Predictive Analytics: Better Commerce Insight | Ariba LIVE Rome

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Predictive analytics is a solution that comprises the collection and analysis of data through quantitative means to draw conclusions and insights, and ultimately predict future events. An increasing number of organizations are turning to predictive analytics to make better decisions and improve performance.

In this session we’ll explore predictive areas such as benchmarking, performance measurement, and commerce insight. We’ll look at practical ways to use these services and solutions to improve sourcing & procurement, sales & marketing, procure-to-pay, and order-to-cash processes.

Come to this session to find out how predictive analytics can be a game-changer and ultimately the best tool in your tool box.

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  • We’re living in the era of the Networked Economy. In our personal lives we are connected to our family, friends, and business colleagues in more ways than ever. In the business community, companies are considered Networked Enterprises when they connect their back office systems, people and processes with their customers, suppliers and trading partners. McKinsey says that Networked Enterprises are 50% more likely to be market leaders.The Networked Economy is transforming business in a couple very important ways. It’s creating a far more efficient supply chain; it’s taking out cost and improving process efficiency; it’s optimizing working capital; and reducing risk while improving compliance.It makes the consumption of software much easier to buy, deploy and use. At Ariba, we pride ourselves on creating a user buying experience comparable to what we experience in our personal lives with Ebay and Amazon. In the Networked Economy we can get information in real-time. But real-time is no longer good enough.Knowing your manufacturing plant just went down. Knowing that you just lost your largest customer to a chief competitor is knowing that far too late.To stay ahead, businesses must be able to:Predict the future with a high-degree of accuracyAssess the best course of action andHave the agility to adapt their business model accordinglyThis is where the cloud, mobility and the Business Network add value. And this is why being a predictive business is essential to success in the Networked Economy.
  • In this session we’re going to explore a variety of predictive analytic use cases, and look at practical ways to transform your business into a predictive business. We’ll share examples of predictive solutions from SAP and Ariba. And we’ll have time at the end for Q&A.You should have three key takeaways from this session:1. Have a better understanding of Predictive Analytics2. Understand how you can leverage Predictive Analytics in your industry and your job function3. Understand how SAP and Ariba can help you with your business challenges on this frontBut before we dive into the power of predictive analytics, I have something special I want to share with you…
  • Before we dive into the power of predictive analytics, I first want to share the top 5 worst predictions ever… dim the lights please
  • http://www.buzzfeed.com/lukelewis/26-shockingly-bad-predictionsHow many of you have used the internet today? Okay… that one was wrong?Did you know there over about 650 Million websites and 56 Billion pageviews – just on reddit.com in 2013?!http://royal.pingdom.com/2013/01/16/internet-2012-in-numbers/
  • http://www.buzzfeed.com/lukelewis/26-shockingly-bad-predictionsHow many of you use an iPhone? That was wrong…Do you know that over 420 Million iPhones have been sold worldwide. 420 Million!!
  • http://www.buzzfeed.com/lukelewis/26-shockingly-bad-predictionsHow many of you have bought something on Amazon or Ebay? Okay, that was wrong.Ebay did over $200 billion of commerce volumein 2013Source: http://finance.yahoo.com/news/ebay-investor-guide-world-largest-134315167.htmlAmazon has over 30 million customers. And you know last year, when Amazon's site went down for 49 minutes, the company missed nearly$6 million in sales. In all seriousness, the reason I wanted to point out the worst predictions in the world is to highlight just how important making a good prediction is. Making the right decision can make or break a company. Let me share an example of a company who used predictive analytics to really turn their organization around….
