Supply Chain Analytics: Sense and Respond. Outline of Presentation

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1 Supply Chain Analytics: Sense and Respond Roberta S. Russell, PhD, CFPIM, CSCP Professor, Business Information Technology Member, Center for Business Intelligence & Analytics Virginia Polytechnic Institute and State University Outline of Presentation Big data Big data and analytics The analytics process Analytics in practice Closed-loop analytics Summary and concerns Selected References 1

2 Data big and growing bigger Wal-Mart> 1,000,000 transactions /hr. Databases > 2.5 petabytes of data. RFIDgenerates 1000X more data than bar codes Facebook> 1 billion users; daily > 250 million photo uploads,900 million objects; reaches 1 in 7 people on the planet Tweets> 400 million per day and counting US Gov. says amount of data collected is doubling every year; measured in yottabytes Data big and growing bigger Source: Wall Street Journal, CISCO 2

3 Big Data presents both problems and opportunities. Volume Variety VALUE Velocity Veracity Big data enables analytics. Analytics finds the value in big data. Structured data Transactional data Performance data Industry data Consumer data Economic data Predicts future based on past Efficient analysis 3

4 Unstructured Data Text, images, video, documents, s Social networks (clicks, blogs, tweets and chatter) Internet of things (sensors, cams, smart devices) Accounts for 90% of all new data created Time-sensitive decisions, emerging trends, course corrections, first mover Predicts future based on real time data Insightful analysis 4

5 2012 Big Work Survey 2012 Big Work Survey 66% Top 4 Issues in Big Data Infrastructure 64% 62% 60% 58% 56% 54% Information Integration Scalable storage High-capacity data warehouse Security & governance 5

6 2012 Big Work Survey 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Analytics Used 0% Query & reporting Data mining Data visualization Predicitive modeling Optimization Simulation Text mining Geospatial analytics Importance of analytics # 1 reason top innovators keep winning? Deep customer insight and analytics Most important elements for business growth? Visibility, flexibility and analytics Cos in the top 20% of growth were twice as likely to use analytics 6

7 The Analytics Process Data Analytics Insight Action Collect Cleanse Aggregate Integrate Extract Manipulate Transform Store/Retrieve Report, OLAP Data Mining Text Mining Simulation Optimization Visualization Predictive Modeling Statistics Customers Products Markets Competitors Processes Suppliers Risk Costs/Revenues Strategies Disseminate Choose Adjust Embed Operate Design Prepare Evaluate Change Justify Transform Decisions based on data Capabilities needed to capitalize on the analytics process Strategy Data Analytics & Insight Awareness of possibilities and basic knowledge or experience Business priorities and deployment strategies IS/IT skills and capabilities Hardware/software capabilities and structure Governance principles and standards Analytics skills and tools Business process and customer knowledge Action Information sharing Empowered decision making Data-driven culture 7

8 Companies most successful with analytics Led with priorities, not data Had both technical and analytic skill sets Demonstrated value Managed scope Had a history of data driven decision making (through six sigma or other quality improvement initiatives) Moved from IT to function-specific to CEO-driven analytics strategy Analytics is used for Customer profiling and segmentation Demand forecasting Demand planning (incl. pricing & promotions) Production planning (incl. product mix, S&OP) Logistics Inventory optimization Supply chain optimization Supply chain design Risk management Demand Supply 8

9 Analytics in Action Pros: Amazon, UPS/FedEx, Google, Facebook Examples: Consumer products - P&G, Coca-Cola Energy Chevron Chemicals Dow Pharmaceuticals/Healthcare Pfizer, McKesson Automotive Electronics Analytics at P&G CIO developed groundbreaking analytics system ahead of the curve; background in IT, Mktg, Ops Decision Cockpit better, faster decisions; mgmt by exception -visual control charts, automatic alerts; 58,000 users, 56% of processes Business Sphere at headqtrs, two 2 x 8 screens: advanced visualizations; 50 globally Embedded analysts adds human factor to analytical tools & data visualization 9

10 Analytics at Dow Chemicals Tradition of long-term planning (several decades ahead) population, energy, environment Began w/scm; staff of 6 BPMs, 2 R&D modelers; saved $2.6 bill in logistics & $4 bill in purchasing Now large analytics dept 10 PhDs, BI staff; 15 outside consultants & 30+ IT professionals avail on project basis; Competency Director for AdvAnalytics 85% of analytics projects tied to bus units Essential for strategic move from chemicals to value added manufacturer Analytics at Chevron Deep water drilling in Gulf shut down >> Chevron spent time developing predictive model to improve drilling prospects (miss costs $ terrabytesof data to analyze per site); improved odds of success to 1 in 3; ranked projects by probability of success 10

