In BYU’s MBA program, I remember using Harvard Business School case-study books to study, discuss, and learn business Operations. At first a lot of us students wondered what kinds of “proprietary calculus” we would be learning.

And while we occasionally had to analyze rates of change (ie. “calculus”), that didn’t happen often. Sometimes we learned how to build and model improvements.

But generally operations cases comprised a massive set of seemingly disparate data from some company that was facing an operational “bottleneck” of some sort. In other words, we had to figure out what was in the way of the organization  achieving more of some desired process or outcome. 

To be sure, automation systems, including types of software (for service organizations) and types of robotics (for manufacturing organizations) account for some of the “great leaps forward” that we can see both at a micro and macro levels of operational improvement over the last five decades. But so have quasi optimization models.

Quasi Optimization Models

I think it can be useful to conceptualize Operations Management from the perspective of another business school tool: optimization models. A consulting firm may seek to maximize the creation and delivery of analyses, subject to various kinds of constraints. And notice that very often, these constraints are not fully quantifiable:

  • Quality of data gathering (a function of both hard work, and trustworthiness, and the client’s perception that it is in their own best interest to provide data);
  • Thoroughness of the data gathering (consultants designing the inquiry or questions to be thorough, and then knowing–often intuitively–when to press for more and when to loop around again for another “visit” later);
  • Brilliance of insight identification (analysis of the data that “gets to meaning” through thematic analysis, content analyses, etc.; see Ethnographic Research, by James Spradley);
  • Consideration of related issues (how many relevant theories has a consultant learned that s/he can bring to bear on the analysis);
  • Creativity of approach (can consultants look at things in an original way; can themes be creatively accounted for and understood, etc.);
  • Honoring and developing the core characteristics of a culture (honesty, cooperation, seeking first to understand before being understood, humility).

Service-based and manufacturing-based companies have many such models to be optimized.  Some examples are business development, cash flow optimization, talent recruiting optimization, oil changes by car mechanics within a period of time, upselling dollars per clerk per register per month, etc., etc.

Importantly for such optimization models, many of these constraints are subjective in nature. So we can’t just plug them into an app and compute answers (although we can do subjective assessments of numbers and then scenario analysis to get a sense of how things “might” relate and to what magnitude.) An operations leader must have the intelligence (emotional, social, otherwise) to understand both quantitatively and qualitatively, as the specific condition requires.

Crucial Dialectic

Anthony Manzo, et al., published an article on Dialectic Thinking (for a published article, go here). They define Dialectic Thinking to be “the ability to view issues from multiple perspectives and to arrive at the most economical and reasonable reconciliation of seemingly contradictory information and postures.” 

Now, some things are clear and don’t need to be reconciled from multiple perspectives. But many things–particularly things that involve services organizations–are both quantitative in nature (numbers of things getting done), and subjectively assessed (meanings and stories from data gathering and “coding.”) Thus an operations analysis must be able to switch between quantitative and qualitative thinking.

Solving operational problems, with known math and known equations, is pretty easy. But when a part of the analysis is subjective, then math alone won’t suffice. Thankfully, there are ways to uncover underlying and sometimes “tacit” subjective problems: rolling up your sleeves and doing the work, AND WHILE DOING THE WORK visiting with others doing the work to understand their perspectives and observations (again, see ethnographic research post referenced above.)

Ethnography includes “Participant observations” that should be done while documenting the patterns of work & social interactions of the work (from the perspectives of participants), and analyzing these for themes that emerge, along with eventual insights. Doing from the perspectives of participants help to make explicit such things as (1) why we do what we do, (2) how we do what we do, and (3) what we think desired outcomes look like. And from such data, insights related to improvements can be identified. Existing management are probably exemplary, but looking at things from deep within an “inside perspective,” can sometimes be more profound that looking at things from a fully fresh and “outside” perspective.

Always the end-goal is to identify and optimize some operational bottleneck. But doing so requires leaders willing to participate or “do the work” (in some realistic, if modest proportion), and in the process to dig progressively deeper in order to “get to meaning.”

Doing this over and over again, and finding and fixing the next biggest operational bottleneck, is a lot of hard work. It requires intellectual curiosity. But in the end, it can yield sustainable operational improvements. 


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