Energy balance in sustainable food supply chain processes

Riccardo Accorsi, Riccardo Manzini, Andrea Gallo, Alberto Regattieri, Cristina Mora,

University of Bologna – Alma Mater Studiorum Italy

corresponding author: Riccardo Accorsi, mail: riccardo.accorsi2@unibo.it,

viale Risorgimento, 2, 40136, Bologna, Italy

Working paper

Abstract The agro-food production and distribution processes are identified as the main source and human-induced greenhouse gases (GHGs) emissions that significantly affect the environmental ecosystems and contribute to global warming and climate change. Furthermore, with the expected raise of the world population by 2050, the global food demand will double, requiring the intensification of more environmentally sustainable practices and choices in agro-food supply chains. Notwithstanding the literature widely debates on the environmental impacts of agro-food ecosystems and processes (e.g., global warming, eutrophication, deforestation, energy consumption), there is still room for tools and decision-support methodologies to help practitioners and policy-makers in merging economic and environmental goals. Considering the energy balance as an important metric of sustainable food supply chains (FSC), this paper proposes a tool for the energy-effective management of time-based food processes and flows from-farm-to-fork. The tool accounts energy costs to the food production, storage and distribution activities and implements a mixed integer programming model (MILP) to minimize the overall energy contributions across the food supply chain. Food miles, logistics infrastructures, and level of technology are enquired to find out the main stressor of an energy-effective food supply chain. A numerical example inspired by the fresh fruit and vegetables supply chains is proposed. The analysed scenario is inspired by the some of the main perishable food corridors across the MED area. The tool underpins the selection of the most energy-efficient food products and processes, and finally shifts the focus from the energy balance to the role of the exergy balance in sustainable FSC.

Keywords: Sustainability, Food supply chain, Energy, Logistics, Food operations

1. Introduction

The modern agro-food systems are identified as one of the main causes of the environmental impacts (e.g., GHGs emission, fossil fuel exploitation, soil erosion, deforestation, eutrophication, acidification) that affect the global ecosystems. The impacts of food systems and operations (i.e., agriculture, packaging, processing, storage, distribution, waste management) on the environment are increasing at such a rate to reduce the ecosystem’s capacity to provide essential nature services for the medium and long-term future. Furthermore, the raise of global population is stressing more and more food supply chains to find out new strategies and patterns for economic and environmentally sustainable agro-food models and processes. The modern agro-food system indeed, diffused since the 70s green revolution, is energy intensive due to the adoption of chemical fertilizers and pesticides, the use of agriculture equipment and vehicles, the powering of irrigation systems, processing and packaging lines, the storage of products, and their distribution. This model is deeply fossil fuel dependent and has been sustained, until now, by the worldwide availability of low cost energy. As a result, the current 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), defined as follows:

2

EE

pliedsup

consumedIS

(1)

Where Econsumed accounts the exploited energy and Esupplied represents the energy content of the products supplied to the consumers. For food products the supplied energy is given by the energy content (i.e., expressed in kcal, Joule, or kWh), whilst the exploited energy is the amount of energy required for agriculture, processing, packaging, storage, and distribution processes. Thus, the global average IS for agro-food products raise from 10 at the beginning of 70s to a value higher than 100 today (Iannetta, 2010). Some examples range from the IS value equal to 127 for salad shipped via air from U.S.A. to U.K., IS equal to 97 for asparagus imported by U.K. from Chile, and IS equals to 66 for South-Africans carrots consumed in U.K. (Church, 2005). A significant part of the overall energy requirements in agro-food supply chain is accounted to logistics operations, including food packaging, storage and distribution. The impact of logistics is even raising due to the markets and demand globalization and the increase of exportation of both commodities and perishable products across the continents. As instance, the whole impact of the Italian agro-food sector is 104 Tg of CO2eq. which roughly represents the 19% of total Italian GHGs emissions (Iannetta, 2010). This amount is split into fractions per different processes/activities as illustrated in Table 1 (Castaldi et al., 2009).

