la logistica intelligente logistics and operations: issues and challenges 23 maggio 2014, cineca,...
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La logistica intelligenteLogistics and operations: issues and challenges
23 Maggio 2014, Cineca, Casalecchio
Prof.Ing.Emilio Ferrari
Dipartimento di Ingegneria Industriale, UniBO
Agenda of the speech• Advanced problems and issues in logistics and operations
• Advanced models and tools supporting decision making in logistics
• Exemplifying problem complexity and results
_Food supply chain
_Picking and correlated storage
_CNH Spare Parts (Eng. Tommaso D’Alessandro)
Issues & challanges (1)Manufacturing and material handling• Flexible manufacturing system (FMS) & cellular manufacturng• Layout determination and optimization• Line balancing (e.g. assembly system)• Reliability and maintenance engineering• Material handling (e.g. automated guided vehicles - AGV, LGV, etc.)
CO2
€
Problem complexity:• Large number of products• Large number of control points• Complexity of BOM and work
cycles• Large number of failure modes,
spare parts, etc.• Automation and human workload
Supporting decision models and tools• Mixed integer programming• Heuristics & meta-heuristics• Dynamic simulation• Clustering and correlation analyses
in presence of big data
Issues & challanges (2)Logistic networks and Freight Intermodality• Planning intermodal freight infrastructure and networks.• Environmental impacts assessment of alternative transport modes.• Distribution planning and scheduling handling operations.• Clustering shipments in distribution planning.• Strategic analysis of urban networks for passengers and freight.
CO2
€
Problem complexity:• Large umber of nodes• Large number of items moving• Lurge number of transp. Modes• Long periods of time• Forward & reverse logistics
Supporting decision models and tools• Mixed integer programming• Heuristics & meta-heuristics• Dynamic simulation• Clustering and correlation analyses
in presence of big data
Reverse networks and waste management• Planning forward-reverse logistic networks.• Design closed-loop supply chain for the management of waste and by-products.• Assessment of environmental KPIs of reverse collection chain.• Measuring environmental performance of alternative packaging materials.• Collection fleet management and routing.• By products management
CO2€
Cluster 1
Cluster 2
Cluster 3
Issues & challanges (3)
Quality traceability and logistics of perishable products• Enterprise touching base.• Tracking shipments with on-board data loggers.• Monitoring environmental stresses (temperature, humidity during logistics processes.• Lab simulation of transport conditions in climate rooms.• Sensorial and chemical analyses on stressed products to assess quality decay due to
logistic processes.
CO2€
Issues & challanges (4)
Issues & challanges (5)Storage and warehousing system• Design order picking systems (OPS) and storage areas.• Storage allocation and storage assignment problems for perishable and non-
perishable products.• Assessment of time, energy and space efficiency in handling and storage operations.• Design unit-load storage systems for beverage and bakery industry.• Simulation and scheduling of storage and retrieving activities.• Order-batching and zoning in OPS.• Automation
CO2€
Case studies
2
• Food supply chain
• Storage system & warehosuing
• CNH Spare Parts (Eng.Tommaso d’Alessandro)
Food Issues & Food Supply Chain
2
Water supply
Climate change
Energy supply
Hunger
Demographic Development
Urban/rural balance
Land grabbing
An Integrated Perspective
4
• The design of food supply chain as a whole, involves a broad set of processes and variables belonging to different stages from-farm-to-fork.
• An innovative approach aims to integrate decisions of agriculture source (i.e., LUAP) with decisions of logistics planning (i.e., LAP) for the design of a sustainable forward-reverse food supply chain.
Spatial gridLatitudeLongitudeAltitudePopulationResources
Solar IrradianceWindTemperatureHumidityRainfallSundays
ThicknessMoistureTextureStructureCarbonateSodiumEvapo-transp.
Manufacturing cap.Manufacturing variable costs Manufacturing fixed costs Manufacturing environmental impacts
Storage cap.Storage modeStorage equip.Transport meanDistribution nodeTransport environmental impacts
Food DemandRetailer node
Packaging Recycling flowsCollection nodeRecycling nodeCollection cap.Recycling cap.
Agriculture decisions Logistics decisions
Geography Climate Soil Processing Distribution Consumption End-of-life
• The proposed land-use allocation (LUA) model supports the design of sustainable agri-food production area.
• Assume to consider the agriculture, logistic, energy and environmental use as potential land-use.
• The objective is the minimization of carbon footprint (tons CO2eq) of the agro-food process including agriculture, food processing, and packaging through the adoption of renewable energy sources and mitigation strategies.
