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 Ferrariemilio.ferrari@unibo.it

University of Bologna

Department of Industrial Engineering

http://warehousing.diem.unibo.it/http://foodsupplychain.diem.unibo.it/