Internet of Things impact on the Intralogistics of tomorrow

Transcription

1 Internet of Things impact on the Intralogistics of tomorrow International Conference on CeMAT 2008 Hanover, 30th May, 2008 Dr. Thorsten Schmidt

2 Panel Jan van der Velden, Vanderlande Industries, Netherlands Manager Distribution Systems Jean-David Attal, Savoye, France Vice President, Directeur du Development Craig Rollason, Daifuku Europe Ltd. Sales manager Page 2 Hanover, 30th May, 2008

3 Introduction Objectives Actual state of the Internet of Things Development trends in Intralogistics Conclusion and outlook Page 3 Hanover, 30th May, 2008

4 Objectives Identification of convergence and separation for Internet of Things - self-controlled material flows Current Intralogistics technologies Specification of development trends within this scope Importance of efficient and reliable operations in logistic systems Page 4 Hanover, 30th May, 2008

5 Definition of Intralogistics Intralogistics is a cutting-edge term in Europe that comprises all technical systems, services and related business involved in the in-house materials handling of industrial enterprises, wholesalers, retailers and government institutions. The processes of the intralogistics domain are vital for managing the flows of goods along the entire supply chain as they provide the reliable and predictable flow of physical goods in the joints of a supply network. Page 5 Hanover, 30th May, 2008

6 State of the art Objectives Actual state of the Internet of Things Development trends in Intralogistics Conclusion and outlook Page 6 Hanover, 30th May, 2008

7 Self-controlled material flows and the Internet of Things Drivers Standardisation Locating Sensor networks Development of AutoID especially of RFID technology Requirements Growing complexity System requirements on plants and networks Reading security Coverage Flexibility Availability Polymer chips Internet of Things Change of paradigms in material flow logic towards self-controlled systems Tasks Planing and modelling e.g. logics and components for simulators and reference models Optimality and availability e.g. comparison of system efficiency, system-related feasibility Design MF technology e.g. adapted components and conveyor elements Page 7 Hanover, 30th May, 2008

8 Self-control / decentral control Basic problems regarding distributed controls Technology and form of the communication among decentral controls Reaction to failures Determination of the optimal operating point without knowledge of the general system status Definition of suitable strategies to optimize the system Handling of sequencing problems and priority rules Page 8 Hanover, 30th May, 2008

9 Example: Comparison of central and decentral strategies in a conveyor-system Capacity utilization picker 100,0% 90,0% 80,0% 70,0% 60,0% 50,0% 40,0% 30,0% 20,0% 10,0% 0,0% Central, 1 order/stat. Bunch-wise bin supply Central, 1 order/stat. Controlled bin supply Decentr., 1 order/stat. Bunch-wise bin supply Decentr., 1 order/stat. Controlled bin supply 500 art art art. Simulation-based analysis of a conveyor-loop in order-picking Capacity of order picking personnel as KPI for system efficiency Comparison of simple and enhanced operating strategies Decentral strategies proof competitiveness Page 9 Hanover, 30th May, 2008

10 State of the art Objectives Actual state of the Internet of Things Development trends in Intralogistics Conclusion and outlook Page 10 Hanover, 30th May, 2008

11 Initial situation Intralogistics Increasing article range / higher shipping frequencies Cost driver human labor Increasing performance of material handling systems Selection and planning are mostly based on rough empirical values; potentials are often not analyzed in detail in advance The control concept, strategies, etc. are often selected by the system provider Effectiveness and efficiency of process and operating strategies are largely unknown Page 11 Hanover, 30th May, 2008

12 Warehouse material flow Functions in warehouse procedures Unitizing Packaging Conveying Handling Storing Most rationalization attempts aim at optimizing the aspects of Storage Inventory management Order picking Reserve Storage and Pallet Picking Direct putaway to reserve Replenishment Replenishment Receiving Direct putaway to primary Case Picking Cross-docking Shipping Broken Case Picking Accumulation, Sortation & Packing Source: GaTech Page 12 Hanover, 30th May, 2008

13 Examples: Rack-Picker Targets: Integration of single functions in an effective system combination of rack feeder, automated gripping technology and automated conveying Optional handling of single cases or full pallets Disposal of picked units by continuous conveyor in order to achieve high system throughput, i.e. separation of the functions handling and conveying Page 13 Hanover, 30th May, 2008

14 Examples: Automated rack feeding Targets: Automation of general industrial trucks Dual usage of vehicles Manual operation Automated operation Regular trucks, like reach mast trucks, automated with sensors for orientation and navigation Page 14 Hanover, 30th May, 2008 Source: Still

15 Examples: Warehousing Shuttle systems with independent vehicles on separate storage levels Targets: Scalability Storage capacity (# and length of aisles) Throughput performance (# of vehicles) Procedures Sequencing with independent vehicles Integrated storage and transport system skipping the handover procedure Page 15 Hanover, 30th May, 2008 Source: FhG-IML

16 Examples: Picking technology Gripping system Traction Gripper Automation of picking operations in order picking scenarios facilitate the comprehensive automation of dedicated processes Pouch picking system Patentanmeldung DE Page 16 Hanover, 30th May, 2008 Source: FhG-IML

17 Synergies by means of data generation: RFID-based picking KUKA depalletizing robot TCP/IP Interface depalletizing control robot control Middleware PLC Roller conveyor readerantenna pallet with RFID-Tag Page 17 Hanover, 30th May, 2008

18 Conclusion and outlook Objectives Actual state of the Internet of Things Development trends in Intralogistics Conclusion and outlook Page 18 Hanover, 30th May, 2008

19 Resumee Scientific research demonstrates suitability of decentral controls for Intralogistics Established strategies of central controls can be transferred only to a limited extent These have to be designed and optimized by carefully selecting suitable strategies The linked communication between the units, e.g. via software agents, offers further potentials for optimization Page 19 Hanover, 30th May, 2008

20 Convergence vs. divergence towards use of IOT in Intralogistics Possible reasons for convergence clear trend towards modular systems increase in computing performance increasing intensity of communication due to required transparency of operations shorter design and ramp-up of complex systems (e.g. 12 months for entire DC) inherent robustness fosters availability requirements Possible reasons for divergence unique positioning in competition change management in company know how operations and handling of interfaces Reluctance of customers for new technology step Page 20 Hanover, 30th May, 2008

21 Panel discussion Jan van der Velden, Vanderlande Industries, Netherlands Manager Distribution Systems Jean-David Attal, Savoye, France Vice President, Directeur du Development Craig Rollason, Daifuku Europe Ltd United Kingdom. Sales manager Page 21 Hanover, 30th May, 2008