If you’ve ever encountered a process at work that made you think, “There has to be a better way to do this…” you know firsthand how fallible companies are to inefficiencies large and small. After all, with so many moving parts in play at any given time, some practices are bound to become outdated or obsolete. The problems start when less-than-optimal processes continue to fly under the radar, wasting money, time and effort for as long as they’re left unchecked.
This is especially true in repetitive environments — like on a factory floor, for instance. Even a tiny inefficiency repeated hundreds or thousands of times per day tends to multiply into a very costly situation over time.
This is where companies stand to benefit immensely from their ability to identify inefficiencies, understand what’s causing them and take action to optimize them as soon as possible.
This is known as process mining.
Here’s more on what process mining entails and how it can help businesses reduce waste.
What Is Process Mining?
As one expert notes for Towards Data Science, process mining lives at the intersection of data mining and business process management. That is, it uses data-driven insights to:
- Clarify existing processes
- Identify deviations from the ideal version of a process
- Come up with possible optimizations for processes
Digitization has enabled companies to collect and store more information pertaining to every step of their processes, which is the jumping-off point for analyzing their efficacies and coming up with ways to make them better.
As Gartner outlines, process mining ultimately aims to improve overall business outcomes by auditing the “efficiency, effectiveness and value” of existing practices. Only when business leaders are able to understand specific facets of current performance, based on data, can they make prudent decisions on where to focus improvement efforts — and which improvements to prioritize.
Artificial intelligence — specifically AI analytics — can help companies with the data mining aspect of process mining, as this tech is capable of uncovering data insights related to operations and performance. Specifically, AI algorithms found in advanced analytics programs uncover anomalies and process deviations, as well as identify key metrics necessary to measure efficiency and set realistic benchmark goals.
Here’s an example from Harvard Business Review: AI algorithms could alert a financial services company that its approval process lags every time a new customer has to wait on a credit check — which would allow them to take steps to reduce this specific source of friction in order to expedite the process.
What Is Robotic Process Automation (RPA)?
One potential response to process mining is determining when it makes financial sense to automate a procedure based on data, especially manual tasks found to be sucking up an inordinate amount of employees’ time and/or energy. This is known as robotic process automation (RPA).
Case in point: An IT department requiring employees to manually evaluate, categorize and assign tickets can benefit from automating this process. Continually applying process mining over time will help ensure the automated system improves alongside the needs of the company — like accounting for new categories and employees as they emerge. The goal of automation in this case would be to free up IT workers to focus on strategic projects rather than getting bogged down in a rolling backlog of tickets requiring manual work. It could also cut down on the instances of human error in dealing with said tickets, as well as reduce the chances one will slip through the cracks.
By investing in process mining tools to evaluate current processes, understand deviations and implement data-driven solutions, businesses can continually reduce wasted time and money.