AlphaFlow BPI is a process mining and intelligence platform. It uses event data from existing systems to reconstruct actual workflows, analyze deviations and bottlenecks, and measure improvements.
Explore how event data becomes a view of actual process performance, with evidence to guide and validate improvements.
BPI / 01
Reconstruct actual process paths from event data. Explore activities, flow volumes and elapsed time to understand how work really runs.
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BPI / 02
Clean, map and join source data in a visual workspace. Build reusable event models with consistent fields and business definitions.
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Compare paths by volume, duration and business attributes. Investigate inefficient or unusual variants and drill down into their cases.
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BPI / 04
Compare the observed process with a reference model. Investigate skipped steps, extra activities and out-of-order execution, then assess the cases affected.
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BPI / 05
Track cycle time, waiting time, rework and other process indicators against defined targets and changing business conditions.
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BPI / 06
Move from an aggregate chart to specific cases and source records to verify the conditions behind a finding.
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BPI / 07
Configure thresholds and monitoring intervals for key indicators. Notify responsible owners when conditions require attention.
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BPI / 08
Create improvement scenarios from process models and operating parameters. Explore resource and routing changes before changing the live process.
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Connect event and business data from databases, files, logs and REST APIs, with full, incremental or scheduled updates. Start with a case ID, activity and timestamp. Clean, map and join records, then add business attributes such as organization, product or amount.
Automatically reconstruct the observed process from event data. Compare paths by volume, elapsed time and business attributes to distinguish common routes from inefficient or unusual variants. Connect events across systems to see the complete business journey.
Analyze waiting time, handling time, rework and overdue steps. Compare nodes, organizational units, roles and business types, then trace an issue to the cases affected. Use these comparisons to identify the conditions associated with poor performance.
Compare observed execution with an EBPA or BPMN reference model to identify missing, additional or out-of-order steps. Complement path analysis with rule checks on individual cases. Candidate rules extracted from policy documents require authorized human confirmation before publication, with conclusions linked to source evidence.
Build dashboards for cycle time, rework, first-pass yield and service-level targets. Combine process indicators with cost, quality and other business measures. Drill down from summary charts to paths, cases and original records so teams can verify the evidence behind a finding.
Set thresholds and monitoring intervals for indicators such as backlog, cycle time and compliance. Notify responsible owners when conditions are met and locate the relevant processes and cases. Use continuously updated data to track whether improvement actions are working.
Simulate changes to resources, activity capacity, routing and arrival volumes using process models and operating parameters. Compare the resulting cycle times and bottlenecks before rollout. Use simulation reports to evaluate options, then validate the selected changes against actual execution data.
Start with a valuable process whose event data is available. Agree goals and definitions with business, process and IT teams, validate models together, and publish dashboards with an action list. Feed findings into EBPA design and BPA execution, then review the results through ongoing monitoring.
PRODUCT QUESTIONS
The minimum event data is a case ID, activity and timestamp. Organization, product, amount and other business attributes support deeper analysis. Teams validate event quality and definitions before interpreting results.
Conformance analysis compares observed execution with a reference process. It identifies missing, additional or out-of-order activities and the cases affected.
BPI combines process discovery, performance analysis, case-level evidence, monitoring and simulation. Teams use the findings to prioritize changes and compare results with subsequent execution data.
Xingfa Aluminium
Materials & manufacturing
Xingfa Aluminium introduced process design, automation, integration robots and process intelligence in a staged manufacturing programme.
Read the implementation story ↗
Delegate process reviews, drafts, guidance and analysis to eight specialized AI teammates. FlowBuddy Core supplies shared capabilities and authorized context, while your team reviews the evidence and confirms the next action.
ExploreBring your team, your systems, and the work you want to improve.