Agro-food Process/Activity CO2eq. (Mtons/year) % CO2eq. Per capite (kg/year)

Agriculture 47.1 45.3 805

Farming, Enteric fermentation 11.6 11.2 198

Farming, Sewage and waste (i.e., N2O, NH4) 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 Table 1. Italian agro-food sector GHGs emissions (Castaldi et al., 2009) Table 1 shows that the large part of GHGs emission due to the Italian agro-food sector are accounted to agriculture (i.e., 45.3% of the total), and logistics processes and operations (i.e., including packaging, storage and distribution) equal to 31.7% of the total. Therefore, assuming the Italian case as picture of the general agro-food systems, significant environmental savings in agro-food supply chains can be achieved through the rationalization of most efficient food corridors and practices from farm-to-fork in terms of suppliers selection, routes planning, and infrastructure design and management. In ordert to address the presented issues and criticalities, this paper proposes and illustrate a decision support tool for the management of sustainable and efficient food supply chains. The proposed tool integrates a mixed integer programming model (MILP) to optimize the energy consumption throughout the food pipeline, across the agriculture, production/packaging, storage and distribution processes. The tool provides quick solutions for supply chain actors, practitioners, and decision-makers in facing food distribution scheduling/planning and infrastructures management. The illustrated decision support tool takes into account energy efficiency and environmental sustainability goals in food supply chains, thereby resulting in cost-efficient network solutions for food logistics management. In section 2 a focused review of the literature is presented. Section 3 illustrates the MILP model and the decision support tool for data collection, optimization run, and solutions reporting. Section 4 presents the application of the tool to a numerical example inspired to some perishable food flows across the Mediterranean area. Section 5 concludes the paper and gives some proposal for further studies.

Energy balance in sustainable food supply chain processes

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2. Literature review

In the last decades, the raising awareness of the environmental impacts of the agro-food sector pushed researchers from multiple disciplines (e.g., agriculture, economics, engineering) to provide models, methods, tools and technical pathways to enhance the environmental sustainability of agriculture and food operations. With respect to logistics and industrial operations, the design of efficient and sustainable food production and distribution networks and activities has gained more and more attention. Since the agro-food chain moves perishable products, the trade-off between stocks, infrastructure and network performances and costs, and the product quality rather than the product expiration is crucial in identifying the optimal logistics choices. For example, the selection of the wrong suppliers or the wrong logistics routes across the network (i.e., packaging/storage facilities) produce useless shelf-life losses, travelled distances, and storage periods, that necessarily affect the quality of products at the retailers or final markets or generate wastes throughout the whole supply chains. In Italy about 1.2 Million tons of agro-food products are wasted at crop level, 2 Million tons at industry/processing level, while 0.3 Million tons are wasted during distribution processes or at the retailing level (Last Minute Market, 2013). These wastes affect not uniquely the economic performance of the supply chains, but produce energy losses equal to the amount of food energy contents plus the energy spent by processing, packaging, storage and transport. Therefore, an urgent need of supporting decision systems enabling the right balance between effectively supplied and consumed energy arises across food supply chain actors and environmental-care policies.