  • Let me close my introductory remarks with a brief video clip from the movie based on the Michael Lewis book, “Moneyball”. Moneyball (Breaking Biases).webmC:\Users\I836040\Documents\Ariba\Ariba LIVE\2014\Predictive AnalyticsUp to 0:47 “using stats the way that we read them, we’ll find value in players that nobody else can see.”They were able to find the right priced and qualified players to fill the positions they needed to build a championship team because they had the right data and the right data models.That’s the real power of predictive analytics. Look at the history of analytics…
  • When we start thinking about use cases for Predictive Analytics, a really good example are the tools that many day traders and professional investors use to predict the direction a stock or an index will take. The ISE Sentiment Index is one of those examples. ISE Sentiment index = (Long Calls Purchased / Long Puts Purchased) * 100The ISI (Int’l Securities Exchange Sentiment Index) uses put and call transactions to predict bullish/bearish market direction. You can see the ISIhas been a pretty good indicator of movement. The index poked its head slightly above the 150 mark a few times in March, and really spiked in April before the huge sell-off. We also see a sharp spike in August, at the last major high. And in Sept we see another pop above 150, but not a huge one. So this is a great example of a good predictive tool, but not one I would bet my life savings on. So what’s the problem?[click]“the extent to which common macroeconomic forecasting variables enhance our understanding of time-variation in volatility is rather limited.” - Do Macroeconomic Variables Predict Aggregate Stock Market Volatility?Bradley S. Paye, Jones Graduate School, Rice University, October 6, 2008[click]In other words - predictive analysis is only as good as the data and the data models you use.Let me share some examples of industries that have successfully deployed predictive analytics…
  • Power/Utility/Oil & GasOil & Gas uses predictive analysis to find oil reserves.The Utilities industry is moving “aggressively” into the predictive analtyicsspace with tools for monitoring usage and outages.Many utilities now analyze automated data from “smart meters” along with environmental data to predict utility consumption. HealthcareSimulate Patient Reported Outcomes (PRO) for care quality improvement.Predict market access and optimize resource allocation for new therapies.Predict high risk patients and reduce hospital readmissions. http://www.icosystem.com/9-ways-to-apply-predictive-analytics-to-healthcare/Public Sector — Predict tax revenue based on historical demographic trends and economic assumptions.Financial ServicesSears Holding Corporation, the parent company of the retailer, uses over 100 performance metrics for financial models to predict deposit revenue based on demographic data, interest rate projections and historical regression trends. Insurance - Every business wants to peer into the future, but few industries rely as heavily on forecasts as insurance. Retail and Consumer Products — Predictive Analytics gives retail marketers the power to see into the future and know with almost complete certainty what products shoppers will purchase and where they will make those purchases.http://www.dmnews.com/predictive-analytics-the-new-retail-currency/article/287244/From what we’ve seen here, these industries have a done a good job getting the right data and refining their data models.
  • Sales/CRM - Develop scoring models at the opportunity level to determine the likelihood of an opportunity closing successfully or not. And prescribe actions that could be taken with individual opportunities to increase the likelihood of success. Use predictive models to help improve the accuracy of revenue forecasts.Another common use of predictive analytics is to identifywhich customers are likely to churn. For example, telecommunications firms can use customer data such as calls made, minutes used, number of texts sent, average bill amount, and hundreds of other variables to find models that will predict which customers are likely to change mobile carriers.Human Resources – We’re familiar with the story of how the Oakland A’s formed used predictive analytics to select players and created a champsionship team with the lowest payroll in all of baseball.U.S. Special Forces uses predictivemodels to assess candidates. They are able to rapidly refine a huge pool of candidates down to a much more manageble size with the most probable success.Theses same kinds of models can be used for virtually any hiring practices.http://www.slideshare.net/SAPanalytics/predictive-finance-22144239human judgment / AssessmentAssess results sooner with greater accuracyRespond faster to actual outcomesApply across company or to specific customers – i.e. well-established cyclical patternsSupply ChainLocationMuch like items “trend” on the Web, predictive models tied to location technology have sprouted up to gauge experiences and expected outcomes for future events. Foursquare, Facebook and other social network purveyors have implemented analytics along incoming location data to assess upcoming events, and vendors like Pitney Bowes are attaching location-based analytic forecasting with its BI suites.Finance - Planning, budgeting, forecasting, and consolidating financials are core, data-driven activities that