The Infosys global supply chain management blog enables leaner supply chains through process and IT related interventions. Discuss the latest trends and solutions across the supply chain management landscape.

September 30, 2015

How will Future Maintenance be?

Tomorrow's enterprise would want to limit their efforts, energies and expertise to the areas of their core competency rather than dwelling on non-core areas. When we look at asset intensive organizations, most of these firms would want to stick to their area of expertise i.e. manufacturing/producing products of value. So, these organizations can spend their resources and energies in innovating and manufacturing products and leave the supporting areas to the experts in those areas like how IT implementations are not carried out in house as it is not their core competency.

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April 16, 2015

Internet of Asset Maintenance

Internet of Things (IoT) is one of the hot topics much discussed in various forums these days. IoT is considered to be an influencer in transforming the way Objects interact with external world. This arouses a curiosity to know how it influences the Asset Maintenance business function as well. A point of debate is that - 'is it a new concept which can improve the maintenance process or is this something we already do in a different way'. A comparative analysis on the basics of IoT and on the latest trends in Maintenance processes can help to find an answer for this debate.

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March 30, 2015

Aspects of Shutdown - Pre & Post Audit

Now that we have seen all the key factors/phases for a successful shutdown such as the scoping, kick off, identification, safety and procurement. A successful execution of shutdown should start with an audit and should be followed by an audit. Let us dive deep now.

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March 27, 2015

The Expert Talk Predictive Analytics

Predictive Analytics in the area of Equipment Reliability has been a key focus area within Enterprise Asset Management(EAM) practice in Infosys. Thought papers, frameworks and real-time project experience to reinforce the subject knowledge did not limit us in our pursuit of seeking the ultimate in this domain. Bringing you one among many such pursuits- our interaction with Yuri Gogolitsyn. Read on as Yuri take us through some un-traversed areas within in the domain of Predictive Analytics.

 About The Expert

Yuri Gogolitsyn is an experienced EAI Technical Architect and Consultant who has worked on numerous multinational projects and  has substantial hands-on experience with many leading integration technologies with exposure to real-time assignments involving predictive analytics. He is based out of the UK and before moving into the professional IT was doing brain research dealing with statistical processing of the brain electrical signals.


Welcome to the first edition of Expert Talks, good to have  your with us today. Could you please share with us your tryst with Predictive Analytics?

Thank you! First of all, I think that Predictive Analytics is still much more a research area than a set of tools or products capable of providing quick and immediate solutions to emerging requirements in various industries.

Quite a while ago, before moving into the professional IT, I was doing a scientific research in the area of the brain science. My main interests were, to state it very briefly, in using statistical methods to detect and evaluate brain electrical responses to various stimuli (pictures, words etc.). As a rule, the responses were tiny and buried in the unavoidable background variations and noise. The goal was to obtain a statistical proof that the response actually exists at all and to provide some estimate of the extent it is consistently repeated when you present the same stimulus under the same conditions. This is just one of the examples of a more general area of Pattern Recognition. I believe that Predictive Analytics belongs to the same general area. This area is really huge both in terms of classes of problems it deals with and methods used.


Brain science and its relation to statistics seems fascinating! Do you think we can relate this concept to an Equipment response as well?

 Yes, in fact the concept finds its application in a wider scope. Look around and you will see the science of correlations in all possible aspects of things.  For example, when analyzing large amounts of data on the contents of the supermarket baskets the researchers recently found that when a person buys baby's diapers, there is a good probability that this person would also buy some beer. It was a bit of a surprise! An immediate pragmatic recommendation from the study would be to keep both items close on the shelves to make it more convenient for the customers. However, the researchers did some more digging and found an explanation for this unusual effect. It turned out to be due to young fathers whom their wives ask to buy diapers for their baby on the way home from work. It often implies that the husband expects to spend evening at home with his family, so he also buys beer for himself.

 An example from a completely different area - the width of the annual growth rings on the tree stumps strongly correlates with the annual number of fatalities from heart attacks. However, there is no causal relationship between the two observed variables in this case. The actual driving mechanism underlying this effect is the annual variations in the solar activity.

