What are Key Performance Indicators? According to Wikipedia:
A performance indicator or key performance indicator (KPI)
is industry jargon for a type of performance measurement. An
organization may use KPIs to evaluate its success, or to evaluate the
success of a particular activity in which it is engaged. Sometimes
success is defined in terms of making progress toward strategic goals,
but often success is simply the repeated, periodic achievement of some
level of operational goal (for example, zero defects, 10/10 customer
satisfaction, etc.). Accordingly, choosing the right KPIs relies upon a
good understanding of what is important to the organization. 'What is
important' often depends on the department measuring the performance -
the KPIs useful to finance will be quite different than the KPIs
assigned to sales, for example. Because of the need to develop a good
understanding of what is important, performance indicator selection is
often closely associated with the use of various techniques to assess
the present state of the business, and its key activities. These
assessments often lead to the identification of potential improvements;
and as a consequence, performance indicators are routinely associated
with 'performance improvement' initiatives. A very common way for
choosing KPIs is to apply a management framework such as the balanced
scorecard.
Key performance indicators define a set of values
used to measure against. These raw sets of values, which are fed to
systems in charge of summarizing the information, are called indicators. Indicators identifiable and marked as possible candidates for KPIs can be summarized into the following sub-categories:
- Quantitative indicators which can be presented with a number.
- Qualitative indicators which can't be presented as a number.
- Leading indicators which can predict the future outcome of a process
- Lagging indicators which present the success or failure post hoc
- Input indicators which measure the amount of resources consumed during the generation of the outcome
- Process indicators which represent the efficiency or the productivity of the process
- Output indicators which reflect the outcome or results of the process activities
- Practical indicators that interface with existing company processes.
- Directional indicators specifying whether an organization is getting better or not.
- Actionable indicators are sufficiently in an organization's control to affect change.
- Financial indicators used in performance measurement and when looking at an operating index
KPIs are often constructed by combining other indicators. This leads to two major challenges:
1. The amount of data that is produced by and available at a modern business is huge. That's why Big Data is today's new buzzword. And problem.
2. Data is often combined via a series of subjective weights to produce KPIs. Consider, for example, Balanced Score Cards. These are basically all about about choosing measures and targets.
The mere presence of subjective weights in any kind of metric, cost function, KPI or a Balanced Score Card means that they lack a solid scientific basis. And when things get really complex, this can mean the difference between being right or wrong. So, how can things be done differently and a bit more rationally?
Quantitative Complexity Management (QCM) technology offers an elegant solution - the Complexity Profile. Complexity is a function of Business Structure (see examples) and entropy (the amount of uncertainty "contained" therein) - the equation is C=f(S; E). Complexity is measured in bits and represents the total amount of structured information within a process or a business or a generic system. For example, the DNA contains enough information to build a complete living organism. More complex DNA more complex (functional) the organism. The same may be said of a business the performance of which one wishers to assess and steer. Complexity is measured by processing the so-called state-vector of a system, a set of measurable N outputs.
An example of such a vector is the set of Balance Sheet entries of an SME, in which N = 26, is this:
(the actual data used in this example is available here).
Now suppose that you wish to build one or more KPIs, combining some or all of the above parameters. Suppose you wish to use weights. What should these weights be? You can, of course, select them subjectively, based on your feeling, or you may use the natural weighting of each parameter which is provided by the Complexity Profile or Complexity Spectrum, illustrated below.
A Complexity Profile quantifies the percentage contribution, or footprint, of a particular variable to the overall value of complexity. Since complexity is a measure of how much information is contained within a system, the Complexity Profiles measures directly how many bits of information a particular parameter contributes to the total. In the case in question - see the Business Structure Map of the SME in the example - the total amount of information in the Balance Sheet data is just over 18 bits. According to the Complexity Profile, "Work in progress" contributes 10.3% of those 18 bits. In other words, the "footprint" of this variable on the system is 10.3%. Consequently, if we wish to weigh this variable in a particular KPI, its natural weight is 10.3%, or 0.103. On the other hand "Total number of overtime hours" has a footprint of 0.23%, or 0.0023, i.e. it is the the variable which should receive the lowest weighing.
