Productivity is a measure of the efficiency of production, expressed as the ratio of output to inputs used. Performance is defined as the accomplishment of a given task measured against preset standards of achievement, such as accuracy, completeness, cost and speed.
In the wider context of performance management, productivity is measured against productivity KPIs. In their simplest form, productivity KPIs, such as # Units per man-hour, stand at the basis of both modern and older performance evaluation systems. However, it is only but natural that we ask ourselves the following question: How much productivity is there left to both measure and reflect on performance?
In her book, The Measurement Nightmare: How the Theory of Constraints Can Resolve Conflicting Strategies, Policies, and Measures (1999), Debra Smith talks to her readers about a real-life situation, based on one of the most common productivity KPIs in use: # Units per man-hour. And it all starts with defining the KPI. According to her, # Units per man-hour is a “summary of standard costing’s use of standard labor hours and standard labor rates, resulting in labor variance analysis and decisions designed to improve.”
“There is not one productivity indicator that does not reflect on performance. And there is not one neglected faction of performance that does not impact productivity in one way or the other.”
From here on, Debra Smith describes this particular situation in which, on an intuitive basis, some executive manager from a manufacturing company decides to increase # Units per man-hour by cutting labor costs with highly automated machines. So, instead of six loom operators, four were assigned to tend to one loom per shift.
And the effect was as expected…at first. # Units per man-hour had increased at the loom. However, because of the downtime of the looms which now increased, the total output of the looms had decreased.
Due to a lack of attending operators, the downtime of the machines escalated up to a point where it impaired all subsequent processes. When that happened, all downstream processes began to suffer from starvation. % On-time delivery of products declined, $ Labor costs went up due to # Overtime and, instead of going up, $ Net profit went down.
Debra Smith’s account of the negative side effects one productivity measure can propagate, when taken out of the context of performance, stand to show that there is more to productivity in performance than counting outputs per unit of input. And this is more visible when dealing with the most popular dimension, which is labor productivity.
In the context of performance management, labor productivity can be translated through individual KPIs. When dealing with employee performance, individual productivity KPIs become part of a more complex performance evaluation system. The overall individual performance index simulates an average between the score of the individual performance scorecard, the individual competencies score, and the employee behaviors score.
Where do KPIs fit into this equation? Productivity KPIs are mindfully incorporated into the individual performance scorecard, to best reflect the quantitative aspects of employee performance. And this is where everything gets tricky and we start asking ourselves: How much of one employee’s performance should be measured in terms of quantity?
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Let’s take, for example, the automotive industry. With automotive manufacturing, productivity is a key performance indicator that measures the total production volume of the actual manpower, while taking into consideration the effective days officially scheduled for each automobile.
The core performance indicator of the automotive industry is # Hours per unit or # HPU, and it reveals the number of hours required to build a car. However, at its basis, this # HPU cannot be measured outside # Available manpower, # Effective working time, and # Individual production volume. Let’s add % Absenteeism rate to this reasoning.
When dealing with target production volumes it is important that the plant works at its full throttle to achieve those targets. Given this requirement, % Absenteeism rates should not be overlooked, as they have a major impact on the # Effective working time, which here on, impacts the # Production volume, and, ultimately, the # HPU.
However quantifiable, % Absenteeism rates also reflect on less quantifiable variables. This further takes us to the issue of % Employee engagement: a roughly quantifiable, uncontrollable driver of not only productivity but of performance as well.
So, how much productivity is there left, to both measure and reflect on performance? A great deal. And maybe the best way to look at it is by envisioning this revolving cartwheel…this continuous circle, which turns productivity into performance and vice versa.
All things considered, there is not one productivity indicator that does not reflect on performance. And there is not one neglected faction of performance that does not impact the former in one way or the other.
For more articles on productivity improvement, click here.
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Editor’s Note: This article has been updated as of September 18, 2024.
The Balanced Scorecard (BSC) is one of the most important performance management tools used to improve business functions and their outcomes. This tool is used not only at the organizational level but also at the departmental level.
