Using Big Data for Machine Learning Analytics in Manufacturing

White Paper

Manufacturing

Jiby JosephBusiness Consultant, Manufacturing Innovation and Transformation Group, Tata Consulting Services (TCS)

Jiby has over 10 years of industry experience in the manufacturing and retail domains across Supply Chain Management, Strategic Cost Reduction, Business Process Re-engineering, Lean Six Sigma, and New Product Development. Jiby holds an engineering degree, along with an Executive Diploma in Management from the Indian Institute of Management (IIM) Calcutta. He is a trained Lean Six Sigma Black Belt professional.

Omar SharifBusiness Consultant, Manufacturing Innovation and Transformation Group, TCS

Omar has over eight years of experience in manufacturing, in the areas of Strategic Supply Chain Consulting, Logistics and Transportation, Warranty and Aftersales, Six Sigma and Quality, and Training. He has worked on projects with leading global auto OEMs, aero-engine OEMs, and large chemical companies. His current focus areas include Analytics and Big Data. Omar holds an engineering degree along with a Post Graduate Diploma in Management.

Ajit KumarBusiness Consultant, Manufacturing Innovation and Transformation Group, TCS

Ajit has over 10 years of experience in manufacturing operations and consulting. His functional expertise comprises Industrial Operations, Supply Chain, and Quality and Manufacturing Analytics. He has successfully executed various cost saving, process improvement and business intelligence (BI) projects for leading manufacturing companies.

About the Authors

About the Authors

Saurabh GadkariBusiness Analyst, Manufacturing Innovation and Transformation Group, TCS

Saurabh has over three years of industry experience in the manufacturing and telecom domains. He has worked in the areas of research and analytics across Supply Chain, Sales and Marketing, Warranty, and New Product Introduction. His current focus is on BI and Big Data Analytics. Saurabh is an engineering graduate with a Diploma in Management.

Aditya MohanBusiness Analyst, Manufacturing Innovation and Transformation Group, TCS

Aditya has over seven years of work experience in the IT industry, with over three years in the manufacturing domain as a functional consultant. He has worked in ERP development and implementation across small and medium businesses in India. He holds a Diploma in Management from IIM Lucknow.

Machine Learning as a concept has been in existence for many decades now. However, most manufacturing operations — such as repairing an aircraft engine, planning the product mix in cement production, or ensuring energy control in a large facility — are still largely dependent on experience-based human decisions. The advent of Big Data technology, coupled with efficient data storage mechanisms and parallel processing frameworks, has found new use for the petabytes of data generated by manufacturing operations. Applying Machine Learning techniques to the shop floor has enabled increased accuracy in decision-making and improvement in performance.

This paper explores how Machine Learning algorithms, in conjunction with Big Data technologies, can help manufacturers bring about operational and business transformation.

Contents

1. The Evolution of Machine Learning 6

2. Approach to Machine Learning 6

3. Applications of Machine Learning in Different Industries 8

4. Case in Point: Application of Machine Learning for Predictive Maintenance in Automotive Industry 10

5. Conclusion 13

The Evolution of Machine LearningArtificial Intelligence (AI), a concept that came into existence in the 1990s, is fast gaining popularity across industries. Deep Blue (the chess-playing computer developed by IBM)¹, Watson (the artificially intelligent computer system specifically developed to answer questions on the quiz show Jeopardy!)², and Google Chauffeur (the software powering Google's driverless car)³ are some landmarks in the field of AI, where computer programs have surpassed the capabilities of the human mind.

Machine Learning is a part of AI that continuously observes a series of actions performed over a period of time, and puts this knowledge to use by devising ways to perform similar processes better, in a new environment. In 1959, Arthur Samuel defined Machine Learning as the field of study that gave computers the ability to learn without being explicitly programmed. From initial efforts to explore whether computers could play games and mimic the human brain, this study has now grown into a broad discipline with the ability to produce statistical and computational theories of learning processes.