Of course, complexity itself, which is a structured function of the said variables, is already a formidable KPI. It takes into account:
Moreover, invoking the concept of Relative Complexity, it is possible, by means of this new KPI, to even compare different systems or businesses.
Are you sure you know the real importance of your business parameters? If not, click here.
www.ontonix.com
1. The amount of data that is produced by and available at a modern business is huge. That's why Big Data is today's new buzzword. And problem.
2. Data is often combined via a series of subjective weights to produce KPIs. Consider, for example, Balanced Score Cards. These are basically all about about choosing measures and targets.
The mere presence of subjective weights in any kind of metric, cost function, KPI or a Balanced Score Card means that they lack a solid scientific basis. And when things get really complex, this can mean the difference between being right or wrong. So, how can things be done differently and a bit more rationally?
Quantitative Complexity Management (QCM) technology offers an elegant solution - the Complexity Profile. Complexity is a function of Business Structure (see examples) and entropy (the amount of uncertainty "contained" therein) - the equation is C=f(S; E). Complexity is measured in bits and represents the total amount of structured information within a process or a business or a generic system. For example, the DNA contains enough information to build a complete living organism. More complex DNA more complex (functional) the organism. The same may be said of a business the performance of which one wishers to assess and steer. Complexity is measured by processing the so-called state-vector of a system, a set of measurable N outputs.
An example of such a vector is the set of Balance Sheet entries of an SME, in which N = 26, is this:
| Orders |
| Backlog end of period |
| Value of production |
| Revenues |
| Costs for purchases |
| Costs for services |
| Personnel costs |
| Financial expenses |
| Trade receivables |
| Other Receivables |
| Inventories |
| Work in progress |
| Cash and cash equivalents |
| Trade payables |
| Others Borrowings |
| Net debt |
| Total number of clients (last 12 months) |
| Total number of suppliers (last 12 months) |
| Total employees |
| Total part time employees |
| Total number of overtime hours |
| Total number of customer orders transactions |
| Total number of orders at warehosue |
| Total number of goods/services |
| Total number of customer transactions |
| Unsold goods/services (gap between closing stock and opening stock) |
(the actual data used in this example is available here).
Now suppose that you wish to build one or more KPIs, combining some or all of the above parameters. Suppose you wish to use weights. What should these weights be? You can, of course, select them subjectively, based on your feeling, or you may use the natural weighting of each parameter which is provided by the Complexity Profile or Complexity Spectrum, illustrated below.
A Complexity Profile quantifies the percentage contribution, or footprint, of a particular variable to the overall value of complexity. Since complexity is a measure of how much information is contained within a system, the Complexity Profiles measures directly how many bits of information a particular parameter contributes to the total. In the case in question - see the Business Structure Map of the SME in the example - the total amount of information in the Balance Sheet data is just over 18 bits. According to the Complexity Profile, "Work in progress" contributes 10.3% of those 18 bits. In other words, the "footprint" of this variable on the system is 10.3%. Consequently, if we wish to weigh this variable in a particular KPI, its natural weight is 10.3%, or 0.103. On the other hand "Total number of overtime hours" has a footprint of 0.23%, or 0.0023, i.e. it is the the variable which should receive the lowest weighing.
Of course, complexity itself, which is a structured function of the said variables, is already a formidable KPI. It takes into account:
- Each variable, together with its intrinsic (natural) footprint within a system
- The structure of the correlations of that variable with all remaining variables
Moreover, invoking the concept of Relative Complexity, it is possible, by means of this new KPI, to even compare different systems or businesses.
Are you sure you know the real importance of your business parameters? If not, click here.
www.ontonix.com
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