Internet of Things Microservices
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The document discusses the integration of IoT micro-services within enterprises, highlighting the process of acquiring and analyzing data from connected devices to drive actionable insights. It outlines the various steps and components involved in turning IoT data into actionable tasks across different functional areas such as customer care, sales, and maintenance. Additionally, it emphasizes the importance of predictive modeling and data lakes in improving operational efficiency and enhancing customer satisfaction.
Internet of Things Microservices
- 1. Copyright © Capgemini 2013. All Rights Reserved 1CWIN München IoT Capone.pptx IoT Micro-Services CWIN ´16 München, Telford, Köln Prof. Dr. Michael J. Capone
- 2. CSX - Data Actionability Performance and usage data is acquired and transmitted every x seconds. Data from many connected devices is aggregated and stored When the data indicates an issue that can cause a problem or opportunity, a case is created and a resolution or „Next Best Action“ is selected An actor takes action: 1. Customer Care provides the operator tips and instructions 2. Sales & Marketing proactively sends the customer offers and orders 3. Technical Service is dispatched to perform maintenance 4. System sends messages to machine or person We turn IoT data into ACTION 1 2 4 5 Data is analyzed in real-time to identify issues or opportunities. 3 Control Tower Connected Service Excellence (CSX) „2 Steps Ahead“
- 3. 5-A Model for Data Actionability Copyright © Capgemini 2013. All Rights Reserved 3CWIN München IoT Capone.pptx Results often in a use case spceific silo Functional Layer Description Technology Action (A5) A task is automatically created for sales, contact center, service, or system. ERP Integration, Contact Center, Field Service Management, Mobile Apps Assignment (A4) A recommendation or „Next Best Action“ is proposed. Knowledge Base, Expert System, Product Information System Analysis (A3) Data is viualized, tabulated, and analyzed. Insights are produced. Dashboards, Reporting, Analytic Tools Aggregation (A2) Data from multiple, different sources are combined and stored. Big Data, Data Lake, In-Memory Database Acquisition (A1) Data is created and transmitted. Sensors, Gateway, Connectivity
- 4. IoT-Cloud State Engine 2. Machine Data 5. Alert & Task Technician ServiceCloud Manager Predictive Model DB R BDLaaS 4. Case 1. Input Stream PLC Data Machine Data Apama Complex Event Processor 3. Descriptive Stream EBS WebMethods Input Orchestrations 2. Complex Stream Streaming Manual Case Management Q: How do I identify bad quality during or before the process? A: PQM Aside from the technician, who else can benefit from preditive quality? What other processes can machine data trigger for which actors?
- 5. IoT-Cloud State Engine 2. Machine Data 9. Alert & Task Knowledge Technician 7. Model + Error Code 8. Article ServiceCloud Manager Assets 6. Model 5. Location + Error Code Predictive Model DB R BDLaaS 4. Case 1. Input Stream PLC Data Machine Data Apama Complex Event Processor 3. Descriptive Stream EBS WebMethods Input Orchestrations 2. Complex Stream Streaming Manual Case Management Q: How do I prevent unplanned downtime? A: PMM Aside from the technician, who else can benefit from preditive maintenace? What other processes can machine data trigger for which actors?
- 6. IoT-Cloud State Engine 2. Machine Data 9. Alert & Task Knowledge Technician 7. Model + Error Code 8. Article ServiceCloud Assets 6. Model 5. Location + Error Code Predictive Model DB R BDLaaS 4. Case 1. Input Stream PLC Data Machine Data Apama Complex Event Processor 3. Descriptive Stream EBS WebMethods Input Orchestrations 2. Complex Stream Streaming Manual Case Management Q: How do I ensure service is performed correctly? A: PMM w/ VR Aside from the technician, who else can benefit from preditive maintenace? What other processes can machine data trigger for which actors?
- 7. Q: How do I know if my patient is doing well on the new treatment plan? A: eHealth Copyright © Capgemini 2013. All Rights Reserved 7CWIN München IoT Capone.pptx • Blood Pressure • Heart Rhythm • Pulse • Blood Sugar • EKG • BMI • Weight • Oxygen Saturation • Movement • Glucose • Temperature • Prescription Adherence Features Best-of-Speed Platform as a Service Streaming Analytics Micro-Services Any Input Motivation Operational Efficiency Customer Satisfaction New Revenue Model Benefits Lower Costs Shorter Treatment Period Lower Side Effects Higher Patient Satisfaction Aside from the physician, who else can benefit from patient monitoring? What other processes can vital signs and adherence data trigger for which actors?
- 8. Q: How do I satisfy the info needs of partners, customers and users? A: Micro-Services Copyright © Capgemini 2013. All Rights Reserved 8CWIN München IoT Capone.pptx Infrastructure Services Deliverables 1. Database Hosting 2. Predictive Modelling 3. Streaming Anayltics 4. Dashboards & Reporting Customer Sales Marketing R&DService Engineering Services Competencies 1. Sensor Design & Prototyping 2. Embedded Software 3. Connectivity 4. Gateways, Hubs, Bridges 5. Testing Application Services Deliverables 1. App Factory (Development) 2. Rules Engine 3. CRM & Contact Center 4. FieldService There are many users of IoT data. These users have different requirements, they use different hardware, have different logic, and want to trigger departmental specific processes. An enterprise-ready data lake consolidates the data from all „smart appliances“ making it available to multiple users and enabling correlative analytics, i.e. an intelligent home. Any device can be made „smart“ by retrofitting or embedding sensors and software. Generating data is only the first step. The data has to be validated and transmitted reliably and securely. Actors Drivers Fleet Managers Dispatchers Trainers Service Stations Repair Centers Manufacturer Motivation Operational Efficiency Customer Satisfaction New Revenue Model Benefits Customer Loyalty User Acceptance New Revenue Models Single Point-of-Truth Lower Operating Costs