- Explore your, and your business partners, event data to discover financial, organisational and process improvement opportunities.
- Become operational process intelligent using the historic, and scored (*) process execution, as reference base to match actual execution for correction ‘in-flight’.
(*) Scored for wanted or not wanted execution
- Support decisions using predictions on the outcome (cost or duration). Create the basis for automatic action taking (based on Prescriptive analytics)
- Operationaly and Financialy Monitor the directional effect (+/-) of the actions/decisions taken.
Where to start? What business objectives to address? Which operational insights to exploit while monitoring the results of the actions while on ‘in-flight’?
The data produced by any transaction system can be turned into vizual process and analytical business insights using process mining in combination with data mining. Being able to manage efficiently both internal and external (client) processes, together with monitoring the directional impact of the decisions taken, help realize the targetted objectives more effectively.
The future starts today. Solutions in answering the needs of ever more demanding customers while facing the increased competition are available and affordable. The solutions offering is called: operational service process intelligent solutions.
Since 2013 we are co-leading the trend to fuse new technologies to create “operational systems based on data driven process analytic insight”. We are advancing our approach and supporting technologies to provide service process execution analytic and monitoring solutions.
The possibilities to exploit are multiple.
- Cost Monitoring: cost reducing where possible, incurring additional cost when beneficial (determine the real and actual processing cost of a Order to Cash, Procure to Pay, Insurance Claim, Intervention, … process). Assess the process execution variations for any of the stakeholders not at the least the clients. Learn from the past, Apply on the current, Steer for the future.
- Assign the best possible resource to the job or activity. Assign the best placed (compentence, experience, budgetairy advantageous) available individual or team. (not only available).
- Use the process and data analytical insights (machine learning, artifical intelligence,…) and embrace foresight (predictive and prescriptive analytics) for guiding and training.
We are able to realise this by embedding automatic process execution discovery algorithms for performance optimisation and compliance management operational solutions.
By blending knowledge & expertise in data management & analytical software, in process & data analytics, the following application area’s come into reach:
- Process Performance Monitoring: Situation: Service Level Agreements can not be reached. Time to deliver is below target. Solution: Weekly or daily event data gathering, preprocessing for analytics, data enrichment are the starting efforts. Conduct Process and Data analytics for ‘Insight’. Facilitate Interactive process execution insight workshops, using factual analytic data extracting trends and prepare predicties. Surface the factual best/worst process execution practices: Identify value leackage like process bottleneck(s), ping-pong behavior or excessive waiting time between activities. Deduct opportunities for improvement with anticipation of the timing of impact. The results becomming real.
The effect of the corrective actions (decisions) can be observed using comparative analytics with a reference data store (learning data base). The actionable results are being provided using cockpits with actual measurements, reference measurements and alarms. The decisions are tracked for impact.
- Process Conformance Monitoring: Situation: Measurement of key activities for them always being: on time, all the time, everywhere and thouroughly performed. Compliance rules do not permit some activities being executed by the same person. Solution: Process Mining with Event-Chain analytics. Potentialy combined with Data and Text-Mining. Together they are effective techniques to obtain factual insights for occurance and the gravity of the defects. The best possible root-cause analyses tooling?
- User adoption monitoring: Situation: People returning to old habits/procedures. Solution: After gaining insight in the real process execution, possible flaws are identified and quantified for their impact. The execution of the changes to the process execution are monitored using work hand-over flows between the resources involved in the process execution. Tracking the effect of the changes based on more discipline in data entry (improving data quality), additional resources or better co-ordination in the organisation (workload balancing based on skill versus availability).
- Intervention Management (Security Services, Repair, Emergency)
- Claims Processing (Insurance Industry)
- Business Process Outsourcing Monitoring (IT Outsourcing and BPO service providers)
- Client Service Monitoring (Telecom Industry, Utility Companies (Electricity, Gas), Banking)
Client centric organisations focus on service excellence with compelling products. Client satisfaction is a very important objective aiming at loyalty and recommendations as a targetted result. Fact based process execution information is the way forward. Automatic process execution discovery starting from event log data is what we do. We analyse and visualise client service process execution with focus the vital client service ‘touch’ points. Process Mining with Event-Chain Analytics and operational process monitoring support demonstrated their positiv impact on Business improvement initiatives (processing cost, external cost in combination with process execution based user satisfaction measurement and anticipation)
Business questions looking for answers.