By using departmental scorecards, managers are able to get detailed insights into the performance of their departments. The scorecards can also determine the responsibilities of the employees in terms of achieving strategic objectives.
To implement an effective balanced scorecard for the departmental level, organizations should take into consideration these best practices.
Develop the Right Template
Employees are often asked to collect data since every manager knows that it is essential in generating qualitative insights. However, the different performance reports could easily lead to different interpretations. A well-designed template leads to a clear, structured reporting and improves communication through standardization.
The template should contain four perspectives that meet the organization’s strategic needs. The most commonly used perspectives are Financial, Customer, Internal Processes, People Learning, and Growth.
Moreover, the template should also display the objectives associated with each perspective and the KPIs associated with each objective. For each KPI, the target and thresholds, the trend, and the previous and current result should also be presented.
Choose the Right Objectives
When preparing a departmental scorecard, one of the most important steps is to select the right objectives for the different categories, and those objectives should align with the organizational and departmental strategy. Through the cascading process, the organizational objectives and KPIs are translated from the strategic level down to the departmental level.
The departmental scorecard must contain some specific objectives depending on the activities of the operations team. The same objective can be cascaded to more departments, each of them measuring it through different KPIs. Some organizational objectives may not be cascaded to lower levels.
For example, the objective of the Financial perspective is to Increase profit. This organizational objective can not be directly cascaded to the human resources department since the human resources department has no direct influence on the revenue of the organization. However, they could reduce their spending in order to increase organizational profit. Therefore, the objective for the human resources department could be to minimize operational costs. Since the sales department is responsible for profit generation, they can cascade down this organizational objective without any modification.
Choose the Right KPIs to Measure Chosen Objectives
As mentioned before, it is recommended not to cascade all objectives and KPIs from the organizational level to the departmental level, but organizations may add specific ones that represent the department. The most important attributes in KPI selection are relevance, clarity, and balance.
In many cases, organizational and departmental scorecards may not be enough to communicate the organizational strategy to all employees. Therefore, individual scorecards should also be created for them.
Data Sources for a Balanced Scorecard
During the scorecard development process, organizations may find it hard to determine the right objectives and KPIs. Objectives and KPIs must be based on relevant data. There are two types of sources of data to consider: primary and secondary.
Feedback from internal stakeholders can be considered as an internal primary data source, while feedback from external stakeholders is an external primary data source. Secondary internal sources could a company’s previous reports and strategy plans, while smartkpis.com and academic articles are external secondary sources.
Figure 2: Marketing Departmental Scorecard Example
“What constitutes a good KPI example?”, “How should KPIs be measured?”, “Which KPI is suitable for our organization?”, and “How well will employees understand and participate in tracking these KPIs?” These questions often loom large for companies seeking to select the right KPIs to accurately measure their performance and progress toward strategic objectives.
According to The KPI Institute’s (TKI) State of Strategy Management Practice Global Report – 2023, choosing the right KPIs ranks as the second most significant obstacle in strategy planning.
The report further reveals a concerning trend regarding the challenges associated with working with KPIs. Results indicate a surge in the hurdles associated with KPI selection compared to the previous year.
Several factors contribute to the challenging nature of KPI selection, including the need to align with strategic objectives; the common practice of defining initiatives before KPIs rather than defining KPIs and targets first and then developing initiatives to reach them; clearly differentiating between strategic and operational KPIs at the departmental level; and focusing too much on task-related KPIs rather than impact KPIs at the employee level.
3 stages of KPI selection
Selecting the right KPIs requires careful planning, analysis, and collaboration across various organizational areas. A rigorous KPI selection process typically involves three major stages (see Figure 1).
Your initial step in the process is to set a clear direction for KPI selection by recognizing the business objectives and goals that must be attained. This is essential to ensure that all personnel are working towards the same objectives and that progress can be efficiently monitored. This stage clarifies the necessity and application of measurement while precisely defining the intended purpose of the KPIs.
Next, conduct thorough research to gather a range of KPI examples. This serves a dual purpose: educating your internal stakeholders and fostering meaningful discussions about KPIs. This process, labelled as the KPI expo, entails compiling a comprehensive list of KPIs that will later be filtered based on a set of criteria.
