impacts of proximity deliveries on e-grocery trips

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Impacts of Proximity Deliveries on e-Grocery TripsBruno Durand, Jesus Gonzalez-Feliu

To cite this version:Bruno Durand, Jesus Gonzalez-Feliu. Impacts of Proximity Deliveries on e-Grocery Trips. SupplyChain Forum: An International Journal, Kedge Business School, 2012, 13. �hal-01770407�

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Impacts of ProximityDeliveries on e-GroceryTrips

10Supply Chain Forum An International Journal Vol. 13 - N°1 - 2012 www.supplychain-forum.com

Bruno DurandUniversity of Nantes (Lemna), France

[email protected]

Jesus Gonzalez-FéliuUniversity of Lyon 2 (Let), France

[email protected]

This article proposes a discussion of three scenarios related to French e-grocery developments, and identifies and analyzes the effect of newforms of proximity deliveries on household shopping trip flows. One ofour objectives will be to consider logistics solutions adopted by onlineretailers. First, we present the two basic models of B2C: order-picking at a dedicated site and in-store picking. Second, we evaluate threedistribution systems adopted by French e-grocery retailers. We focus inparticular on the impact of these systems on consumers' purchasing tripsand, to this end, we use an empirical simulation approach to make acomparison of the systems studied.

Keywords: e-grocery, warehouse-picking, store-picking, home delivery(HD), out-of-home delivery (OHD)

Introduction

After a slow start, particularly in France, B2C (business-to-consumer) services are nowbooming, which sometimes leadsto fractures, especially in logistics(order-picking and deliveries). Ittherefore seems urgent to worryabout deliveries to Internet users,either directly at home or at pick-up points, because city logisticscould become a key factor in thesuccess or failure of online selling.In past decades, city logistics dealtwith the main problems of urbanfreight distribution, studyingfreight movements in urban areas and proposing solutions toreduce congestion and pollution.Moreover, end-consumer movements,related to household supply, haverecently been studied from a citylogistics point of view (Gonzalez-Feliu et al., 2012). However, most ofthese studies take into accountonly traditional shopping trips,avoiding several categories of trips related to e-commerce andteleshopping distribution channels.

Moreover, e-commerce-relatedstudies focus on customer choicesor optimization approaches infields such as culture and clothing(Rohm & Swaminathan, 2004;Taniguchi & Kakimoto, 2003),whereas e-grocery, one of the fieldswith a stronger potential, is lessstudied (Durand & Vlad, 2011).

For this reason we decided to focuson e-grocery. We wish, in particular,to focus on interactions between e-grocery end-consumer flows andcity logistics systems. Thus, one ofour first objectives will be toconsider logistics solutionsadopted by online retailers. Wepresent the two basic models of e-grocery distribution: order-pickingat a site dedicated to this and in-store picking. Second, we evaluatethe three distribution systemsadopted by French e-groceryretailers. We focus in particular onthe impact of these systems onconsumers' purchasing journeysand, to this end, we propose asimulation approach empiricallybuilt from data surveyed to make acomparison of the systems studied.

© Copyright BEMISSN print 1625-8312ISSN online1624-6039

An International JournalSupply Chain Forum

E-Supply Chain Management

Logistics plays a major role in e-commerce success, yet its statusremains secondary. Indeed, whenonline shoppers receive their orderunder expected conditions, there isno reason to linger there. However,when logistics leaves something tobe desired (delay, theft, loss, etc.),it can make consumers lessinterested in continuing topurchase using the website.Logistics performance is thereforean obviously integral part of theonline transaction.

At the same time, as underlined byBaglin et al. (2005), B2C imposesspecific logistics that, in particular,depend on the products sold. Thereare almost as many kinds of e-logistics systems as families ofproducts; cyber-storekeepers areguided, of course, by the nature oftheir products in choosing them. Italso depends on the nature of theretailer: a storekeeper whose onlypresence is online will not choosethe same options as a colleaguewho also sells in-store. Essaysconcerning typologies are regularlythe object of academic research inthis area, in particular concerningmodel choice criteria (Durand,2008).

According to Dornier and Fender(2001), logistics is an essentialcomponent of web-based retailers'strategies, also called e-tailers.More precisely, two main

components can be identified instrategic logistics management fore-commerce activities: inventorystrategies and transport schemes.If we observe online order-picking(related to inventory), we candefine two basic organizationalmodels (Paché, 2008): (1) order-picking at a dedicated site, forexample, an upstream national orregional warehouse (warehouse-picking) or closer to the place ofconsumption in a downstream localdepot (depot-picking); and (2)store-picking.

