negotiations, potential rebates, and economies of scale
  • 48.
    53 Price History: Supplier Result ofAccurate Data Unification at Scale Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Category Spend Mfg.: $42M Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207
  • 49.
    54 Result - Unableto achieve Goals Incorrect Info, Uncertainty and Gaps hinders accurate Analytics Where Used (by Qty): ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● ? ● ? ● ? Award History Year Type Limit Unit $ 2015 RFQ? 15% ? 2019 SS 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Spend Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 ? Alternate Murata Elec... ? Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 ? Alternate ? Supplier: Digikey Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Mfg. Part Description: Supplier PN: GRM0335C1H100JA01J Category: Passives >> ……….. >>................ ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO ... ERP Fields: MG, PL Price History: Supplier ? Category Spend Supplier: $310M ? Annual Part Spend: ? Category Spend Mfg.: ? Annual Mfg. Spend: ? Annual Supplier Spend: $17M ? Category Spend: Murata vs Digi-Key ? Spend Ranking: Digikey 10?
  • 50.
    57 Machine Learning Proofof Concept Results Goal: Categorize parts into USPSC taxonomy with at least 80% accuracy using source data “as-is” - P/N, Description, Commodity Group, Product Line Demonstrated a reduction in manual effort and time required to classify parts ✓ 1.5 weeks to build and train the model, which can be easily generalized to new data ✓ 87.2% of the data was categorized by Tamr’s machine learning capabilities ✓ 85% accuracy after just a few iterations ✓ With only a few hours of training by Subject Matter Experts ✓ Demonstrated the ability of Tamr to reliably classify parts with minimal feedback from SMEs ✓ Continued use of the system will yield further improvements in accuracy and reductions in SME effort ✓ Governed categorization workflow can be generalized to other use cases including BOM parts for improved down stream predictive spend analytics
  • 51.
    73Confidential 73 Executive Initiativesthat Tamr Accelerates ● Consolidated View of Spend as a Negotiation Lever ● Improve Performance of Supply Chain with better Convergence between Forecast, Plan and Schedule ● Accelerate Data Unification post M&A ● Innovate and Improve Design with Closed Loop Quality
  • 52.
    74 Implementation Strategy Parts ClassificationDown Stream Analytics BOM Parts Mastering Supplier Classification
  • 53.
    75 Day in theLife: Data Pipeline Workflow High confidence Low confidence Notification: Manual Review Push to MDM Does the model need to be retrained? Yes No 300 - 400 new SKUs per month Down stream analytics Data analysts report data quality issues Additional: Exploded BOMs, new parts, new product lines
  • 54.
    76 Case Study: GeneralElectric Agile, multi-domain entity mastering drives $500M+ in Value “We’ve seen firsthand the transformative results that Tamr’s technology has on an enterprise of GE’s scale. When the cost and complexity of bringing together enterprise datasets is massively reduced, the resulting analytic breakthroughs create opportunities that were previously inaccessible.” Lisa Coca Managing Director, GE Ventures ● < 6 months from pilot to globally deployed data pipeline; 2M records consolidated to 700k ● 25M reduced to 6.4M unique parts; 5- tier analytic-ready classification ● Data from 3 acquisitions integrated with GE’s in <2 weeks Technical Outcome ● Suppliers: Build an integrated view from 75+ ERP systems and 2M supplier records ● Parts: 25M non-unique parts in purchasing systems across 8 BUs ● M&A: Integrate data from acquired entities with existing master views Business Outcome “The supplier data integration was a big win.” Bill Ruh CEO GE Digital & Chief Digital Officer at GE ● $80M savings in Year 1 from Tamr- mastered unified supplier view ● $300M in annual savings identified (0.5% reduction of direct spend) ● Supplier, purchasing, and customer base opportunities quickly identified ● Suppliers: Get GE’s best terms with a given supplier in every negotiation ● Parts: Optimize sourcing strategies to most cost-effective suppliers ● M&A: Increase the velocity of realizing synergies post-acquisition Technical Challenge Business Challenge
  • 55.
    77 Price History: Supplier Result ofAccurate Data Unification at Scale Drive Actionable Insights trapped in your Data Silos Where Used (by Qty): ● PCA, SYS I/O, XL2x260w ● Aruba IAP-224 (RW) Instant 3x3:3…. ● HPE USB US Keyboard/Mouse Kit ● HPE DL325 Gen10 7351P 16G 8SFF…. ● HPE ZApollo 4200 Gen10 48SFF CTO.. ● PCA, Control SYS I/O ZSCLR12… ● PCA, Control SYS I/O XSLX ROHS... ● Click Here for 25 more Manufacturer: Murata Electronics MPN: GRM0335C1H100JA01J Category: Passives >> Capacitors >> Ceramic Capacitors Category Spend Supplier: $503M Annual Part Spend: $1.67M Mfg. Part Description: CAP CER 10PF 50V NP0 0201 Supplier: Digi-Key Supplier PN: 490-17811-1-ND Category Spend Mfg.: $42M Annual Mfg. Spend: $0.35M Annual Supplier Spend: $22M ERP PN 8601J-32 ERP Part Description: CAP 50V 10pf C0G/NPO CER ... Award History Year Typ e Lim Unit $ 2015 RFQ 15% $0.0015 2019 Sole 30% $0.0012 Type P/N Desc. Supplier Unit $ Indirect Product Preferred 8601J-32 MLCC 50V 10pf Murata Elec... $0.0012 no Alternate 8601J-33 50V, 10PF, NP0, Multi…. Murata Elec... $0.0012 yes HPE USB US Keyboard... Alternate 78785-01 MLCC-SMD/SMT 50V…. Kemet $0.0015 no Alternate 8459X-05 SMD/SMT 50V10pF 201 AVX $0.0014 yes HPE ZApollo 4200Gen1... Category Spend: Murata vs Digi-Key Spend Ranking: Digi-Key 6/207
  • 56.
    78 Tamr’s Focus -Power your Analytics and Applications through Data Unification BI / Data Viz / Analytics Data Wrangling Sources UsesPeople, Process, Tools, Services, Data Enterprise Data Apps: APS, CRM, ERP, SCM, Quality Supply Chain Data Logical Models – Tamr Automation, Logging Provenance Movement Feedback – Steward Governance Raw Source Catalog Unified Data Hub Multiple ERP Multiple Excel: RFQ, AVL, Allocations Multiple Applications Supplier ERP Extracts Inventory, Quality, Logistics…. Data
  • 57.