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<article>
<header>
<h1 class="entry-title"><a href="/blog/2013/06/03/we-are-hiring/">We Are Hiring!</a></h1>
<p class="meta">
<time datetime="2013-06-03T06:00:00+05:30" pubdate data-updated="true">Jun 3<span>rd</span>, 2013</time>
</p>
</header>
<div class="entry-content"><p>At System Insights, we build <a href="http://systeminsights.com/vimana/">vimana</a>, the world’s most advanced platform for manufacturing data analytics. We are a small, fast-growing company with 15 employees globally and we are looking to grow our R&D center in Chennai, India. We are currently hiring for the following positions in Chennai:</p>
<p><strong>Manufacturing Engineer</strong></p>
<p>You will be responsible for leading the deployment of vimana at customer facilities around the world, and working with our users to deliver vimana’s value.</p>
<ul>
<li>B.E/B.Tech from reputed a college/university, preferably in Mechanical Engineering (or equivalent) with strong academics</li>
<li>2~3 Years experience</li>
<li>Hands-on shopfloor experience with Production/Process Engineering, New Product Development, Industrial Engineering, Manufacturing Systems</li>
<li>Experience/familiarity with Value Stream Mapping, 6 Sigma, TPM etc., a plus</li>
<li>Excellent communication skills</li>
</ul>
<p><strong>SW Developer</strong></p>
<p>You will be responsible for developing the cutting-edge software technologies that power vimana. You will be working closely with our development team based in Berkeley, California.</p>
<ul>
<li>Strong experience in Ruby, AJAX, Java Script, HTML/CSS (at least 2 years)</li>
<li>Experience with Web 2.0 development, Object Oriented Design and MVC Frameworks</li>
<li>Experience with SQL and noSQL technologies</li>
<li>Knowledge of HTML5, Git</li>
<li>Experience in using testing frameworks and unit tests</li>
<li>Excellent communication skills</li>
</ul>
<p>If you are interested, please fill out <a href="http://systeminsights.wufoo.com/forms/z7x4m1/">this application</a> (don’t forget to attach your resume!).</p>
</div>
</article>
<article>
<header>
<h1 class="entry-title"><a href="/blog/2013/05/30/energy-consumption-in-manufacturing/">Top Manufacturing Energy Consumers: A Close Look at the Paper Industry</a></h1>
<p class="meta">
<time datetime="2013-05-30T12:00:00+05:30" pubdate data-updated="true">May 30<span>th</span>, 2013</time>
</p>
</header>
<div class="entry-content"><p>Jesus Nieto Gonzalez from the UC Berkeley Haas School of Business Class of 2013 worked with us on a research project to identify the top energy consumers in the manufacturing sector. Here are some of his findings:</p>
<ul>
<li>The top five energy-intensive industries are Paper, Petroleum and Coal products, Nonmetallic Mineral Products, Chemicals, and Primary Metals (measured based on BTU/$ output)</li>
<li>The paper industry has the highest electricity consumption as well, followed by Primary Metals and Textile Mills (measured based on kWH/$)</li>
<li>The EU countries (particularly Italy and Germany) have some of the highest electricity costs for Industrial consumers, followed by the BRIC countries and the US</li>
<li>The paper industry offers a lot of opportunities for energy efficiency and electrical energy reduction. The industry is highly fragmented and the major paper producers are concentrated in parts of the world with high electricity costs.</li>
</ul>
<p>The entire report is available <a href="https://s3.amazonaws.com/manufacturingbigdata-blog-static/pdfs/jesus-nieto-energy-consumption.pdf">here</a>.</p>
</div>
</article>
<article>
<header>
<h1 class="entry-title"><a href="/blog/2013/04/01/chennai-ruby-meetup/">chennai.rb Meetup</a></h1>
<p class="meta">
<time datetime="2013-04-01T14:45:00+05:30" pubdate data-updated="true">Apr 1<span>st</span>, 2013</time>
</p>
</header>
<div class="entry-content"><p>We will be hosting the next chennai.rb meetup on the 6th of April in our <a href="http://systeminsights.com/locations">Chennai office</a>. The talks include:</p>
<ul>
<li>Hadooping with Ruby</li>
<li>Large Scale Web Apps with Ruby</li>
</ul>
<p>We will be posting more details shortly – watch this space!!!</p>
</div>
</article>
<article>
<header>
<h1 class="entry-title"><a href="/blog/2013/04/01/green-manufacturing-book/">The Green Manufacturing Book</a></h1>
<p class="meta">
<time datetime="2013-04-01T11:15:00+05:30" pubdate data-updated="true">Apr 1<span>st</span>, 2013</time>
</p>
</header>