Reference in FSC Problem form / Solving method

Model Decision support

tool

Problem characteristics

Multiple stage

Multiple product

Multiple period

Multiple modal

Multiple obj

Temp based

Quality based

Waste Env

Ljungberg et al., 2006 ILP/Analytical ✓

✓

✓

Van der Vorst et al., 2009 Simulation

✓ ✓ ✓ ✓

✓ ✓

✓

Rong et al., 2009 MILP ✓

✓ ✓ ✓

✓ ✓ ✓

Ahumada and Villalobos, 2009 MILP ✓ ✓ ✓ ✓ ✓ ✓ ✓

Oglethorpe, 2010 Goal Programming ✓

✓ ✓

✓

✓

Bosona et al., 2011 Clustering

✓ ✓

Zucchi et al., 2011 MILP ✓ ✓ ✓ ✓

Gamberi et al., 2013 LP/Procedure ✓ ✓ ✓ ✓ ✓

Accorsi et al. MILP ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓

Table 2. Review of the literature in FSC In order to summarize the contributions from the literature on sustainable food supply chain management, Table 2 illustrates a classification of the crucial aspects to be taken into account when modelling food logistics processes. The column named Model indicates whether the reference proposes a mathematical formulation of the analyzed scenario, whilst the column named Decision support tool indicates if an informatics tool is designed to support decision-makers. Table 2 also reports if the proposed model considers multiple stages (e.g., growers, suppliers, producers, distributors, retailers), multiple products, single or multiple periods (i.e., necessary to meet product shelf life), or if consider single or multi-modal transportation. Other drivers taken into account are the temperature for product storage and transport and the related quality level, as well as the amount of waste generated by supply chain processes. Finally, the objective function of the model is identified in accordance with single or Multi-objective problem, and on the bases of environmental (i.e., column named Env in Table 2) considerations. Ljungberg et al. (2006) collected different issues in food supply chain management. Specifically, they presented a ILP formulation to optimize profit from wheat production-distribution considering the costs of drying, storage and transport in a two-periods problem (i.e., summer-winter periods). They also assessed the impact of transport modality choices for regional grain distribution. Ahumanda and Villalobos (2009) developed a comprehensive and valuable multi-period MILP formulation to support the cost-effective management of a fruits and vegetables supply chains. Their model accounted the agriculture costs and capacities (e.g., including water availability), crops seasonality, production and transport costs, and packaging activities. Rong et al. (2009) proposed a crucial contribution on this field through a cost-based model that accounts the overall food supply chain costs due to production, storage, transport, and waste. They proposed a multi-period, multi-stage MILP model that takes into account product quality and perishability. The level of quality of food depends on the number of periods spent by the product throughout the supply chain process and the conservation temperature for both storage and transport. The minimum level of quality accepted at each supply chain stage determines the

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resulting flow of waste (i.e., not accepted or expired food). Rong et al. defines the formula of the decreasing of quality q with respect to time t as:

nkqdt

dq

(2)

Where k is the ratio of degradation and n can be set to 0 for fresh fruits and vegetables, thereby assuming a linear relationship between quality and time. According to Rong et al. the ratio of degradation k can be defined through the Arrhenius equation:

RT

Ea

ekk

0 (3)

Where k0 is a constant, Ea represents the molecular activation energy, R the gas constant (i.e., 8.31 J/mol K), and T is the absolute temperature. Therefore, (2) and (3) allows to find out the quality degradation level of food product with respect to conservation temperature and time. In order to obtain the adequate value of quality degradation per each food product (e.g., fruit and vegetable, dairy, edible oils, wine, chocolate), these renowned formulas can be improved by in-lab empirical analysis to assess the decay of product due to environmental stresses (i.e., heat, cold, light exposure, humidity, vibration) occurring in handling, storage and transport activities (Manzini and Accorsi, 2013). Zucchi et al. (2011) proposed a MILP formulation to establish the optimal slaughters location for Brazilian meat considering the production and distribution costs across a multi-modal global supply chain. Van der Vorst et al. (2009) developed a simulation tool (i.e., ALADIN™) to support decision-making on product quality, sustainability and logistics. Different supply chains scenarios are proposed and analyzed for pineapples production and distribution. The results of the what-if simulation analysis are collected in terms of logistics costs, transport mode, energy consumption, time spent by each supply chain stage. Bosona et al. (2011) implemented an hybrid methodology to cluster the Swedish growers on the bases of the distance between each other, and establish the optimal location of warehousing/storage facility per each cluster. A multi-objective problem is proposed by Oglethorpe et al. (2010) which adopted a goal programming approach to consider the maximization of ROS (i.e., Return On Sales), as well as the minimization of GHGs emissions, water requirements and food fats. Gamberi et al. (2013) integrated a single-period LP formulation for fresh food supply chain design with an iterative procedure to account the product quality decay across supply chain processes. They focused on the assessment of the equivalent thresholds in transport modality choices for the adequate conservation of fresh food products. This paper builds-up upon existing researches (i.e., Rong et al., 2009 and Van der Vorst et al., 2009) and provides a decision support tool that integrates the main objectives and constraints for green food supply chain management with a user-friendly software interface aiding practitioners and policy-makers in sustainable mid-long terms decisions.

3. Decision support tool

The proposed decision support tool integrates a MILP formulation for the design and management of an energy-saving food supply chain into a user-friendly software platform, aimed to store and organize supply chain data, to optimize the problem, and finally to illustrate the results of the analyzed scenario. This tool aims to define the optimal supply chain configuration considering:

food flows allocation from growers to markets across intermediate nodes (i.e., packaging, distribution facilities),

scheduling of transport activities from-farm-to-fork,

transport modality choices,

transport and storage temperature,

accepted level of food quality at each stage,

energy consumption for all logistics processes. The following sub-sections presents both model objective and constraints and illustrates the tool architecture.

