How to Cheat and Make Better Decisions with Predictive Analytics. Track 1 Session 3

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1 How to Cheat and Make Better Decisions with Predictive Analytics Track 1 Session 3

2 Robert Heaney - Title: Lead Analyst, Supply Chain Management - Company: Aberdeen Group Phone: Company Website: 2

3 Abstract Pass or punt? An acquisition? Another beer? Hindsight confirms that success or failure is simply a result of how well decisions are made along the way. This nationally recognized expert will help us understand how gathering and applying data to your gut feel decisions will wildly improve your win rate. We will learn how the many analytical apps available today can guide you to making brilliant foresighted decisions, as well as hear what the next generation of tools and processes means to the business world. 3

4 Agenda Business Context- Gut Feel and Complexity Tradeoffs / Strategies In Predictive Analytics Aligning Predictive Analytics to Supply Chain - Strategies and Focus - Capabilities of Leaders - Technology Enablers traditional to social media Case Studies and Best Practices Key Takeaways Q&A

5 Gut Feel Decisions and Supply Chain Executives 25% rely on gut feel supported with supply chain data to make strategic decisions 45% rely on gut feel supported with supply chain data to make tactical decisions With today s Business and Predictive Analytics tools these decisions can be better and more informed 75% of Executives report that if armed with the proper analytics they could make better decisions Chief Supply Chain Officer Survey, 2012; n=191 5

6 Supply Chain Complexity is a Given Component Factory Customers Raw Material System Factory Distributors Retail Store Enterprise Suppliers 6

7 Today s Complex Demand-Supply Network is More Unpredictable Component Factory Customers Raw Material System Factory Distributors Retail Store Enterprise Suppliers Conversion of linear supply chain into multi-enterprise networks - Results in loss of control and visibility and predictability - Lead-times are increasing due to expansion of the network - Planning collapsing into execution - Sustainability embedded into decision-making - Risks are proliferating 7

8 Integrated Demand-Supply Networks Supply Network System suppliers, Contract Manufacturers, ODMs, Raw material suppliers Demand Network - Buyer of services and products For e.g: Retailers, Distributors, Value Added Resellers (VARs), Component Factory Customers Raw Material System Factory Enterprise Sales, Marketing, Operations, Manufacturing, Procurement Retail Store Enterprise Suppliers Logistics Network 3PLs, Shippers, Carriers Financial Network - Flow of physical goods, supply chain data and financial information 8

9 Demand-Supply Synchronization = Increased Predictability International visibility through satellites and cloud technology DEMAND Global Air Customers Home Delivery Raw Materials Supplier Component Supplier Retail Store Global DC Retail Store Ocean Component Supplier Global DC Raw Materials Supplier SUPPLY 9

10 Globalization & Multi-Channel Logistics Transportation Modes and Distribution Nodes Trucks (TL and LTL) 92% Ocean 70% Air 68% Parcel 56% Rail 40% We do not operate a distribution center and have no plans to Multi-modal 35% 22% 0% 20% 40% 60% We 80% do not 100% operate a distribution center, but 3% Percent of Respondents, Percent of Respondents n=191 are planning to 11% We are 100% managed by a third party service provider(s) 25% We currently operate a distribution center 15% 24% We currently operate 2-5 distribution centers We currently operate more than 5 distribution centers 10

11 CSCO Supply Chain Disruptions Supply Chain Disruptions Discrete Increase in customer demand 47% Supplier / carrier capacity did not meet our demand 45% Raw materials price volatility 42% Shipment delayed / damaged / misdirected 34% Commodities price volatility 29% Reduction in customer demand 33% Product quality issues leading to recalls 26% Unfavorable change in currency exchange rate 19% Complexity and Volatility and Gut Feel decision making is mitigated by Big Data and Predictive Analytics 11

12 Energy and Globalization Drive Actions Improve energy efficiency or use alternate sources of energy 60% 55% Changing the way we manage our waste and disposal Introducing more efficient / environmentally friendly packaging options 42% 35% 39% 35% Changing transportation / logistics strategies Integrating supply chain process across the extended supply chain Focusing on inbound supply chain initiatives Changing the way we market our products 27% 24% 18% 16% 15% 13% 9% 8% Best - in - Class All Others 0% 15% 30% 45% 60% Percent of Respondents, n=191 12