Today, although the field of Machine Learning is still nascent, it has found its way into daily user experience through applications like Google Maps⁴ that present accurate geographical data from satellite view to street view, and Netflix,⁵ which simulates the user experience through patterns of movie viewing habits. Other examples include applications used for speech and gesture recognition (Kinect), natural language processing (Siri), facial recognition (iPhoto), web search, spam filters, ad placement, credit scoring, fraud detection, stock trading, and drug design.

Big Data technology, with the capacity to process large volumes of data, is accelerating the growth of Machine Learning applications. Apache Mahout, a machine learning library for Hadoop, has a collection of scalable Machine Learning algorithms, executed in quick cycles with the innovative 'MapReduce' technology.⁶ These algorithms are remarkable in their ability to bring out hidden relationships among data sets and make predictions.

Approach to Machine LearningWhile Machine Learning techniques have found an increasing level of applicability and relevance to real world scenarios, they pose a few implementation challenges.

First among them is the lack of expertise in applying Machine Learning techniques to business problems. Not many data scientists have experience in the manufacturing industry, along with a strong knowledge of statistics and the ability to derive analytical insights from manufacturing data.

6

[1] ‘Deep Blue, Icons of Progress’ IBM, accessed on 5 May 2014, http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/

[2] ‘IBM's Watson supercomputer crowned Jeopardy king’, BBC News, 17 February 2011, accessed on 6 May 2014, http://www.bbc.com/news/technology-12491688

[3] ‘Inside Google's Quest To Popularize Self-Driving Cars’, Popular Science, 18 September 2013, accessed on 6 May 2014, http://www.popsci.com/cars/article/2013-09/google-self-driving-car

[4] Andrews Ng, ‘Lecture 1, Machine Learning’, Stanford,, 22 July 2008, accessed on 6 May 2014,http://www.youtube.com/watch?v=UzxYlbK2c7E at 54 minutes

[5] ‘Collaborative Filtering’, Centre for Computational Statistics and Machine Learning, accessed on 6 May 2014,http://www.csml.ucl.ac.uk/courses/msc_ml/?q=node/40

[6] The Apache Mahout™ Machine Learning Library, accessed on 8 May 2014, http://mahout.apache.org/

The second challenge is the lack of a culture that can apply the Machine Learning process to day-to-day operations. This does not mean the mere usage of mobile devices with system failure prediction capabilities, but rather a high level of involvement from the operations team in the process of Machine Learning, for better prediction accuracies and process improvement results.

The third challenge is the availability of the right data from various operations and processes. Machine systems such as Programmable Logic Controllers (PLC) and Supervisory Control and Data Acquisition (SCADA) may capture a lot of machine data, but this data may not be relevant. PLC and SCADA do not store the entire data set required for creating a predictive analytics solution based on Machine Learning.

The fourth challenge is the lack of technological competence in using Big Data for Machine Learning algorithms. While software vendors have a growing list of Machine Learning algorithms, they are mostly unsupervised learning algorithms that are used to derive inferences without setting the expected responses. Applying them to a specific problem requires a lot of effort in fine tuning the parameters and validating the results.

As these challenges would be more or less relevant for different organizations, the implementation approach must be tailored for specific requirements. The reference model depicted in Figure 1 enumerates the various elements to be considered for the successful implementation of Machine Learning.

7

Figure 1: Approach to implementation of Machine Learning techniques

Ensure management commitment and investment for people, process, data, and technology

Gain competence on Big Data technology and forge

alliances with technology

partners

Collaborate with

universities and get

training on Machine Learning models

Prioritize business

challenges

Build data

infrastructure

Prepare and understand

the data

Develop right Machine Learning models

Set up the Big Data platform

Test model for continuous improvement

Deploy and monitor solution

MachineLearning

Framework

An organization needs to establish a clear link between its business imperatives and Machine Learning program strategy. The initiative requires widespread executive sponsorship and business commitment to yield the expected benefits. The following checklist can help in the implementation process:

Define the business case with a focus on prioritized opportunities, possible solutions, and return on investment.

