Usage Data Navigator

18 representative use cases showing how to profitably leverage user data
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Planning usage of fertilizer in agriculture

 

🏭 Industry: Agriculture

🗊  Application Scenario: Planning resource allocation (Agriculture)

 

𝌎  Cluster:

    • Function Supercluster: Operations
    • Function Cluster: Performance improvement
    • Cluster Benefit: Optimize production planning

 

Precision farming equipment with an internet connection to data collection about weather conditions and ground sensors can calculate the conditions of plants and calculate the individual farming (2) of each field e.g. using fertilizer on areas that need more nutrition

  sensor-driven decision making, enhanced situational awareness

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Customer referenced product presentation on billboards

 

🏭 Industry: n/a

🗊  Application Scenario: Adjustments on presentation of product range

 

𝌎  Cluster:

    • Function Supercluster: Marketing & Customer loyalty
    • Function Cluster: Performance improvement
    • Cluster Benefit: Tailored marketingplanning

 

"Billboards in Japan peer back at passersby, assessing how they fit consumer profiles, and instantly change displayed (3) messages based on those assessments"

  sensor-driven decision making

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅  
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Behavior based payment in Insurance Sector

 

🏭 Industry: Insurace

🗊  Application Scenario: Usage based pricing

 

𝌎  Cluster:

    • Function Supercluster: Cost and activity accounting
    • Function Cluster: Performance improvement
    • Cluster Benefit: Behavioral / dynamic pricing

 

Insurance companies use sensor data transmitted by consumer's car to calculate the price based on the driving behavior and where the consumer travels. So the pricing can be adapted to the actual risk (4) and not based on general information e.g. driver's age and gender. The insurance companies can provide their own sensors or use the information of the originally integrated sensors by the car manufacturer. 

  ℹ tracking behavior and location

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
✅    
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Optimize rental car revenues by monitoring usage (Zipcar)

 

🏭 Industry: Automotive

🗊  Application Scenario: Usage centered service-design

 

𝌎  Cluster:

    • Function Supercluster: Service & Product deployment
    • Function Cluster: Performance improvement
    • Cluster Benefit: Functional adjustment

 

Zipcar embedded sensors and network connections in their rental cars. Ipcar monitors the usage of the car (including location) and the availability. They are using an additional app so that the user can book the car via the app instead of a rental counter. The rental counter become unnecessary and the cars can be leased for short time spans to registered members of their car service. Additionally, each car's use can be optimized for higher revenues (5). "Zipcar has pioneered this model, and more established car rental companies are starting to follow."

  ℹ tracking behavior, condition and location, usage of additional digital services (apps)

 

Transmitted Data: 
locationconditionavailabilityusage
 ✅✅
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Image monitoring for illness detection

 

🏭 Industry: Healthcare

🗊  Application Scenario: Presenting user relevant information

 

𝌎  Cluster:

    • Function Supercluster: Service & Product deployment
    • Function Cluster: Stabilization / Retention
    • Cluster Benefit: Support health condition

 

Pill-shaped micro cameras within the human digestive tract transmit images to detect the source of illness (1) within the human body. Based on this information the diagnosis can be more precise and medication more suitable. Additional, the transmitted images provide information the doctors usually have no access to. 

   sensor-driven decision making

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅  
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Embedded location sensors in shopping bags to provide marketing information about nearby products (Tesco)

 

🏭 Industry: Retail

🗊  Application Scenario: Adjustments on presentation of product range

 

𝌎  Cluster:

    • Function Supercluster: Marketing & Customer loyalty
    • Function Cluster: Performance improvement
    • Cluster Benefit: Behavioral offering

 

"In retailing, sensors that note shoppers' profile data (stored in their membership cards) can help close purchases by providing additional information or offering (6) discounts at the point of sale. Market leaders such as Tesco are at the forefront of these uses."

