The Right KPI Model: Optimizing Performance Measurement in Order to Cash

You’ve likely heard plenty of clever advice about how to construct a list of performance metrics and build a key performance metrics (KPI) model. The likely reason you’re still here reading about KPIs is that the solution is not effortless and evident.

Keep in mind the main reasons for running any sort of measurements when designing KPI models: to provide transparency to improve the process, service, and outcome, and support a business in reaching its targets. Without knowing what drives the process results, there is no way to change the outcomes. Therefore, the simple prerequisite to start with is data.

We need good quality data to work with. We need organizational structures, process flows, and above all, technology to enable us to look at data and analyze it. This might be obvious, and for many potentially a needless recap, but the issue is that despite knowing this, many organizations do not have the foundations in place. They jump to a KPI list to select from and quickly assemble a list of metrics they feel suits their purpose. Of course, it’s easier and more fun to participate in the KPI selection than in building foundations, but the results will likely be less than satisfactory.

Selecting metrics should be aligned with the organizational or functional targets, e.g., GBS's own targets or OTC process targets, and with the overall enterprise agenda. This requires pulling in people from leadership levels and several units and functions to gather holistic data and provide a complete view. “The difficult way is the right way” in this case, and you will reap the benefits at the end of the journey. A certain understanding and philosophy from leadership is required for this to work.

In terms of the actual metrics, the general guidance of how to build KPI models and balanced scorecards is not new.

What is new are the possibilities provided by technology to pull together masses of data to generate something useful across silo borders and to balance individual views and perceptions.

The situation has also changed due to the many crises we have experienced over the past years. A recent OTC research report from SSON Research & Analytics revealed that the relevance of working capital has risen, with 94% believing it is very important or “their life depends on it.” And, 94% of companies said DSO has the most impact on their total working capital (TWC or CCC), up from 84% before the pandemic.

Working capital metrics are a key part of an OTC/TWC-related performance measurement approach, but is DSO a good metric? For example, 78% of companies use DSO, 75% use “Best possible DSO,” and 37% use average days delinquent (ADD). While on a global level and in external comparisons DSO can be useful, for internal improvement efforts, ADD or similar metrics are more realistic and helpful. ADD currently stands at 7 days, with a very large range of deviating results.

We can see most everyone follows DSO or similar metrics, but is this helpful? Potentially we can keep the cash flowing, but what about overall long-term business targets—what should we measure to achieve those? Are our “OTC targets” or “finance targets” the right targets or are they conflicting with the business's goals? In an environment in which we are not fighting for survival, the long-term company targets should precede situational, functional, or individual targets.

In terms of metrics, an example of “zero bad debt” sounds like a good metric to look at: 16% of companies use “% of Bad Debt” measurements and the bad debt has come down to about 1%. Using this metric, it’s possible to show an improvement that would not be visible on several working capital metrics (ADD and similar metrics are basically unchanged). Cycle time metrics are basically unchanged as well. “Time-to-Bill” is at 3-4 days, so a limited set of KPIs would have provided an incomplete view of the development.

But what about above metrics like “zero bad debt?” It sounds great, but how much sales revenue are you losing? Interestingly, there is little to no measurement or comparison of this, so a reasonable assessment of what you’ve gained (lower bad debt) and what you’ve paid for the gain (lost sales) is not done, but it should be to find a healthy balance! Sales people typically know why deals are lost—these reasons are recorded for sales meeting discussions. It would require limited effort to roughly assess which sales losses are linked to applying overly restrictive rules.

It’s always good to run forecasts, such as for sales and revenue. After all, the key purpose of OTC/TWC support work and measurement is to support the business.

Depending on the type of business, these could be metrics on sales revenue forecast, sales pipeline, and sales backlog. During the year, the forecast combines the “as-is-numbers” for past periods with the existing budget for the remaining year. We then generate an estimate (forecast) of what we’ll end the year with. In this situation, it’s advisable to detach yourself from the mathematical calculations and turn on the thinking for the following reasons:

• We know from past research that manual forecasts are, in general, extremely unreliable. Most of them are off by 5% or more, even for short timeframes of one or a few months. Considering many organizations are publicly listed, it’s understandable that analysts sometimes shake their heads.

• Budgeting is a fairly unreliable process since it’s done at a time when limited information is available, and the defined desired results are often wishful thinking more than supported by a full set of necessary measures. There are significant interests and biases in play, and we need to accept it will never be precise.

• Most of us do not live in a plan economy. So why would we expect to fulfill a plan and hit a number defined, as mentioned, under high uncertainty and limited transparency? The argument of capital markets expecting you to perform as in a plan economy is acceptable, but I would argue positive surprises are even better.

During the year when the forecast shows a deviation from budget, instead of only trying to hit the plan numbers and finding ways to get there, it’s advisable to also analyze if trends, markets, technologies, people, and regulations have changed, making the budget not fully achievable.

A key point here is that companies often cannot cope with the speed of information and don't have enough resources to manage all data. Technology support can be a game-changer for many, and the accuracy of forecasting can be uplifted. Remember: technology cannot solve issues that are intrinsic to the process, for example, that we simply do not know what will happen in future or that people often act based on biases.

Measurement should be constantly reviewed in terms of composition of metrics, and the findings should lead to course correcting when necessary.

Based on the previously mentioned report from SSON Research & Analytics, another issue is that most companies claim their technology cannot compare forecasts to actuals. They also claim it cannot pool cash and cannot calculate cost of borrowing. The above discussion remains theoretical without the means to measure and analyze.

That leads us to the final point in discussing a good set of KPIs.

In today's market situation, we must include a view on resources, people, skills, and capabilities. We need a special lens to analyze our work in the OTC and cash space in terms of providing stable operations now and in the future. Technology and talent are two sides of the same coin—we need both. Technology availability seems abundant, and we should select and implement the fitting technology solutions. Finding and utilizing the required skills can be a much tougher task (in certain markets).

These so-called soft metrics are only soft because of a missing understanding for the impact and significance of them. This can be changed by data to prove the linkages and outcomes, at least to some extent. Metrics on training, coaching, and career progressions (from job rotation to enterprise-wide transfers) are widely agreed upon metrics to start with. In OTC, for example, 88% of practitioners (in GBS and outside) have access to trainings. Why the remaining 12% don’t is something only those employers can explain.

The information we’ve discussed can be summarized as follows:

• KPI models are not only useful, but essential, especially in today's volatile uncertain world.

• Selecting the right KPIs is not a trivial task. It requires a good understanding of company targets, situational aspects, and organizational capabilities.

• Anything forward-looking is intrinsically uncertain, but our predictive capabilities can be improved significantly with a combination of technology, big data, and the right capabilities.