Set in place a core team with expertise in business processes, Machine Learning models, data architecture, and Big Data technology. Build a team of data scientists with such expertise, either by hiring external resources or through internal grooming.

Get guidance from renowned universities on established Machine Learning models.

Deploy dedicated operational resources, build the infrastructure to source data, and set up the Big Data technology platform.

Institute change management programs for improved processes and intelligent ways of working.

Applications of Machine Learning in Different IndustriesMachine Learning can be applied to high volumes of data in order to gain deeper insights and to improve decision making. Figure 2 depicts some emerging applications of Machine Learning.

8Figure 2: Machine Learning applications across industries

Predictive maintenance or condition monitoring

Warranty reserve estimation Propensity to buy Demand forecasting Process optimization Telematics

Predictive inventory planning Recommendation engines Upsell and cross-channel

marketing Market segmentation and

targeting Customer ROI and lifetime

value

Alerts and diagnostics from real-time patient data

Disease identification and risk stratification

Patient triage optimization Proactive health

management Healthcare provider

sentiment analysis

Aircraft scheduling Dynamic pricing Social media – consumer

feedback and interaction analysis

Customer complaint resolution

Traffic patterns and congestion management

Risk analytics and regulation Customer Segmentation Cross-selling and up-selling Sales and marketing

campaign management Credit worthiness evaluation

Power usage analytics Seismic data processing Carbon emissions and trading Customer-specific pricing Smart grid management Energy demand and supply

optimization

Manufacturing Retail Healthcare and Life Sciences

Travel and Hospitality Financial Services

Energy, Feedstock, and Utilities

Manufacturing organizations generate a lot of data in the course of operations, but currently, are not collecting, storing, managing, and using this data judiciously in order to improve process performance. Machine Learning systems can estimate the predicted outcome accurately based on training set data or past experiences. By gathering valuable insights for better and more accurate decision-making, Machine Learning systems can help manufacturers improve their operations and competitiveness.

The following are some potential game changing use cases from across industries.

Case 1 – Condition Monitoring

Airline companies no longer pay upfront for engines; instead they pay per hour of ‘time on wing’, a measure of operational reliability of the engine or aircraft system. This forces engine manufacturers, currently engaged in diagnostics of engine defects for service requirements, to try to improve the engine’s reliability. Big Data applications for Machine Learning techniques carry out pattern matching for fault isolation and repair support using multiple operational and external parameters received in real-time from sensor data. This helps manufacturers accurately predict failure in engine operations well ahead of time, thus increasing the service revenue and reducing the cost of service.

Similarly, the heavy engineering industry is moving away from the typical long term service contract to an ‘Analytics-as-a-Service’ model. This enables manufacturers to predict the health of the equipment in real time, allowing customers to release the equipment for maintenance only when necessary. Machine Learning techniques such as neural networks, support vector machines, and decision trees are capable of identifying complex interdependencies within operational parameters and detecting anomalies that can lead to equipment failures. Manufacturers of power generation turbines, electrical substation equipment, and building equipment have implemented these models and techniques. The vehicle telematics industry is also using this model, enabling a shift in the service business for automotive dealers from regular service visits to service based on analytical findings.

Case 2 – Quality Diagnostics

Machine Learning techniques can potentially eliminate the process of testing, by predicting quality early on in the manufacturing process. This can change the paradigm for precision manufacturers who are currently unable to detect micro shrinkage or porosity of castings, and for engine manufacturers spending hundreds of hours in test rigs before shipment. Big Data applications collect data from manufacturing operations and from the various processes across the supply chain, to decode the behavior of material transformation and engine operation in order to detect potential defects. ⁷

9

[7] ‘ Igor Santos, Javier Nieves, Yoseba K Penya, and Pablo G Bringas, ‘Optimizing Machine-Learning-Based Fault Prediction in Foundry Production’, Deusto Technology Foundation, accessed 10 May 2014, http://paginaspersonales.deusto.es/ypenya/publi/penya_DCAI09_Optimising%20Machine-learning-based%20Fault%20Prediction%20in%20Foundry%20Production.pdf

10

Case 3 – Energy Optimization

With growing privatization and volatile markets, the power generation and transmission industry is looking at energy service deals as a new avenue for growth. Machine Learning techniques can help these power generation and transmission organizations predict the demand fluctuations from energy consumption patterns and arrive at the optimum demand response in real time, thereby optimizing the demand and supply from various sources at minimum cost. Building management companies can also benefit from such an application, since it helps them optimize their energy consumption and keep energy costs in check.