Energy balance in sustainable food supply chain processes

5

3.1 Model

The proposed MILP model optimizes the overall energy consumption due to food supply chain processes from-farm-to-fork including processing, storage and transport. The energy-based objective function allows taking into account the energy balance between the energy consumed by the supply chain and the energy content of food supplied to the consumers. This trade-off is a metric of the sustainability of the supply chain and can lead decision-makers towards innovative pathways and methodology for more efficient, responsible and fair agro-food systems. Furthermore, given the environmental objective function, cost-based consideration can be quickly portrayed through the costs of energy. The model involves four supply chain levels (i.e., growers, packaging nodes, storage nodes, demand nodes) and transport multi-modality choices. The MILP formulation is built upon existing research (Rong et al., 2009), but further includes transport modality choices and capacities (i.e., integer constraints) and adds the growers stage to involve fresh products seasonality and account agriculture energy requirements in the problem.

Distribution Centre (D) Retailers, markets (S)Growers (L) Packaging (P)

Fig. 1. Network configuration Set of indeces i = 1,…, I product q = 1,…, qmax quality level k = 1,…, K temperature l = 1,…, L grower p = 1,…, P packaging node d = 1,…, D storage node s = 1,…, S retailer, market (i.e., demand node) m = 1,…, M transport mode t = 1,…, T period

SDPPDSpds cluster of packaging, storage, and demand nodes

DPPDpd cluster of packaging and storage nodes

Set of parameters demandi,s,t Demand of product i by node s at period t cci,l,t Harvest of crop i from grower l at period t pci,p,t Packaging capacity of product i by node p at period t dcpd Storage capacity at nodes p and d mcm Transport capacity of transport mode m weighti Weight of handling unit of product i cei,l Energy need to crop one handling unit of product i by grower l pei,p Energy required by packaging/processing node p to process one handling unit of product i storageei,pd Energy required by climate controlled chamber to store one handling unit of product i at nodes p and

d

6

mem Energy required by transport mode m (i.e., kWh/km) qmini,pds Minimum level quality accepted for product i at nodes p, d, and s wei Energy losses for product i decay (i.e., waste) coolemk,m Energy required to set the transport mode m at temperature k coolepdk,pd Energy required to set the facility temperature at k for bot p and d nodes timelpm,l,p Periods required by transport m to move from l to p timepdm,p,d Periods required by transport m to move from p to d timedsm,d,s Periods required by transport m to move from d to s distlpl,p Routing distance from l to p distpdp,d Routing distance from p to d distdsd,s Routing distance from d to s varqpdi,k,pd Quality level degradation of product i stored at node p or d at temperature k varqmlpi,k,m,l,p Quality level degradation of product i transported by m from l to p at temperature k varqmpdi,k,m,p,d Quality level degradation of product i transported by m from p to d at temperature k varqmdsi,k,m,d,s Quality level degradation of product i transported by m from d to s at temperature k Set of variables inventoryi,q,k,pd,t Stock of product i in period t at node p or d with temperature k at quality level q transportlpk,m,l,p,t Number of transport means m at temperature k used to move products from l to p at period t transportlpk,m,p,d,t Number of transport means m at temperature k used to move products from p to d at period t transportlpk,m,d,s,t Number of transport means m at temperature k used to move products from d to s at period t xlpi,q,k,m,l,p,t Flow of product i transport by mode m at quality q at temperature k leaving from l to p at period t xpdi,q,k,m,p,d,t Flow of product i transport by mode m at quality q at temperature k leaving from p to d at period t xdsi,q,k,m,d,s,t Flow of product i transport by mode m at quality q at temperature k leaving from d to s at period t wastei,pds,t Flow of expired/decayed product i at period t at any supply chain node

otherwise 0

t period on k etemperaturset at is d or p node if 1z tpd,k,

otherwise 0

t period in k etemperatur at p to l from travels m meantransport if 1t,p,l,m,ko

otherwise 0

t period in k etemperatur at d to p from travels m meantransport if 1t,d,p,m,ko

otherwise 0

t period in k etemperatur at s to d from travels m meantransport if 1t,s,d,m,ko

Objective function The objective function (4) minimize the overall energy consumed across the food supply chain from growers to consumers. The three first contributions account the energy consumed by transport activities as the sum of the energy required by the transport mode plus the energy required to maintain temperature k multiplied by the routing distance and the number of means required. The second three contributions accounts the energy consumptions due to agriculture, packaging and storage processes. Furthermore, the energy contribution to power the climate controller chamber at packaging and storage facilities is considered. Finally, the energy content of products expired and decayed is accounted.