13 Agenda Business Context- Gut Feel and Complexity Tradeoffs / Strategies In Predictive Analytics Aligning Predictive Analytics to Supply Chain - Strategies and Focus - Capabilities of Leaders - Technology Enablers traditional to social media Case Studies and Best Practices Key Takeaways Q&A

14 Aberdeen s Methodology The PACE Framework Pressures: External and internal forces that impact an organization s market position, competitiveness, or business operations. Actions: The strategic approaches that an organization takes in response to industry pressures. C Capabilities: E Enablers: The business competencies (organization, process, etc ) required to execute corporate strategy. The key technology solutions required to support the organization s business practices

15 Tough Tradeoffs Growing Globalization Growing complexity of global operations (e.g., longer lead times and lead-time variability, or increasing numbers of suppliers, partners, carriers, customers, countries, logistics Rising supply chain management costs (e.g., total landed costs, fuel, labor costs) 26% 52% 46% 52% Increased demand volatility 28% 28% Escalating demand for service from customers 22% 37% Increased regulatory compliance mandates reduced supply material & capacity 5% 12% Best-in-Class n=58 All Others Chief Supply Chain Officer Survey, 2012; n=191 0% 10% 20% 30% 40% 50% 60% Percent of Respondents 15

16 Predictive Analytics Definition Predictive analytics = mathematical model to use as the basis of predictions Today s Supply Chain BIG DATA - hundreds thousands of pieces of data to segment by... - Customer - Product - Logistics Channel and Profit Beyond BI- data richness and the rigor of the mathematical modeling distinguish predictive analytics from business intelligence 16

17 Predictive Analytics Priorities in Customer Marketing Improve targeting of marketing offers 55% Obtain a 360º view of the customer 45% Build unique customer profiles and personas 29% 0% 10% 20% 30% 40% 50% 60% Percentage of respondents, n=112 17

18 Predictive Analytics Usage in Marketing 24% of companies have adopted predictive analytics for marketing An additional 33% of respondents plan to implement this approach within the next 12 months Segmenting components include - Customer - Product - Logistics Channel and Profit 18

19 Predictive Analytics Capabilities in Customer Contact Centers Customer facing skills understood and grown / hired 37% 50% Predictive model used to drive decisions in real time 18% 32% Leaders Followers Ability to make proactive inbound offers 20% 29% 0% 10% 20% 30% 40% 50% 60% Percentage of Respondents, n=112 19

20 Predictive Analytics Requires Data : Structured, Unstructured and Social Access to customer behavior data Access to all customer transactional data Access to internal unstructured data Access to social media data 49% 33% 41% 24% 81% 63% 78% 71% Leaders Followers To a large extent, the quality of the model is only as good as the quality of the data that you have. But now quantity of social data is growing and unstructured ~ CRM Manager, European Utility 0% 20% 40% 60% 80% 100% Percentage of Respondents, n=112 20

21 Agenda Business Context- Gut Feel and Complexity Tradeoffs / Strategies In Predictive Analytics Aligning Predictive Analytics to Supply Chain - Strategies and Focus - Capabilities of Leaders - Technology Enablers traditional to social media Case Studies and Best Practices Key Takeaways Q&A

22 Predictive Analytics Focus Aligns with Supply Chain Mission Cost savings opportunity area 33% 40% Customer service competitive differentiator Market strategy competitive differentiator 18% Officer 19% 20% 27% Cost center necessary to conduct business 10% 22% Profit center - a potential source of new revenue/profit 5% 7% Best-in-Class n=58 All Others n=106 0% 15% 30% 45% Percent of Respondents Chief Supply Chain Officer Survey, 2012; n=191 22

23 Top Strategic Actions in Predictive Analytics Taken by Companies Internal vs. External Improve internal integration process for creating forecasts, pricing and promotion plans and making mid-course corrections 44% Optimize end-end inventory based on predictive customer demand or service levels 37% Transform supply chain organization 35% Create a more predictable cost optimized network 32% Create a tighter feedback loop from actual market activity to demand assumptions and plans 30% 0% 20% 40% 60% Percent of Respondents 23

24 Predictive Analytics Usage in Supply Chain Organization is Growing 35% of companies have adopted predictive analytics for supply chain segmentation An additional 23% of respondents plan to implement this approach within the next 12 months Segmenting targets include - Customer /supplier - Product - Logistics Channel - Profit contribution 24

