There are periodic discussions on various forums about how to define operations research, and the even trickier question of how one distinguishes operations research from management science from industrial engineering (or even if such distinctions are meaningful). So it comes as no surprise to me that the definition of "industrial engineering" may be evolving. It did come as a bit of a surprise, however, when I was notified today by Rachel Stevenson, co-founder of Engineering Masters, that this blog had made their "Top 50 Industrial Engineering Blogs" list. (Not as big a shock as when I found myself teaching organizational behavior, though, and considerably more pleasant!) I was particularly gratified by my #8 ranking ... until I realized it was alphabetical by blog title within groups.
The categories listed in the Engineering Masters blog post tell us something about the range of interests people with IE degrees might have these days. Besides operations research, they have operations management, project management, financial engineering (where the intent is to make something out of nothing and recent practice, at least, has been to make nothing out of a whole pile of something), machine learning, statistics (which I find interesting since AFAIK the average engineering major takes approximately one statistics course), analytics (another definitional debate in the making), and life-cycle management (who knew that would spawn so many blogs?).
Like any blog roll, there's a reciprocity at work here: they link my blog (and maybe drive a second reader to it), and I link their blog (and maybe drive my one current reader to it). I'm okay with that: it's free (unlike the occasional pitches I get for including me in the who's who of academic bloviators, presumably to get me to buy the book), I'm not embarrassed to point to their site, and I'm pretty sure it generates no money for terrorists, organized crime or political parties. (Apologies if those last two are redundant.) If nothing else, I appreciate their efforts compiling the list of blogs because I see a few entries on it that look promising, of which I would otherwise be unaware.
Friday, December 17, 2010
5 comments:
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All that hype about analytics might be an advantage for us OR people. I mean nobody really like the name OR and few outside our field know what it is. I read in analytic magazine that I actually work in "predictive analytics" now :-) I welcome such a change and hope it will make our field more like-able.
ReplyDelete@Bo: Since I'm on the verge of retirement, I'm past the point of worrying if anybody knows what it is I do. :-) Still, I tend to prefer "operations research" to "analytics". I think the former sound more active ("operations" implies doing something) and the latter more passive (analyzing stuff). If "analytics" gets the field more traction among organizations that might want to use our services, perhaps I'll become a fan. I wonder, though, whether it will be easier or harder to convince a student to major in "analytics" than in OR (or IE or MS).
ReplyDeleteSorry should have been "Prescriptive Analytics" and not "Predicitve Analytics", though both IMHO can contain OR techniques, the first is much more optimization driven.
ReplyDeletemy expectation based on what i have seen is that OR will absolutely make its presence felt but it will take a while. Right now, companies are giddily happy with the many TB of data that they have and are mainly looking for exploratory tools that provide actionable insights that have an immediate, big impact. Once everybody does this, its back to OR types to build decision support systems as the next value layer.
ReplyDeleteInteresting point. It makes sense that, with the explosion of available data, data-mining will be the immediate attention-grabber. I'm not sure how much of what you get from data-mining is immediately actionable, and how much just points you in a certain direction. For instance, data-mining might suggest certain combinations of products that would make good bundles; but will it tell you how to price the bundles, or do you need an analytical model for that?
ReplyDelete