every business must execute in some way, shape or form. These activities are also instrumental in achieving a competitive advantage, identifying new revenue opportunities, increasing profitability, improving customer service and driving operational efficiencies — all of which are named top benefits of predictive analytics by participants in the Ventana Research survey.http://www.optimalsol.com/bpc-on-hana-ground-zero-for-predictive-analytics/SAP Business, Planning and Consolidation (BPC), the centerpiece of SAP’s Enterprise Performance Management (EPM) portfolio, combines planning, budgeting, and forecasting capabilities with management and legal consolidation functionality in a single application, providing business users with the intuitive, role-based tools, structured processes and centralized data they need to integrate corporate and departmental planning, shorten budget cycle time, close the books faster and ensure compliance with regulatory and financial standards. Performance ManagementAutomate planning and forecasting and enhance accuracySet benchmarks to assess performanceGenerate alerts to accelerate cashFocus on potential problems (proactive) rather than fighting fires (reactive)Cash forecasting – Analyze accounts to identify slow and fast payers to see if there are systemic issues that should be addressed or actions that could be taken to better manage receivables. For example:Receivables aging process – If analysis shows that a specific customer reliably pays within say, 28 days, set n alert if payment is not received by day 32 to trigger an automated process for contacting the customer.Optimize payables – In today’s low-interest rate environment there is a limited value to holding onto excess cash, especially since incentives for early payment can provide high-yield, risk-free returns. Manage profitability – Individual departments and business units typically focus on their own profit or cost objectives. Product organizations try to maximize product profitability, sales organizations try to maximize revenue, call centers try to minimize costs. Individually, each move may be rational, but collectively they can work at cross-purposes, and few companies focus on managing the conflicts (i.e. procurement and finance).Performance ManagementAutomate planning and forecasting and enhance accuracySet benchmarks to assess performanceGenerate alerts to accelerate cashCash forecasting – Analyze accounts to identify slow and fast payers to see if there are systemic issues that should be addressed or actions that could be taken to better manage receivables. For example:Receivables aging process – If analysis shows that a specific customer reliably pays within say, 28 days, set n alert if payment is not received by day 32 to trigger an automated process for contacting the customer.Optimize payables – In today’s low-interest rate environment there is a limited value to holding onto excess cash, especially since incentives for early payment can provide high-yield, risk-free returns. Manage profitability – Individual departments and business units typically focus on their own profit or cost objectives. Product organizations try to maximize product profitability, sales organizations try to maximize revenue, call centers try to minimize costs. Individually, each move may be rational, but collectively they can work at cross-purposes, and few companies focus on managing the conflicts (i.e. procurement and finance).Source: http://www.slideshare.net/SAPanalytics/predictive-finance-22144239Sourcing/ProcurementProcurement, for instance, can be alerted to potential future risks in the sub-tier supply chain by triangulating a myriad of real-time supplier performance inputs such as change in payment status, loss of a key customer, change in leadership, commodity price or supply fluctuations, with historical results, to highlight patterns that correlate with disruptive events. These alerts can be supplemented with recommended responses or alternative suppliers based on community-generated ratings and buying patterns for similar network participants.These functional areas have leveraged the data available to them and put together powerful forecasts and predictions that are having a significant impact on the bottom line.
  • These examples illustrate that much of the data you need for effective predictive analytics already exists and is available to you.The key to becoming a predictive business is accessing the right data and creating the right predictive models to get the best results. “In the future, businesses will be expected to possess the talent, tools, processes, and capabilities to analyze past business performance and events to gain forward-looking insight to drive business decisions and actions.” PREDICTIVE BUSINESS ANALYTICS: FORWARD LOOKING CAPABILITIES TO IMPROVE BUSINESS PERFORMANCE, Laurence Maisel, Gary CokinsSo we have explored the definition, challenges, and use cases for Predictive Analytics. Now I’m going to turn things over to David where he will provide an overview of some of the solutions available to you from SAP and Ariba to help you on your journey to becoming a predictive business.
  • Here are a couple of examples of results obtained by the KXEN customers who use InfiniteInsight® for their PAKindly note that such results are usually obtained in a few days, or weeks – we are not talking about months of deployment, here.
