1. APICS PDM Big Data in Supply Chains Uses & Challenges cliff allen

2. Agenda: Big Data & SCM What is Big Data and how does it relate to SCM? Using Data on “the edges” (NPI & Fulfillment) The role of forecasting; is it changing S & OP? The sweet spot: Reverse Logistics Omni-channel & SCM Displaying meaningful results / communication Encryption / Safety Moving forward Q & A

3. Traditional ERP vs. Big Data ERP is not going away……however Emerging are: The Edges: – Channels – Social media – RFID – PoS – GPS – Blueprint data

4. Big Data Lots of data is being collected and warehoused – Web data, e-commerce – purchases at department & grocery stores – Bank/Credit Card transactions – Social Networks – GPS location

5. © Deloitte & Touche LLP and affiliated entities. Digital Convergence of small businesses now get at least one quarter of new customers via social media. of young people refer to social media to decide where to go when they go out. of Americans check their social networks several times a day. of Americans check brand pages regularly as part of their social media activity. of time spent online is for social media. If Facebook were a country, it would be the world’s 2 nd largest - 1.3B Percent of 18-34 year olds who check Facebook when they wake up - 48 % Social Media has overtaken adult content as the #1 activity on the web 1 out of 8 couples married in the US last year met via social media 78% 61% 27% 35% 27% Digital engagement is the future Digital Convergence

6. Big Data: How Much Google processes 20K TB a day Facebook has 2.5 PB of user data + 15 TB/day eBay has 6.5 PB of user data + 50 TB/day NSA touches 29K TB a day *1000 gigabytes = 1 Terabyte *1000 Terabytes = 1 Petabyte

7. A critical mass of new technologies and consumer and client demand is ushering in a new era of computing, and with it the “Post Digital Age” 6 Billion People worldwide have access to a mobile phone The number of mobile- connected devices exceeded the word’s population in 2012 1 2 3 4 Projects measured in years Vast divide between IT and business Long adoption curves Projects measured in years Vast divide between IT and business Long adoption curves Projects measured in months Bridging gaps between IT and business Accelerated adoption Projects measured in weeks IT and business collaboration Accelerated adoption Mainframe Client/Server Web Digital 7

8. Complexity of data

9. Internet of Things

10. Internet of Things : Marketing & SCM Internet of things brings real-time data via scanners and sensors to the channels and suppliers. Creates real time Point of sales data.

11. Making analytics relevant to the heart of clients’ business with Analytics domains Finance Analytics Risk Analytics Workforce Analytics Supply Chain Analytics Customer Analytics Companies should have a more complete intimate understanding of their customers to get them, grow them, and keep them. Many leaders want to take advantage of the benefits of risk analytics to limit risk exposure or to take certain risks to generate returns. Finance managers have applied analytics to better understand the present and more accurately predict the future. Workforce reporting and analytics achieves greater visibility and deeper insights into the most complex workforce- related challenges. Apply analytics to achieve forward-looking insights combined with the disciplined execution of the supply- chain function.

12. Big Data – Why Supply Chain? INCREASE CUSTOMER ENGAGEMENT—lost market share IMPROVE PRODUCT/SERVICE QUALITY—Toyota OPTIMIZE OPERATIONAL EFFICIENCIES—SW Airlines PROVIDE FASTER TIME –TO-MARKET POTENTIAL FOR GREATER REVENUE RECOGNITION—Apple & Samsung lead in APP market SENSE SMALL EVENTS TRIGGERS POINTS (BEFORE THEY BECOME BIG IMPACTS PROBLEMS/PROXIES)—Nokia/Blackberry since 2008 IMPROVE RISK MANAGEMENT—Cost of BP oil spill

13. APICS Big Data Survey,2012 Supply Chain Inventory Levels Competitive Trends Actual Product Usage Forecasting/planning/ scheduling Actual/Real Time Demand 68% 34% 37% 79% 60%

14. Prescriptive Analytics The emerging technology of prescriptive analytics goes beyond descriptive and predictive models by recommending one or more courses of action -- and showing the likely outcome of each decision.