Order-picking at a dedicated site

According to De Koster (2002),when the number of SKU (stockkeeping units) for B2C is large(several tens of thousands) andwhen the online activity is notmarginal (several hundreds oforders a day), storage at a specificsite, dedicated to e-commerce,seems a necessity. Three differentinventory schemas are considered:(1) upstream storage, in producers'warehouses for slow-moving items;(2) more downstream storage, forfast-moving products in national(or interregional) warehousesdedicated to e-commerce andmanaged by distributors and/orLSPs (logistics service providers);and (3) far downstream storage, forvery fast-moving articles in urban(or suburban) depots, directlyconnected to online salesstructures and directly managed bydistribution companies.

Let us specify that the firstalternative, that of the order-picking at producers' warehouses,contains several variants (Durand,2010). We will look at the variantthat minimizes the number of HDs(home deliveries) and examine itsprocess (see Figure 1). First, onlineconsumers place orders of severallines (that is to say of several SKUs)on a retail website. Then, the cyber-storekeeper relays the orders to the specific producers. Theseproducers carry out order-picking,giving their parcels to a solitaryLSP to avoiding multiple deliveries(Monnet, 2008). For that, the LSPgroups parcels by customer (it's atype of cross-docking operation)and the suppliers' orders are thusstrengthened. Once assembled, theLSP starts to deliver orders to theInternet users. A single HD perhousehold makes this alternativeunmistakably the most economicand the most ecological variant.

Let us add that we regularlyencounter this first alternative inthe editorial e-supply chainbecause of the several millionarticles available online. However,it is absent in the e-grocery sector.Indeed, the offer of cyber-marketsis only composed of approximatelyfive or six thousand very fast-moving articles. Consequently,grocery items are more oftenstored downstream in warehouses(or depots) allocated todistributors. It corresponds to the two other order-picking


Figure 1Upstream warehouse-picking and in-transit merge operations (Adapted from Chopra & Meindl, 2004)

(Adapted from Chopra & Meindl, 2004)

alternatives. According to Yrjölä(2003), a logistics unit dedicated toe-grocery operations justifies itselfbecause the number of onlineconsumers per km2 is increased. Interms of final delivery, we alsoobserve several variants: themanagement of HD being integratedin or delegated to LSPs, or a hybridof the two.

Store-picking

Online retailers who choose to leanon a network of existing stores optfor a very simple process and onethat is quickly operational. Thismodel, which was the cornerstoneof Tesco's e-grocery success, isbased on online orders beingtransferred to the store nearest tothe e-consumer's location. Order-picking is often made by employeesof the store concerned (they pickarticles from shelves) and, once thebasket has been filled, HDs are, in

general, made by the storekeeperor by an LSP, with a multi-temperature vehicle. So, usingexisting infrastructures, store-picking is characterized by areduced investment and, therefore,by a very short ROI (return oninvestment). Another asset of thismodel is the fact that onlineconsumers can opt to pick upgoods purchased directly in-store(as shown in Figure 2), reducinglogistics costs in this way. So, thismodel also constitutes an OHD(out-of-home delivery) alternative.However, this second modelcontains a risk: that of thedisturbance of traditional in-storecustomers by pickers. Faced withthis eventuality, which could resultin leaks of consumers, Ogawara etal. (2003) suggest adoptingwarehouse-picking as soon as thecustomer catchment area has goodpotential. In any case, the store-picking model constitutes the proof

that online business does not meanthe death of outlets: indeed, theirmobilization could be an invaluablesupport to e-logistics.

These two basic models of B2Clogistics continue to be the objectof academic works (Marouseau,2007). But, we already can findthem in the practices of onlinestorekeepers, in particular in theFrench market.

Logistics practices observed by French cyber-traders

To sketch a state-of-the-art logisticssystem practiced by French cyber-storekeepers, we adopted aresearch methodology (see thebox) that we apply to threebusiness sectors: floral, editorial,and food.

The research shown in Table 1gives a summary, allowing us tonote that the studied e-supplychains often lean on organizationsstemming from the old economyand therefore already integratepreoccupations about urbanlogistics.