Land-use allocation Model
4
Location-allocation model
4
• The proposed location-allocation model supports the design of sustainable food forward and reverse distribution networks.
• Reverse networks support the collection of packaging materials, by-products or waste generated by production, storage or consumption.
• The objective function account two-fold objectives of minimizing carbon footprint or costs of the closed-loop supply chains.
Forward FlowReverse Flow
An Integrated Procedure
4
• Supporting the connection of agri-food production areas and demand over a global scale through the design of sustainable food supply chain:
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EForward food flows
Reverse package flows
AS-IS TO-BE
monitoring, simulation and optimisation
Case study 1 - Supply Chain assessment
Logistic network of fresh products for a retailer company
As-Is vs To-Be – Impact Categories
Effetto Serra (GWP)
CO2 CH4 HC
N2OCO
Assottigliamento Strato Ozono Atmosferico
HC
Acidificazione
SO2
NOx
HC
NH3
Eutrofizzazione
N2ONOx
NH3
Smog Fotochimico
CH4
NOx
CO
HC
Anidride Carbonica
Ossido di Azoto
Ossido di Zolfo
Protossido di Azoto
Idrocarburi Metano
Particolato AmmoniacaMonossido di Carbonio
Case study 1 - As-Is vs To-Be – Impact KPIs
Obtained Results• Reduction of travelled distances (-50%)• Reduction of Co2eq (-50%)• Increase in saturation level of vehicles• Reduction in the number of vehicles moving• Reduction of congestions• Reduction of shelf-life erosion• Reduction of storage levels (-20%)• Increase of safety and quality of food supply chain• Etc.
Storage system & Warehousing Global supply chains continuously face criticalities related to material handling and
logistic network. Enterprises need to lead products from processing towards final consumer in a
global context. Logistics represents an opportunity as well as the main source of waste and costs.
Distribution Center (DC) Warehousing system
Material handlingInventory management
Receiving/shipping Order picking
Add value service
Supply Chain and Warehousing
Distribution Center (DC) Warehousing system
Material handlingInventory management
Checklist Add value service
Product Supplying
WIP Supplying
Customer Demand
Order Picking
Unit-load picking
sorting
shipping
receiving
costtime
Order Picking Systems ORDER PICKING: process of retrieving products from a storage area in
response to a specific customer request.
Reducing travelled distance and time for retrieval missions
Order Picking Efficiency
Decrease logistic costs.Minimize customer response time.Increase service level.
3 main problems in Fast Pick area optimization:1. Which items we need to store in fast pick area?2. Stock inventory level for each item in fast pick?3. Where are the most suitable locations for each item?
Which items we need to store in fast pick area? Stock inventory level for each item in fast pick? Where are the most suitable locations for each
item?
STORAGE ASSINGNMENT RULES
STORAGE ALLOCATION STRATEGIES
Try to establish how much goods stored in Fast Pick area is
required.
Try to establish where allocate each stock within the Fast Pick
area.
2 3
Questions in OPS
ITEM CLUSTER SIMILARITY POPOUT UBICAZIONE
1507301 Cluster 35 1 7 06F06F02
1507300 Cluster 35 1 7 06F55F11
1103398 Cluster 06 1 1 04F30A01
5037269 Cluster 06 1 1 10F01F05
1344376 Cluster 06 1 1 01F24A01
1440472 Cluster 06 1 1 09F21D02
1518007 Cluster 25 1 2 04F34F01
1704635 Cluster 25 1 2 06F20D01
1383759 Cluster 11 0,5 2 03F25D02
1383759 Cluster 11 0,5 3 09F09F03
1365753 Cluster 11 0,5 5 10F21F02
…just an example, before the application of the correlated storage assignment
CASE STUDY 2 - CORRELATED STORAGE ASSIGNMENT
Problem complexity:• Large number of products• Different products (shape, density, etc.)• Large number of locations• Less than unit load picking• Different processes and storage modes• Ergonomics implications• Automation and human workload
Supporting decision models and tools• Mixed integer programming• Heuristics & meta-heuristics• Dynamic simulation• Clustering and correlation analyses
in presence of big data
Obtained Results• Reduction of travelled distances (-50%)• Increase of the throughput (+15%)• Reduction in the number of vehicles moving (-20%)• Reduction of congestions• Reduction of storage levels (-20%)• Etc.
Prof.Ing.Emilio [email protected]
University of Bologna
Department of Industrial Engineering
http://warehousing.diem.unibo.it/http://foodsupplychain.diem.unibo.it/