  ℹ tracking behavior

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅ 
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Ensure order by tracking products with RFID tags

 

🏭 Industry: Manufactoring

🗊  Application Scenario: Tracking products in supply chain

 

𝌎  Cluster:

    • Function Supercluster: Supply-Chain-Management
    • Function Cluster: Monitoring & Analysis
    • Cluster Benefit: Improve inventory/Stock management

 

By tracking the inventory and device through the whole supply chain, the product can put back into order if there is a mismatch Additionally the product can be everywhere in the inventory, because it can be automatically located and a connected machine can pick up the device (7). So there is an improvement of inventory management while reducing working capital and logistic costs --> many use cases with tracking products 

  ℹ tracking location / behavior

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅ 
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Proactive maintenance based on tracked conditions in Aviation 

 

🏭 Industry: Aviation

🗊  Application Scenario: Predictive maintenance

 

𝌎  Cluster:

    • Function Supercluster: Operations
    • Function Cluster: Stabilization / Retention
    • Cluster Benefit: Prevent downtimes

 

Sensors send continuously data to the manufacturer (via a network) so that based on the conditions and travel routes airplanes can be checked proactive. This reduces downtimes and enables proactive maintenance (8)

  ℹ tracking condition, predicitve maintenance

 

Transmitted Data: 
locationconditionavailabilityusage
 ✅✅ 
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Detect unauthorized individuals, using image footage and detectors

 

🏭 Industry: Security

🗊  Application Scenario: User Authentication

 

𝌎  Cluster:

    • Function Supercluster: Service & Product deployment
    • Function Cluster: Stabilization / Retention
    • Cluster Benefit: Increase understanding

 

Security personnel can spot unauthorized individuals who try to enter restricted areas based on image footage and detectors. E.g. areas can be overviewed without local presence, because of the embedded sensors and connected security equipment. Face recognition (9) can be made automatically. Depending on the intelligence of the product ("thing") the product can make the connection between real time situation and information in an external database itself and react to it or only inform the supervisor (which is not in the supervised area) about the match. Furthermore, these technologies are getting so small that they can be tagged directly on devices with small surfaces.

  ℹ enhanced situational awareness

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅ 
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Routing adjustments based on weather und traffic patterns

 

🏭 Industry: Logistics

🗊  Application Scenario: Optimize routing

 

𝌎  Cluster:

    • Function Supercluster: Supply-Chain-Management
    • Function Cluster: Performance improvement
    • Cluster Benefit: Optimize routing

 

Logistic managers include knowledge of weather conditions, traffic patterns, and vehicle locations to make constant routing adjustments (10) that reduce congestion costs and increase a network's effective capacity. The information about the situation can be send by the vehicle itself and will be enriched by further data sources. 

  ℹ enhanced situational awareness

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅ 
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Providing information about location of gun fire

 

🏭 Industry: Security

🗊  Application Scenario: Condition monitoring

 

𝌎  Cluster:

    • Function Supercluster: Service & Product deployment
    • Function Cluster: Communication
    • Cluster Benefit: Increase understanding

 

The usage of sonic sensors can provide information about the point of gun fire to officers (11).

  ℹ enhanced situational awareness

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅  
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Searching resources by analyzing the condition of soil

 

🏭 Industry: Mining

🗊  Application Scenario: Environment monitoring & anomaly detection

 

𝌎  Cluster:

    • Function Supercluster: Operations
    • Function Cluster: Monitoring & Analysis
    • Cluster Benefit: Access to new resource possibilities

 

Analyzing the condition of earth with sensors (12) placed in the earth crusts to find potential fields of gas and oil e.g. location and structure. The placed sensors transmit information without being at the spotted place. So companies can look for resources in more dangerous areas where the local presence of humans is not possible. The sensors can be controlled remotely and transmits the data via the internet.

