This document discusses autonomous logistics and describes several key aspects:
1) Autonomous logistics aims to give each entity such as products and processes autonomy through decentralization, allowing them to independently manage and transmit data.
2) Implementation involves simulation, identifying errors, and refining the process to create the optimal autonomous control model.
3) An agent-based approach using tools like ALEM represents the logistics system with autonomous agents making decentralized decisions.
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OT - How IoT will Impact Future B2B and Global Supply Chains - SS14Mark Morley, MBA
This presentation was originally given at an EDIFICE plenary in Brussels in May 2014. EDIFICE is a European based industry association driving B2B standards across the high tech industry. The presentation discussed digital disruption and how the Internet of Things is likely to impact future design of B2B platforms and global supply chains. - Updated March 2015
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The supply chain of the future will accelerate due to assistance from autonomous technologies and processes. These are slides from our webinar Supply Chain and the Autonomous World webinar featuring special guests Jim Lawton, Chief Product and Marketing Officer, Rethink Robotics and Andy Souders, SVP Products and Strategy IoT/Big Data, Savi Technology, as they join Lora to discuss the impact of technologies such as robotics, self-driving vehicles, wearable devices, cognitive learning, 3-D printing and other evolutions on current and future supply chains.
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As big data concepts have germinated, and are combined with new and improved data visualization techniques and faster processing capabilities, the opportunity to inject intelligence into ITS has finally emerged. Users for the first time have the tools available to data mine, actively spotting trends which were nearly impossible previously.
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This presentation identifes how ANPR compares to other sensor technologies, identify the areas where ANPR can and will make a difference as well as identify limitations in the current ITS philosophies.
1. Reactive:
This kind of monitoring can be achived by the orchestration engine updates the monitoring system.
Example:
Puppet: if any changes to configuration happens it revert back to the actual configuration which management config needs
2. Proactive:
This kind of monitoring can be achived by adding precautionary measures for the known issues, where, if the issue occurs it immidiately starts the precaution to eradicate the fault.
3. Adaptive:
This is better suited for monitoring a frequently changing system like docker containers, as it can adapt itself to the micro services that get intorduced into the containers. Now the question is “Is the adaptive montoring a full solution to the abovementioned challenges?"
- Answer is “NO”
- We need solutions at different levels
Sponsored by Smeal College of Business at Penn State University, Infosys Consulting, Penkse Logistics and Korn Ferry, the 22nd Annual 3PL Study investigates digitization and automation in logistics, blockchain for supply chain, and risk and resilience in 3PL shipper relationships.
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The supply chain of the future will accelerate due to assistance from autonomous technologies and processes. These are slides from our webinar Supply Chain and the Autonomous World webinar featuring special guests Jim Lawton, Chief Product and Marketing Officer, Rethink Robotics and Andy Souders, SVP Products and Strategy IoT/Big Data, Savi Technology, as they join Lora to discuss the impact of technologies such as robotics, self-driving vehicles, wearable devices, cognitive learning, 3-D printing and other evolutions on current and future supply chains.
Keep your SCM application Agile with Panaya
Upgrading to the latest SAP SCM EHP offers new features and functionalities to improve supply chain management in the age of digital disruption. However, like any change, SCM EHP upgrades face operational challenges of time, cost, complexity and risk that can delay or even derail an upgrade.
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Learn how companies are seeking to transform their supply chains in the new digital networked economy. This session will include a discussion about the major pillars for transformation that can act as a concrete framework for initiatives to realize new digital opportunities and business value. You will be introduced to SAP’s extended supply chain portfolio and learn how this can be used for collaborative business planning across the network to integrate supply chain with sourcing and purchasing, and to orchestrate internal and external operations across short-, medium- and long-term time horizons. SAP customer use cases and examples from various industries will be used.
Learn about the latest market trends and technology innovations in supply chain from Lora Cecere, CEO and founder, Supply Chain Insights, and glean lessons learned and key takeaways from practitioner Elliott Wolf, director of analytics at Lineage Logistics.
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Thinking about your sales team's goals for 2017? Drift's VP of Sales shares 3 things you can do to improve conversion rates and drive more revenue.
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Will camera technology become an ITS sensorAllied Vision
As big data concepts have germinated, and are combined with new and improved data visualization techniques and faster processing capabilities, the opportunity to inject intelligence into ITS has finally emerged. Users for the first time have the tools available to data mine, actively spotting trends which were nearly impossible previously.
While the introduction of wide spread ANPR has been reasonably slow, both camera and ANPR technology has become mainstream. In addition, this same camera when used in conjunction with clever software can also: identify wrong way running, speed enforcement, Amber, Silver and LEO alerts, average speed analysis, objects in a road, vehicle classification and a host of other capabilities, which would otherwise require a barrage of additional sensors.
The dawn of integrated ANPR solutions is upon us, these systems have been widely used in a variety of applications from congestion charging to open road tolling, and have been proven to be robust, reliable, and accurate – with many vendors offering systems with accuracy level well in excess of 95%, even in free-flow environments.
This presentation identifes how ANPR compares to other sensor technologies, identify the areas where ANPR can and will make a difference as well as identify limitations in the current ITS philosophies.
1. Reactive:
This kind of monitoring can be achived by the orchestration engine updates the monitoring system.
Example:
Puppet: if any changes to configuration happens it revert back to the actual configuration which management config needs
2. Proactive:
This kind of monitoring can be achived by adding precautionary measures for the known issues, where, if the issue occurs it immidiately starts the precaution to eradicate the fault.