In the context of Equipment, an example would be to note a response against parameters such as load, pressure, rotations per minutes etc. and try correlating it with the failure pattern. With the objective of optimizing equipment performance, one can study these specific parameters and try channelizing it towards a safer zone. This way, we essentially work on a need based maintenance as we know whether a failure is imminent and could avoid the pit fall of overdoing the maintenance activity.


If you want to describe the Predictive Analytics to a novice in this field, how would it be?


The logic underlying Predictive Analytics could be outlined as follows. A combination of parameters is repeatedly measured for a system under observation. At some moment in time an important event occurs due to an unknown reason - the system noticeably changes its behavior in some way (e.g., breaks or stops functioning). Over a substantial period of observation a large volume of data on the values of parameters that precede the important event's occurrence has been accumulated. The question to answer is to what extent it is possible to predict that the important event is imminent by looking at the current values of the measured parameters.


One fundamental aspect should be stressed here - the repeatability of the important event.  It is impossible to predict events that are unique or occur very rarely indeed - statistical methods just do not work under such scenarios.  On a lighter note, this is nicely illustrated by the following joke.

A University professor is conducting a seminar on telekinesis. He explains to his students that telekinesis is an ability to move objects using just one's will power and says:

-          Let's now all close our eyes, concentrate for one minute and try moving ourselves outside this room into the corridor.

In a minute they open their eyes and are very surprised to see that one person is missing! The professor, stunned not less than his students, asks them for comments. One of the students is doing a course in statistics. He says:

-          I am not sure you would be able to prove that this effect is significant using statistical methods...


 Which industry according to you  would have the most requirements of Predictive Analytics?

Everyone would like to know the future!  The quality of prediction benefits from the careful statistical analysis of the available data. Unfortunately it may often be the case that even the very large volumes of data do not allow prediction with any usable degree of confidence - we do not know if parameters we are monitoring indeed have the required predictive power. You are unfortunately not guaranteed a success when you start dealing with a prediction task. A very good example in this respect is a long history of attempts to predict earthquakes and volcanic eruptions. We are still very far from where we ideally would like to be in this area. You really need not have to put this in an Industrial perspective.


Based on your experience, could you please tell us about the tools/software widely used in the field of Predictive Analytics? Is there a best -of- breed solution available?

There is a huge number of packages for the statistical data analysis available. You can do a lot in Excel, for example, regression models. You can try the machine learning algorithms or even neural networks. In addition to this, there are online courses available on latest analytics tools such as DataStream, Hadoop etc. which can be tried as well. However, I believe that the tools used should be chosen after considering the nature of the problem in details.  You should decide on the approach first, and then pick up the right tool. Also, to work in Predictive Analytics a very good understanding of statistical methods and models is required.


You mentioned about the models, could you please elaborate on this? Is there a best of breed which one can pursue in this regard? According to you, what are the key determinants/factors to ensure accuracy of analysis?


To make a prediction you need a model. A model here is a very general concept. Depending upon the approach and techniques you use the model could be explicitly presented as a formula (e.g., regression models) or, like in neural networks, be not directly visible - embedded in the structure of connections between the neurons in the network. The outline of the general approach used in Pattern Recognition is as follows. Use some part of the data to build a model. Then test the validity of your model by feeding it the data from the other part.  The second step shows how good your model is.

In addition to this, The Data to be analyzed needs to represent an actual behavioral pattern or a trend which can be analyzed using a statistical model forming a basis for drawing meaningful conclusions. It is therefore essential to gather data from a real scenario.


The data gathering aspect is becoming more promising as we move towards the Internet of Things. Utility companies have now started offering the home hubs enabling their domestic customers to monitor energy consumption and control home appliances remotely from smartphones, say, switching on the heating some time before arriving home. Actually, Infosys was already involved in integration aspects of one of such projects.


Everyone is talking about the transition to Strategic Maintenance Practices and the Prescriptive Maintenance practices lately, what are your thoughts on this?

