According to Peter Drucker (1999), there are six factors that determine the productivity of a knowledge-worker (a knowledge-worker is one who has to organize and execute knowledge-based tasks; in contrast, a manual worker executes mechanical tasks defined by machinery):
- Task (job) definition: Workers are no longer dependent on machinery that defines the tasks to be done. They now have to decide what tasks are needed to do their job.
- Worker autonomy: Workers are responsible for their own contributions.
- Continuing on-the-job innovation: Workers have unique insights on how to improve their own tasks.
- Continuing learning and teaching by the worker: Continuing innovation requires continuous learning and systemic innovation requires teaching.
- Quality – at least on equal footing as quantity – of output: Quality is tightly related with the task being done, so task definition is crucial.
- Knowledge-worker seen as an asset, not as a cost: Assets are nurtured, contrary to costs which are to be minimized. A worker’s knowledge is a non-fungible asset to (not of) the organization.
Yet many business leaders we encounter face difficulties applying all of these factors to improve the productivity of their knowledge-based workforce. Although each organization has reasons for not achieving the productivity they deserve (based on talent they employ), we distilled a list of common reasons observed in a cross-section of our clients:
- Pre-defined jobs that do not take into account a worker’s input and self-reflection.
- Micro-managing workers, curtailing their autonomy.
- Inflexible approval guidelines for changing (and improving) how tasks – and jobs – are accomplished.
- Perennial focus on inputs (e.g. hours worked) versus outputs (e.g. the quantity and quality of the work-product) and lax definition of output quality.
- Knowledge worker’s salaries seen as cost that needs to be contained.
Drucker highlights that one of the biggest challenges organizations face in the 21st century is to increase the productivity of their knowledge workers. In the 20th century, industrial firms were successful in increasing the productivity of manual workers by applying specific Industrial Engineering techniques (such as Total Quality Management, Operations Management, Minimalist Manufacturing, Rationalization, etc.).
We believe knowledge worker productivity can improve substantially by embracing these time-tested techniques wrapped around a framework of cooperation and mutual accountability. Our early implementations suggest better alignment amongst workers, better tasks definition, reduction of extraneous tasks and improvements in worker morale that led to more output per worker.
We are now seeking business leaders who want to tackle the challenge of improving the productivity of their knowledge workforce by piloting this approach. Please send us an email at hello [at] expertcollective [dot] org if you are interested in collaborating.
Reference:
Drucker, P. (Winter 1999). “Knowledge-Worker Productivity: The Biggest Challenge.” California Management Review, vol. 41, No. 2, p. 83-84.