  • Not good enough to manage 1st-tier suppliersPace of innovation creating more single and sole source suppliers in value chain40% of disruptions caused by sub-tier issuesRecent Examples:Air India claimed revenue loss of around $1.5 billion due to 787 production delays due to supplier performance problems and is negotiating with Boeing for compensationToyota’s quarterly profit crumbled over 75% after part suppliers in Japan shut down due to natural disastersToymaker Mattel experienced brand damage and costs of $30 million after recall of Chinese-made toys for inclusion of lead paintTo manage risks, companies need:Commerce graphAccess to real-time eventsEarly warning systemsView of the crowdAbility to separate wheat from chaff
  • Powerful predictions about n-supplier risks  receive real-time alerts and predictions on any supplier and their suppliersProactive monitoring and prediction of supply risks in real time across your multi-tier supplier network (your suppliers, your suppliers’ suppliers)Uses leading-edge machine learning and statistical analysis toolsPowerful, tailored benchmarking across your business network  compare supplier performance relative to how they perform for others (OTD, quality); are they improving or deteriorating?Benchmark supplier performance for your company, your industry peers and against other suppliersSet user-specific alert thresholds to monitor suppliers’ KPIs relevant for your businessIdentify significant shifts and trends in supplier performanceAggregate and transform supplier data to deliver instant insights into the operational health of the supply baseManage the complex process of cleansing, harmonizing, matching, enriching, anonymizing and connecting 16,000+ sources of structured and unstructured supplier dataIntuitive ways to explore and visualize the operational health of the n-tier supply base at many levels of depthEfficient, secure cloud-based service, simple to deploy and cost effective to use
  • Powerful predictions about n-supplier risks  receive real-time alerts and predictions on any supplier and their suppliersProactive monitoring and prediction of supply risks in real time across your multi-tier supplier network (your suppliers, your suppliers’ suppliers)Uses leading-edge machine learning and statistical analysis toolsPowerful, tailored benchmarking across your business network  compare supplier performance relative to how they perform for others (OTD, quality); are they improving or deteriorating?Benchmark supplier performance for your company, your industry peers and against other suppliersSet user-specific alert thresholds to monitor suppliers’ KPIs relevant for your businessIdentify significant shifts and trends in supplier performanceAggregate and transform supplier data to deliver instant insights into the operational health of the supply baseManage the complex process of cleansing, harmonizing, matching, enriching, anonymizing and connecting 16,000+ sources of structured and unstructured supplier dataIntuitive ways to explore and visualize the operational health of the n-tier supply base at many levels of depthEfficient, secure cloud-based service, simple to deploy and cost effective to use
  • Powerful predictions about n-supplier risks  receive real-time alerts and predictions on any supplier and their suppliersProactive monitoring and prediction of supply risks in real time across your multi-tier supplier network (your suppliers, your suppliers’ suppliers)Uses leading-edge machine learning and statistical analysis toolsPowerful, tailored benchmarking across your business network  compare supplier performance relative to how they perform for others (OTD, quality); are they improving or deteriorating?Benchmark supplier performance for your company, your industry peers and against other suppliersSet user-specific alert thresholds to monitor suppliers’ KPIs relevant for your businessIdentify significant shifts and trends in supplier performanceAggregate and transform supplier data to deliver instant insights into the operational health of the supply baseManage the complex process of cleansing, harmonizing, matching, enriching, anonymizing and connecting 16,000+ sources of structured and unstructured supplier dataIntuitive ways to explore and visualize the operational health of the n-tier supply base at many levels of depthEfficient, secure cloud-based service, simple to deploy and cost effective to use
  • To wrap things up, I want to take a moment to share with you how SAP and Ariba can help your organization become a Predictive Business…
  • Ariba has been a pioneer of the Business Network. For over a decade companies have used the Ariba Business Network to improve the way they buy, sell and manage cash. Ariba has the largest Business Network of its kind with nearly 6 million users from over 2K companies that source, procure and pay for over $650B of goods and services with over 1.4 million companies. This means on any given day, people enjoy the same kind of buying experience in their business as they do in their personal lives transacting over $500M in commerce, over 100K PO’s and Invoices, and realizing over $65M in savings in 95 countries and 72 currencies.The Ariba Business Network extends the value of your existing ERP, MRO, procurement and Finance/AP systems with a single connection to thousands of your suppliers anywhere in