 If we are talking about Prescriptive Maintenance of some expensive equipment in utilities etc., I think that the organizations that should look in this direction are the companies that actually make the equipment. They are in the best position in terms of being able to collect vast amounts of data from many installed pieces of this equipment. They also should have a better understanding of what needs to be monitored. This increases chances of success. 


I am a bit skeptical about quick success in scenarios like "It costs me a lot to maintain my three expensive gadgets/widgets, and one of them failed recently causing me a lot of problems. How nice would it be to use the Predictive Analytics to warn me when one of my gadgets is close to failure? Those guys need to tell me what exactly I should start monitoring. I am sure there are some best practices somewhere".


So quick result is a challenge, what are the other challenges you think one may face while approaching a Predictive Analytics Solution?


From just a task it may develop into a serious research project that would start consuming all your time. Do not expect readily available best practices and universal recipes. You will need to understand a lot about the target process. It takes time and many iterations until (with substantial degree of luck) you arrive to something usable. Furthermore, the most common pitfall I would suggest any analyst is be wary of is generalizing an Asset class, in I think generalizing an Asset class across domains are also not intended. Another common problem I have seen companies struggling with is having huge set of data and having no clue on what to do with them. A predictive data analytic model cannot be generic, it differs case by case. For performing predictive analysis in Asset Management, each Assets specific information needs to be viewed specifically and the asset specific predictive factors determined accordingly.


What are your thoughts on the heavy investments which this area entails? something which Predictive Analytics is infamous for!

I need to make it clear that I am on the side of skeptics in relation to Predictive Analytics, those who tend to believe that the number of scenarios where it is potentially possible to provide a prediction with a reasonable degree of confidence is rather small, definitely much smaller than the number of scenarios where it is not possible. The best negative example we all know about is prediction of the share prices.

The investments in this area should be probably considered as spending on research and development. Usual considerations are valid here - the investments are heavy indeed and in no way they guarantee the desired solution. However, I think that the beneficial side of heavy investments in research is clear - it may lead to better technologies, algorithms etc. that would have much wider usage and substantial benefits.


Besides investments on research, do you think there are other avenues of higher spends which the organization should watch out for?


Certainly the investment cost are higher, the early adopters of predictive analytics would certainly have challenges in substantiating the cost. The investments could range from gathering Instrumentation controls and analytic tools to the company personnel who need to get trained on the using the technology and deciphering the results to act on them. However, looking at the advantages in terms of catching failure before it causes beyond repair damages, the investments seem to be promising.


What would be your advice to Organizations attempting to go the Predicative Analytics route?


Be ready for a trial and error approach albeit at a smaller scale, have some experts who has good qualification in statistical computations. I have often seen companies providing research grants to universities, there is a cost advantage to this. Collaborating with equipment manufacturers also helps as they bring in a consolidation of   data to cover the expanse of operational scenario which is a must thing in predictive analytics. Role of Equipment manufacturer and critical component (e.g. Bearing, Bushes) manufacturer are key and should be partnered with, in the journey of Predictive analytics. Every equipment and machine is different and unique therefore developing predictive analytical model would turn out to be very time consuming and costly at times.
Above all, you also need immense patience to succeed in this domain.  Never expect to master the art and also do not expect a radical result. Taking things one at a time would help and yeah -All the Best!

The future of Manufacturing from an IT perspective

During my MBA days, I had the opportunity to learn a lot about Manufacturing, Operations, Supply Chain management and of course a lot of other courses in the form of lectures/case studies/web/books. When we spoke about process improvements during those days we spoke more often than not about the lean manufacturing concepts, the six sigma's, the Toyota production systems, the value stream maps, business process reengineering's and all this was not long ago (last decade). Today's organizations when challenged with these issues have started relying more and more on their data and this data is generated from none other than their own backyard "The Shop floor" powered with Big Data. 

Manufacturing Operations has become more complex and intriguing than ever before. The key driver for this has been the ability to generate data from the shop floor, analyze the data and then be able to take the right/much required kind of business/manufacturing decisions.

