La logistica intelligente Logistics and operations: issues and challenges 23 Maggio 2014,

1. La logisticaintelligente Logistics and operations: issues and challenges 23 Maggio 2014, Cineca, Casalecchio Prof.Ing.Emilio Ferrari Dipartimento di Ingegneria Industriale, UniBO

2. Agenda of the speech • Advanced problems and issues in logistics and operations • Advanced models and toolssupportingdecisionmaking in logistics • Exemplifyingproblemcomplexity and results • _Foodsupplychain • _Picking and correlatedstorage • _CNH SpareParts (Eng. Tommaso D’Alessandro)

3. 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 € • Problemcomplexity: • Large number of products • Large number of control points • Complexity of BOM and work cycles • Large number of failuremodes, spareparts, etc. • Automation and human workload • Supportingdecisionmodels and tools • Mixed integerprogramming • Heuristics & meta-heuristics • Dynamicsimulation • Clustering and correlationanalyses in presence of big data

4. 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 € • Problemcomplexity: • Large umber of nodes • Large number of itemsmoving • Lurgenumber of transp. Modes • Long periods of time • Forward & reverse logistics • Supportingdecisionmodels and tools • Mixed integerprogramming • Heuristics & meta-heuristics • Dynamicsimulation • Clustering and correlationanalyses in presence of big data

5. Issues & challanges (3) • 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 €

6. Issues & challanges (4) • 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 €

7. 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 €

8. Case studies • Foodsupplychain • Storage system & warehosuing • CNH SpareParts (Eng.Tommaso d’Alessandro) 2

9. Food Issues & Food Supply Chain Water supply Hunger Energy supply Demographic Development Urban/rural balance Climate change Land grabbing 2

10. An Integrated Perspective • 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. Climate Soil Processing Distribution Consumption End-of-life Geography Agriculture decisions • Logistics decisions 4

11. Land-use allocation Model • 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. 4

12. Location-allocation model • 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 Flow Reverse Flow 4

13. V Layer An Integrated Procedure • Supporting the connection of agri-food production areas and demand over a global scale through the design of sustainable food supply chain: Forward food flows Reverse package flows 4

14. Case study 1 - Supply Chain assessment monitoring, simulation and optimisation Logistic network of freshproducts for a retailer company AS-IS TO-BE

15. As-Is vs To-Be – Impact Categories Effetto Serra (GWP) Eutrofizzazione Smog Fotochimico CO2 NOx HC NOx CH4 HC N2O NH3 CO N2O CH4 CO Acidificazione Assottigliamento Strato Ozono Atmosferico SO2 HC NH3 NOx HC

16. Case study 1 - As-Is vs To-Be – Impact KPIs Anidride Carbonica Ossido di Azoto Protossido di Azoto • ObtainedResults • Reduction of travelleddistances (-50%) • Reduction of Co2eq (-50%) • Increase in saturationlevel of vehicles • Reduction in the number of vehiclesmoving • Reduction of congestions • Reduction of shelf-life erosion • Reduction of storagelevels (-20%) • Increase of safety and quality of foodsupplychain • Etc. Monossido di Carbonio Particolato Ammoniaca Idrocarburi Metano Ossido di Zolfo

17. 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 handling • Inventory management • Receiving/shipping • Order picking • Add value service

18. SupplyChain and Warehousing • Distribution Center (DC) • Warehousing system • Material handling • Inventory management • Checklist • Add value service CustomerDemand ProductSupplying WIP Supplying OrderPicking sorting receiving shipping Unit-loadpicking time cost

19. 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 Systems Order Picking Efficiency • Decrease logistic costs. • Minimize customer response time. • Increase service level.

20. Questions in OPS • 3 main problems in Fast Pick area optimization: • 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? 2 3 STORAGE ALLOCATION STRATEGIES STORAGE ASSINGNMENT RULES Try to establish where allocate each stock within the Fast Pick area. Try to establish how much goods stored in Fast Pick area is required. • 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?

21. Case study 2 - Correlated Storage Assignment …just an example, before the application of the correlatedstorageassignment • ObtainedResults • Reduction of travelleddistances (-50%) • Increase of the throughput (+15%) • Reductionin the number of vehiclesmoving (-20%) • Reductionof congestions • Reductionof storagelevels (-20%) • Etc. • Problemcomplexity: • Large number of products • Differentproducts (shape, density, etc.) • Large number of locations • Lessthanunitloadpicking • Differentprocesses and storagemodes • Ergonomicsimplications • Automation and human workload • Supportingdecisionmodels and tools • Mixed integerprogramming • Heuristics & meta-heuristics • Dynamicsimulation • Clustering and correlationanalyses in presence of big data

22. Prof.Ing.Emilio Ferrari emilio.ferrari@unibo.it University of Bologna Department of Industrial Engineering http://warehousing.diem.unibo.it/ http://foodsupplychain.diem.unibo.it/