the world. What this means to you is control over spend and contracts, improved vendor management, faster transaction cycles, data accuracy, lower supply chain risk, and improved working capital. As finance executives, you are responsible for settling all the spend in your organization regardless of whether it is direct, indirect, MRO, complex goods or services. You have to pay all the bills. And many of you are now responsible for the procure-to-pay functions, so you have a stake in controlling spend and providing a secure and reliable solution to do that. And what does the Business Network mean to your suppliers? It means they are part of the largest business network of its kind with over 90% of the G2000 companies. Suppliers are able to grow revenue through new sales opportunities. Improve order-to-cash processing to drive down costs and accelerate cash flow, and perhaps most importantly suppliers are able to improve customer satisfaction by streamlining the buying and paying processes for their customers. Let me give a couple examples of how companies are leveraging the Ariba Business Network to drive strategic savings…
  • SAP and Ariba have very deep experience with analytics and big data. Your Predictive Analytics success is only as good as the data and the data models. The Ariba Business Network extends the value of a Buyer’s existing ERP, MRO, procurement and Finance/AP systems with a single connection to thousands of your suppliers anywhere in the world. What this means to you is control over spend and contracts, improved vendor management, faster transaction cycles, data accuracy, lower supply chain risk, and improved working capital. And what does the Business Network mean to suppliers? It means they are part of the largest business network of its kind with over 90% of the G2000 companies. Suppliers are able to grow revenue through new sales opportunities. Improve order-to-cash processing to drive down costs and accelerate cash flow, and perhaps most importantly suppliers are able to improve customer satisfaction by streamlining the buying and paying processes for their customers. Cash forecasting – Analyze accounts to identify slow and fast payers to see if there are systemic issues that should be addressed or actions that could be taken to better manage receivables. For example:Receivables aging process – If analysis shows that a specific customer reliably pays within say, 28 days, set n alert if payment is not received by day 32 to trigger an automated process for contacting the customer.Optimize payables – In today’s low-interest rate environment there is a limited value to holding onto excess cash, especially since incentives for early payment can provide high-yield, risk-free returns. Manage profitability – Individual departments and business units typically focus on their own profit or cost objectives. Product organizations try to maximize product profitability, sales organizations try to maximize revenue, call centers try to minimize costs. Individually, each move may be rational, but collectively they can work at cross-purposes, and few companies focus on managing the conflicts (i.e. procurement and finance).This is the beauty of SAP and Ariba… we have the data and we have the tools to help you create the best predictive models and become a predictive business. David and I have shared a lot of information on SAP and Ariba’s approach to predictive analytics and becoming a predictive business.So I want to leave you with a very important question… is your predictive scorecard smart enough?
  • Thank you for your time. My SAP/Ariba colleagues and I will be here the rest of the day, we look forward to discussing your unique business challenges and opportunities with you.Thank you!The Business Network is leading the Networked Economy transformation… one in which networked enterprises are 50% more likely to be market leaders, have higher margins, and increased revenue. Thinking Differently means looking beyond traditional process savings to other areas where the P2P/AP/Shared Service function can truly be a strategic weapon to not only improve process efficiency, but also impact supply management, maximized contract and discount savings, impact working capital, and comply with not only regulatory requirements, but also pricing, preferred vendors, and contracted terms. Source: McKinsey & Company, “The rise of the networked enterprise, Web 2.0 finds it’s payday.” Survey of 4,394 executives. December 2010And don’t forget to fill out your session survey (next slide)
  • Predictive Analytics: Better Commerce Insight | Ariba LIVE Rome

    1. 1. #AribaLIVE © 2014 Ariba, Inc. All rights reserved. Predictive Analytics Is Your Scorecard Smart Enough? James Tucker, Sr. Director, Business Network Strategy james.tucker01@sap.com | @jbtucker3 David Charpie, Development Executive david.charpie@sap.com
    2. 2. Agenda • Use Cases for Predictive Analytics • Challenges with Predictive Analytics • A Predictive Analytics Framework • SAP/Ariba Solution Set • Q&A © 2014 Ariba – an SAP company. All rights reserved.2
    3. 3. The Top 3 Worst Predictions Ever
    4. 4. #3. Worst Prediction Ever… Robert Metcalfe, inventor of Ethernet, in InfoWorld magazine, December 1995.
    5. 5. #2. Worst Prediction Ever… Microsoft CEO Steve Ballmer, 2007.
    6. 6. #1. Worst Prediction Ever… Time magazine, 1968.