25 Predictive Analytics Enablers : Adoption Levels in Demand-Supply Customer segmentation tool 59% 71% Demand management software 51% 63% Social media monitoring and analysis tools 27% 39% Leaders Followers Supply Chain automation with embedded predictive analytics 20% 34% 0% 10% 20% 30% 40% 50% 60% 70% 80% Percentage of respondents, n=

26 Leaders More Likely to Align and to Act Business managers can take action on predicted insights Able to align to execute on predicted outcomes 30% 28% 45% Leaders Followers 45% 0% 10% 20% 30% 40% 50% Percentage of respondents, n=112 26

27 Agenda Business Context- Gut Feel and Complexity Tradeoffs / Strategies In Predictive Analytics Aligning Predictive Analytics to Supply Chain - Strategies and Focus - Capabilities of Leaders - Technology Enablers traditional to social media Case Studies and Best Practices Key Takeaways Q&A 27

28 Schwan Foods Adopts Predictive Analytics, Tailors Delivery Assortments Schwan's Home Service is the largest direct-tohome food delivery provider in the United States Home Service markets and distributes more than 400 products, yet only 78 can fit on a truck 500 sales-and-distribution centers located throughout the United States with 5,700 delivery vehicles Home Service markets and distributes more than 400 products, yet only 78 can fit on a truck Predictive Analytics and route/customer segmentation used to stock trucks and support suggestive selling 28

29 Schwan Foods Adopts Predictive Analytics, Tailors Delivery Assortments Most popular item is vanilla ice cream, yet have 43 flavors of ice cream alone After reviewing assortments by route and driver a predictive analytics model was used Determine which specific 78 items to place on each daily route Predictive analytics program is now over 7 years old and is ingrained in inventory allocation, routing and demand-supply planning Results: 30% increase in sales overall and 13% increase in incremental and suggestive sales 29

30 Users of Predictive Analytics Leverage Customer Segmentation and Social Up to 2x as likely to posses 90% 80% 70% 60% 82% 68% Predictive analytics No predictive analytics 61% 50% 45% 38% 39% 40% 30% 20% 10% 0% Customer behavior used to segment and target Deliver specialized offers to high value customers Marketing messages customized based on Social media behavior 30

31 Schwan Foods and Predictive Analytics Using predictive analytics to support the sales and marketing function is different from using predictive analytics to support strategic planning. When sales and marketing initiatives are supported by this technology, insight from the solution and real-time social media trends can be injected into customer interactions. That means that employees lower down the management ranks and even in our delivery fleets must be empowered to act on those insights and we have seen a 13% increase in suggestive sales ~ CRM Manager, Schwans 31

32 Predictive Analytics Usage in Supply Chain Organization is Growing 55% of Leaders are using social network analysis tools, compared with 36% of followers Social network feeds provide a public database of intentions and sentiment Up to 60% of marketing departments are paying closer attention to social data as a source of predictive insight 32

33 Sullivan Tire on Segmented Predictive Analytics By breaking customers up into different segments, I was able to see which customers are loyal and strong, who only comes in occasionally, and also identify who I can likely turn into a loyal customer who uses more services and comes in more frequently. ~ Mike Panarelli, Database Manager, Sullivan Tire 33

34 Agenda Business Context- Gut Feel and Complexity Tradeoffs / Strategies In Predictive Analytics Aligning Predictive Analytics to Supply Chain - Strategies and Focus - Capabilities of Leaders - Technology Enablers traditional to social media Case Studies and Best Practices Key Takeaways Q&A

35 Key Takeaways 25-45% of CSCO utilize gut feel decisions 75% of executives feel that they could make better decisions with better analytics Predictive Analytics assists - Segmentation of supplier/products/customers - Inbound to outbound decision making - Adding structure to Social Media and Big Data Leaders are 2 times as likely to segment data Predictive Analytics can mitigate suboptimal segmentations and decision making 35

36 Agenda Business Context- Gut Feel and Complexity Tradeoffs / Strategies In Predictive Analytics Aligning Predictive Analytics to Supply Chain - Strategies and Focus - Capabilities of Leaders - Technology Enablers traditional to social media Case Studies and Best Practices Key Takeaways Q&A

37 Questions?

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