K

k

M

m

L

l

P

p

T

t

t,p,l,m,kp,lm,km ptransportldistlp)coolemme(min1 1 1 1 1

K

k

M

m

P

p

D

d

T

ttdpmkdpmkm dtransportpdistpdcoolemme

1 1 1 1 1,,,,,, )(

K

k

M

m

D

d

S

s

T

ttsdmksdmkm stransportddistdscoolemme

1 1 1 1 1,,,,,, )(

(4)

Energy balance in sustainable food supply chain processes

7

I

i

q

q

K

k

M

m

L

l

P

p

T

ttplmkqili xlpce

1 1 1 1 1 1 1,,,,,,,

max

I

i

q

q

K

k

M

m

P

p

D

d

T

t

t,d,p,m,k,q,ip,i

max

xpdpe1 1 1 1 1 1 1

I

i

q

q

K

k PDpd

T

tpditpdkqi storageeinventory

1 1 1 1,,,,,

max

K

k PDpd

T

ttpdkpdk zcoolepd

1 1,,,

I

i PDSpds

T

ttpdsi wewaste

1 1,,

Set of constraints

k k

tpkqpdqipi pkiinventoryinventory 1, ,,var,t,k,q, , ,,

k m d

tdpmkqik l m

timelptplmkqmlpqi xpdxlpplmlmki ,,,,,,,,,,,var, ,,p ,,,,

t,q qp,i,pi,min

(5)

k k

tdkqpdqidi dkiinventoryinventory 1,Din ,,var,tD,in k,q, , Din ,,

k m s

tsdmkqik p m

timepdtdpmkqmpdqi xdsxpddpmmki ,,,,,,,,,,,var, ,,dp,,,,

t,q qd,i,Din di,min

(6)

ts,i,

DSin ,min

,, ,,,,,,,,

Psi

sdmqq k m d

tsitimedstsdmkqi demandxds

(7)

tl,i, ,,,,,,,, q k m p

tlitplmkqi ccxlp

(8)

tp,i, ,,,,,,,, tpi

q k m d

tdpmkqi pcxpd

(9)

Pin ,,,,,, ,, pi q k m l

itimelptplmkqi dcweightxlpplm

i q k m d

itdpmkqitPinpkqi weightxpdinventory ,,,,,,1, ,,,tp,

(10)

Din ,,,,,, ,, di q k m p

itimepdtdpmkqi dcweightxpddpm

i q k m s

itsdmkqitDindkqi weightxdsinventory ,,,,,,1, ,,,td,

(11)

tp,,qi, Pin ,pk,i, min1- tP,in k,,varqpdq,,,,,,, pi

qinventoryxpdk

pi

k m d

tdpmkqi

(12)

td,,qi, Din d,dk,i, min1-tD,in ,,varqpd,,,,,,, i

qinventoryxdsk

dkqi

k m s

tsdmkqi

(13)

k qpdqq

tpkqitpi

pdkiPDSpdi

inventorywaste

,,in ,min var

1,d,,,,d,

tpd,i,

(14)

8

sdmkisiqmdsqq k m d

tsdmkqiti xdswaste

,,,,Sin ,min var

,,,,,,,Sin s,

ts,i,

(15)

i q k m

itplmkqi

k

tplmkmc

weightxlpptransportl

,,,,,,,,,,

tp,l,m,

(16)

i q k m

itdpmkqi

k

tdpmkmc

weightxpddtransportp

,,,,,,,,,,

td,p,m,

(17)

i q k m

itsdmkqi

k

tsdmkmc

weightxdsstransportd

,,,,,,,,,,

ts,d,m,

(18)

tpdk

i q

tpdkqi zMinventory ,,,,,, tpd,k,

(19)

1,, k

tpdkz

tpd,

(20)

m

tplmk

i q m

tplmkqi oMxlp ,,,,,,,,,, tp,l,k,

(21)

m

tdpmk

i q m

tdpmkqi oMxpd ,,,,,,,,,,

td,p,k,

(22)

m

tsdmk

i q m

tsdmkqi oMxds ,,,,,,,,,,

ts,d,k,

(23)

1,,,, k

tplmkotp,l,m,

(24)

