www.wipro.com Manufacturing - How to maximize the use of data Prasad Shyam, General Manager & Global Business Head MFG, HLS & ENU Analytics and Information Management(A&IM) Wipro Technologies Table of Contents 1. Capitalizing Data to Transform Manufacturing Operations ............................. 03 2. Freedom from spread sheets .................................................................................. 04 3. What you should do right now .............................................................................. 04 4. How did analytics get to be center stage? ........................................................... 04 5. High impact tool ........................................................................................................ 05 6. The 8 key success factors ........................................................................................ 06 Capitalizing Data to Transform Manufacturing Operations Manufacturing is no stranger to data. However, over the last decade or so, new sources of data are leaving manufacturing buried under piles of numbers. This is because manufacturing continues to wield traditional tools such as spreadsheets. These tools are incapable of handling the new volumes, velocity and variety of data. Is it time for manufacturing to ponder over how to maximize the use of data? Recent research commissioned by Wipro showed that five out of six manufacturers do not have appropriate data management strategies to help improve the quality of their decision-making. The problem demands urgent attention. An Economist Intelligence Unit survey, 2013, commissioned by Wipro Technologies called `The Data Directive' showed that manufacturing (16%) and retail (13%) are the least prepared with data management strategies (See Figure 1 for a comparison between manufacturing and other industries from the research). Other industries are getting ahead of manufacturing in the use of modern data management and analytics. The study indicates that manufacturing has much ground to cover before it can begin to leverage data and analytics. The challenge before manufacturing is to integrate vast volumes of core ERP data with market intelligence, Data-driven industries (% respondents) 56.3 60 Manufacturing Financial Services 45.7 50 Professional Services 35.1 37.8 40 We have a welldefined data management strategy that focuses resources on collecting and analysing the most valuable data We understand the value of our data and are marshalling resources to take better advantage of them We collect a large amount of data but do not consistently maximize their value We collect data but they are severely underutilized 2.2 3.3 3.1 2.2 0 3.5 6.5 6.7 6.7 6.3 8.8 10 15.2 20 12.5 13.3 15.8 20 21.9 30 Technology, Media and Telecom 30 30.4 40 36.8 40 Retail and Consumer Goods We do not prioritize data collection Figure 1 Source: Economist Intelligence Unit Survey 03 customer behavior, social data, , device data, logistics, partners, channel performance, service opportunities, etc. In real terms it means overhauling the tools used for data management. Manufacturing needs to switch to sophisticated cutting edge tools to improve the efficiency of its operations and the accuracy of its decision-making. The Economist Intelligence Unit study showed that amidst the data stockpiling now under way in manufacturing, clarity on which data matters most is the biggest barrier to move forward (40%). As many as 34% of the executives in the study worried “that the quality of their decisions are actually being impaired by data overload.” Over the last decade analytics has grown in importance. It has become the central tool to improving efficiencies, reducing costs, adhering to environmental norms, improving compliance monitoring/reporting and addressing security concerns. Today it is indispensable. Manufacturers are indicating their acute need for predictive analytics through questions such as:  How can I reduce down time of equipment without the expense of routine preventive maintenance?  How can I discover a potential product defect even before the customer knows it?  How can I improve traceability of defective components? Freedom from spread sheets Fortunately, for some manufacturers, the historical dependence on spread sheets is on the wane. They are the ones that are building a strategic response to fluctuating economic conditions, rise in raw material costs, volatility in demand, higher customer expectations with regard to quality, shrinking product cycles, and shorter forecast horizons. These select manufacturers are capitalizing on data, maximizing its value, leading to significant transformation in their manufacturing operations. The Economist Intelligence Unit Report clearly points to analytics being a differentiator in many businesses. The report shows that the key to success of high growth firms is “the fact that they have done more to reorganize their structures and leadership around data and to introduce data management strategies.” As an example, the study showed that 10.7% of high-growth firms collect machine generated data (sensors, RFID, network logs, telematics, etc). Ironically, 14.3% no-growth firms – a shade more than high-growth firms - collect the same data. The obvious question that begs an answer is: if no-growth firms collect more data than high-growth firms, why do they lag in growth? This is because 46.6% of high-growth firms collect and analyze the data while only 33.3% of nogrowth firms do the same. The difference lies in analyzing the data, not collecting it. Other independent research has begun to show that those investing in analytics were also the most likely to be innovative and experience an increase in operational efficiencies. Interestingly, one study showed that only 10% were using predictive analytics. This means that manufacturers who use predictive analytics and go from being reactive to being proactive can gain substantial advantage over those who don't. These are the manufacturers who are extending their focus from traditional market surveys and pilot products to leveraging social media, telemetry data from products to discover new services that customers want, and reduce after sales costs. And this is just the beginning of what data and analytics can enable. What you should do right now Analytics until recently uncovered trends, sentiments, root cause for failure, etc. by digging into data from the past. It produced reports that helped answer modest questions: how much did that delay in delivery cost us? How much did we add to bottom line by shaving off part of a process?  