Awareness Session. Robotics. 18 April 2018

Transcription

1 Awareness Session Robotics 18 April 2018

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3 Three activity spheres within the current business function 3

4 Commodore 64 4

5 Productivity yy Awareness session Robotics Cognitive automation will enable next wave of automation 4 5 year Cloud 5 Cognitive systems year Mechanic systems 2 35 year Information systems 3 15 year Internet systems Time 5

6 Advancements in Process Automation Awareness session Robotics Two dominant routes leading to Cognitive Robotics Rules mapping Sreen scraping Workflow Natural Natural Language language Processing processing Big Data Analytics Big Data Analytics Artificial IntelligenceArtificial Intelligence Basic Process Automation Autonomic/Cognitive Robotics Adaptive Adaptive alteration alteration Large-scale Large-scale Processing Processing Machine Machine Learning Learning Processing of Unstructured Data and Base Knowledge Human labour Robotics (Enhanced Process Automation) Advancements in Machine Intelligence 6

7 The influence of Robotics on the business function Business Partner 7

8 Shifting human workforce to value-adding activities Business Function 2016 Business Partner Business Function 2020 Functional Business Business Partner Transactional Business Functional Business Transactional Business Cognitive Robotics Process Robotics 8

9 Point-to-Point Generic Awareness session Robotics Robotics solution overview Cognitive RPA SDA ERA PtP Process robotics Enhanced & Machine Learning Cognitive robotics 9

10 Process Robotics key ingredients Process mapping Object studio Work Queues Control room Visual representation of tasks and steps performed by robot A library of reusable objects that can be used when performing processes The work queue holds the backlog for each of your defined processes Manage your robots from the control room Map your processes using level 4 detailed working instructions Highlights specific elements of SAP/Oracle/Websites/ Excel Keep track of your backlog Decide which processes to run Most tooling allows the process owner to monitor the processes (e.g. how often does a specific exception occur?) Process mapping speed increases as more objects have been defined Decide which processes to run based on the size of the work queue Assign processes manually, or automatically (when the work queue >100 items, every night at 03.00h or when a file is saved at a specific folder location) 10

11 Demo RPA BleuPrism 11

12 Demo process example Open Mailbox & Read Structured Invoice Request Open SAP Use data from to make Intercompany booking Generate invoice Store outcomes & Send to requestor 12

13 Benefits of Robotics Accuracy Costs Benefits Efficiency Quality Governance Control Compliance Process Improvement 13

14 Demo example of IPsoft Amelia 6 Demo 1 Demo 2 How does IPsoft Amelia work? Implementation of IPsoft Amelia at public sector company (Enfield Council London) 14

15 A typical Process Robotics journey Understand the impact of Finance Robotics on the business Raise awareness Usually C-level audience Understand business target areas Identify potential (E2E) target processes for Finance Robotics Usually process owner audience Perform vendor selection process (including longlist, short-list and demonstrations) Implement Finance Robotics for the happy flow of the target process(es) to demonstrate the product feasibility Use design thinking and rapid prototyping to finalize detailed solution architecture Integrate with enterprise systems Configure and teach software robots Test and deploy solution Build the Finance Robotics solution for each use case Enable and train users in Finance Robotics tool for adaptation and implementation purposes Run Finance Robotics solution once steady-state is achieved Roll-out to other identified target processes Awareness Workshop Process Workshop Proof of Concept Pilot Implementation & Further Roll-out 2 hours 1 week 2 weeks per POC process Depending on scope Depending on scope 15

16 Robotics implementation framework 6 People and Competences 1 Organisational Structure and Governance 2 Process delivery and deployment 5 Performance and Risk Management Robotics 4 Vendor Management 3 Technology 16

17 Evaluatie van doelprocessen voor Robotics Proces Robotics criteria Proces Robotics kan werk sneller en efficiënter uitvoeren dan de huidige handmatige methode. Als we processen evalueren voor Proces Robotics, dan identificeren we transactionele activiteiten die voldoen aan de volgende criteria: Criteria Rule Based Transactioneel Hoog volume Gestructureerde Data Herhaalbaar Geavanceerde / Cognitieve Robotics criteria Geavanceerde / Cognitieve Robotics kan gebruikt worden om taken die gespecialiseerde menselijke kennis nodig hebben te transformeren. Als we processen evalueren voor Geavanceerde / Cognitieve Robotics, dan identificeren we activiteiten die voldoen aan de volgende criteria: Criteria Gedragspatronen Meerdere Databronnen Ongestructureerde Data Complexe Processen Grote Data Sets 17

18 Earlier experience with project implementations Our findings from previous Finance Robotics project implementations can be clustered into five themes: 1 Finance Robotics implementation is not an IT project 2 Detailed working instructions and specific training materials are required 3 After cost savings, quality improvement is a key advantage of implementing Finance Robotics 4 Support functions like IT, HR, Legal and Tax must be involved with the Finance Robotics Implementation 5 Change management is a critical success factor 18

19 How tangible is Robotics for you? 19

20 Awareness Impact of disruptive session technologies Robotics The Digital Finance Stack requires a selection of solutions Cognitive Natural language processing Use of behaviour patterns for cognitive hypothesis generation / advanced predictive analytics Support the best possible business decision and transaction Machine learning Use of algorithms that iteratively learn from structured and unstructured data Generation of data-driven predictions or decisions Robotic process automation ibpms Use of structured data within well defined parameters Use of technologies like screen scraping, rules engine and process orchestration Autonomous completion of transactional and rule-based tasks Digital analytics and delivery Data management & data mining System backbone: Cloud ERP and EPM Management of flexible data structures coming from heterogeneous data sources without a predetermined scheme Process mining technology provides insights into processes System backbone is the basis of the digital finance stack Structured and unstructured data with an increasing variety and complexity 20

21 Awareness What is Appian? session Robotics KPMG Digital Chain Adaptive alteration Cognitive Natural lang. process. Artificial intelligence Use of behaviour patterns for cognitive hypothesis generation / advanced predictive analytics Support the best possible business decision and transaction Machine Learning Use of algorithms that iteratively learn from structured and unstructured data Generation of data-driven predictions or decisions Learning Algorithms BPM RPA Use of structured data within well defined parameters Autonomous completion of transactional and rule-based tasks Process Orchestration Rules engine Screen scraping Big Data analytics Management of flexible data structures coming from heterogeneous data sources without a predetermined scheme Data ingestion and integration Advanced Search Process mining ERP / DWH Documents and s System backbone Machines Devices / IoT Social Media / Web Internal and external data sources Structured and unstructured data with an increasing variety and complexity 21

22 Process Value Awareness session Robotics Robotics focuses on the long-tail of automation IT Resourced and Delivered Enterprise IT Business Resourced Procedural Automation Previously Uneconomic Projects Short Term Regulatory Requirements Competitive or Rapid Response Business Ownership System Coverage 22

23 Thank you Contact Joris Juttmann Robotics & Process Excellence Director KPMG Advisory N.V. Tel: Mob: juttmann.joris@kpmg.nl