Hitting the Muggles from All Sides

The theme of the June INFORMS Blog Challenge is "O.R. for Muggles". I mention that at the outset because I do not want to be accused of using the word "Muggles" to drive search engine traffic to my blog. (For that I use references to naughty videos featuring Dumbledore.) The rationale for the theme is articulated as follows:

[T]hese conversations tend to reach an already involved audience. We’d love to spread the word about OR/MS and analytics to a wider audience.

 So we shall assume here that "Muggles" are people who are not part of the O.R. community (and not students likely to join the O.R. community).

If our goal is to spread the word about the value of O.R. and analytics, and particularly if the ultimate aim is to generate work for O.R. experts and to raise its prestige (and priority) in academic institutions, we might want to adopt a three-pronged strategy.

Top Down

This approach targets C-level executives (or at least executives as high in the corporate food chain as we can reach) along with their governmental counterparts. The key is to convince them that they can achieve competitive advantage using O.R. and analytical modeling (or that they will suffer a competitive disadvantage if they fail to do so). Hopefully they will then give marching orders to subordinates to seek out opportunities to exploit this wonderful new (?) type of magic.

Business honchos are famous both for being very busy and for having short attention spans. Discussing mathematical or statistical details would be the kiss of death here. What might work would be to present them with short, non-technical but compelling stories of organizations that have benefited significantly from O.R. or analytics. Benefiting from O.R. models hard-coded into "canned" software is not what I have in mind; the stories need to involve customer-specific analysis by human (or plausibly human) experts.

I'm pretty sure consultants have been doing this for years, and I think INFORMS is doing a decent job as well. Publicizing the Edelman awards is a step in the right direction, although my impression is that the Edelman videos are way too long to engage a C-level executive. We should probably be doing more along this line, though, perhaps including more focus on non-profits and government organizations. How you get to these people is a question above my rather modest pay grade. Articles in key publications (Forbes? Business Week? Harvard Business Review?) may be part of the answer. Guest speakers at business round tables may be another component.

Bottom Up

Here we try to plant seeds in the minds of students about to enter the working world. I don't mean students majoring in O.R., industrial engineering, management science and augury (oops, make that "analytics"). We need to target generic business students (especially MBA candidates) and try to convince them that there is an arsenal of really useful tools that may avail them down the road (provided they acquire artisans capable of using those tools).

I'm inclined to grade our performance here as at best a C. When I began my academic career (about the same time transistors -- not integrated circuits -- were pushing vacuum tubes out of the computing business), it was fairly common for both undergraduate and graduate business curricula to include mandatory "quantitative methods" courses. Unfortunately, we tended to shoot ourselves in the foot (repeatedly) by putting way too much emphasis on theory and hand computation, too little emphasis on the business consequences of the solutions to the models ("you will save $XXX") ("and earn a quick promotion"), and way too little emphasis on problem identification and classification (where you will actually see this problem in the "real world", how your problem might be amenable to a "transportation model" even though it has nothing to do with transportation, why your actual mess will not be nearly as neatly structured as a textbook problem, etc.). This was exacerbated by a lack of user-friendly software, or perhaps any software at all.

The result was that we produced, at least in the U.S., generations of business graduates who's main take-away from their quant methods course was that they hated it and never wanted to deal with that stuff again. Their lack of enthusiasm when giving feedback to administrators paved the way for other disciplines to push aside quant methods courses and grab their space in the curriculum.  Quant methods is still required at many schools, and is very popular at some, but its "footprint" in the curriculum is often diminished, and it is all too frequently no longer required at all (excluding perhaps a basic statistics course).

Software and hardware are ubiquitous now, and the software quality is quite good. Based on recent market-leading textbooks, though, I believe we are still focusing general quant methods courses heavily on application of specific O.R. tools (linear programming, simulation, decision trees). Some skill building is fine, but it is hard to say whether planting graduates with good skills at small-scale modeling in supply chain, marketing or finance positions will lead organizations to attack larger and more complex problems (where O.R. analysts are needed), or will diminish the need for workers with O.R. training (because the generic business graduate can do enough of the analysis on his or her own). Perhaps more critically, it is not clear to me that a non-O.R. graduate with decent modeling skills for the scope and scale taught in a classroom will necessarily be able to identify larger opportunities that would justify bringing in consultants or creating an internal O.R. group.

The key here may be to dial back a little on teaching specific models and algorithms, and put more emphasis on general modeling and problem-solving skills (including recognizing the actual problem and delineating its scope).

Sideways

This is the direction that I think is most often overlooked. Small to medium businesses (SMBs), small non-profits and small governmental institutions (think your local school district) are probably underserved by the operations research community, particularly as they may tend not to be lucrative potential clients for consultancies. The key decision makers are often not business school graduates, and may have no idea that O.R. and analytics even exist (unless they know "analytics" in the sense of parsing web server logs). Presentations at local Chamber of Commerce meetings, participation in local business forums (do your local businesses have a LinkedIn group?), and even pro-bono consulting will help show people both what O.R. is about and how it can pay off. My guess is that this group is particularly ripe for word-of-mouth marketing.

As I said, this segment is probably not particularly lucrative monetarily. Every once in a while, though, one of those SMBs will take off and become a large company; hopefully they will remember the role O.R. played in their growth. Organizations in this category that benefit from O.R. help may also have the ear of politicians and university administrators, which may foster some growth in O.R. curricula. Finally, press reports of success stories at this level may catch the eyes of executives in larger firms.