    7. 7. “Using stats this way, we’ll find value in players that nobody else can see.” - Moneyball
    8. 8. Is Your Scorecard Smart Enough? Raw Data Cleansed Data Standard Reports Ad Hoc Reports & OLAP Generic Predictive Analytics Predictive Modeling Optimized Actions What happened? Why did it happen? What will happen? What is the best course of action? CompetitiveAdvantage Analytics Maturity Embedded Predictions End Users © 2014 Ariba – an SAP company. All rights reserved.8
    9. 9. “The extent to which common macroeconomic forecasting variables enhance our understanding of time-variation in volatility is rather limited.” Bradley S. Paye Jones Graduate School Rice University In other words – predictive analysis is only as good as the data and the data models you use. ISE Sentiment Index x 100 Long Calls Purchased Long Puts Purchased
    10. 10. Industry Use Cases © 2014 Ariba – an SAP company. All rights reserved.10 Financial Services • Revenues • Interest Rates Utilities • Usage / Outages • Smart Meters • Environmental Forecasts Healthcare & Life Sciences • Patient Outcomes • Market Access • High Risk Patients Public Sector • Tax Revenues • Economic Assumptions Retail and Consumer Products • Shopper preferences • Mobile alerts / Location
    11. 11. Sales/CRM • Revenue Forecast • Customer churn Functional Use Cases © 2014 Ariba – an SAP company. All rights reserved.11 Sourcing / Procurement • Supplier Risk • Price Fluctuations Supply Chain • Supplier Risk • Inventory Finance • Planning/Budgeting/Forecasting • Revenue Opportunities • Profitability / The Street • Working Capital HR • Candidate Acquisition • Human Judgment
    12. 12. The Road to the Predictive Business Where to Begin – Where it Will Take You
    13. 13. SAP PREDICTIVE ANALYTICS Modeling & Forecasting for the Front-Line User © 2014 Ariba – an SAP company. All rights reserved.13
    14. 14. Predictive Analytics Enabling a New User Base • Create complex predictive models and simulations • Validate predictive business requirements • Publish results back to source Data Scientist .001% Representative User Base • Transform and enrich data source(s) • Create simple predictive models and simulations • Visualize results and publish to BI Platform Data Analysts ~3% 97% • Interact with published predictive analysis • Visualize results in context of use case • Collaborate with colleagues toward closure/action Executives/ Business Users © 2014 Ariba – an SAP company. All rights reserved.14
    15. 15. Insight from social graphs built on 400M calls Built 11x more models Campaign response rates up 260% Doubled campaign take-up rate Identify half future churners in a 5% target list Detected half future churners in a 10% target list Decreasing 3% monthly churn rate on postpaid 400% increase in campaign response rates Churn and X-sell management with 700 models Superior churn model built in only 2½ days Improved model accuracy by 50% using SNA Predicative Analytics The Potential Results… © 2014 Ariba – an SAP company. All rights reserved.15
    16. 16. Predictive Analytics Automated Model Creation and Selection Automatically builds a description of your dataset Selects modeling approach to balance data fit and predictive power © 2014 Ariba – an SAP company. All rights reserved.16
    17. 17. Predictive Analytics Self-service Support for Less Experienced End-Users Identifies key contributors to best prediction Drills down to see if a multiclass model is appropriate Pinpoints need for additional models 17
    18. 18. © 2014 Ariba – an SAP company. All rights reserved.18 MACHINE HEALTH CONTROL CENTER Automated Maintenance & Service Predictions
    19. 19. Predictive Maintenance and Service Building the foundation for systematic learning based on purpose driven data collection to improve product design, reliability, service revenue, customer satisfaction and operator experience. Dealer Sales “How can I improve my customers satisfaction and loyalty?” Service Service “How can I provide the best service at the right time and place?” Owner/Operator Assets/ Fleet “How can I reduce service and operational cost?” Operator/ Driver “How can I improve yield, reduce energy consumption and improve safety?” OEM R&D “How can I improve my product’s reliability?” Warranty “Which service level do we need to provide?” Spare Parts “Which spare parts are needed where and when?” Production “How can I minimize problems in production?” © 2014 Ariba – an SAP company. All rights reserved.19
    20. 20. Costs of Ineffective Maintenance Europe 60 Annual maintenance costs Loss due to ineffective maintenance Maintenance Costs 2010 1 in billion EUR Sources: 1 ConMoto, Wertorientierte Instandhaltung (2011); 2 ARC Advisory Group, Predictive Maintenance Survey US Maintenance Costs 2011 2 in billion USD Annual maintenance costs Loss due to ineffective maintenance 70 450 200 20 © 2014 Ariba – an SAP company. All rights reserved.