3. Adaptive:
This is better suited for monitoring a frequently changing system like docker containers, as it can adapt itself to the micro services that get intorduced into the containers. Now the question is “Is the adaptive montoring a full solution to the abovementioned challenges?"
- Answer is “NO”
- We need solutions at different levels
Sponsored by Smeal College of Business at Penn State University, Infosys Consulting, Penkse Logistics and Korn Ferry, the 22nd Annual 3PL Study investigates digitization and automation in logistics, blockchain for supply chain, and risk and resilience in 3PL shipper relationships.
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Hub and Spoke Methodology followed by FedEx. 3PL (3rd Party Logistics) and its Quality Driven Management (QDM)
2. About Autonomous Logistics
• Autonomy with each entity
• Robust, flexible and quick:
Decentralized control
Independently manage and process information
Receive and transmit data
Run decisions and initial actions remotely.
Autonomous Logistics
Product and
processes
Connectivity
Sensors
2
3. Process of implementation
• Run simulation
• PLASMA
• Track unwanted behavior
• Smooth running system
• Identify errors
• Rectify process
• Choose the best configuration
• Final model
• Define objectives
• Define all the controls• Identify existing elements and
relationships
• Represented using agent based systems
• Tools like ALEM assist
• Autonomy in decision making
3
4. Autonomous Control Modeling Techniques
Different algorithms
have shown similar
results independent
of size of production
network
Autonomous control modelling techniques attempts to implement
capability in a system, process or an item to design its input-,
throughput- and output-profiles as an anticipative or reactive
answer to changing constraints of environmental parameters.
Ant Colony Control
• Implemented on shop floor
• Different jobs with pheromones
• Machines develop pheromone concentrations
• Pheromones expire over time
Market Based
• Each part carries list of required operations
• Cost assigned to distance travelled
• Budget specified to each par
4
5. Autonomous Logistics Engineering Methodology
(ALEM)
Models a system by defining specifications of Autonomous Logistics
Identifies, Designs & Allocates Decision Processes
Components of ALEM :
ALEM Notation
ALEM Procedure
ALEM Tools
Gives the notational elements to be
used in views (to show specific aspects
of the logistics system)
A procedure model which acts as a
guideline for modelling autonomous
logistic processes
Software tool to aid a logistics engineer
to model and designing automation
logistics processes
5
7. Agent Components
• Sensors – quality of shipments
• RFID – stores information in label
• Standard Protocols:
Unanimous communication
EPC global architecture
• PLASMA Simulation
7
8. Semantic Mediator
• Smart data detection & integration
• Serves as “GO” between diff agents
• Translates message, filters information
Cloud Computing
• Decentralized decision making
• Software agents on: Embedded systems Or Central server
• Types :
1) Infrastructure as service
2) Platform as service – JADE, PlaSMA
3) Software as service
4) Process as service
• Advantage ?
8
9. Product Variation
• Product decides
Operation Order
Machine
Product variant
Customer
Collaborative Transport Planning (CTP)
• Exchange of customer Requests
• Create optimal routes
• Everyone’s benefit
9
10. Smart Vehicles and DLRP
• Use of automated guided vehicles
Automated systems
Capable of all operations
Integrate with model
Speedy and eliminate traffic problems
• Distributed Logistics Routing Protocol
Decentralized
Node to node configuration
Best available route is chosen
Helps avoid Delays and errors
10
11. Benefits
• Quick decisions and responses
• Automates the whole process
• Minimal human intervention (less errors)
• High efficiency
• Quality of product
• Order traceability
• Optimizes and chooses the best path
possible
• Enables collaboration between enterprises
to get higher profit
• Detailed representation of logistics system
• Improved communication and inter-
objects interaction
Challenges
• Any anomaly or change needs in depth
examination
• Prolonged Simulation time
• Enterprises with different strategies lead
to differences in model
• Computational power
• Interaction complexity
• Power constraints
11
12. REFERENCES
• Autonomous logistic processes– new demands and first approaches – b. Scholz-reiter (2), k. Windt, m. Freitag (2003)
• 2. An agent-based approach to autonomous logistic processes collaborative research centre 637: autonomous cooperating logistic
processes - jan d. Gehrke · otthein herzog · hagen langer · rainer malaka · robert porzel · tobias warden (june 2010)
• 3. A classification pattern for autonomous control methods in logistics - katja windt • till becker • oliver jeken • achim gelessus (june
2010)
• 4. Applying autonomous sensor systems in logistics—combining sensor networks, rfids and software agents - reiner jedermann ,
christian behrens, detmar westphal, walter langa – (march 2006)
• 5. The autonomous logistics engineering methodology (alem)- bernd scholz-reiter, jan kolditz, torsten hildebrandt- (september 2007)
• 6. Cloud computing for autonomous control in logistics - a. Schuldt, k. A. Hribernik, j. D. Gehrke, k.-d. Thoben, and o. Herzog –
(october 2010)
• 7. Autonomous control methods in logistics – a mathematical perspective - sergey dashkovskiy, michael görges, lars naujok – (july
2012)
• 8. Limitations in modeling autonomous logistic processes - bernd scholz-reiter, daniel rippel, steffen sowade (june 2011)
• 9. An internet of things for transport logistics - an approach to connecting the information and material flows in autonomous
cooperating logistics processes - hribernik karl a, warden tobias, thoben klaus-dieter, herzog otthein-(2010)
• 10. Analysis of mobile agents considering the fan out - mobile agents for autonomous logistics - markus becker, gulshanara sayyed,
bernd-ludwig wenning, carmelita g¨org - (2006)
• http://www.uni-bremen.de/en.html - BIBA-Bremer Institut für Produktion und Logistik GmbH
12
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