Process Mining has the potential to save tons of dollars by reducing process inefficiencies, detecting process non-compliances and breaking separated company silos by analysing end-to-end business processes. As a result, multiple process mining software vendors were established and achieved significant growth. Nevertheless, some companies experienced a high total cost of ownership (TCO) as well as time-consuming implementation phases.
In the following, I am going to give you an overview of the possibilities Alteryx provides in the area of Process Mining without having the need for an extensive implementation phase.
The Process Mining Manifesto defines process mining as follows.
“Process mining is a relatively young research discipline that sits between computational intelligence and data mining on the one hand, and process modeling and analysis on the other hand. The idea of process mining is to discover, monitor and improve real processes [...] by extracting knowledge from event logs readily available in today’s (information) systems [...].
Process mining includes (automated) process discovery (i.e., extracting process models from an event log), conformance checking (i.e., monitoring deviations by comparing model and log), social network/organizational mining, automated construction of simulation models, model extension, model repair, case prediction, and history-based recommendations.”
(van der Aalst, W. et al., Process Mining Manifesto, 2012, p. 172)
Therefore, process mining serves as a bridge between process science and data science as shown in Figure 1.
Even though process mining is such a young research discipline, it can possess a significant success already:
“Remarkable success stories have been reported on the applicability of process mining based on event logs from real-life workflow management/information systems. In recent years, the scope of process mining broadened from the analysis of workflow logs to the analysis of event data recorded by physical devices, web services, ERP systems, and transportation systems. [...] The insights obtained through process mining are used to optimize business processes and improve customer service.”
(Bose, Mans, & van der Aalst, Wanna improve process mining results?, 2013, pp. 1–2)
Before outlining the core capabilities of Process Mining by Alteryx, I want to highlight the task of data collection and preparation. Data collection and quality (e.g. timestamps) are the Achilles' heel of Process Mining, esp. if the data comes from systems, for which no standard connectors and data knowledge exists. According to this report, on average, most analytics projects spend only 20 percent of their time on actual data analysis and 80 percent of their time on finding, cleaning and blending huge amounts of data, which limits the final optimization potential drastically.
Alteryx helps you to reduce the time you need to find and prepare your data significantly: Alteryx Connect for the fast identification of relevant data sources and data collection; and Alteryx Designer for accessing multiple data sources, cleansing, blending and preparing the data for analyses & modeling.
Visualize the process flow graphically in Designer by leveraging the Network Analysis tool:
There is also the possibility to read-in multiple process instances/cases. In the following example, two process instances are analysed. Every instance receives an own arrow:
This can quickly look messy if we analyse multiple process instances and differs from classic Process Mining visualizations, which are only labeling the arrows (e.g. instance count) and do not draw the same arrows multiple times.
In order to monitor the performance of your processes, it is recommended to set-up specific process KPIs known as PPIs, or process performance indicators. For example, classic PPIs to monitor are rework rate, throughput time, automation rate, instance/case, and activity count.
In the following, I present some PPI examples based on a Purchase-To-Pay Process:
Process mining is often used to detect automation potential within the processes. As a result, you can focus your automation initiatives (Alteryx workflows, RPA bots etc.) on those areas, which involve high manual efforts:
This is a more sophisticated PPI that identifies incompliant purchasing behaviour or rather purchases, which were conducted without the involvement of the Purchasing department (e.g. a controller buys a new laptop via amazon.com instead of the company catalog). This increases the cost unnecessarily, as we are missing out on contract discounts.
In the following example there are over 2000 cases of Maverick Buying that are causing 442k€ additional purchasing costs:
Alteryx Server accelerates your time to analytical insights and empowers analysts and business users across your organization to make informed, data-driven decisions. Using a scalable platform to deploy and share analytics, you and your team can easily collaborate on business-critical decisions.
With analytic apps, every employee in your company can create dynamic reports on Server in a self-serving fashion. For example, by entering date and product name you can automatically create filtered analyses and review only those PPIs that are relevant for each end-user.
In addition, direct actions can be automatically triggered. For example, updating wrong master data in IT systems or sending emails to the users, who are conducting Maverick Buying:
For collecting and preparing Process Mining data as well as for analysing and reporting process KPIs, the Alteryx Platform is a good fit. In addition to classic process KPIs, Alteryx is a great enabler for advanced use cases in the area of spatial and predictive process analytics. Alteryx is also a good booster for operationalizing the process mining insights, by automatically triggering process actions.
You can review the Process Mining examples (small and full scope) with the attached Alteryx file. In addition, I can recommend the following Community article, which contains a discussion about Process Mining with Alteryx.
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