5 Essential PPI’s To Track With Process Mining

A Process Performance Indicator (PPI), a type of Key Performance Indicator (KPI), measures the success of each process within an organization. It provides valuable insight into each event that occurs and explains the reasoning for the success or failure of each.

Through Process Mining, you can track these 5 essential PPI’s…

1: Effectiveness measures the actual results against the expected results. There is often a desired outcome that businesses aspire to reach through their product or service. The question is: Was the process effective enough to produce that outcome? If not, where did everything go wrong? Process mining can help uncover the answer to that question. With a look at every detail of the events that led up to that outcome, you can find exactly what went wrong.

2: Efficiency evaluates the actual results against the resources utilized to complete those results. It is important to understand how much resources are being used per event within a process to achieve a desired outcome.  

  • Why are some processes utilizing more resources than others?
  • When are these inefficiencies occurring?
  • Why are they occurring?
  • Who is working when these occur?

These are some questions that you may be pondering when taking a look at efficiency within your processes. With process mining you can uncover how much waste is within each process and the reason for such occurrences.

3: Productivity is the ratio between the output produced per resource inputted. Essentially, productivity is how efficiently and effectively your workers are utilizing their time and resources.

  • What day is more work being completed?
  • Who is working then?
  • What shift is being worked?
  • Why is productivity better on certain days compared to others?

Process mining can help pinpoint these inefficiencies and help you in understanding why certain days, times, and shifts are producing more or less output than others. It helps you understand where your productivity is greater or where you need to improve your processes for this to increase.

4: Quality is the relationship between output and output that can actually be used. There are going to be defects, deficiencies, and variations that occur throughout the production process and when a product displays one of these variations, the quality is reduced. Therefore, it is important to near-perfect the production process of quality products. Bad quality means bad revenue and process mining can help uncover these faulty processes before they get worse.

5: Capacity is the amount that can be produced within a certain amount of time. Often times, businesses expect the greatest quantity of quality products to be produced within the shortest amount of time. But what happens when that doesn’t occur? You often start to question others why this may be happening. The answer may be simple or complex but the only way to find out is to look at the end-to-end processes within your organization. Process mining can help you find a way to achieve the greatest sustainable output rate within your business.

In conclusion, these five process performance indicators provide information not only as to how your business is performing but why specific events or processes are resulting in such success or failure. Process mining uncovers defects, deficiencies, variations, deviations, and inefficiencies within your businesses processes. There are many process performance indicators that you can track for your company, but these are of the most important for your success. It gives you the essential information you need to make changes or improvements for your organization going forward.  

About the author

An aspiring Business Analyst, Emily Atkinson is currently a student at Widener University in Chester, Pennsylvania studying Business Analytics with a minor in Operations Management. She is also in enrolled in the SAP Student Recognition Award Certificate Program. Emily plans to graduate in the Spring of 2020 and is seeking a career in the Healthcare industry with a special interest in data and data science.

July 18, 2019