Case 4 – Demand Prediction

Machine Learning techniques based on natural language processing and speech recognition can process large amounts of social media data. This can be helpful for the automotive industry and other B2C markets. Insights from these pockets of data help in near-accurate demand prediction in response to an organization’s and its competitors’ market campaigns and product launches. Several Big Data applications are available in the text analytics space to analyze data from various sources like call centers, blogs, survey results, and visit notes.

Case 5 – Propensity to Buy

With competition getting fiercer by the day, automotive Original Equipment Manufacturers (OEMs) are keen to understand factors influencing the propensity to buy and the appropriate mix of customer incentives to fuel their product demand. Though estimation of the propensity to buy is not a new concept, it has so far been limited to major events, such as new product launches, and a narrow set of data. Machine Learning techniques can use vast amounts of data to unearth vital insights like consumer attitudes and perceptions towards the brand. This can help marketers improve conversion rates and formulate successful and economical up-selling, cross-selling, and retention strategies for targeted customer segments.

Case in Point: Application of Machine Learning for Predictive Maintenance in the Automotive IndustryBusiness Scenario

Consider the scenario of a stamping plant at an automotive OEM that manufactures vehicle panels. The operation involves an intensive workload, requiring high availability of hydraulic press lines. The Overall Equipment Effectiveness (OEE) of the press line was as low as 65 percent, with the breakdown time ranging from 17-20 percent. Though the press manufacturer offered closed loop control systems, they were limited to validation against a static range of values and did not read all the variables affecting the failure event. Hence they were not effective in improving the OEE. In addition, the maintenance process was largely based on preventive scheduling, leading to high unplanned downtime and maintenance cost as well as lost capacity during the maintenance tasks.

11

The OEM wanted to improve the equipment availability through accurate prediction of potential events such as part failure and functional degradation. Through this proof of concept, TCS recommended the use of Machine Learning analytics to achieve the same.

Business Process Change

The process of maintaining the press line was studied in detail. It was found that Maintenance Engineers spent a lot of effort in attending to breakdowns and consequently, less time was spent visiting the floor and allocating resources for planned maintenance. A Machine Learning solution could enable the condition monitoring process, reducing the need to attend to breakdowns (see Figure 3).

Figure 3: Business Process Changes for a Maintenance Engineer at the OEM Plant

Traditional

Big Data

Shop floor visit for equipment

observation

Study the equipment log

Identify any abnormalities and

decide on maintenance task

Review absenteeism and critical

spares availability

Allocate resources

for planned maintenance

Review and approve the spare orders

Receive notification

on equipment breakdown

Redeploy the people to

attend breakdown maintenance

Diagnose the equipment fault by analyzing the

performance indicators with the known techniques

Identify potential

options for fixing the problem

Try one of the options

If unsuccessful, try another option

until problem is fixed

Real time observation of failure probability

over mobile

Follow the directions for maintenance event based on probability

Review the absenteeism and

critical spares availability

Resource allocation for planned

maintenance

Review and approve the spare orders

Receive notification

on equipment breakdown

Redeploy the people to attend

breakdown maintenance

Study the trend of operation parameters to accurately

identify the right root cause

Identify the right option for fixing

the problem

Execute the option to fix the problem

30% time in analyzing the problem

70% time in fixing the problem

70% time in analyzing the problem

30% time in fixing the problem

Standard Process change

Analytical Solution

Stochastic Gradient Descent (SGD), a Machine Learning technique, was used to predict failure events using the sensor data of all three machine systems in the press line – hydraulic press, blank holder, and lifter. The technique maximizes the likelihood of classification into the defined categories using Logistic Regression. The algorithm uses weight factors associated with the sensor data to classify the equipment events into the two categories: 'fail' and 'not fail'. It uses an iterative process to calculate new weight factors through the observation of equipment data.

