Energy and Operations Post #1: Why should operations (and you) care about energy waste?
In my introductory post about energy and operations, more a reflection on my current internship at Raytheon in Andover, MA, I spoke about my experience in broad terms and all the things I’ve learned in the four months I’ve been at that plant. But, I realized that I hadn’t done a good job motivating anyone to care about energy or my project in the first place. In this first post in the informal weekly series which will hopefully coalesce into a decent sized portion of my thesis, I will be motivating the problem. I’ll be largely drawing on previous LGO theses, and the second chapter in Mike Norelli’s in particular.
Basically, corporations should care about energy if for no other reason than it costs money to use – duh. Electricity bills from where I work, a 1.1 million square foot manufacturing plant in Andover, MA, run about $9 million a year. Of course, there are other forms of energy than electricity – plants also use a lot of natural gas for heating, coal or a variety of other sources. But electricity, at least in Andover, is the most expensive form of energy per watt-hour, and that’s what I’ve been focusing on in my internship.
In fact, I should point out now that electricity in Andover, MA is A LOT more expensive than in other places around the country. According to the Department of Energy, retail electricity in 2010 costs about $0.14 per kilowatt-hour for industrial customers in MA ( and $0.19 for residential customers). That puts MA just behind Connecticut ($0.15) for the most expensive industrial electricity costs in the lower 48 states. For comparison, the average national retail price is half as expensive, at $0.07 , and the cheapest electricity for industrial customers was in Utah, coming in at $0.04/kWh. In China, it is approximately $0.11/kWh, which is actually surprisingly high. These estimates are all blended rate estimates, which is a weighted average of demand costs and consumption costs.
However, it’s one thing to say that electricity costs a lot, it’s another to say that a lot of it is wasted, and yet another to say that something can be done about it. But to start, we need to see where all that electricity is going in the typical manufacturing plant.
From a 1998 survey (yeah, it’s old) done by the DOE the typical electronics manufacturer in the U.S. uses 65% of its electricity on “direct uses”, which basically means the “plug load” of floor equipment (heating, cooling and machine drives) and offices (computers). The remainder is used on “indirect uses” like HVAC (, lighting and other facility support (kitchens). I’m not quite sure how much I can reveal about my specific company yet, but the numbers I’ve gathered are actually quite similar at the IADC.
Energy use of a typical electronics manufacturer in 1998
| End Use | |
|---|---|
| TOTAL FUEL CONSUMPTION | 100% |
| Direct Uses-Total Process | 65% |
| Process Heating | 18% |
| Process Cooling and Refrigeration | 4% |
| Machine Drive | 40% |
| Electro-Chemical Processes | 2% |
| Other Process Use | 2% |
| Direct Uses-Total Nonprocess | 29% |
| Facility HVAC (f) | 15% |
| Facility Lighting | 13% |
| Other Facility Support | 4% |
| End Use Not Reported | 5% |
There is a plethora of information out there about how to best reduce some of the costs for indirect uses, and even some of the direct uses. Two great resources are the Manufacturing Institute’s energy efficiency toolkit and the Environmental Protection Agency’s Lean and Energy Toolkit. Personally, I have been focusing on the direct uses – the 65%. Within that 65% at the IADC, however, is a variety of machinery and technology ranging from thirty-plus year old ovens to the latest in component placement equipment for circuit card manufacturing. I’ll drill down a little bit more into some of my findings from my data gathering over the past couple months to put some of the numbers in perspective. But as a teaser, what I have found is that the energy use of equipment greatly depends on the schedule of operations, which lends itself to some (hopefully) interesting analysis and optimization possibilities.