11 Analytics at Coca-Cola Supply chain visibility- $4 bill Wal-Mart VMI Vending operations -customized pallet delivery; sequence loaded side trucks; variable pricing vending machines; freestyle vending machines (with 125 choices for mixing) Juice production NFC vs. FCOJ; advanced analytics; black book; in-season/off-season mix; blending technicians; agriculture & processing guidance Analytics at McKesson High volume, low margin healthcare products > $2 mill orders/day; $8 bill in inventory Used analytics to determine cost to serve; used simulation to optimize inventory placement; saved $100 mill in working capital; 6 Σexception-based approach; both process & analytic knowledge important Analytics at Pfizer Motivation - expiration of patent on key product Collected activity-based data on customers; able to predict patterns of behavior; fine-tuned sales pitch and services Applied analytics outside of scientific fields 11

12 Analytics for VMI Manage inventory levels across multiple tiers of supply-chain hubs Data integration, monitoring (shortage, surplus, caution, OK), analytics; visualization and dashboards Inputs:daily and weekly data feeds from tier 1 and tier 2 suppliers; external events Analytics: non-linear programing model to determine buffer stock levels subject to operating, financial and liability constraints Decision options: supply rebalancing, demand promotions, substitutions 10,000 hubs currently monitored; $$$ saved Analytics to detect vehicle defects Text mining of social media sites for threads on vehicle defects Can be used to detect defects before reports from dealers, or to produce more service parts Found that traditional sentiment analysis not appropriate for vehicles Trained network with automotive experts, then tested on separate data base 12

13 Vehicle Defect Discovery System (VDDS) Source: A. Abrahams et. al., Vehicle defect discovery from social media, Decision Support Systems 54 (2012), 90. Closed-Loop Analytics Big data and analytics promise deep customer insights and predictive powers; however, this insight rarely reaches the supply chain portion of the organization. Supply chain analysts are optimizing purchasing, production and inventory models built for existing customer & supplier dynamics. As a result, the optimized supply chain may be unable to handle the rapid changes initiated by sales and marketing, and the new sales initiatives may cause shortages and cost overruns at a later data. 13

14 Closed Loop Analytics, cont. This is the type of problem S&OP was designed to solve, but the process is often too slow and cumbersome for today s rapid pace of change. There is a need for shared decision making, as well as shared visibility; and for a more rapid or revised S&OP process. This includes an evaluation of decisions made. New models that simulate the affect of decisions on the entire value chain are needed, as well as better process knowledge. Thresholds of change that can be made unilaterally will help make the execution of plans more effective. Big Data and Analytics Sharing data across functions Historical Snapshots Shared visibility of data flows Real-time Information Reacting Sense and Respond Establish a framework for shared decision making. 14

15 Summary Organizations have more data now than they know how to use. Companies mired in data can become immobilized. Analytic tools may seem daunting. Companies with experience with structured decision making are more likely to embrace analytics. Others may find analytics suspect. Siloed analytics can lead to unintended results. Companies successful with analytics lead with priorities, not data. They have strong capabilities in IT and analytics, as well as customer and process knowledge. Concerns about Analytics Does real-time data lose perspective? (Is this a trend or just a one-time blip in the data?) Better data or better analytics? Do numbers imply certainty? Will big data make sampling obsolete? Will analytics completely replace intuition? The lure of big data can actually slow decision making. What is a reasonable timetable for results? Are companies repeating the same IT hype cycle? 15

16 Selected References: Center for Business Intelligence and Analytics D. Kiron, et. al., Analytics: the widening divide, MIT Sloan Management Review Research Report, Fall S. LaVelle, et. al., Big Data, Analytics and the Path from Insights to Value, MIT Sloan Management Review, Winter M. Schroeck, et. al., Analytics: The real-world use of big data, IBM Global Business Services Executive Report, T. Davenport & J. G. Harris, R. Morison Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press, Boston. P. Mitchell, Supply Analytics: An Overlooked Opportunity, Supply Chain Management Review, July/Aug S. Kapoor, et.al. Sense-and-respond supply chain using model-driven techniques, IBM Systems Journal, 46 (4), A. Abrahams et. al., Vehicle defect discovery from social media, Decision Support Systems 54, Survey 16