You should review both internal and external data sources (see Figure 2) to leverage existing practices while also gaining insights into industry best practices. The KPI expo can include existing KPI lists from various organizational levels, which may already be in use or have been tested within your organization.
In the next stage, use intelligence gathering and conduct workshops to identify suitable KPIs. You can obtain insights from a diverse range of stakeholders, including clients, suppliers, employees, and management. This approach will foster broader buy-in and support.
TKI recommends the following selection methods to ensure the identification of relevant KPIs:
Question framing: Guide discussions toward relevant contexts and gather participant perspectives. Questions might include, “How many KPIs should we select?” or “What is the procedure for validating the selected KPIs?”
Value flow analysis: Examine the flow of value within business processes—from inputs to outcomes—to understand how objectives can be measured from different perspectives.
KPI balancing: Avoid narrow perspectives by selecting at least two complementary KPIs per objective, ensuring the measurement of both quantity and quality, subjectivity and objectivity, and efficiency and effectiveness.
Additionally, among the existing criteria in practice, TKI suggests using these five to ensure KPI relevancy:
Measurable: Can the KPI result be quantified?
Accessible: Can your organization feasibly gather the necessary data?
Specific: Does the KPI address a specific issue you have?
Actionable: Does it provide information for decision-making?
Balanced: Does it reflect various facets of performance?
The final stage in the KPI selection process involves monitoring the selected KPIs for necessary recalibrations. This can be achieved through two key activities: KPIs documentation and the performance review meeting.
KPI documentation can reveal limitations associated with data collection or reporting and gaps in the cost-benefit analysis of the KPI’s usage. Develop a comprehensive set of information for each selected KPI to facilitate data collection, reporting, and analysis.
Use a standard template, known as a KPI documentation form (see Figure 3), capturing each KPI’s details, definition, calculation formula, target, data source, reporting frequency, KPI owner, and data custodian. For more examples, you can explore TKI’s comprehensive repository of KPIs at smartKPIs.com.
The first reporting and performance review meeting for the new KPIs will reveal their utility for decision-making. It provides managers with an overview of how the KPIs cover all aspects of the business and helps identify necessary adjustments to the corporate scorecard, ensuring that the most relevant data is available for decision-making. Facilitate this first meeting through your strategy office.
After this final stage, your KPIs can be maintained as initially selected, recalibrated and updated, or even phased out of use based on their effectiveness and relevance to your organizational goals.
By following these stages, you can select and implement KPIs that accurately measure performance and support strategic objectives, ultimately driving your business success and growth.
Ready to take your KPI selection to the next level? Head over to the KPI section on our website for more in-depth articles and expert advice.
Key performance indicators (KPIs) have been the north star guiding business strategy for decades. These criteria measure not only sales and revenue but also customer satisfaction as well as employee engagement.However, as the business landscape continues to evolve at an unprecedented pace, the need for deeper insights and more agile measurement arises. This is where the potential of generative artificial intelligence (GenAI) shines, opening doors to a new era of KPI innovation.
GenAI goes beyond automation to produce entirely novel content. It is a creative catalyst, opening up unprecedented possibilities for KPI innovation. Forget rigid, one-dimensional metrics. Powered by GenAI, KPIs become fluent, adaptive, and poetic, capturing not only the whats but also the whys and what-ifs.
Reimagining KPIs for exponential growth
From static to dynamic: GenAI is capable of integrating dynamic KPIs, meaning they can evolve alongside the company that uses them. KPIs also fit seamlessly into a changing market, with trends and strategies naturally shifting along the way.
Unveiling the unseen: Traditional KPIs often fail to hit the nail on the head by overlooking key, intangible factors that could affect performance. GenAI, however, can delve much deeper. With the help of GenAI, it is possible to determine brand sentiment before a particular campaign is launched, anticipate employee engagement within remote teams, or even predict customer turnover before it happens.