  ℹ sensor driven decision making

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅  
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Detect shopping routes to optimize retail layout

 

🏭 Industry: Retail

🗊  Application Scenario: Adjustments on presentation of product range

 

𝌎  Cluster:

    • Function Supercluster: Marketing & Customer loyalty
    • Function Cluster: Performance improvement
    • Cluster Benefit: Optimize POS

 

Companies include sensors into shopping bags to gather location and behavioral data. This data combined with video footage provides information about the most frequent used routes. This combined with the information what the customer ultimately bought can increase revenue by optimizing the retail layout (13)

  ℹ sensor driven decision making

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅ 
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Monitoring and warning of patients 

 

🏭 Industry: Healthcare

🗊  Application Scenario: Warning and recommending in extraordinary situations

 

𝌎  Cluster:

    • Function Supercluster: Service & Product deployment
    • Function Cluster: Communication
    • Cluster Benefit: support health condition

 

Patients health equipment have sensors with network access. Connecting these devices with product clouds to store their usage data can provide more insights into the health condition of the patient. The data link in real time improve the diagnostic and prescribe tailored treatments. Additionally, patients with chronic illness can be monitored remotely and continuously. The connection of the devices with the hospital monitoring system or emergency system enables early warnings (14) and amplify faster reactions.

  ℹ sensor driven decision making

 

Transmitted Data: 
locationconditionavailabilityusage
 ✅✅
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  

 

  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Remotely chance position of objects in assembly line

 

🏭 Industry: Manufactoring

🗊  Application Scenario: Process-adjustment and improvement

 

𝌎  Cluster:

    • Function Supercluster: Operations
    • Function Cluster: Performance improvement
    • Cluster Benefit: Optimize asset allocation

 

Making sure the physical object is in place in the assembly line (15). Using actors to change the position of the physical object

  ℹ n/a

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅  
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Adjust process-temperature based on sensors (paper industry)

 

🏭 Industry: Manufactoring

🗊  Application Scenario: Process-adjustment and improvement

 

𝌎  Cluster:

    • Function Supercluster: Operations
    • Function Cluster: Performance improvement
    • Cluster Benefit: Increase process productivity

 

In the paper industry the temperatures during the production needs to be adjusted very often (16). With the help of sensors the production outcome was raised by 5% because of the sensors 

  ℹ process optimization

 

Transmitted Data: 
locationconditionavailabilityusage
 ✅✅
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Smart metering (Pacific Gas and Electrics (PG&E))

 

🏭 Industry: Energy

🗊  Application Scenario: Changing behavior of user by comparing

 

𝌎  Cluster:

    • Function Supercluster: Service & Product deployment
    • Function Cluster: Performance improvement
    • Cluster Benefit: Behavioral feedback

 

PG&E integrated sensors into homes and creates an overview about the energy usage and the real-time costs. Based of the real time information residents can use their electronic devices during off-peak usage time to reduce the price (17).

  ℹ optimized resource consumption

 

Transmitted Data: 
locationconditionavailabilityusage
  ✅✅
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  
  • Overview
  • Project information
  • Data information
  • Functionalities & Network

 

Predictive maintenance in Aviation (Etihad Airways)

 

🏭 Industry: Aviation

🗊  Application Scenario: Predictive maintenance

 

𝌎  Cluster:

    • Function Supercluster: Operations
    • Function Cluster: Stabilization / Retention
    • Cluster Benefit: Prevent downtimes

 

Etihad installed sensors on every plane to generate massive data about the condition and location. In turn they are able to control their fleet and use it for predictive maintenance (18).

  ℹ predictive maintenance​

 

Transmitted Data: 
locationconditionavailabilityusage
✅✅ 
Data Analytics:
descriptivediagnosticpredictiveprescriptive
   

 

Level of decision:
MonitoringControlOptimizationAutonomy
   
Level of connectivity:
Eventcontinousinterval
  

Acknowledgement

M.Sc. Julian Wilberg

Technical University Munich

Chair of product development (Prof. Volk komm.)

  • Fax:     +49 (89) 289 – 15144
  • Room: 5506.02.634

PDF: Wilberg (2018) – Development of a use case catalogue supporting idea generation for IoT[1847]