Also, the flavor of these posts will be on industrial and operational energy use. However, a great resource I’ve used to learn some of the basics and get some easy and (generally) very practical tips and data about reducing energy use in the home is the Mr. Electricity site. (One word of warning – he lives in Austin, TX, so when he advocates using only a space heater to heat the rooms you are in, I would take it under advisement…)
Information/Decision systems in retail
Sam’s club recently started offering their customers personalized deals based on their past buying history:
Linda Vytlacil, vice president for member insights and innovation at Sam’s Club, said coupons normally had a response rate of 1 percent or 2 percent. With eValues, she said, as many as 20 percent to 30 percent of eligible customers collect the discount they are offered.
The program is called “eValues” and it is the “latest iteration in the fast-growing field known as predictive analytics, which uses vast amounts of data to spot trends and anticipate consumer behavior.”
In a related article in the New York Times (yes, occasionally I do read other stuff) there was a description of how people are being offered product deals to share their personal data on sites like mint.com and foursquare.
While taking Intro to Marketing this past semester, I was struck by how much the field could benefit from quantitative techniques like machine learning and decision/control theory, and how the best retailers (like Wal-Mart) have already harnessed the concept of real-time/individualized data analysis. The push only appears to be accelerating. After all, as a shopper, don’t you want the best deals on things you actually want to buy? It seems like a win-win.
I imagine the trick would be to judiciously use the data to “nudge” people to buy things they normally wouldn’t buy – isn’t that the point of sales in the first place? That could get pretty tricky, but I can see how the data could be used to do that in an optimal sort of way. For instance, you could use the data to group similar products and then predict whether this individual is likely to stray from his/her preferred brand on that day and then give the perfect coupon tailored to that person for a rival brand.
Of course, if that fails, free samples always work…
I prefer the chicken teryaki...
Announcement – New LGO EECS track: Information and Decision Systems
The Information and Decision Systems (IDS) track is designed for LGO students in EECS who want to both explore and develop practical skills in how to apply the latest algorithms and mathematical analysis in real operational settings.
The goal of the track is to make LGO the premier training program for leaders who will use advanced data analysis to make smarter operational decisions. The track includes five courses in four areas: 2 courses in tools/theory, 1 course in design, 1 course in communication and 1 course in an engineering elective specific to an application area.
We have a great adviser in Professor Patrick Jaillet, who is associated with LIDS and is also the new co-director of the Operations Research Center at Sloan.
In the coming months, we will be preparing track materials for the LGO Open House and fleshing out how to make this track an active group in the LGO community, much like the sustainability program in ESD. Shoot me an email if you are interested or have ideas…
Google and IBM say we need to train more supercrunchers
There was an article in the New York Times today about the effort that companies like Google and IBM are making to allow university students access to very powerful computing environments to allow engineers and scientists to plow through massive data sets. Their argument is that students are being trained right now to think on a gigabyte scale (if they’re lucky enough to be trained how to analyze real data at all), when all the breakthroughs are happening with datasets in the tera and peta-byte scales.
I couldn’t agree more with this analysis. If people are serious about analyzing those “very rare events”, “long tails” or whatever that can make the difference between a profit and loss, success or failure, or even life or death, then we can’t continue running around assuming things because the model fits 80% of the time and anyways, it’s too hard to do that level of analysis. We all saw what happened with that idea.
When I was working at Lincoln, we created a highly accurate model of U.S. near mid-air collisions. We did this by analyzing about 5 terabytes worth of radar data from across the country (about 8 months worth). Nobody had ever done this before on anything close to that scale.
As a result, we had orders of magnitude more data on near mid-air collisions (a very rare event) than the last model in the early 90′s. Without this data, and the high-powered systems available at Lincoln that we used to analyze it, our model would have suffered from the same assumptions and modeling error as previous attempts, and that is just not good enough for developing something as important as the next generation of collision avoidance systems for manned and unmanned aircraft, which people are now doing at Lincoln, largely as a result of that effort.
The ability to analyze massive data sets has been proven again and again as a competitive advantage in bio-tech, finance (those who do it correctly), internet, and even marketing, making those companies who developed those competencies hundreds of billions of dollars.