<div class="entry-content"><p>Had questions about Green Manufacturing but were too bashful to ask? You are in luck, you can pick up a copy of the excellent “Green Manufacturing: Fundamentals and Applications” from <a href="http://www.amazon.com/Green-Manufacturing-Fundamentals-Applications-Technology/dp/1441960155/">Amazon.com</a> and other fashionable purveyors of fine reading material. Full Disclosure: I have co-authored 4 chapters in the book, including the penultimate chapter “Enabling Technologies for Assuring Green Manufacturing”, which, among other things, talks about the need for a highly scalable software architecture to enable realtime monitoring and reporting of manufacturing performance (hint: we have a built a little <a href="http://www.systeminsights.com/vimana">product</a> to address that need).</p>
<p>“Green Manufacturing” is based on research from the <a href="http://lmas.berkeley.edu">Laboratory for Manufacturing and Sustainability</a> and is edited by Prof. David Dornfeld, the Director of LMAS and the Chairman of the Department of Mechanical Engineering at UC Berkeley. For more information, you can check out his excellent <a href="http://green-manufacturing.blogspot.com">blog</a>.</p>
</div>
</article>
<article>
<header>
<h1 class="entry-title"><a href="/blog/2013/01/08/javaone-presentation/">JavaOne Presentation</a></h1>
<p class="meta">
<time datetime="2013-01-08T09:28:00+05:30" pubdate data-updated="true">Jan 8<span>th</span>, 2013</time>
</p>
</header>
<div class="entry-content"><p>We presented at <a href="http://www.oracle.com/javaone/index.html">JavaOne</a> in early October 2012 in San Francisco on applying Embedded technologies in enabling Manufacturing Big Data. Here are our slides.</p>
<script async class="speakerdeck-embed" data-id="50726683b1808300020091bb" data-ratio="1.7444633730834753" src="//speakerdeck.com/assets/embed.js"></script>
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<article>
<header>
<h1 class="entry-title"><a href="/blog/2013/01/08/precision-manufacturing/">Precision Manufacturing Lecture Videos</a></h1>
<p class="meta">
<time datetime="2013-01-08T09:00:00+05:30" pubdate data-updated="true">Jan 8<span>th</span>, 2013</time>
</p>
</header>
<div class="entry-content"><p>I am back to my regular blogging duties after a hiatus of a few months.</p>
<p>Part of the reason I was away was because I was teaching the graduate course “Precision Manufacturing” at UC Berkeley in the Department of Mechanical Engineering.</p>
<p>Thanks to the webcast facilities at Cal, videos of all of the classroom lectures are available on Youtube <a href="http://www.youtube.com/watch?v=vqxpT3GBB8U&list=PLbCV7-4PxKsQJ7VtY13oipOEDrCVXdkhy">here</a>. If you have any questions or feedback, please leave it in the comments!</p>
<iframe width="560" height="315" src="http://www.youtube.com/embed/vqxpT3GBB8U?list=PLbCV7-4PxKsQJ7VtY13oipOEDrCVXdkhy" frameborder="0" allowfullscreen></iframe>
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</article>
<article>
<header>
<h1 class="entry-title"><a href="/blog/2012/09/04/imts-oracle-partnership/">Oracle Partnership at IMTS</a></h1>
<p class="meta">
<time datetime="2012-09-04T09:53:00+05:30" pubdate data-updated="true">Sep 4<span>th</span>, 2012</time>
</p>
</header>
<div class="entry-content"><p>We are very happy to announce that we will be partnering with <a href="http://www.oracle.com">Oracle</a> during <a href="http://www.imts.com/">IMTS</a> to showcase integrated solutions for manufacturing data interoperability, collection, and analysis. We will be discussing the use of Java SE Embedded in building a ubiquitous embedded platform for collecting data from the shopfloor. <a href="http://www.oracle.com/technetwork/java/embedded/overview/javase/index.html">Java SE Embedded</a> has been used in products across a diverse spectrum of industries, ranging from RFID readers to parking meters to ATMs to in-flight video systems to POS terminals to wearable systems. This makes it a great fit in the manufacturing world, where we need hardware devices that can seamlessly and ubiquitously integrate data across the shop floor.</p>
<p>Java SE Embedded will be applied in developing a hardware platform that can collect data across proprietary interfaces in the shopfloor, perform basic analysis, and then stream it in the MTConnect standard. Selecting MTConnect was a no-brainer – it is widely supported by the industry, it is lightweight and easy to implement, and gives us a lot of flexibility in deployment. MTConnect is also natively supported by our <a href="http://www.systeminsights.com">vimana</a> platform for manufacturing big data analysis, and MTConnect allows us to quickly integrate the factory data feeds for further analysis and reporting using vimana.</p>
<p>We are very excited about our partnership with Oracle, and will be talking more about it at IMTS. You can catch us Booth N-6995 with Oracle, between September 10th and 15th, at McCormick Place, Chicago.</p>
</div>
</article>
<article>