The Predictive Maintenance Solution

Sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) was collected from the equipment every 15 seconds for a period of 12 months. The components of the solution output are depicted in Figure 4.

12

Figure 4: The Predictive Maintenance Solution for the OEM Plant

Calculate the OEE of the entire plant (OEE = Availability* Quality * Performance)

Drill down to derive equipment level OEE and identify the bottlenecks

Estimate the time to fail in correlation with machine event failure probability

Establish failure probability of the machine based on the real time operational parameters

Plant OEE OEE by Equipment

Time to Fail

Probability of Failure

13

Benefits of the Solution

The potential benefits that the OEM can realize by implementing this solution are:

The ability to predict a failure event before it occurs, with an accuracy of more than 92 percent and a cut-off probability of 40 percent

Increase in OEE from the industry average of 65 percent to a benchmark figure of 85 percent

Greater efficiency in the scheduling and planning process, ensuring minimal loss of production

Improvements in asset reliability and product quality

ConclusionAcross industries, Big Data technology has tremendous potential to leverage Machine Learning capabilities in enabling accurate decision-making for superior performance. There are many applications of Machine Learning techniques in the manufacturing industry, but successful implementation requires commitment from top management to enable changes in processes, active involvement of operational resources, availability of data, and collaboration with academia and technology partners with expertise in Machine Learning models and Big Data technology. The solution for predictive maintenance analytics using Stochastic Gradient Descent, as presented in this paper, demonstrates how Machine Learning can enable accurate prediction of failure events in the press line.

Recent developments in advanced computing, analytics, and low cost sensing have the potential to bring about a transformation in the manufacturing industry. The implementation of Machine Learning and Big Data may drive the next wave of innovation and may soon prove to be an unavoidable tactical move in achieving higher levels of optimization.

All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. Copyright © 2014 Tata Consultancy Services Limited

IT ServicesBusiness SolutionsConsulting

Subscribe to TCS White PapersTCS.com RSS: http://www.tcs.com/rss_feeds/Pages/feed.aspx?f=wFeedburner: http://feeds2.feedburner.com/tcswhitepapers

ContactFor more information about TCS’ Manufacturing Business Unit, visit: http://www.tcs.com/industries/manufacturing/Pages/default.aspxEmail: manufacturing.solutions@tcs.com

About the Manufacturing Solutions Unit

Global manufacturers are trying to reduce operational expenditure, invest in process improvement, utilize existing capacity optimally and increase efficiencies, while maintaining product quality and meeting safety and regulatory norms. TCS' Manufacturing Solutions provide you the bandwidth to innovate on business models, leveraging contemporary technology solutions.

We believe in leveraging learning from across the segments in developing business solutions. Be it in applying the concepts of lean new product introduction from discrete industries to a chemical manufacturer, or leveraging the aerospace industry experience in service management for the automotive sector, our dedicated Manufacturing Centers of Excellence (CoEs) under these focus vertical industries are continuously looking at breakthrough solutions. Clients can benefit from our rich experience in both the discrete (automotive, industrial machinery and equipment, aerospace) and process industries (chemicals, cement, glass and paper).

About Tata Consultancy Services (TCS)Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match.TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering and

TMassurance services. This is delivered through its unique Global Network Delivery Model , recognized as the benchmark of excellence in software development. A part of the Tata Group, India’s largest industrial conglomerate, TCS has a global footprint and is listed on the National Stock Exchange and Bombay Stock Exchange in India.

For more information, visit us at www.tcs.com

TCS

Des

ign

Serv

ices

I M

I 07

I 14