How can I avoid shipping defective components and reduce returns?  How can I contain warranty costs? Manufacturers are saying, “I don't want reporting on yesterday or alerts on today; just tell me what my business should be doing this morning.” In fact analytics is providing insights into what manufacturers should be doing at this very moment. This is a sophisticated capability, unthinkable until now. It is possible to achieve this because conventional enterprise data sources (SCM, CRM, HCM, etc.) are now being supplemented by data from social media (Twitter, Facebook, YouTube, etc.), device and machine data (sensors, meters, RFID tags, CDR, mobile devices, computer logs, cameras, GPS, etc.) and interaction data (credit cards, clickstream, etc.). 'Listening' to the enormous data stream being generated by men and machines can show up patterns that help predict the future: How much time is left between a vehicle component failure and an accident? Can we call the vehicle in for a check before the accident? When is a turbine likely to go down? Can early intervention reduce plant downtime when the turbine fails? How can I overcome my current manufacturing constraints when demand bumps up? Or better still, can I predict the surge in demand and prepare my plant for it? Sophisticated models and algorithms can forecast the future and provide decision support to manage those emerging scenarios in real time. How did analytics get to be center stage? There are radical changes sweeping the industry and the way it consumes information. Data has become a key driver of manufacturing and is the fundamental building block of smart systems. It is fueling new waves of efficiency and productivity. Manufacturing is moving away from descriptive reporting (What happened? When? How? How often? What was that outlier trying to say?) and query for detail (What is the excitement about? Why did it happen? What is the problem?). It has also begun to evolve beyond alerts (What is the required action now? What is likely to happen?) towards predictive forecasting (What could happen in the near future? What is this trend trying to tell us? How should we respond?). In many ways we are at the most complex level of generating and consuming business intelligence - the prescriptive level (What is the best outcome if we don't omit all the variables?). 04 Evolution: How we consume data There are radical changes sweeping the industry and the way it consumes information. Data has become a key driver of manufacturing and is the fundamental building block of smart systems and is fuelling new waves of efficiency and productivity. t pac im ess in Volume and complexity of data Bus Reporting Descriptive reporting: What happened? When? How? How often? What was that outlier trying to say? Query Alerts What is the excitement about? Why did it happen? What is the required action now? What is likely to happen? Predictive What could happen in the near future? What is this trend trying to tell us? How should we respond? Prescriptive What is the best outcome if we don't omit all the variables? Based on sensors, CEP, Social network analysis etc. Enterprise manufacturing intelligence High impact tool Analytics has become a high impact tool across manufacturing: Enhancing quality: Analytics can surface patterns from product problems. These patterns can be used to improve product quality, thereby increasing customer satisfaction, product safety and reducing potentially expensive product returns. Reducing returns through identification of defects before shipment can save millions of dollars in recalled products. Toyota alone, for example, recalled 2.7 million vehicles late last year for steering and water pump related problems at an estimated cost of half a billion dollars1. Superior warranty management/product returns/fraud detection: Adequately armed with predictive analytics, manufacturers can understand when products or parts are likely to fail. This can help organizations create better service, repair and warranty policies. Quality departments can also drill down to the level of batch or unit that is likely to fail, predicting product returns. This gives manufacturers an opportunity to pro-actively recall products, send replacements and ensure customer dissatisfaction is minimized. Increasingly, predictive analytics is being used to detect suspicious or fraudulent warranty claims, thus reducing financial loss. Improved maintenance capabilities: One of the major reasons that can be attributed to maintenance costs of equipment is that manufacturers often fall back on periodic maintenance schedules to prevent downtime. Downtime can generate negative customer sentiment, impact SLAs, loss of reputation, inability to meet compliance norms (in cases such as medical equipment), and can add to costs through standby equipment, high spare parts inventory or result in financial impact due to disruption in production. Manufacturers have also invested in service teams to provide on-site maintenance. Servicing can become expensive if the service team does not have the right spares before the service request is made. In cases involving medical equipment, valuable time can be lost in patient care. Device data combined with regression models that are at the core of predictive