<header>
<h1 class="entry-title"><a href="/blog/2012/08/31/traceability/">Process Traceability and Big Data</a></h1>
<p class="meta">
<time datetime="2012-08-31T22:47:00+05:30" pubdate data-updated="true">Aug 31<span>st</span>, 2012</time>
</p>
</header>
<div class="entry-content"><p>In a previous post we looked at the different things we can do with Big Data. Lets examine one of the applications in more detail: Process Traceability.</p>
<p>Process Traceability can be understood as being able to trace every process that happened to a part as it got manufactured. Its important to make a distinction here between <em>Part</em> Traceability and <em>Process</em> Traceability. Part Traceability primarily deals with what part was manufactured, and when. Process Traceability, on the other hand, builds on this information, and expands it greatly to understand exactly how the part was manufactured. Process Traceability is a key requirement in Aerospace manufacturing since manufacturing defects can have severe impacts on the quality of parts, and with capable Process Traceability systems we can go back in time and find out exactly how and when the manufacturing defect was introduced into the part. Of course, Process Traceability has applications in other quality-critical domins as well including medical devices and engine components.</p>
<p>Currently, most companies only have Part Traceability systems (if any) – they know when which part was manufactured. When automated, this type of data is collected by MES or ERP systems, and with the right kind of aggregation and rollups, the data can reveal when a particular batch of parts was manufatured and what was the heat number (or lot number) of the castings/forgings that went into it. But this is not enough to fully understand what happened when a part was made. To build a full-fledged Process Traceability system, lets see what kind of data we can collect from the shopfloor:</p>
<ul>
<li><strong>Identity</strong> data is the most basic kind required for part traceability. This data tells us what is being made, how much was made, and when was it made. <em>Examples: Heat ID, Batch ID, Operator ID.</em></li>
<li><strong>Operational</strong> data can tell us what the machine was doing when it had a part associated with it. We can understand the utilization of a device when it was operating on a part, how long the device was in “auto” mode versus “manual” mode, and the different downtimes that device experienced when it was working on the part. Knowing the downtimes, for instance, can indicate potential issues with part quality. For example, if a device had repeated unplanned maintenance downtimes when it had a part on it, its quite likely that the part has some quality problems as well, and might require additional metrology. <em>Examples: Device uptime, downtime, modes, states.</em></li>
<li><strong>Diagnostic</strong> data can further embellish the correlations that we can get started with using Operational data. Alarm and condition data can reveal specific issues in the device that is manufaturing a part. <em>Examples: Alarms, warnings, messages, notifications.</em></li>
<li><strong>Process</strong> data can help us get into a lot more detail, and can reveal how specific features were generated on the part. For example, in high speed machining of aerospace alloys, its very important to preserve a specific chip velocity when features are being created. But interpolation errors and machine tool limitations can result in significant variations between the actual feedrate and the planned feedrate. With process data, we can precisely know when these deviations happened, and can use it in understanding its impact. <em>Examples: Positions, velocities, acceleration, flow rates.</em></li>
<li><strong>Environmental</strong> data can tell us the impact of the part as its being manufactured. With detailed knowledge of the resource flows associated with the part, we can estimate its environmental impact. A high level of detail can also reveal which stages of the production process have the greatest impact, and that knowledge can help in targeting energy efficiency improvements. <em>Examples: Resource usage, energy consumption, effluents and emissions.</em></li>
</ul>
<p>The bigger challenge, is to be able to intelligently and efficiently operate on this massive set of data, and find pertinent information associated with a part. And here we can broadly look at three kinds of queries:</p>
<ul>
<li><strong>Part Search</strong>: Here we are trying to find all the information associated with a specific part (or family of parts). We start with some identifying characteristic for the part (or the family of parts). This can be a Part ID, a Heat ID, or even the day the part was manufactured. We can also identify parts based on other events, like the first part manufactured after a power outage. Information associated with a part can include all of the five kinds discussed above.</li>
