Managing Under Uncertainty
By Spencer, Robin W | |
Proquest LLC |
In his 2006 book Science Business,
Business uncertainty is the sum of all the unknowns sur- rounding a decision: unknowns about competitors, suppliers, and partners and their intentions; about consumer trends and political and financial shifts; about coming technical changes. There is a rich literature on the topic;
In an uncertain world, the wise manager will envision modes of failure and put plans in place to avert, minimize, or recover from them. Insurance is one form of planning-for- failure.
Strategies Against Uncertainty
There are a host of approaches to managing uncertainty, but they can all be grouped into three general strategies, illus- trated by a personal approach to wet weather:
* Buffer: Always keep a raincoat and umbrella in the trunk of the car.
* Plan: Listen to the weather forecast and bring a raincoat if the prediction warrants.
* Adapt: Do nothing now. If it rains, find an umbrella somewhere, run, or get wet.
Which strategy you adopt may depend on elements that are outside your control. If you have slack resources (a car with a big trunk), then you can buffer; this is the approach on which the insurance industry is built (cash today against bad news tomorrow). If you have a reliable model of the future (a weather forecast), then you can plan. If you have neither, then you must adapt because you have no alternative.
But it's important to be honest about your capabilities. Science and engineering managers love to build quantitative models to guide planning. The danger is that modeling can take on a life of its own, restrict itself to knowable but minor factors, and mask the ugly reality that a lot of what happens isn't very predictable. In pharmaceuticals, despite years of in- vitro and animal testing and sophisticated computer model- ing, about 90 percent of new drugs fail when they reach human testing (Kola and
At the strategic level, adaptation means moving from push to pull as defined by Hagel and
Pull had its genesis in Toyota's "lean production" system, in which reduced inventories and just-in-time supply lines shift the uncertainties of markets and demand to suppliers. Pull can extend beyond the supply chain; at the extreme, a virtual company can be nearly 100 percent pull, functioning from a tiny core that executes through a fluid community of contractors and suppliers. Thus, pull can mean going outside the firm, often via the Internet and perhaps through match- makers like Innocentive, YourEncore, and NineSigma that tap into networks of potential problem solvers. While exter- nalization and crowdsourcing may not "change everything," as some suggest (Tapscott and Williams 2006), it is certainly a valuable hedge against some sources of uncertainty.
But pull is more general than this. It's about finding knowledge and resources when they are needed, instead of paying to have them standing by, and this can begin within the corporation at very low cost and risk. Plus, there are added benefits to using internal enterprise-wide challenges to solve problems: not only do such efforts smooth out the uncertainty in determining when the expertise will be needed and pull it to the problem just in time, but they hedge against the likelihood that a small static team will become out of date (Spencer 2013).
Project-Level Defenses Against Uncertainty
These corporate-level approaches to uncertainty are impor- tant, and they may well influence what resources you have to manage uncertainty at the project level. A couple of spe- cific tactics may help address uncertainty associated with par- ticular projects.
Modular design can provide a hedge against the possibility of future catastrophes.
We can go farther and suggest that wherever possible, a project should also be designed to have finite value-not just resistance to failure-at a number of intermediate stages. For example, a project may be initially intended for local produc- tion, but if it is designed as a sequence of autonomous mod- ules that can be shunted to outlicensing, outsourced for manufacturing, or transferred to overseas marketing as events unfold, then it is far more likely to deliver value to the company than another project that lacks such alternatives. The movie industry offers many examples of films that bombed at the domestic box office but were saved (finan- cially at least) by DVD, cable TV, or international licensing.
The second project-level tactic to mitigate uncertainty is to do "killer tests" as early as possible in a project's timeline. In a competitive technical arena, the killer test might be a thor- ough check for encumbering patents. In drug development, it's usually the first test of a new drug against human disease, a Phase II clinical trial. With a salesperson and customer, it might mean asking an intent-to-purchase question before starting an elaborate demo. Because failing such a test ends the project, it should be done as soon as possible to reduce sunk costs and time. It takes courage and confidence for the project manager to look disaster in the eye, but denial and delay are far worse.
In summary: develop failure scenarios and plan against them. Since your resources and predictive models can't be complete, be honest about your need to adapt, which means adopting pull techniques. Design projects to be robust against failure or flexible enough to embrace alternative views of success, and face the worst case early. The good news is that the necessary skills for the adaptive manager-efficient in- formation gathering and crisp decision making-are worth developing even for a perfectly predictable future.
References
Brown, J. S., and Hagel, J. 2005. From push to pull: The next frontier of innovation. McKinsey Quarterly 3: 82-91. http:// johnseelybrown.com/pushpull.pdf
Hadfield, C. 2013. An Astronaut's Guide to Life on Earth.
Hagel, J., and Brown, J. S. 2008. From push to pull: Emerging models for mobilizing resources.
Kola, I., and
Pisano, G. 2006. Science Business: The Promise, the Reality, and the Future of Biotech.
Simon, H. 1996. The Sciences of the Artificial. 3rd ed.
Spencer, R. 2013. Reality check: Your teams aren't good enough. Research-Technology Management 56(1): 60-61.
Spencer, R. 2014. Reality check: Managing complexity. Research- Technology Management 57(3): 53-55.
Tapscott, D., and Williams, D. 2006. Wikinomics: How Mass Collaboration Changes Everything.
DOI: 10.5437/08956308X5705007
Copyright: | (c) 2014 Industrial Research Institute, Inc |
Wordcount: | 1659 |
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