15. Big Data – The Mystery Questions for executives: What happens in a world of data transparency? If you could test all decisions, would you be more competitive? How would your business change with real-time data? Can data replace some management? Are Amazon, Alibaba, & Zulily Marketing or Supply Chain Companies? Is Data security is an growing concern when increasing trends for complex gathering and harnessing of data are exploding?

16. Big Data – Game changers

17. Big Data - Amazon Amazon has filed a patent for a shipping system designed to cut delivery times by predicting what buyers are going to buy before they buy it — and shipping products in their general direction, or even right to their door, before the sales click.patent

18. Big Data: Supply Chain Game Changing Technologies RFID & PoS Data: Real time consumption Shelves become the inventory manger Too much inventory can push “deals” out to users while shopping via GPS Merchandising and product location Users include Office Max & Best Buy

19. Big Data: Supply Chain Customer Analytics Blueprints Customer Profile: 360-degree view of the customer Micro-segmentation: Create segments of one Next Action: Predict and Influence customer decisions Loyalty Programs: Keep customers by using data applied to particular segmentation

20. Big Data: Supply Chain Eyesee: The eye recognition cameras Eyesee: $5,100 Mannequin uses IBM Cognos software – collects data from patrons — logging things like age, gender and ethnicity – recognizes words to allow retailers to eavesdrop on what shoppers say about the mannequin’s attire

21. Eyesee: The eye recognition cameras Calo (2009): People can be so fake: Truth in privacy overcomes truth in observed situations

22. Big Data: Supply Chain Customer Analytics Blueprints In store cameras with consumer behavior Walmart: Shopperception – Avg Visit duration – % of vistors thru Transit Zones – Touches per product / Pick-ups – Return to shelf – Conversion: Touches and not returned – Heat maps: color coded

23. 7x24 shelf analysis with multiple and simultaneous people tracking: Traffic Flow analysis based on zones/time Heat Maps of conversion rates for each SKU. Hot activity zones in shelf More Shopper insights: Multiple events on the shelf. Entrance / bounce paths Average times in zones Product traction analysis Real comparative shelf layout performance

24. Omni-Channel changing everything

25. Omni channel is here to stay… - Make Up For Ever – The cosmetics company put iPads in some of its stores to let shoppers browse products and virtually try various make- up combinations by uploading their own photos - Loyalty cards are on their way out and will be replaced by customized rewards that incorporate social information, shopping behavior, and more.

26.  Near Real-time Data & Dashboards  Identifies Actual & Predictive OOS & Overstock Issues At SKU/ Store Level  Enables Root Cause Analysis  Actionable Tasks Prioritized By Profitability  Drive Sales & Execution ‒New Product Introductions ‒Closing Distribution Voids ‒Promotion Execution & Effectiveness ‒Store Merchandising & Replenishment ‒Order & Shipment Forecasts ‒Retail Pricing Compliance PepsiCo Believes In The Power Of Data & Analytics To Drive Supply Chain P

27. Big Data - Visualization

28. -Visual Analytics methods allow decision makers to combine their human flexibility, creativity, and background knowledge to gain insight into complex problems. -Example: - To predict demand, Amway China applied SAS time series forecasting to data from 70 million orders placed over the past three years improving delivery and inventory by +20%

29. Utilizing Big data to discover and explain Is not as easy as you might think… – Poor and sparse samples, surrogates, bias… – As number of dimensions increases it becomes increasingly difficult to add in any data point without giving rise to some kind of statistically significant ‘pattern’ or ‘cluster’ – And parametric distributions become unreliable – It is very difficult to discover useful things that are unknown by experts

30. Utilizing Big data to discover and explain

31. Data Visualization

32. Once Visualized The sweet spots for Big Data & SCM S & OP Reverse Logistics and Sustainability

33. The Role of Forecasting Forecasting is a vital function and impacts every significant management decision…. And is always inaccurate Finance and accounting use forecasts as the basis for budgeting and cost control Marketing relies on forecasts to make key decisions such as new product planning and personnel compensation Production uses forecasts to select suppliers, determine capacity requirements, and to drive decisions about purchasing, staffing, and inventory

