Process mining value: What it means for your business

"Speed is everything. It is the indispensable ingredient in competitiveness." ~ Jack Welch

Speed in business is all about process efficiency and optimisation. Business process management (BPM) uses process mining to analyse operational processes. Process mining uses event data to create process models that help pinpoint bottlenecks, highlight deviations from the norm, provide predictions, and make recommendations. Before process mining, improvements to processes were based on human opinion, which is highly subjective and biased, and as a result, the process outcome, too, was skewed.

Process mining encompasses several distinct stages, including event data extraction, process discovery, conformance checking, and process enhancement. Event data is the foundation of process mining. It tracks the execution of business processes and is used to model each process. The event data requires a case id to identify the process instance, activity labels that describe the operation, and run timestamps. Event data is created out of the operations IT system log. The level of detail in the event log depends on the process analysis that is being done. The data model of the event log should be finalised after a thorough study as the process analysis outcome depends on it. 

Process discovery uses the selected event data to create a process model. Process discovery algorithms read the event log data and build a model that best represents the process behaviour. Any new sequence of activities coming from the event log either fits the process model in its current state or causes some adjustment in the model discovered so far. When the algorithm reads through the entire event log, it has covered all the various behavioural instances of the system. It then presents the final process model based on the system behaviour as depicted by the event log. It also explains the optimal process path.

Conformance-checking algorithms compare the process execution instances in the event log against the process model obtained via discovery to reveal the alignment between the model and reality. The algorithm then reports the details regarding the conformances and violations when checked against the process model. Conformance-checking is used to inspect compliance with the defined process boundaries, evaluate process discovery outcomes, and review adherence to the process specifications.

Process enhancement extends and improves the theoretical reference model with discovered information. The extension integrates details about performance and resources into the model to fine-tune the specifications. Model improvement is about highlighting and altering the specifications that lead to unwanted behaviour. Similarly, executions that enhance performance are assimilated into the model.

AI technologies like NLP, machine learning, and computer vision bring automation and predictive analysis to process mining. With AI, there is continuous, real-time acquisition of different event data, including video, which is analysed and input to algorithms for process discovery. With AI, conformance-checking of video procedural data can be done against the process model. AI-based predictive analysis facilitates forecasting business processes' behaviour, performance, and outcomes during runtime. It allows the identification of problems before they occur and the re-allocation of resources for optimal performance.

Process mining creates an objective, data-based model of an optimal baseline process. It helps organisations to comprehensively understand their operations and identify process improvement and automation opportunities. It provides data-driven process insights to make a persuasive case for transformation initiatives. The process visualisation created by intelligent process mining tools gives a clear picture of the inefficiencies, bottlenecks, variations, and repetitive tasks ideal for automation, allowing the organisation to execute targeted corrective action. After implementing the intervention, whether it is the deployment of RPA bots or modifications to the process, the impact of the intervention can be gauged using process mining. Process mining is used for regulatory compliance by monitoring policy adherence and identifying infringement. It helps determine the root cause of the violation and helps work out the optimal intervention. It allows for real-time monitoring of processes to track KPIs. Predictive analytics with process mining can estimate the time and outcomes for ongoing operations. It can be used to predict cost and risk and generate recommendations. Process mining is the enabler for the creation of the digital twin of an organisation which is a virtual reflection of assets, services, or processes and is used to simulate different scenarios and analyse hypothetical scenarios. The virtual twin helps organisations study the impact of introducing any change to the system before finalising and implementing the change.

AI-enabled process mining gives organisations the data-driven, in-depth insights that are the key to running their business functions optimally. If implemented, this optimisation will let businesses be more than the sum of their parts, enabling their ascent to market leadership.

*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.

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