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Ubiquitous Computing in Business Processes
Part III
Prof. Dr. Lutz HeuserUrban Software Institute
Prof. Dr. Zoltán NochtaSAP SE
DarmstadtDecember 18th, 2015
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1. Recap
Outline
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RFID supported Asset Maintenance Process
3.) Execution of
maintenance and repair
activities
RFID tag
on asset
1.) Technician reads
information stored on
RFID-tag
4.) Information update
on RFID tag after
maintenance has been
performed
2.) Maintenance and
repair planning based
on information on RFID
tag loaded
Source: SAP AGUbiquitous Computing in Geschäftsprozessen / Lutz Heuser, Zoltan Nochta / Page 3
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ERP
RFID in Order Fulfillment Process
Retailer
WM
Vendor
Middleware
Delivery
Purchase Order
Adv. Ship
Notification
Cust.
Order
Post
Goods Issue
Create HU
Pickor
Produce
Scan IDs
Issue Goods
(Loading)Build
Handling
Unit (HU)
Associate
Boxes & Pallet
Tags
DeliveryDelivery
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ERP
Let’s have a look into the Factory!
RFID in Discrete Manufacturing Process
Retailer
WM
Vendor
Middleware
Delivery
Purchase Order
Adv. Ship
Notification
Cust.
Order
Post
Goods Issue
Create HU
Pickor
Produce
Scan IDs
Issue Goods
(Loading)Build
Handling
Unit (HU)
Associate
Boxes & Pallet
Tags
DeliveryDelivery
Ubiquitous Computing in Geschäftsprozessen / Lutz Heuser, Zoltan Nochta / Page 5Source: SAP AG
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EPCglobal Network Architecture
Search and
Discovery
Event
Registries
Source: EPCglobal
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2. Beyond RFID – How to construct smart shelves without tagging?
Outline
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Principle of Load Sensing
Load sensors under corners of a surface (e.g. pallet, shelf, floor) detect :
Mass of objects on surface
Position of objects on surface
Interactions with surface, e.g. adding or removing objects
Table equipped with load
sensors
1kg load sensor(Photo: Bongshin)
50kg load sensor50t load sensor
(Photo: Bongshin)
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Basic Calculations
Fi,1 i 4: Forces at sensors after action
F0i,1 i 4: Forces at sensors prior to action
Weight:
xmax: length of surface
ymax: width of surface
x: x-coordinate of point of action
y: y-coordinate of point of action
Force Fx
at (x,y)
Force F1
at (0,0)
Force F3
at (xmax
,ymax
)
Force F4
at (0,ymax
)
Force F2
at (xmax
,0)
4
1i
ix FF
4
1
00i
ix FF
xx FFW 0
)0(
)0()0( 3322max
xx FF
FFFFxx
)0(
)0()0( 4433max
xx FF
FFFFyy
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Advantages
Cheap: load cells at less than $100
Robust: works in almost every environment (e.g. metal environment)
Everyday compatible: no tagging of objects required
Flexible:
Feasible for any kind of object
Feasible for any size of object (load cells from 1kg to 200t)
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Source: SAP AG
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Business Potential
Capabilities
Object counting, e.g. number of items in a case
Object tracking, e.g. trolleys in warehouse or finger on table
Load balancing (trucks, helicopters, …)
Application Areas
Inventory management
Warehouse management
Supply chain management
Transportation management
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Source: SAP AG
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3. Beyond RFID - Quality Management in Supply Chains
Outline
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End-To-End Quality Management Goods
1. Vendor: Goods
Issue
2. Carrier:
Transportation
3. Customer: Goods
Receipt
4. Transfer of data to
Inspection lot
5. Usage decision
6. Reset
Logger
Source: SAP AG
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Sensor-aided Supply Chain Monitoring
Technology Sensors (temperature, acceleration, …) Local memory to store sensor and
identification data
Intermittent connection to backend
Example: ESYS – MINIDANTEMP0,1
Temperature logging
Temperature range: -40 °C to +85°C Accuracy: 0.1 °C or 0.5 °C Storage: 16.000 to 32.000 values depending on
accuracy
Potential Integrate shipping / transport of goods into
Enterprise Quality Management processes
Source: SAP AG
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Aware Goods @ Infineon
Scenario
Shipley: Supplier for Infineon
Polishing liquid for wafer production
Liquid has to be transported within certain temperature range
Key Requirements
Easy handling for customer and supplier
One-step integration with SAP PLM avoiding manual data transfer
Solution
Temperature logging during transportation based on ESYS MINIDANTEMP0,1
Source: SAP AG
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4. Introduction Smart Items in Smart Cities of the Future
Outline
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The world’s cities are on a collision course with the future.
