Third International Workshop on Food Supply Chain (WFSC 2014) Making Food Supply Chains Efficient, Responsive and Sustainable ENERGY BALANCE IN SUSTAINABLE FOOD SUPPLY CHAIN PROCESSES Riccardo Accorsi , Riccardo Manzini, Andrea Gallo, Alberto Regattieri, Cristina Mora San Francisco, CA 4 th – 7 th November, 2014
Aim & Goals of the Research The scope of this working paper are: • Highlight the role of supply chain decisions in addressing environmentally care behaviour, • Assess the energy trade-off in perishable food supply chains, • Provide a decision-support tool to design sustainable supply chains for perishable food products from-crop-to-fork, • Identify the most energy-intensive operations/activities in perishable agro- food supply chains. Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
Problem statement • The agro-food systems are identified as the main source and human-induced greenhouse gases (GHGs) emissions (Desjardins et al., 2007) • Modern agro-food system (post 70s) are energy intensive and highly fossil fuel dependent due to: • adoption of chemical fertilizers and pesticides, • use of agriculture equipment and vehicles, • irrigation systems, • processing and packaging lines, • Climate-controlled storage of products, • transport and product distribution Agro-food systems depend by worldwide availability of low cost energy and • are not sustainable in long term Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
Some Evidences CO 2 eq. CO 2 eq. Per capite Agro-food Process/Activity % (Mtons/year) (kg/year) Agriculture 47.1 45.3 805 Farming, Enteric fermentation 11.6 11.2 198 Farming, Sewage and waste (i.e., N 2 O, NH 4 ) 6.9 6.6 117 Storage/Distribution 19.8 19.1 339 Industrial processing 5.5 5.3 94 Packaging 13.1 12.6 224 Italian Agro-food Sector 104 18.8 1778 Italian Total GHGs emissions 553 100 9543 Italian agro-food sector GHGs emissions (Castaldi et al., 2009) Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
Agro-food system assessment • The intensive agro-food system is one of the least efficient since consumes much more energy than it provides. • A renowned metric for the assessment of the environmental efficiency of a product or a processes is the index of sustainability (IS): E IS = consumed E sup plied Where E consumed accounts the exploited • energy and E supplied represents the energy content of the products supplied to the consumers. Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
Agro-food system assessment For food the supplied energy is given by its energy content (i.e., kcal or kWh). • • For food the consumed energy is the amount of energy required for agriculture, processing, packaging, storage, and distribution processes. Some examples (Church, 2005) : • IS = 127 for salad via air from U.S.A. to U.K. • IS = 97 for asparagus from Chile to U.K. • IS =66 for South-Africans carrots consumed in U.K. • 600 500 E IS = Food IS avg 400 consumed E 300 sup plied 200 100 0 1900 1970 2000 2050 Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
Agro-food operations The design of efficient agro-food operations from-farm-to-fork is widely • debated by literature: Problem Problem characteristics formulation/ Math Decision Reference in FSC Multi- Multi- Multi- Multi- Multi- Temp- Quality- Was Environ Solving model support tool stage product period modal objective based based te goal approach ✓ ✓ ✓ ✓ ✓ ✓ Van der Vorst et al., 1998 Simulation ILP/Analytical ✓ ✓ ✓ Ljungberg et al., 2001 approach ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Rong et al., 2009 MILP Goal ✓ ✓ ✓ ✓ ✓ Oglethorpe, 2010 Programming ✓ ✓ Bosona et al., 2011 Clustering ✓ ✓ ✓ Zucchi et al., 2011 MILP ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ This paper MILP This paper builds-up upon existing researches (i.e., Rong et al., 2009 and Van • der Vorst et al., 2009) and provides a DSS for energy balance in food supply chains Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
A MILP model A MILP model is implemented into a user friendly windows application to • support decision-making. The MILP model: Includes 4 stages: crop, processing/packaging facility, storage facility, • consumer centers (e.g., markets, retailers) Considers multiple periods and multiple products. • Involves storage temperature and shelf life issues in accordance with a time- • based qualit y curve (Rong et al., 2009). Aims to minime the overall energy consumed by agro-food operations plus • the lost energy given by expired products. Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
A MILP model The food supply chain network considered by the A MILP model: • Packaging (P) Distribution Centre (D) Retailers, markets (S) Growers (L) Inventory Inventory Waste Waste Proc/Storage Storage Capacities Capacities xpd xlp xds Harvest Transportpd Transportlp Transportds capacity Load Load Load Waste Capacity Capacity Capacity Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
A MILP model The objective function of the model includes the energy contributions given • by: K M L P T ∑ ∑∑∑∑ + + min ( me coolem ) distlp transportl p , , , , , , m k m l p k m l p t = = = = = k 1 m 1 l 1 p 1 t 1 Crop • K M P D T ∑ ∑∑∑∑ + + + Processing/Packaging ( me coolem ) distpd transportp d • m k , m p , d k , m , p , d , t = = = = = k 1 m 1 p 1 d 1 t 1 Storage • K M D S T ∑ ∑∑∑∑ + + + Transport ( me coolem ) distds transportd s • m k , m d , s k , m , d , s , t = = = = = k 1 m 1 d 1 s 1 t 1 Expired food • q I K M L P T max ∑ ∑ ∑ ∑∑∑∑ + + ce xlp i , l i , q , k , m , l , p , t = = = = = = = i 1 q 1 k 1 m 1 l 1 p 1 t 1 q I K M P D T max ∑ ∑ ∑ ∑∑∑∑ + + pe xpd i , p i , q , k , m , p , d , t = = = = = = = i 1 q 1 k 1 m 1 p 1 d 1 t 1 q I K T max ∑ ∑ ∑ ∑ ∑ + + inventory storagee i , q , k , pd , t i , pd = = = ∈ = i 1 q 1 k 1 pd PD t 1 K T I T ∑ ∑ ∑ ∑ ∑ ∑ + + coolepd z waste we k , pd k , pd , t i , pds , t = ∈ = = ∈ = k 1 pd PD t 1 i 1 pds PDS t 1 Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
A MILP model The set of constraints formulated in the model includes: • Food demand fulfillment • Stock balancing within processing/packaging and storage • facilities Capacity constraints for harvest, processing, storage operations • Flow balancing across supply chain stages • Expired food product flows • Integer number of transport means • Quality level degradation (Rong et al., 2009) • Storage temperature settings • The MILP formulation is built upon existing research (Rong et al., 2009), but • includes transport modality choices and capacities (i.e., integer • constraints) adds the harvest capacities to meet products seasonality • account agriculture energy requirements in the problem. • Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
A Decision-support tool The MILP model has been integrated into a decision support tool developed • in C# language and available for any Microsoft based operative system (OS). The tool includes: a database management system (DBMS) for data collection • a geographic informative system (GIS) • an AMPL interface to run the model with a commercial linear solver • a graphic user interface (GUI) for user-friendly analysis and outputs • visualization. Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
A Decision-support tool The MILP model has been integrated into a decision support tool developed • in C# language and available for any Microsoft based operative system (OS): Period setting GIS Interface Quality setting IS value Nodes saturation Report Energy balance in sustainable food supply chain processes Third International Workshop on Food Supply Chain – WFSC 2014 – San Francisco, CA, November 4 th -7 th , 2014
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