Is it then a stretch to say that the next lucrative opportunity in operations management will be to develop the capabilities to harness the massive amounts of data companies already generate every day? I’m talking about everything from inventories to machine control outputs and even to intra-company emails. There are signals in that data, just as there are signals in everything from our DNA to the stock markets, if you look hard enough.
To be honest, I don’t know (I’m new to this stuff!) but that’s why I and several of my classmates are trying to start a new track for LGOs in the EECS department this year called Information and Decision Systems. The focus in this track is to develop the theoretical, practical and communication skills for students who want to take on this operations challenge in the real world, for real companies. That means not just studying and learning the algorithms, but also getting a design background in the networking, database and parallel computing systems that are critical enablers of this type of work. It also means developing specialized communication skills to explain the opportunities and the results, because like the NYT article said, most people have not been trained to think on this scale before.
I could talk for pages more about this topic, but lets just leave it at that for now. I just had to write something because I’m obsessed with this idea, and this article got me all excited. I’m definitely going to look into Hadoop…
Introduction to Operations Management
Introduction to Operations Management was the title of just one of the classes I took this summer (15.761) at MIT, but it may well have described my entire experience this summer. I spent 3 months taking 5 courses (15.761, 15.064: Engineering Probability and Statistics, 15.066: System Optimization and Analysis, 15.317: Organizational Leadership and Change, and ESD.60: High Velocity Organizations) with 46 other people in the class of ’11 (Snake-Eyes class). Needless to say, it ended up being a ton of work, more than maybe I expected. But when there is a ton of work, that means there is usually a lot of accomplishments, and this was no exception.
One of the classes I really enjoyed (not including the probability stuff in 15.066 – Arnie Barnett is the man!) was the Systems Optimization class, taught by Jeremie Gallien. This class (really well taught) focused on the use of linear optimization techniques (including Integer and Mixed Integer Programs) to solve various types of problems, including planning problems, network flow problems (max-flow, transportation) and scheduling problems. The class, being taught as part of Sloan, was more about problem formulation and applications, rather than the algorithms to solve them. I appreciated this approach, because I took a class as an undergrad that was called Intro to Operations Research where we just did the Simplex algorithm by hand over and over and over again, and I got basically nothing out of it. By contrast, this class had a lot of real world examples that Prof. Gallien presented from his experience consulting for companies. In addition, we did a final project as part of the class where we applied the techniques we used in the class to a real life Operations problem, at the Pre-Admissions Testing Clinic (PATA) at Mass General Hospital, which ended up being a great success, thanks to our collaboration with Kelsey McCarty, a Sloan ’10 who was interning at PATA for the summer (read our final report here). Our team, with Prof Gallien, is continuing to collaborate with the managers at PATA as a follow-on, and there is a good chance our model will help them make decisions about how to schedule patients and providers in the future to reduce patient waiting times in the clinic, which is pretty cool.
Maybe some of the most important things I learned was about what it means to work on a team in a high-pressure environment. I had 4 other people on my summer team, Five-Alive, and we did basically all of our work together. We also did a lot of team building exercises including Outward Bound and a really cool Leadership Reaction Course (obstacle course with water – I didn’t get wet) at Camp Edwards, an Air National Guard base on Cape Cod. I learned a lot about teamwork from all of these experiences, but one key takeaway was that one of the most critical traits of a leader is to listen – not only in the sense of not talking when somebody else is, but to really concentrate on what others are saying and converse. It sounds simple, and maybe it is, but I noticed that we tended to stop doing that as work kept piling up and that led to a lot of re-work, wasted effort, and frustration.
- My “photoshopping” skills are limited…
In the end, all our hard work paid off (I think we consistently produced some of the best work in the class), and through all our time we spent together, I got to know four other awesome people with really interesting backgrounds and perspectives.
I’m looking forward to fall semester (orientation starts tomorrow) but I hope it doesn’t kill me – if I thought 5 classes were bad, I’m taking 7 this fall!