34. Sales & Operations Planning Is an executive decision-making process Balances demand and supply Deals with volume in both units and $$$ at aggregate level Ties operational plans to financial plans: one set of numbers Is the forum for setting relevant strategy and policy

35. From APICs : – Deep Analytics Analytics-based reporting tells the S&OP planning teams: The data and the application of analytics is at the heart of S&OP Where they are (Current state of the business) What actions need to be taken and driven down into tactical and operations S&OP processes What results and trends are emerging from their decisions What corrective steps do the S&OP planning teams which to take

36. Sales & Operations Planning (Can be real-time With Analytics) Master Scheduling Detailed Planning & Scheduling CAPACITYPLANNINGCAPACITYPLANNING F O R E C A S T I N G & D E M A N D Business Planning High Level Enterprise Resource Planning Model Annually Bi-Monthly Weekly Daily Strategic Planning 2-10 Years Forecast Only Forecast PoS real time Forecast & Orders Orders Only Rough-cut Capacity Planning Capacity Requirements Planning Resource Planning

37. The monthly sales and operations planning process & Collaborative Planning, forecasting, & replenishment with Big Data End of month STEP 1 Data Gathering STEP 5 Exec S&OP Meeting STEP 4 Pre-S&OP Meeting STEP 3 Supply Planning STEP 2 Demand Planning Statistical forecasts Field sales worksheet Management forecast 1-st pass spreadsheets Capacity constraints 2-nd pass spreadsheet Recommendations For executive S&OP Decisions Wallace: 2 nd edition Sales & Operations Planning First real time data check Second real time Data Check With Analytics & CPFR this is real time cutting 1 week or more in POS data

38. Data and S &OP

39. Big Data – CLSC & Reverse Logistics

40. Utilizing Big data Improve CLSC Supply Chains Supply Chains and Marketing converge with improved POS, velocity with RFID, reduction of lead-times with “make->sell” compression of data & inclusion of “sell-> return.”

41. Reverse Logistics: Hi Tech Trash Two Million tons of e-waste goes to landfills each year 163K PCs & TVs become obsolete every year

42. Eight categories of reverse flows 1.Products that have failed; are unwanted, damaged, or defective; but can be repaired or remanufactured and resold. 2.Products that are unsold from retailers, usually referred to as overstocks that have resale value. 3.Products being recalled due to a safety or quality defect that may be repaired or salvaged. 4.Products needing “pull and replace” repair before being put back 5. in service. 6.Products that can be recycled such as pallets, containers, computer inkjet cartridges, etc. 7.Products that are old, obsolete, or near the end of their shelf life but still have some value for salvage or resale. 8.Products or parts that can be remanufactured and resold. 9.Scrap metal that can be recovered and used as a raw material for further manufacturing. No V A L U E A D VALUEADDVALUEADD

43. Reuse can cycle quickly but what about the others? With Analytics prescrpitive works for all 3

44. A business process approach Product acquisition is a major driver of success Creating effective remarketing channels is another major driver Research emphasis has largely been on reverse logistics, disassembly and remanufacturing operations; not acquisition timing; This is where Prescriptive analytics takes place Product returns represent a value stream, not just a waste stream

45. Time-sensitive product return streams Short life-cycles; high obsolescence risk Returned products losing value rapidly “Value of time” a key prescripter – Examples: PCs Printers and Computer Peripherals Mobile Phones Telecommunications Equipment

46. Product Acquisition The collection of used products potentially accounts for a significant part of the total cost, which can be compared with the last mile issue in distribution of products in the forward supply chain. The collection may occur by door to door, through service center, through sales center and sometimes by customers. Answer: Proximity and ease of access for customers & timely returns based on prescriptive analytics

