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Are you measuring Knowledge Worker Productivity across all 3 levels?

The first step towards improving an organization’s productivity is to measure the current productivity level. Only when you know the current productivity level of the organization, you can work towards improving it. So how do you measure productivity?

Measuring productivity is easier at the organization level. This is because you can focus on high-level metrics like revenue per FTE, profits per FTE, revenue to employee cost ratio, etc. While these metrics are easier to measure, these alone will not help you identify the root cause of productivity problems in the organization. Individual-level productivity, on the other hand, is difficult to measure and is prone to errors by the person carrying out the study, particularly in case of more complex knowledge-based jobs.

Therefore in order to accurately identify and solve productivity problems, productivity must be measured across all 3 levels:

1. Organizational

2. Team

3. Individual

Moreover, limiting the scope of the study to one particular level typically results in resistance from individuals operating at those levels. Broadening the horizon of the study brings in more acceptance from employees. Given that employee resistance is one of the biggest challenges to productivity improvement initiative, it’s even more critical to not limit the scope to just one or two levels of analysis.

At each of these levels, there are several factors that impact productivity. Understanding and analyzing these factors in the context of the organization will help in getting to the core of productivity issues:

1. Organization — At the organization level, factors like the type of org structure, level of centralization, the collaboration mechanisms, the level of specialization, the extent of technology adoption, the culture of the organization, decision rights, or delegation of authority could impact productivity.
2. Team — Most of the organization level factors are also applicable at the team level. But apart from those, factors like physical or location constraints, the quality of talent, tools used, the extent of non-value adding activities, and process efficiency could impact productivity at the team level.
3. Individual — At the individual level, factors like utilization, focus on the primary task, role-capability mismatch, and motivation of the individual could impact productivity.

Although the study must be as exhaustive as possible, sometimes it’s not possible to analyze all these factors in a limited time span. Just measuring productivity should not take up a lot of time, because a larger amount of time must be spent on making changes and improving productivity. So how do you prioritize and do a more focused productivity improvement exercise while ensuring productivity is measured across all 3 levels?

The generic rule is — As the jobs become more judgment oriented rather than process-based and rely more on individual expertise rather than set procedures, the focus should shift towards softer factors.

But one standard approach will never work across all organizations. Most of the time the approach must be tweaked even when moving from one function/department to another. The factors to be analyzed must be finalized depending on organization’s or team’s specific context while adhering to the 3-level productivity rule.

Whatever factors you are focusing on, a work-content or job analysis interview must be carried out to understand where, why and how time is spent across all jobs in the organization. But as the focus shifts towards the softer factors, the way these work-content or job-analysis interviews are carried out must also change and should emphasize more on understanding how work gets done rather than where time is spent.

Finally, productivity improvement has to be a continuous exercise. Processes must be established to continuously measure the identified factors across all 3 levels. Moreover, the factors impacting productivity also change as the organization evolves and moves forward in its productivity journey. So what is important to measure and solve now may not be so important in the future.

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Originally published at https://www.linkedin.com.