analytics could help ensure just-in-time preventive maintenance. Regression models create relationships between interacting elements and 05 variables to forecast breakdowns, thereby helping optimize field personnel, availability of spares, etc. saving millions in unnecessary routine maintenance costs. Enabling service discovery: Service discovery can be the key to creating fresh revenue streams. Services typically result in between 7 to 12% of revenues through the lifecycle of a product. Service discovery can be based on data from remote monitoring of the asset, analyzing equipment usage, service schedules, customer feedback (CRM + social), contracts (AMC), etc. As an example, telemetry data from a vehicle may suggest that a battery failure is eminent. The manufacturer can call in the vehicle for a replacement before the next scheduled maintenance. Driving regulatory compliance: With increased regulatory pressure and safety standards, traceability of products and their components is becoming extremely important. Manufacturers, especially in industries such as automotive, medical devices, high tech and defense need to be able to capture, maintain and recall mountains of very granular data such as component number, model, manufacturer/supplier/place of origin, lot/ serial number, plant floor for assembly, production date and time, dispatch, distribution etc. When required, analytics can sift through this data to generate compliance reports. But more importantly analytics can predict when compliance norms will be violated and how these violations can be prevented. Impacting demand planning and inventory management: Analytics is turning manufacturers from being product centric to being customer centric. Insights into sales and markets are helping manufacturers adjust their inventory levels, production schedules, order fulfillment rates, shipment, etc. Even a 5% improvement in demand planning – based on data for holidays, seasonal changes, etc. -- can lead to significant bottom line gains. The 8 key success factors The decision to leverage analytics begins with two simple questions, “Is it worth analyzing the data? What is the return I can expect from investments in analytics?” The analytics journey begins by identifying business areas where it can create impact. Prioritizing these opportunities becomes the next step. We believe that success is dependent on eight factors: 1. Identifying key business processes that can create impact 2. Creating a business case and obtaining executive sponsorship 3. Availability of the right data 4. Strategy and plan + supporting infrastructure for analytics 5. Implementation partner capability, domain expertise and solution assets 6. Effective pilot for demonstrating potential business impact and value 7. Change management 8. Measuring impact/ value created to improve on the analytics journey Organizations today need to move towards an analytics culture and deploying their people, processes and technologies towards fact-based decision-making. Mixing data from within (production, sourcing, resource availability, CRM, etc.) and outside the enterprise (weather, demographic profiles, insurance claims, etc.), can help them anticipate and develop a faster response to volatile markets. They can solve complex business problems with greater accuracy and confidence. Above all, using analytics, manufacturing can maximize the use of data to stay on top of the key drivers of financial performance in a world where change is completely unpredictable. References / Citations 1. http://www.ibtimes.com/auto-industry-churning-out-more-lemons-or-more-recalls-880154 06 About the Author Prasad Shyam is the General Manager & Global Business Head for Analytics and Information Management (A & IM) focusing on Manufacturing, Automotive, HiTech & Consumer Electronics, Pharma and Healthcare, Energy, Utilities industries. He carries P&L responsibility, strategy and operations of this unit globally. A&IM helps customers derive valuable insights out of integrated information by bringing together the combined expertise of Analytics, Business Intelligence, Performance Management and Information Management. The group provides consulting, business centric and technology specific analytical solutions and data management frameworks developed through a complete ecosystem of partners, focusing on industry specific analytics, optimization and operations analytics, Enterprise Data Warehouse, MDM, Data quality and data life cycle management. Prasad has 18+ years of experience in IT industry, and is strategic advisor to many Fortune 500 organizations focusing on analytics and information management. He is one of the founding members of Business Intelligence and Data warehouse practice in Wipro. Prasad holds a bachelor of engineering in Electronics and Communications. About Wipro Council for Industry Research The Wipro Council for Industry Research, comprised of domain and technology experts from the organization, aims to address the needs of customers by specifically looking at innovative strategies that will help them gain competitive advantage in the market. The Council, in collaboration with leading academic institutions and industry bodies, studies market trends to equip organizations with insights that facilitate their IT and business strategies. For more information, please visit www.wipro.com/insights/business-research/ About Analytics and Information Management Services Wipro is a leading provider of analytics and information management solutions - enabling customers to derive actionable business insights from data to drive growth, enhance cost management and strengthen risk management. Wipro works with customers to develop end-to-end analytics and information strategy leveraging process assets and solutions based on analytics, business intelligence, enterprise performance management, and information management. 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