1,,,, k

tdpmko

td,p,m,

(25)

1,,,, k

tsdmko

ts,d,m,

(26)

The constraints (5) and (6) balance the stock of product i at quality level q stored in the processing/packaging/storage nodes p and d with temperature k. As instance for (5), the inventory at period t equals the inventory level at period t-1 with quality level q + varqpd plus the incoming flows from growers l departed at period t – timelp with quality q + varqmlp minus the outcoming flows of product i departing from p at an acceptable level of quality. The parameter varqpd represents the quality degradation of product i during a period t. The constraints (7) ensure the fulfillment of demand of product i by retailers s, which equals at least the flow of product i departing from any d at period t – timeds with acceptable level of quality. The constraints (8) respects the harvest capacity of grower l per product i at period t. These constraints allow taking into account seasonality for fresh and perishables agro-food products. The constraints (9) impose the packaging capacity at p node, whilst (10) and (11) limit the storage capacity at packaging and storage facilities. The constraints (12) and (13) ensure the flow transfer balance across the supply chain stages. The constraints (14) and (15) accounts the product waste due to expiration or decay across the supply chain. The constraints (16), (17), and (18) represent one of the main novelty of the proposed formulation. They indeed allow accounting the integer number of means of transport mode m necessary to ship products across the supply chain stages. Finally, the constraints from (20) to (26) handle the temperature settings and product flows at each temperature k across the supply chains nodes and transport activities.

Energy balance in sustainable food supply chain processes

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3.2 Tool

In order to support decision-making in planning sustainable and energy-saving food supply chain, 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 and inputs/outputs arrangement,

a geographic informative system (GIS) to get routing distances and upload optimization results,

an AMPL interface to run the model with a commercial linear solver (i.e., Gurobi),

a graphic user interface (GUI) for user-friendly analysis and outputs visualization.

The data architecture rationale is to collect all required inputs to run the MILP model illustrated in Section 3.1. A not exhaustive list of such data includes:

food seasonality in terms of harvest and demand,

food characteristics in terms of energy content and handling unit,

food shelf life and quality decay with respect to different storage conditions (i.e., temperature),

potential network configuration (i.e., set of growers, nodes, markets),

energy infrastructure and equipment for agriculture, packaging, storage,

energy requirements for different transport mode. The tool enables to set the time batch considered as a period t (i.e., hour, days, or weeks). The selected time batch allows scaling accordingly all the parameters and inputs (e.g., node capacities, crop harvest, product demand) collected to analyse the observed scenario.

Fig. 2. Main tool GUI The GUI illustrated in Figure 2 allows the decision-maker to choose the different level of quality identified along the product life cycle, from harvest (i.e., maximum level of quality) to expiration (i.e., minimum level of quality). Through an automatic connection with the embedded GIS, the tool receives the routing distances and time for all the

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potential growers, packaging and storage facilities, retailers and sets the timelp, timepd, timeds, distlp, distpd, and distds parameters. The run of the MILP model results in the optimal energy-effective allocation of food flows across the supply chains, the assessment of the network nodes saturation, and the evaluation of the IS rate (1) per product, per stage, per node, per process and for the entire supply chain as a whole.

4. Numerical example

The proposed decision support tool has been tested with a numerical example inspired to small and large scale fruit supply chains across the Mediterranean areas. The fruits considered for the testing phase are pears, peaches and apricots. For the small analyzed dataset, these products are supplied by two growers, packaged in four facilities, then stored by three warehousing facilities, and finally ship to four retailers across the regional area of Emilia-Romagna, Italy. These products are assumed to experience the same quality degradation process as well as they share roughly the same optimal storage conditions (i.e., 0 °C conservation guarantees quite long shelf-life). Ten level of quality are considered across the food life cycle in order to determine the minimum acceptable quality for product at each supply chain step. The observed horizon of analysis is 20 days and all the model parameters are daily scaled. The handling unit is the common fruit case, while considered transport modes include two type of trucks. Average values have been assumed to account the energy consumption of agriculture and packaging/processing activities. The analysis has been iterate according to the following scenarios:

Scenario 1. The first scenario considers a small scale food supply chain within the regional Emilia-Romagna area.

Scenario 2. The second scenario considers a large scale supply chain where growers are located in Spain for peaches and apricots and Portugal for pears.