• 1 million people move to cities every day
• 1 billion people call slums home today
• There are 21 megacities with over 10 million people
• China alone will create 81 new major cities by 2025
By 2050, cities will be home to 70% of the world’s people. This rapid urbanization is will increasingly stress city resources, leaders and citizens. Fortunately, technology can play an enormous role in naming our cities smarter and much more livable, workable and sustainable.
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The Challenge
Source: smart cities council brochure
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Smart Cities Wheel
source: Boyd Cohen, http://smartcitieshub.com/2012/11/11/smart-cities-ranking-methodology/
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6. Open Urban Sensor-Data Platforms
Outline
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Definition of Urban Platform
What does the MOU Group mean by ‘an Urban Platform’? …,
… the implemented realisation of a logical architecture/content/design that brings together (integrates) data flows within and across city systems
… and exploits modern technologies (sensors, cloud services, mobile devices, analytics, social media etc)
… providing the building blocks that enable cities to rapidly shift from fragmented operations to include predictive effective operations, and novel ways of engaging and serving city stakeholders
… in order to transform, in a way that is tangible and measurable, outcomes at local level (e.g. increase energy efficiency, reduce traffic congestion and emissions, create (digital) innovation ecosystems)
Source: https://ec.europa.eu/digital-agenda/en/news/memorandum-understanding-towards-open-urban-platforms-smart-cities-and-communities
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Memorandum of Understanding - Towards Open Urban Platforms for
Smart Cities and Communities
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Key Aspects of an Urban (Sensor Data) Platform
Real-time sensor data platform offered in the cloud
Event-based processing
Stream analytics of incoming real-time data streams
Dedicated to urban sensors by semantically enriched data storage
Connectors to devices as well as 3rd party management systems
Multi-side, multi-service platform as a mediator
Integrate the vast amount of data generated in a city into an open urban platform.
The real time intelligence is then used for evidence based planning and decision making
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Reference Architecture - [ UrbanPulse ]
Connectors
Data
analytics
and event
processing
Apps &
Smart
Services
Platform
Management
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6. Group Exercise
Outline
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Goal: Develop a Smart City Service Business Case including PoC
Team of 4 -5
Exercise consists of two parts
Build a business case for a Smart City Service
Description of the Service
Deployment and operation of the Service
Business Plan using a business model canvas (see attached)
Build a PoC using UrbanPulse and [ui!] COCKPIT
Identify the smart data source and connect it via [ui!] CONNECTOR
Visualize the Service using [ui!] COCKPIT
(Build an App)
Kick-off on January 15th, 2016 with a tutorial on UrbanPulse and [ui!] COCKPIT
(Review / Feedback opportunity on January 29th, 2016)
Jam Session on pitch on February 5th, 2016 – first two hours
Successful presentations will count for 50% of exam (TBC)
Content of January 15th is part of the lecture and exam
Group Exercise - Setting
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Business Model Canvas
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Smart City Dashboard – Shows the urban data of the city
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Smart City Dashboard - Sample Urban Sensor Data
Example: Environmental Data
Identify thresholds of air quality
Stake holder: City Authorities
Technical Solution:
Multi-sensor nodes as smart items collect data and send
them to a sensor data platform for evaluation.
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Team 1This could
be yourservice?
Smart City Dashboard – PoC – Build your own Tile
Team 2This could
be yourservice?
Team 3This could be your
service?
Team 4This could
be yourservice?
Team 5This could
be yourservice?
Team 6This could be your
service?