Aug-2019

Digitalisation is transforming KPI setting

Digitalisation means turning numbers on a dashboard into profit on your income statement.

Duncan Micklem
KBC (A Yokogawa Company)

Article Summary

Calculating and reporting key performance indicators (KPI) is a routine practice in the refining and chemicals industries. It is key to helping decision makers act, and to helping managers oversee performance. Digitalisation tools like Business Intelligence (BI) and dashboards are helping to increase the visibility and timeliness of KPIs, break down organisational silos, and drive consistency across organisations. However, this is not enough in itself.

The challenges of poor underlying data, difficulties of setting targets and the interactions between conflicting KPIs mean that, all too often, KPIs are not effective in delivering any true improvement in performance. At the same time, digitalisation is changing organisational responsibilities, and predictive analytics are changing information from being about the past to being about the future.

In this article, we reprise the basic principles and objectives of KPIs with regard to achieving operational excellence, discuss some of the root causes of ineffective KPIs, and then explain how various digitalisation technologies and approaches can transform the effectiveness of KPIs in your business.

Purpose and principles of KPIs
KPIs are an important ingredient in achieving operational excellence. KBC believes the two facets of operational excellence are:
• Making better decisions, faster
• Perfect execution of those decisions, every time.

Within the first, leading KPIs provide timely decision support information to support people to make the right decisions in a timely way.

In the second area, lagging KPIs measure the effectiveness of the entire process (both decision making and execution) to allow fine tuning or modification of the work processes, physical processes or organisation to take place.

Given that leading KPIs are intended to support decision making, it is essential to understand how people make decisions. In particular, how people make decisions in environments where there is limited time and limited information, such as most refining and chemicals facilities.
In this type of environment, decisions are typically made via recognition primed decision (RPD) making, which is fast, natural and intuitive, and effective when deployed by experts. The typical process is as follows:

1. Situational awareness
• Based on visual information about the situation, the expert makes a mental ‘pattern match’ to determine what is happening.
• The expert interprets the meaning of the event, and then makes a mental simulation of what will happen next.

2. Identify options
• Determine the root cause of the issue.
• Search for solutions, typically from experience.
• As solutions are found, check if they will suffice to solve the problem.

3. Implement the first ‘sufficient’ solution, then stop searching for more solutions.
Leading KPIs play an important role in this process by providing a top level of situational awareness, by flagging up and highlighting issues, and potentially providing a simple overview of all the possible issues that need to be considered.

KPIs can help provide situational awareness to less expert people, by explicitly identifying the most important factors rather than hoping it will be spotted amongst the weeds of many data points.

Current simple digitalisation technologies such as data integration and dashboards greatly enhance the power of situational awareness KPIs. Data visualisation such as colour coding changes the KPI from a number on a screen somewhere to something highlighting action may be required.

Data aggregation, to bring together data from multiple sources, can allow KPIs to be viewed in comparison to each other, and makes decision makers aware of situations outside their direct silo. This can make decision making more holistic without needing meetings or calls to exchange information.

KPIs can be applied on a number of different timescales and at all levels in an organisation. Some of the decision timescales are illustrated in Figure 1.

So, for instance, top level business financial results (lagging KPI) are reported quarterly or annually, whereas operational decision making takes place on a timescale of days, hours or minutes.

One of the ways digitalisation adds value to an organisation is compressing the decision timescales, thus reducing losses due to uncertainty. This is key to understanding how digitalisation changes KPI management, as will be discussed later.
 
KPI pitfalls
Having established the theory behind KPIs, we will now examine some of the things that make KPIs ineffective.
 
Rubbish in, rubbish out
KPIs and the situational awareness they bring are only as good as the data being used to calculate them. It is commonplace for experienced operators to ignore or reject data when they ‘know’ (either rightly or wrongly) a certain meter might be wrong. The entire system is dependent on the inputs being correct, and being able to detect and highlight errors in the data.