software design for smart logistics - universiteit twente · software design for smart logistics....
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Lunch talk BMS Smart Industry Research Roadmap
05-06-2018
Software design for Smart Logistics
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Current role and ambition:• Business Development Manager at CAPE Groep• Prospective industrial PhD candidate University Twente
Professional experience:• 10 years industry experience in supply chain, logistics and IT• Member of TKI Dinalog supervisory board 4C program 2020• Involved in multiple topsector projects: 4C & Synchromodal Logistics• Part-time lecturer at University Twente, Saxion UAS, NHTV UAS• Initiator of the vLm round table Twente
Educational background:• Master International Supply Chain Management• Bachelor Business Engineering• Bachelor Human Resource Management
Sebastian Piest
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Partnerships
Agility
Innovation
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Relations
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TMS
WMS
APS / Maps
Finance
Salaris
Back-officeiPaaS
Customs
Re
ceiv
er
Proof of Delivery (POD)
Track & Trace / ETA
Dispatching & Labeling
Shipment planning
Ass
ets
/ L
SPs
/ C
arri
ers
Cu
sto
me
r
Orders (EDI / Web)
Quotes (km / € / CO2)
aPaaS
ERP
FMS
Etc.
Data analyses / BI
Reports / Dashboards
Message exchange
Notifications & Alerts
Invoicing & self billing
B2B / Devices
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8
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Triple helix projects; making science actionable
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• Based on Artificial Intelligence (AI) and the Intelligent Amplification (IA):
– IA is first proposed in 1950s and 1960s, but contrasted by AI developments
– IA uses information technology in augmenting human intelligence
– IA proposes a symbiotic partnership between human and AI
– Combining AI and IA utilizes strengths of both concepts
• “Human-in-the-loop” concept:
– Human defines strategy, oversees AI, corrects when needed
– AI executes routine tasks according to strategy
• Fly-by-wire example
Human in the loop concepts for decision making in logistics
Source: A. Dobrkovic (2016)
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• Identify suitable AI environment:– Repetitive tasks
– High workload
– Limited external knowledge required
– Time limits
• Access soft knowledge via IA:– Information available to human
decision maker
– Not every case can be included in the model, exceptions will keep occurring
– Creativity is essential for making the “right” decisions on complex problems
AI/IA in operational business environments
Source: A. Dobrkovic (2016)
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Framework for Enhancing Scheduling Processes
Source: A. Dobrkovic (2016)
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Learning / personalizing optimization policies
Scenario Input
States
TacticsThresholdsTolerances
PolicyGoals
data mining
Domain expert
Source: A. Dobrkovic (2016)
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Execution flow
PolicyGoals
Scenario Input
Intelligentagent
Consult
Execute
Transfer control
low conf.
Source: A. Dobrkovic (2016)
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Level 1: top level processes
Level 4: decomposed process elements
Level 3: decomposed processes
Level 2: process categories
RFI RFQAnalysis &
negotiationContracting
PO Management
Container trucking
Customs services
Freight insurance
Carrier selection
Freight allocation
Sea- Air FreightTransportation Warehousing Distribution
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Ocean carrier websites Sailing schedules and departure times
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Marine TrafficReal-time actuals on live map
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Terminal websites Terminal planning and arrival times
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Purchase order statusMonitor the actual- and estimated departure and arrival times
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• Increase productivity of the workforce:– Automate manual routine tasks with AI– Extend professional judgement & experience with IA
• Change management:– Stimulate the willingness to share individual knowledge– Bundle individual tactics into a shared policy and goals– Learn to trust and work with data captured by agents– Feed the system and continously refine data quality– Change the way of work
• Integration with 3rd party websites:– No API to connect with– Some require user log-in / commercial subscription– Have their own structure / use different input values
Synchromodal IT goal and challenges
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Web
site scrapin
g
Integratio
n layer
Visib
ility layer
Back-office
NLIP Services
iSHARE
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Real-time ETA monitoring of perishables
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Daily operations - perishables
Real-time:
ETD/ATD
ETA/ATA
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Webscraper robots
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Real-time, open data
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Integration of ETA Monitor and webscraper
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ETA Monitoring Dashboard
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• AI will dramatically change the way we work, but is not a “silver bullet”
• Routine tasks in daily operations and decision making can be automized by AI
• Soft knowlegde of domain experts can be accessed via IA to optimize policy goals
• The “Human-in-the-loop” concept combines the best of both approaches:– Human defines strategy, oversees AI, corrects when needed
– AI executes routine tasks according to strategy
• At the end of the day, complex problems and exceptions still require human intelligence
Lessons learned from Synchromodal IT
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Current research projects
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EMAGIZOrchestration, integration & monitoring
SMEUSERS
MENDIXSaaS basedSwiss Knife
solution withAPIs
ALGORITHMSAPI enabled
micro- servicesMINERS
User interface CLDM Connected algorithmsAction based learning
“Autonomous Logistics Miners for SMEs”
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DataRel: Big Data for Resilient Logistics
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Smart pallet pooling / VMI
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Real-time traceability in food retail
Ahrma
Cloud
Place totes
on roll
containers
Load trucks
with roll
containers
Deliver roll
containers
to X-Dock DC
Return roll containers
IoT
data
Reporting /
Dashboards /
Portal(s)
Order
data
& IoT
data
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Software design for Smart Logistics
Lunch talk BMS Smart Industry Research Roadmap
05-06-2018
Thank you for your attention