- Can we predict the number of medical examinations in the ‘pre-opening’ phase of a claim?
- At which point in time can we anticipate the duration and complexity (number of handlings) of a claim ?
- Will it be possible to prescribe the assignment, or re-assignment, off a particular case to a better qualified claim handling officer?
Determine mathematically the likelyhoods of being able to answer ‘THE’ questions.
Are the data available for these analysis? Is it economically feasible to collect the data related to the volume, variety, velocity of the data? How do we deal with data security and privacy? Is the data quality OK? (completeness, consistency) Which data would make a difference? If ‘key’ data are missing, can we collect them (acquire, extract, transform) from open data sources? Could the data be produced by adding an extra process step, by adding extra data fields in the claim handling recording and claim process management?
The innovative approach: Proof of Possibilities Project with process discovery and analytics.
Blending skills, experience and data in-a-single-environment bringing incremental insights and ultimate monitorng.
- climb the learning curve which and how much value is hidden in your data. Use data driven process discovery techniques finding answers to the ‘dream’ questions.
- get proficient in the competence area’s like Data Pre-Processing (enriching), Process Discovery, Process-Data-Text (P-D-T) Analytics, Process Conformance analytics and Performance analytics.
- use the techniques for process flow extraction (process mining), people collaboration & system interaction (resource mining), prescriptive and predictive modeling (process, data and text mining) of the available ‘event’ data.
Asses the possibilities for business improvement(s).
Determine the likelyhood of an answer to a ‘key’ business question. (below is an extract and example checklist)
With the current data availability and quality, the accuracy of predicting the number of medical examinations in the pre-opening phase is 78%.
Follow-on effort: With a consistent (more automatic) tagging of the health-issue category, the accuracy of the prediction can be progressively improved over the duration of a claim.
Business impact: Less external exams will lower the cash-out, improve overall handling time (benefits the available people capacity to handle claims) and helps with the overall client satisfaction (less unneeded exams, faster communication, timely payment of disability compensation)
With the current data availability and quality, with the assessed modelling technique , the assignment of a case to the most qualified claim officer, or team, is 83%.
Business Impact: This parameter allows to organise (recalibrate) the case distribution to a mixed team with more junior officers, freeing up time from more senior officers to be dedicated to those cases that will profit from their judgement (experience).
Diligent assignment of officers will help with lowering the impact (better manage) of lenghty, ongoing, even ‘fraudulent’ cases directly and indirectly resulting in the best possible user satisfaction.
The launch of Rialto Process is a fact.
On top of the Rialto foundation platform now a Rialto extensions exist for Process Mining.
Rialto Process is an embedded data and process analytics platform supporting a mix of tasks (acquisition, filtering, data mining, text mining, data and process visualization, …) and modeling (predictive, prescriptive, sentiment, …). Our vision from analytics to operations becomes reality.
Connect to and blend a range of very big, big, smart or small datasources and ultimately deliver an analytics based monitoring app that can be encapsulated in your companies operations monitoring and management systems. Industrial Analytics enabled
The process analytics modules are:
- Process Data Management with Acquisition (OpenXES 2.0 –eXtensible Event Stream) , Enrichment (Anonymisation, Add Start and End events, Missing Value addition), Blending of separate data sets based in ‘connect’ fields, Time and Rule Bases Filtering/Trimming, Complex Rule based Tagging, Data Quality Analyses, Export,…
- Activity based analyses/analytics (Process Mining) with Event Log Play-In, Process Statistics and Process Flow Visual Analyses, Performance-Conformance-Process Variant Analytics, Time Drift and Concept Drift Analyses, …
- Resource based analyses/Analytics (Social Mining) with Event Log Play-In with automatic discovery and visualisation of the work-handover process model, Work Hand-Over Variations with impact statistics tables and interactive charts
- Blended Analytics (Examples)
Process and Data analytics. Use of Machine learning modules (decision tree) to determine (predict) the resource that will end the case in the shortest possible time.
Process and Text Analytics. Text mining of a filelist to extract the last document change date or e-mail correspondance exchange file with the extraction of the from->to and return mail datestamp, to determine start and end time of a particular activity.
The full feature – function list exeura.eu