Automotive Analytics Re-Imagination Journey

Transcription

1 Automotive Analytics Re-Imagination Journey Seema Paranjape BI Program Manager FIAT Chrysler Automobile Mumbai , Nita Khare ATU Lead, IBM Analytics Mumbai , ABSTRACT Automotive industry is striving for design anywhere, build anywhere & sell anywhere theme. Digital technologies are disrupting every industry, making executives question their strategy. The only way to sustain competitive advantage in the age of the customer is to make their experiences more personal. To get the data to make this happen, you need a next-gen analytics platform that can harness big data and predictive analytics along with traditional analytics. In this paper, we have tried to portray Analytics Re-Imagination journey for Auto industry. 1. INTRODUCTION The Analytical re-imagination journey will not only enhance the way automobile companies do business but will also enable their business process transformation journey. 1.1 What is Analytics Re-Imagination The digital revolution has impacted enterprises globally on an unprecedented scale. Companies are realizing the tremendous business value unleashed by a combination of the digital five forces to reimagine their businesses in fundamentally new ways. Inundated by enormous volumes of data churned out each day, enterprises find themselves grappling with its diversity and velocity of growth. In order to efficiently utilize this wealth of information harvesting and harnessing the data for improved decision making a shift towards increased adoption of Big Data analytics is imperative. The 3 critical analytics trends which currently prevail in the market are Mobility, Advanced analytics and performance management. Information at your fingertips is the new mantra. In the current data explosion era, enterprises are looking for an Analytics system which offers an integrated, cost-competitive business reporting and analytics solution to quickly and securely deliver vital business insights to the users across the enterprise. It should also support operational analytics, deep mining and analytical reporting with minimal data movement. The desire of the customers to move from Standard analytics to Predictive and Prescriptive analytics in a guided manner is opening the market for advanced analytics including Big Data and forecasting for improved and better decision making. This entire journey needs to be planned and is termed as Analytics Re- Imagination. 2. AUTOMOTIVE BUSINESS VALUE CHAIN The figure below depicts the components of an Automotive Business Value Chain Figure 2 Automotive Business Value Chain Following table lists the key areas handled by automotive business value chain: Figure 1 Digital Five Forces Finance Legal & Finance management Loans management Legal & Audits Dealer Audit - Profiling all region dealers for conducting Sales Copyright 2015 Page 1 of 6

2 Product Development Supply Chain Manufacturing Sales Marketing Services Warranty Incentives Audit Risks Identification & Management Product Development Product Development Plan focus on reliability performance planning, volume planning, cost planning sub functions Innovation & KM Business function focus for technology progression for a vehicle Change Management - business process helps to increase vehicle profitability by R&D Forecasting and Planning Capacity Management Supplier Management IBT & OBT Inventory Management Production planning & execution Production network and rule management Production planning & scheduling Inventory management Production performance management Demand Planning Sales & Operations Planning Order capturing & processing Vehicle scheduling Fleet Branding Campaign management Marketing product information mgt. CRM Social Media benchmarking study Call Centres Service contract management Vehicle Consulting Customer Satisfaction Study Warranty management Sales Claims Loss Ratio 3. CURRENT ANALYTICS MATURITY Figure 3 - Current Analytics Maturity Figure 3 depicts the different types of analytics commonly seen in the industry. We clearly see the trend shifting from Descriptive and Diagnostic analytics to Predictive and Prescriptive analytics. The Auto industry is now keener to be more fore-sighted than before. Currently, we are in the era of Analytical Optimization. Figure 4 - Analytics Maturity Curve Figure 4 covers the complete Analytics Maturity journey. Auto Indutry can use the curve to plot and determine their position in the Analytics maturity curve. Typically, the automobile industry as seen today are at the middle of the analytics curve. There is definitely some work happening in silos w.r.t. forecasting and Prediction but still there is a lot to be done w.r.t the analytics maturity. Copyright 2015 Page 2 of 6

