Once, when designing and implementing a direct marketing program for a startup, the CEO took me aside and said, "Marketing is all good, but most former engineers I know were meant to be future COOs." This makes sense; operations are the engine under the hood of any company. You only notice when it purrs like a high performance machine or when it's broken; if it just works it's easy to forget how much effort and coordination it takes to keep that car running. Engineers are well suited to be the "get-it-done" corporate mechanics and ChE provided an excellent background for ops.
So, what concepts from ChE and related disciplines come in handy for business ops?
- Process Control/Improvement
- Design of Experiment
Process Control/Improvement (PI)
I recently went to a mixer at the University of Washington for current ChE grad students to meet with alumni in industry. People were surprised I made the transition to business and especially when I mentioned that my ChE thesis was in process control. "Oh god," the reaction would typically begin, "All I remember is all the Fourier & Laplace Transforms and I lost interest." This was disappointing, as Process Control/Improvement is one of the most useful concepts I learned in ChE and one that I've been able to apply time and time again in business.
A central Buddhism tenant is that suffering comes from struggling with what is (article). Much of our lives are spent trying to enact change, and process control (in practice) lays out the perfect framework:
What you hope to effect = what you have control over + what you don't + what you don't even know
Controlled Variable (CV) = what you hope to effect
Manipulated Variable (MV) = what you have control over
Disturbance Variable (DV) = what you don't have control over
CV = f(MV,DV) + unknown
Process Control is really about improving what you have control over and understanding what you don't have control over, given your resources. Remember our AT&T case from last time? Customers were leaving due to poor customer service. Better plans didn't affect customer retention, so it would have a negligible effect on churn.
Design of Experiment (DOE)
Before tackling any project, it's worth using Design of Experiment to validate: "Do the resources and levers I have at my disposal have any effect on changing my Key Performance Indicators (KPIs)?" Let's say I want to increase revenue for my wholesale distribution system. Using the marketing material balance:
?Customersin - ?Customersout = ?CustomersAccumulated
My customer retention could be better, so I've asked a multidisciplinary team to brainstorm on solutions:
- Twitter / Facebook campaign
- Account management practice
- Quality control (on-time & error-free delivery rates)
- Product offerings
Our firm has multiple levers at our disposal with varying degrees of:
- Time to implement
For example, I could quickly implement a Twitter campaign, but do wholesale distribution partners really use Twitter to decide whether or not they maintain a working relationship? Asking these questions (DOE) and stack ranking all of your levers based on effectiveness (PI) are key in an increasingly cost-sensitive and analytic business environment. Don't let the cry of "analysis paralysis" deter you, as you aren't engaging in a 10-year longitudinal study. It's better to ask the question and do the research necessary to answer the question before spending time and energy on sub-optimal solutions. Much of the work in business is around enacting change. A recent buzzword is being a "transformational agent," where you are expected to lead in an environment where you need to make decisions to make the best use of your resources.
Typical CVs & MVs in healthcare are patient outcomes and revenue (CVs) to staff and patient beds (MVs). If you hit a bottleneck, hire or build. In an increasing competitive and cash strapped environment, these are fewer available MVs (article). Process control teaches us that MVs can number 1 to n with varying degrees of bang for your buck. Creativity, innovation, "boot strapping" (a favorite venture capitalist & entrepreneur term) can come from asking the question"What can I do with what I have?" or "What MVs do I have at my disposal?" Your CVs become your strategic objectives and your MVs are the resources that you can bring to bear that can actually make a difference in your CVs.
Most engineers in business school find that if they could understand systems of differential equations in undergrad, business analytics should be quite easy by comparison. The analysis techniques I most frequently use for my clients in industry come from the following core topics:
- Statistics [is this data meaningful?]
- Multivariate analysis [how do I use data to predict behavior?]
- Time-series analysis [how does behavior change with time?]
- Optimization [how do I maximize KPIs given constraints?]
If you have a firm grasp on how to use these concepts, your analytical confidence will be one of the most empowering tool kits you can have at your disposal. The most important lesson I learned from Purdue when writing my master's thesis and preparing my research for publication was learning how to deconstruct the work of others and then apply the same critical lens to my own work before it ever sees the light of day. The only way to discover the truth behind "lies, damn lies, and statistics" is to be able to take outside analysis to task and deconstruct others' work.
Engineers are trained to solve problems, making them ideal "get-it-done" corporate mechanics. Industrial engineers get specific training for business operations, but ChE training provides the analytical skills that rounds out our invaluable tool belt. Need to solve an ambiguous problem using an analytical framework while staying within resource constraints to maximize NPV? No problem. Attributing ChE benefits only to creative analytical problem solving would be too easy, so we'll next talk next about what you inherently learn from being a ChE that isn't explicitly part of any curriculum.
When have you used your ChE background as a corporate mechanic?
Photo: Robert J.Pennington, www.rhizomeimages.com
(C)2011 Arkan Kayihan, used with permission