<li><strong>Similarity Search</strong>: Here we are trying to find similar parts based on one or more specific parts that have been identified. The idea here is that we have flagged a certain part or set of parts, and we want to scan our historical system and find other parts that share a similar process history. This is very useful when parts are being quarantined after a quality spill and we are trying to find all the other parts that need to be quarantined. If we know the ID of one part, then we can find other parts similar to it based on a variety of criteria, including: heat code, operator who made the defective part, machine condition, and alarm sequence during manufacture.</li>
<li><strong>“Black Swan” Search</strong>: This is perhaps the most interesting application of a Process Traceability system, and it looks at identifying “black swans”, or the rare events that are anomalous to the norm. These queries will attempt to find parts that have been manufactured differently from the rest (starkly or subtly). These could reveal potential problems in the production process before it is identified by the customer. Examples include: excessive spindle loads, erratic feedrate override, and anomalous energy consumption during machining.</li>
</ul>
<p>Now, in order to bring this level of Process Traceability to the manufacturing shopfloor we need software that is capable of handling massive amount of shopfloor data and operating on it to enable the kind of querying and decision making discussed here. Our vimana manufacturing big data platform does just that. You can learn more about it <a href="http://www.systeminsights.com/vimana">here</a>.</p>
</div>
</article>
<article>
<header>
<h1 class="entry-title"><a href="/blog/2012/08/22/updates-and-imts/">IMTS 2012</a></h1>
<p class="meta">
<time datetime="2012-08-22T13:54:00+05:30" pubdate data-updated="true">Aug 22<span>nd</span>, 2012</time>
</p>
</header>
<div class="entry-content"><p>We are back after a brief hiatus. The last few weeks have been eventful, to say the least, as we get ready for <a href="http://www.imts.com">IMTS 2012</a> in Chicago between September 10th and 15th. At IMTS we will be showcasing the <strong>vimana</strong> software platform for manufacturing productivity improvement and the <strong>ConnectOne</strong> hardware platform for MTConnect-based data acquisition and interoperability.</p>
<p>Catch us at S-8300 with <a href="http://www.imts.com/visitor/exdir/exhibitor_details.cfm?exhid=00000021">Mazak</a> and at N-6995 with <a href="http://www.imts.com/visitor/exdir/exhibitor_details.cfm?exhid=00062662&CFID=11122638&CFTOKEN=40146151">Oracle</a>.</p>
</div>
</article>
<article>
<header>
<h1 class="entry-title"><a href="/blog/2012/07/10/tneb-regulations/">Energy Regulations in Tamil Nadu, India</a></h1>
<p class="meta">
<time datetime="2012-07-10T11:00:00+05:30" pubdate data-updated="true">Jul 10<span>th</span>, 2012</time>
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<div class="entry-content"><p>Industrial energy consumers in Tamil Nadu, India have seen a sharp increase in energy costs beginning from April 2012. In this post we look at the revised tariff of the Tamil Nadu Electricity Board (TNEB), and examine its impact on the overall energy costs for a manufacturing plant.</p>
<h2>The Tariff</h2>
<p>The revised Tariff for Industrial Consumers (HT1A) is as follows (available <a href="http://tnerc.tn.nic.in/orders/Tariff%20Order%202009/2012/T.O%20No.%201%20of%202012%20dated%2030-03-2012.pdf">here</a>):</p>
<h3>Basic Charges</h3>
<ul>
<li>Demand Charges – INR 300 / kVA / month</li>
<li>Energy Charges – INR 5.50 / kWh</li>
</ul>
<h3>Restrictions and Surchages</h3>
<h4>Power Factor:</h4>
<ul>
<li>Power Factor => 0.9 – No Surcharge</li>
<li>0.9 > Power Factor => 0.85 – 1% of Current Consumption Charges for every 0.01 reduction in PF from 0.9</li>
<li>0.85 > Power Factor => 0.75 – 1.5% of Current Consumption Charges for every 0.01 reduction in PF from 0.9</li>
<li>Power Factor < 0.75 – 2% of Current Consumption Charges for every 0.01 reduction in PF from 0.9</li>
</ul>
<h4>Billable Demand:</h4>
<ul>
<li>Demand Charges will levied on Maximum Demand that has actually been registered for the month or 90% of the Sanctioned Demand, whichever is higher.</li>
</ul>
<h4>Peak Hour:</h4>
<ul>
<li>HT Industrial Consumers will be billed 20% extra on the Energy Charges for the Energy recorded during the Peak hours</li>
<li>Duration of Peak hours will be 6:00am to 9:00am & 6:00pm to 9:00pm</li>