3 4. RE- IMAGINATION VISION 5. RE-IMAGINED AUTOMOTIVE BUSINESS VALUE CHAIN In this section, we will the Re-Imagine the Automotive business areas shown in the Figure 5 under Business Value Chain. 5.1 Finance & Legal Integrate social media data with the existing capital data to predict the customer pipeline in order to plan finance and vehicle quantities. Prevention of dealing with fraudent dealers - Integrate current profiling of all-region dealers using Dealers scorecard, Risk score, Sales of vehicle under Incentive program for Sales Incentive audits of dealers. Figure 5 - Automotive Analytics Re-Imagination Vision Figure 5 depicts our vision for the Automotive Analytics Re- Imagination. The real power of new automobile may not be just in its engine, but in its many data-rich sensors. The solution offers the following key benefits: Advanced analytics with full auditability Built-in, extensible automobile industry data model Customer profile and integrated customer activity history Predictive models for marketing, sale of new/additional services Process integration Integrated with website, customer contact center, vehicle service locations, mobile apps. Integrated with marketing and sales automation systems Google glass integration with vehicle in case of kidnapping to find out where that person is. Google map integration to trace back vehicle in case of theft 5.2 Product Development Typical Product Development chain is shown in the Figure 6 below: Figure 6 Product Creation Process Top product development challenges Frequent engineering challenges Increasing product complexity Disconnected processes Projects are understaffed Current scene is that the deviation in the design to target cost is huge. We need to bring it down in our Re-Imagination journey. Create sophisticated predictive models to reduce the deviation in the cost by using various parameters in the alogothirms such as hypothesis analysis, geography information, labor cost information, actual data for similar types of vehicles over the past years. The figure 7 below illustrates the Re-Imagined Product planning phases with cost benefits. Data sources Internal data from CRM, other apps Website, Service activity, in-bank transactions, customer contact centres Social networks Facebook, LinkedIn, Twitter, Google+. Sensors data Usability/Installation/Deployment Search, Q&A interface, socialized analytics SAAS, PAAS rapid on-premise deployments using templates Web, mobile access Customizations using business rules, industry standard components Figure 7 Re-Imagined Product Planning Copyright 2015 Page 3 of 6

4 5.3 Supply Chain This is the typical supply chain of an automotive organization 5.5 Sales Customer Satisfaction Survey (CSS) Figure 8 Supply chain of an Automotive organization Forecasting Accuracy Analyze historical data to find patterns or trends of vehicle parts. Using these models we can perform predictive analysis to suggest parts inventory in months/weeks with the accuracy beyond the established tolerance limits. This may contribute to increase the accuracy of the forecast and consequently provide cost benefits with leaner inventory (both for parts and assembled vehicles). Demand Mix analysis We can analyze the data to find out the distribution of vehicles by size (Small, Midsize, Grande, Truck etc.) and show seasonal variations. Based on this we can suggest appropriate vehicle mix over the coming 1-2 year timeframe (predictive analysis). Global Demand trends - We can analyze the data to establish patterns by geographies and then drill down from regional level to Country Level to state level and below. Based on the analysis, we can decide on which regions to be focused more and consequently generate more demand. 5.4 Manufacturing This is the typical Automotive Manufacturing Process: Figure 10 Re-Imagined CSS Figure 10 shows the Re-Imagined CSS journey. Customer Engagement demands more data to make it personal. Current Customer data management needs to change to result in a multidimensional customer data management platform. Customer Experience Insights with Social Media Data Analytics Data Analytics on voice-of-customers from social website (Facebook, Twitter, blogs, etc) for actionable insights in service, sales, product creation. Analytical Model for Social Media Analytics Predictive analytics - Optimize business strategies by forecasting future behaviors and events from patterns in historical data. Analytics for Rich Customer Experience Fusion of Social Media Analytics and BI with CRM systems to make social insight accessible throughout the company (e.g. Contact Center agent sees information about customer's preference, likes/dislikes, social feedback, etc along with the regular BI while servicing the customer. Figure 9 - Typical Automotive Manufacturing Process Manufacturing Intelligence to predict process bottlenecks and problem root causes in assembly line by analyzing historical voluminous manufacturing process, plant, machine data in assembly line. Predict the down times of assembly lines in a plant to achieve projection accuracy. Copyright 2015 Page 4 of 6