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March 9, 2015

Moving on with Mobility for Asset Maintenance

Most of us have a smart phone / tab nowadays.  Earlier, we found our laptops to be an innovative gadget making our lives easier. Now we have shifted our focus towards our smart devices since it has replaced many of the activities we do in our computers with ease. Mobile technology coupled with internet has opened up a new gateway of communication. I sometimes get perplexed to see how different ends of the world are being connected in fraction of seconds. We do shopping, check our mails, recharge our mobiles, take pictures and videos, chat with friends, read books, track flight status, play games,......a whopping list of endless features. We sometimes don't even reveal our lack of awareness of any such feature in the smart device, to avoid being considered as 'outdated'.

While Smart devices have changed our life style in different ways, it has also influenced the way we do our asset maintenance. As software vendors look out for opportunities to develop mobile apps for different uses, some of them have already placed their footprint into Enterprise Asset Management arena. Many mobile versions of EAM software are made available now a days to capitalize on the potential market in maintenance segment. However, Maintenance Organizations are gradually but carefully changing their processes in this direction since it involves analysis of application fit, investment plan and implementation strategy. This trend is picking up its pace since the benefits are coming out to be evident. However gradual pace of this shift is accountable to few challenges faced by these organizations in implementing a mobile EAM application.

Let us see how few of these challenges are being met in recent times:


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March 3, 2015

Best Practices in Facilities Management

Facility management is a business practice that optimises people, processes, assets, and the working environment to support the delivery of the organisation's commercial objectives. It ensures that the customer's facility is in optimum operational condition and that they are receiving services in a prompt and organized manner. The Facility Management Services could range from maintaining building's air conditions, electrical network, plumbing to cleaning building premises, maintaining landscapes, provide catering services etc. It is about improving and maintaining the quality of life within a facility. It is the role of facility management service provider to ensure that everything is available and operating properly for building occupants to do their work.

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March 1, 2015

Importance of Prototying in Package Implementation- II

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Importance of Prototying in Package Implementation- I

Catching on latest happenings in LinkedIn; reading through some wonderful posts and getting involved in discussions have been some of my favorite unwinding lately. The guys out here are really cool, there is a lot to learn from them. The knowledge they possess, the experience they have and the way they articulate things have been inspiring. Read a comment from Biju Varughese recently on how a wrongly done requirements gathering could be a precursor to a painful IT Implementation. Actually Biju's note resonated with folks I met recently during a recent customer meet. They had concerns on how requirements are destined for frequent changes. While the folks discussed emphatically on how requirements are changed as late as User Acceptance test, I actually got tele-transported back to my stint in a process optimization exercise at one of the largest utility companies. I would like to share this bit of my tryst with you in a hope that it would add value to your projects.

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January 22, 2015

Configuration Management for Complex Assets

We are getting into an era where tremendous inventions and engineering marvels taking shape in front of our eyes. At one time flying across continents was considered to be challenge. When this become reality with aircraft, focus shifted to improve the efficiency of these marvels. Latest of the latest aircrafts being designed are expected to offer increased operating efficiency, enhanced passenger comfort and lesser noise. In future hope we will even never realize a takeoff and landing! While it becomes imperative that manufacturing being one side of the coin, maintaining these marvels working for the intended purpose is the other side of it. It is the daunting responsibility of airlines to keep it running. Latest engineering advancements have paved the way to develop huge complex assets like aircrafts, locomotives, automotive, earth movers etc. However, when it comes to maintaining these assets, one important challenge is to manage their whole assembly structure, which we call as 'Configuration Management'.

Some key reasons for this are:
1. Need for visibility of Asset configuration which comprises of various major component assemblies like Engines, Landing gears, Transformers, Traction Motors, Rectifiers etc. This helps in maintaining the Asset configuration up to date which ensures that the asset is intact with all mandatory components
2. Ability to track component and sub components at serial number level inside the major component assemblies with their designated positions on the Main assembly (like Aircraft, Locomotive, etc.). This helps is following the movement of life limited subcomponents across different parent assemblies and implementing engineering changes (Service Bulletins, Directives, Campaigns, etc.)
3. Integrating configuration information with maintenance (includes maintenance programs, inventory optimization for alternates and spares, etc.)
4. Preserving maintenance history of each components for regulatory compliance and warranty tracking


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