Personalized insights, enhanced action: Data mountains no longer need to be intimidating.GenAI transforms data into personalized narratives, crafting stories tailored to individual stakeholders. Sales teams can access actionable insights, marketing managers can monitor real-time customer sentiment, and CEOs can explore what-if scenarios for strategic foresight. This data-driven storytelling fosters informed decision-making and ignites action across the organization.
A practical guide to unlocking GenAI’s potential for KPI innovation
To effectively utilize GenAI tools like Gemini and ChatGPT for KPI innovation, follow these guidelines:
Define goals and challenges: Clearly articulate objectives, whether uncovering customer sentiment or anticipating market shifts.
Frame specific prompts: Use concise prompts such as “generate potential KPIs for measuring brand sentiment on social media.”
Provide relevant context: Enhance responses by furnishing background information about your industry, business model, and existing KPIs.
Experiment and refine: Iterate prompts, rephrase questions, and provide feedback to improve AI understanding.
Collaborate with experts: Involve human expertise in evaluating and implementing AI-generated insights.
While GenAI’s potential for KPI innovation is undeniable, it thrives on synergy, not substitution. The point is this: human guidance is essential. Act now, invest in your future, and become a master of the new KPI era by enrolling in The KPI Institute’sCertified KPI Professional course.
Today’s organizations drown in information waves. When leveraging data for actionable insights needed to drive strategic decision-making and sound performance measurement, visualization makes that data comprehensible and accessible. Specifically, key performance indicator (KPI) data visualization aims to communicate key performance metrics and trends in a way that is clear, concise, and impactful.
KPI data visualization benefits for organizations
KPI data visualization offers a multitude of benefits for organizations seeking to make data-driven decisions:
Enhanced data understanding: Visualizing KPIs makes it easier and faster to grasp complex data sets, identify patterns, and uncover hidden trends that would otherwise remain obscured in numerical form. KPI visualization provides insights regarding the entity’s current situation and helps a better understanding of the market.
Improved decision-making: Providing a clear and concise overview of key performance metrics, empowers decision-makers as KPI data visualization prioritizes evidence rather than intuition.
Effective communication and collaboration: Visual representations of KPIs facilitate effective communication and collaboration across teams by enabling stakeholders to share insights, align strategies, and achieve desirable goals. Additionally, KPI data visualization fosters accountability by transparently tracking performance against established goals, motivating individuals and teams to take ownership of their results, and promoting a data-driven culture within organizations to encourage data-informed decision-making at all levels.
Popular formats for KPI data visualization
The art of data visualization lies in presenting complex information in an informative and engaging way for all stakeholders. The most popular and effective techniques are as follows:
Charts and graphs: Bar charts and line graphs are effective ways to show trends and comparisons. Bar charts are effective in category comparison within a single measure. The line graph is mostly used to visualize changes in one value relative to another.
Maps and heatmaps: These visual tools are perfect for showcasing geographical data and identifying areas of concentration or dispersion.
Dashboards: Combining multiple visualizations on a single screen provides a comprehensive overview of KPIs (see Figure 1).
Figure 1. An example of medical center management performance dashboard | Source: The KPI Institute (2023), Medical Practice Dashboard
Major principles for effective KPI data visualization
Clarity and simplicity: Prioritize clarity and simplicity in data visualizations by avoiding cluttered charts and excessive complexity that may obscure insights.
Contextualization: Provide context for visualized KPIs by including relevant information, such as benchmarks, targets, and historical trends.
Visual Hierarchy: Establish a clear visual hierarchy to guide the viewer’s attention towards the most important KPIs and trends.
Storytelling: Utilize data visualizations to tell a compelling story, highlighting key insights and communicating performance trends effectively.
KPI data visualization has emerged as a transformative tool to support organizations in extracting meaningful insights from their vast data repositories. The first move for effective KPI data visualization is to embrace data culture across all organizational levels. The second step is to determine data constraints, such as the type of data, the number of variables, and the type of pattern one is trying to show (comparison, part-to-whole, hierarchy, etc.).
If you want to achieve effective KPI visual representations to support the decision-making process,? sign up for The KPI Institute’s Certified Data Visualization Professional course.