5 5.6 Marketing 5.8 Warranty Processes of coverage design and repair data capture can be reengineered to build a foundation for safeguarding against attempts to submit fraudulent claims which can empower coverages and Repair Data to prevent Warranty Fraud Management in a cost effective manner. 6. AUTOMOBILE RE-IMAGINED BI ARCHITECTURE USING IBM COMPONENTS Figure 11 Re-Imagined Marketing Inclusion of following data sources for Re-Imagining Marketing: Web: Web sales and traffic rates, subscription rates, store cannibalization, Web traffic segmentation, target marketing ROI and Web spend effectiveness. Social media: influencers and customers, competitive and competitor intelligence, customer insights and target audience community building. Customers: lifetime value and profitability, segmentation, cross-sell options through optimized next best offer, brand switching and loyalty metrics. Campaigns: attribution, campaign effectiveness, channel optimization and mark-down elasticity. 5.7 Services Cars will capture its own data of accidents, repairs, services and that can be used by insurance company or dealers for service, warranty etc. Rather than getting data from third party agency with cost. Infotainment Video games on back sits Vending Machine Live streaming for Messages from Auto OEM Recall messages Old car replacement schemes Car discount schemes anytime Theft Resolution Google Map and Sensor Integration to know current location of vehicle in case of theft of a vehicle. Prevent Failures Better Service Sensors will pick parts break down and suggest nearest service centre details. Long Journey Trip Guidance Figure 12 Reporting & Application IBM Reference Architecture The solution is applicable and qualified for all other platforms and products suites. 7. PROPOSED METHODOLOGY 7.1 Phases The key phases involve: Inception Elaboration Construction Transition Figure 13 Typical Phases Copyright 2015 Page 5 of 6

6 7.2 Typical Implementation Plan Need to capitalize new market opportunities Need to win customers with products tailored to their need Need flexibility to develop / manufacture in multiple supply chain or factory locations Manufacturers are under pressure to improve performance in several dimensions; well defined metrics can quantify above mentioned benefits/improvements. Figure 14 - Typical Implementation Plan 8. BENEFITS Accuracy in true economic cost vs assumed economic cost by 30 50% Enhanced Accuracy in forecasting of parts during supply chain by at least 10% Increased responsiveness to market changes and customer needs with new/enhanced product and services Increased customer loyalty Reduce marketing costs and better use of marketing spend with better targeting of customers with more effective campaigns More effective upsell/cross-sell, increased share of wallet, higher life-time value Personalized, differentiated services based on a more complete understanding of customer and his/her needs Applying advanced analytics to every customer interaction Higher customer satisfaction Increased customer responsiveness based on better understanding of customer, customer needs, sentiment and feedback On-time delivery to commit Overall equipment effectiveness 9. SUMMARY The major automobile industry pain areas mentioned below can be easily addressed in the Analytical Re-Imagination journey. Auto OEMs driven by Cost Pressures - Top pressure driving improved product development, increase price competition Customer demand for lower cost Need to launch product quickly Analytics Re-Imagination is a journey that needs to be well - planned and timely executed. 10. ABBREVIATIONS Abbreviation KM IBT OBT CRM CSS ROI SAAS PAAS IVR OEM IRC 11. REFERENCES Table 1. List of Abbreviations Description Knowledge Management Inbound Transportation Outbound Transportation Customer Relationship Management Customer Satisfaction Return On Investment Software As A Service Platform As A Service Interactive Voice Response Original Equipment Manufacturer Information Research Centre IRC - The Manufacturing Insider - Mar'15.pdf Copyright 2015 Page 6 of 6