    21. 21. Example Algorithms to Support Analysis Association Rule Learning • Concept – Selects a group of products and – Specifies the kind of problem for the analysis. • Algorithms… – Identify autonomously relevant rules, i.e. relationships between attributes and problems, – Sort the rules according to their statistical relevance, and – Present the rules in a human-readable way. Algorithm ✓ ✘ ✓ ✘ ✓ ✘ ✓ ✘ ✓ ✓ ✓ ✓ Set of machines or products Rules / Relationships Feature C Country F ✘ ✘ ✓✓ ✘ ✘ ✘ ✓ 21 © 2014 Ariba – an SAP company. All rights reserved.
    22. 22. Application Example Machine Health Control Center 22
    23. 23. SUPPLIER INFONET Supply Chain Disruption & Performance Predictions © 2014 Ariba – an SAP company. All rights reserved.23
    24. 24. Complex Value Chains Create New Risks Dependency on suppliers increases disruption potential • Companies must understand supply network to mitigate impacts of unexpected operational events • New systems needed to reveal relevant events, enabling near real-time responses Revenue loss – Air India claimed revenue loss of around $1.5 billion due to 787 production delays Reduced profits – Toyota’s profit crumbled over 75% after part suppliers shut down Brand damage – Mattel experienced brand damage and costs of $30 million after recall for lead paint. • By enabling software with content, companies combine internal knowledge with external insight – and can act in real time upon unforeseen events 1 Gartner, IDC
    25. 25. To address these critical issues, Ariba is offering a cutting-edge solution that can leverage the intelligence resident in the Ariba Network to provide an early warning system to identify potential disruption in the value chain Properly leveraging this solution will provide important benefits, including: Establishing more trust with your customers by delivering increased transparency throughout your entire supply chain Providing your executive team more peace of mind through early warnings of potential supply chain disruptions Demonstrating organizational commitment to your shareholders by predicting and avoiding the unecessary costs associated with poor supplier performance And because this is a cloud-based solution, it is straightforward and cost-effective to deploy Ariba’s Solution – Supplier InfoNet © 2014 Ariba – an SAP company. All rights reserved.25
    26. 26. Uniquely Informed Decision Making Big Data Analytics, Machine Learning Algorithms, NLP Supplier InfoNet Data Pools Your Business Systems Supplier Network Information 3rd Party Information Predict company performance using KPIs Machine learning algorithms Extract and classify news events Language processing and text analysis Combine structured & unstructured data Network Intelligence 26
    27. 27. Uniquely Informed Decision Making Big Data Analytics, Machine Learning Algorithms, NLP © 2014 Ariba – an SAP company. All rights reserved.27
    28. 28. Uniquely Informed Decision Making Big Data Analytics, Machine Learning Algorithms, NLP © 2014 Ariba – an SAP company. All rights reserved.28
    29. 29. THE BUSINESS NETWORK Big Data © 2014 Ariba – an SAP company. All rights reserved.29
    30. 30. Business Network Ariba Network Imagine the Possibilities “Other” Buyers Suppliers 5.7 million users from 2K+ organizations … … conduct over $650B on the Ariba Business Network … … with over 1.4M+ global suppliers annually Every day these companies transact: • $500 million in commerce • 100K+ purchase orders and invoices • $65M+ savings • 95 countries • 72 currencies© 2014 Ariba – an SAP company. All rights reserved.30
    31. 31. Business Network Is Your Predictive Scorecard Smart Enough? “Other” Buyers Suppliers © 2014 Ariba – an SAP company. All rights reserved.31
    32. 32. Being a Predictive Business is Key to Success in the Networ ked Economy James Tucker Senior Director, Business Network Strategy james.tucker01@sap.com | +1 650 390 1702 @jbtucker3 David Charpie Development Executive Ariba david.charpie@sap.com | +1 781 315 7253
    33. 33. Please Complete Session Survey Locate Session 33 Click Surveys Button Select Breakout Survey Rate Session Thank you for joining us © 2014 Ariba – an SAP company. All rights reserved.

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