47. SortingPurificationCompounding

48. Analytics in non-traditional supply chain markets

49. Advanced Technology & Hospitals – Doctor data tracking has helped reduce the average stay for adult inpatients from 4.2 days in 2011 to four days in 2012. – Such efforts also have reduced the average cost per admitted patient by $280, which saved the health system a total of $13.8 million from 2011 to 2012. Lean beyond shop floor Current State data

50. Advanced Technology Process: – Surgical Supplies Pick & Return

51. Advanced Technology Success: – Eliminated 12,000 supply errors –Saved 600 hours of O.R. time – Reduced inventory by 15% –Real-time Performance Reporting

52. Usage, Benefits, and Success of BA The data Scientist and business exec cannot communicate.  Why BI/BA projects fail 1.Failure to recognize BI projects as cross- organizational business initiatives and to understand that, as such, they differ from typical standalone solutions 2.Unengaged or weak business sponsors 3.Unavailable or unwilling business representatives from the functional areas

53. Usage, Benefits, and Success of BA  Why BI/BA projects fail 4.Lack of skilled (or available) staff, or suboptimal staff utilization 5.No software release concept (i.e., no iterative development method) 6.No work breakdown structure (i.e., no methodology)

54. Usage, Benefits, and Success of BA  Why BI/BA projects fail 7.No business analysis or standardization activities 8.No appreciation of the negative impact of “dirty data” on business profitability 9.No understanding of the necessity for and the use of metadata 10.Too much reliance on disparate methods and tools

55. Big Data: Is Our Security Keeping Pace?

56. Are We Headed Towards “Impossible Privacy”? Another Case: Google Google has every single email you ever sent using Gmail. They have it stored, indexed, and they have built models of your behavior. Yahoo and Facebook have been doing similar things. How secure do you feel?

57. Big Data: Is Our Security Keeping Pace? The “Cloud”: The Risks “The internet of things” Internet security breaches happen often. If the server goes down, your devices can’t access data. (Both Amazon and Gmail have gone dark). Lack of access if you have no Internet access If a hacker gets your password, you may be locked out of all your devices. Your security is only as good as the weakest link in the chain

58. Big Data: Is Our Security Keeping Pace? In December & January Target reports another hack for 110 million records Was this done by a global cybercrime group or an individual?

59. Main Big Data Technologies HadoopNoSQL Databases Analytic Databases Hadoop Low cost, reliable scale-out architecture Distributed computing Proven success in Fortune 500 companies Exploding interest NoSQL Databases Huge horizontal scaling and high availability Highly optimized for retrieval and appending Types Document stores Key Value stores Graph databases Analytic Relational DBMS Optimized for bulk-load and fast aggregate query workloads Types Column-oriented MPP In-memory

60. A new Language Major Hadoop Utilities Apache Hive Apache Pig Apache HBase Sqoop Oozie Hue Flume Apache Whirr Apache Zookeeper SQL-like language and metadata repository High-level language for expressing data analysis programs The Hadoop database. Random, real -time read/write access Highly reliable distributed coordination service Library for running Hadoop in the cloud Distributed service for collecting and aggregating log and event data Browser-based desktop interface for interacting with Hadoop Server-based workflow engine for Hadoop activities Integrating Hadoop with RDBMS

61. Big Data: SCM Jobs

62. Careers in Analytics

63. PORTLAND STATE UNIVERSITY MS IN GLOBAL SUPPLY CHAIN MANAGEMENT PREPARE FOR AN INTEGRATED FUTURE Supply Chain & Your Career Vice President/General Manager $175,260 Corporate Division Manager $142,000 Supply Chain Director/Manager $114,275 Logistics Director/Manager $109,760 Business Analyst / Data Analyst $101,000 Operations Manager $98,235 Purchasing/Procurement Director/Manager $85,070 Traffic Manager $69,480 Warehouse Director/Manager $84,730 Coordinator/Analyst $67,000 *Data from Logistics Management 30th Annual Salary Survey, released April 2014. Salary potential may vary depending on location, experience and education. What type of salary can you expect from supply chain positions?*

64. Big Data is easy! Questions?