Scenario 3. The third scenario considers again the small scale regional supply chain but assumes to adopt more energy-efficient equipments/plants for agriculture and packaging facilities. The input energy requirements for agriculture and packaging processes are between 10-12% less than the Scenario 1.

Scenario 4. The fourth scenario stresses the regional supply chain by reducing the storage capacities and packaging capacities of the available facilities.

Scenario 5. The last scenario builds upon the second one, but reduces the storage capacities of distribution nodes and decreases of 10% the energy efficiency of warehousing.

Fig. 3 and 4 illustrates a brief summary of the results obtained by the optimization of the aforementioned scenarios. In Fig. 3 the number of supply chain nodes (i.e., growers, facilities, warehouses) per class of energy consumption are illustrated and compared.

0

1

2

3

4

5

6

7

0-10 10-100 100-1000 1000-10000 10000-100000

Su

pp

ly C

ha

in N

od

es

Energy Consumption (MWh/horizon of Analysis)

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Fig. 3. Scenarios comparison: energy consumption of supply chain nodes In Fig. 4 the same statistics is illustrated per each scenario in accordance with the level of saturation generated per each product. Fig. 4 highlights which product is more responsible than the others to the supply chain energy needs and mostly contribute to a generic class of consumption per each scenario.

Energy balance in sustainable food supply chain processes

11

Peach Pear Apricot

Scenario 1

0

1

2

3

4

5

0-10 10-100 100-1000 1000-10000 10000-100000

Su

pp

ly C

ha

in N

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es

Energy Consumption (MWh/horizon of Analysis)

Somma di Peach

Somma di Pear

Somma di Apricot

Scenario

Scenario 2

0

1

2

3

4

5

6

0-10 10-100 100-1000 1000-10000 10000-100000

Su

pp

ly C

ha

in N

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Energy Consumption (MWh/horizon of Analysis)

Somma di Peach

Somma di Pear

Somma di Apricot

Scenario

Scenario 3

0

1

2

3

4

5

6

7

0-10 10-100 100-1000 1000-10000 10000-100000

Su

pp

ly C

ha

in N

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es

Energy Consumption (MWh/horizon of Analysis)

Scenario

Scenario 4

0

1

2

3

4

5

6

0-10 10-100 100-1000 1000-10000 10000-100000S

up

ply

Ch

ain

No

des

Energy Consumption (MWh/horizon of Analysis)

Scenario

Scenario 5

0

1

2

3

4

5

6

7

0-10 10-100 100-1000 1000-10000 10000-100000

Su

pp

ly C

ha

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od

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Energy Consumption (MWh/horizon of Analysis)

Scenario

Fig. 4. Scenarios comparison: energy consumption of supply chain nodes Finally, Table 3 summarizes the overall supply chain energy performances of each scenario in terms of consumption per processes and the comprehensive IS value.

Table 3. Multi-scenario analysis: results comparison.

Agriculture (MWh) Processing/Packaging Storage Transport Total

Scenario 1 3401 66258 258 33 69950215 663520 105

Scenario 2 2648 62354 243 161 65405429 630400 104

Scenario 3 2937 56318 243 28 59524827 630400 94

Scenario 4 2993 59689 238 35 62954498 663520 95

Scenario 5 1728 57775 270 129 59901523 646980 93

ISEnergy Consumption (MWh)

Scenario Energy Supplied (MWh)

12

5. Conclusions

The rational of the illustrated multi-scenario analysis is to highlight the potential applications of the proposed decision support tool. The tool handles typical logistics decisions for the supply chain management as network design, distribution planning, energy infrastructures and plants design, multi-modality opportunities. Given a specific food supply chain network, the tool optimizes the foods cycles from crops to consumers, leading toward a more energy-saving approach in food supply chain management. Statistics and KPIs in terms of energy requirements (and related costs) for each process, product and node are collected and summarized within the comprehensive and renowned Index of Sustainability (IS). Further studies will certainly include a comprehensive analysis of which are the main drivers and leverages toward a more sustainable food supply chain. A not exhaustive list of potential drivers we will take into account is:

the size of the supply chain (local vs. global vs. glocal, i.e., global when need, local when possible),

the product seasonality (seasonal products vs. off-season products) and the product varieties,

the industrial infrastructure efficiencies,

the transport mode efficiencies,

the intermodality opportunity,

the consumers habits (e.g., diets and quality acceptance).

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