</ul>
<h4>Night Hour:</h4>
<ul>
<li>HT Industrial Consumers will get a reduction of 5% on the Energy Charges for the Energy recorded during the Night hours</li>
<li>Duration of Night hour will be 10:00pm to 5:00am</li>
</ul>
<h4>Demand Integration Period:</h4>
<ul>
<li>Maximum Demand Integration period will be 15minutes</li>
</ul>
<h4>Harmonics:</h4>
<ul>
<li>Total Voltage Harmonic Distortion should not exceed 5%</li>
<li>Total Current Harmonic Distortion should not exceed 8%</li>
<li>If the harmonics level are not within the limits, then the consumer has to pay 15% of respective tariff as Compensation</li>
</ul>
<h2>Power Scenario in the State</h2>
<p>The state of Tamil Nadu has installed capacity of 10,364.5 MW and the average power availability is about 8500 MW. The demand ranges from 11,500 MW to 12,500 MW which gives a clear indication that the state has a shortage about 3000 to 4000 MW of power. Moreover if we see the growth of consumers, it keeps increasing at a rate of 5% every year. Because of this gap between demand and supply, TNEB has taken the following mitigation measures:</p>
<ul>
<li>40% cut on demand and energy for High Tension Industrial and Commercial Services</li>
<li>Load shedding of 2 hrs in Chennai and its suburbs</li>
<li>Load shedding of 4 hrs in other urban and rural areas</li>
<li>10% of power supply during Peak hours for Industrial and Commercial Services</li>
<li>Power Holiday for all HT & LT consumers</li>
</ul>
<p>These restrictions can be relaxed based on the power availability. However, HT consumers are allowed to make power purchase to inter- and intra-state Open Access providers where cheaper power may be available.</p>
<h2>So… What’s the Impact?</h2>
<p>Lets examine how these revised prices impact an average manufacturing plant.</p>
<p>Lets consider a Manufacturing Facility, with the following cost structure:</p>
<ul>
<li>Permitted Demand: 1000 kVA</li>
<li>Permitted Energy Quota: 300,000 kWh</li>
</ul>
<p>Based on this structure, lets assume that the energy consumed and the costs incurred during a representative month is as follows:</p>
<iframe width='410' height='500' frameborder='0' src='https://docs.google.com/spreadsheet/pub?key=0AjFwRioMlxbbdEptNTRoLVdtQUpsa1pIMW9NSXl5S1E&single=true&gid=0&output=html&widget=true'></iframe>
<p>Lets see how costs change with the new pricing under different scenarios:</p>
<h3>Case 1: Grid Only</h3>
<p>The revised grid costs are Rs. 5.50/kWh and the plant faces a 40% reduction on its demand and energy limits. The revised permitted demand is 600 kVA and the permitted energy is 180,000 kWH. The plant is penalized at twice the price for exceeding the energy or demand limits.</p>
<iframe width='410' height='600' frameborder='0' src='https://docs.google.com/spreadsheet/pub?key=0AjFwRioMlxbbdEptNTRoLVdtQUpsa1pIMW9NSXl5S1E&single=true&gid=1&output=html&widget=true'></iframe>
<p>If the plant is purely dependent on the grid (EB), then monthly energy costs grow by more than 120% (more than doubles!!!).</p>
<h3>Case 2: Grid and Diesel</h3>
<p>If the plant offsets 500 kVA of demand and 130,000 kWh of energy by running a diesel generator, which costs Rs. 15/kWh:</p>
<iframe width='410' height='650' frameborder='0' src='https://docs.google.com/spreadsheet/pub?key=0AjFwRioMlxbbdEptNTRoLVdtQUpsa1pIMW9NSXl5S1E&single=true&gid=2&output=html&widget=true'></iframe>
<p>Even with using an auxilliary Diesel Generator to supplement grid energy, the plant spends 82% more on energy.</p>
<h3>Case 3: Grid and Power Purchase</h3>
<p>The plant purchases 130,000 kWh at Rs. 8/kWh, and gets a equivalent deemed demand of 260 kVA:</p>
<iframe width='410' height='700' frameborder='0' src='https://docs.google.com/spreadsheet/pub?key=0AjFwRioMlxbbdEptNTRoLVdtQUpsa1pIMW9NSXl5S1E&single=true&gid=3&output=html&widget=true'></iframe>
<p>The plant still sees an increase of about 60% after purchasing power from third party suppliers.</p>
<h2>What do we do?</h2>
<p>Its clear that there are no simple ways of reducing or even maintaining energy costs at the “pre-hike” levels in Chennai. Simply changing the energy source to Diesel is not an option either, and buying third-party power can be just as expensive as using energy from the TNEB grid. What this calls for is more aggressive and hands-on management of energy consumption in the manufacturing facility, looking at which machines and systems consume the most energy, and finding ways to decrease their usage. Our vimana platform does just this and we will be back with a followup post on how <a href="http://www.systeminsights.com/vimana">vimana</a> can be applied in improving energy efficiency and reducing energy costs in a manufacturing facility.</p>
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