Of the three business sectors, wehad time only to look at e-groceryin depth; we conducted numerousinterviews in this particular sector.It is for that reason that we aregoing to limit our article todiscussing e-grocery, focusingspecifically on the evaluation ofthree distribution systems that theFrench distributors Intermarchéand Auchan have developed. In thefuture, we will conduct additionalinterviews in the floral and editorialsectors and analyze them.

The Expressmarché logisticsmodel Having consumers pick uptheir goods directly in the store, analternative to store-picking, seemsto have convinced the mosthesitant French distribution brands (Durand, 2009). The grocery retailer Intermarché chosethis model to control its logistic costs. Three hundred of their supermarkets, calledExpressmarché, are now cyber-markets. Intermarché chose to takeadvantage of the density of itsnetwork (a selling point every 18


Figure 2Downstream store-picking and e-consumers pick-up operations(Adapted from Chopra & Meindl, 2004)

The mobilized approach is of a qualitative nature. Fifteen semi-directiveinterviews, of an average duration of one hour and thirty minutes, wereconducted: three in the floral sector (Interflora, Aquarelle, and Bebloom), twoin the editorial sector (Fnac and Alapage), and ten in the food sector withseven French large distributors (Carrefour, Auchan, Cora, GaleriesLafayette, Intermarché, Système U, and Leclerc). These conversations werethen the object of an accurate analysis of the speech, in the same way asPaillé and Mucchielli (2003), and therefore revealed the perspectives of 12e-supply chains.

Frame 1Methodology of research used

km). Not only can HD beaccomplished because of this verygood territorial cover, butExpressmarché also made availabletwo pick-up or OHD alternatives:the classic in-store pick-up and thedrive-through, which means thatInternet users do not need to leavetheir vehicles.

The Auchandirect logistics modelAuchan is one of the first largeFrench retailers to have invested inthe e-grocery market by launchingAuchandirect in 2001. At this time,the customer catchment area,served by the central warehouse ofChilly-Mazarin (near Paris), waslimited to the southern region ofParis. Since then, while remainingcommitted to warehouse-picking,Auchandirect has widened itsnational coverage by opening fivenew sites: a second in Ile-de-Franceand four near major cities (Lyon,Lille, Toulouse, and Marseille). In2004, Auchan expanded its digitaldistribution, developing analternative parallel cyber-marketcalled Chronodrive.

The Chronodrive logistics modelThe Chronodrive alternativecorresponds to an original OHDconcept. Orders are prepared innearby depots that are located in

big city suburbs. To differentiatefrom warehouse-picking, we use theterm depot-picking to describe theactivity of these infrastructures,which are exclusively dedicated tostorage and to order-picking (theyare not stores). Internet users cometo pick up and adjust their orders. If warehouse-picking can beassociated only with HD and if thestore-picking authorizes both HDand basket pick-up, Chronodriveallows only the order pick up.Except for the fact that it favors theterritorial extension of the ofAuchan's e-grocery activities, theChronodrive alternative allows thedistributor to bypass the HDproblem. Currently about 20 sitesare operational in France andprofitability seems satisfactory.New depots are expected to open,the objective being, according toSilly (2008), to quickly reach onehundred sites.

We have just sketched a partialstate of the logistical alternativesused by French cyber-storekeepers,more exactly, by French e-grocersin addition to their brick-and-mortar systems, where it ispossible to receive backing from anexisting network of stores. It isindeed necessary to know thatthere had been some French pure

cyber-storekeepers, who dashedinto e-grocery and failed to delivergoods to their customers because,effectively, they didn't havenetworks of stores. In summary, wehave to underline that, faced withdifficulties caused by HD, French e-grocers are more and moreinterested in two types of OHD: (1)pick-up directly from their stores;(2) pick-up from suburban depots,such as used by Auchan via itsChronodrive model.

Let us underline that, generallyspeaking, OHDs are less expensivethan HDs, at least for e-grocers.Nothing proves, however, theecological interest of OHD. Browneet al. (2005) show that OHDs canresult in an increase of greenhousegas (GHG) emissions. This systemcan generate more movementsthan traditional in-store shopping.Such uncertainties about theadvantages of OHD over HD requiresimulations of typical scenarios ofurban logistics and, especially,comparative analysis of theenvironmental disturbancesproduced by each of thesescenarios to be carried out. Thenext section will look at this bystudying three e-grocery logisticsmodels.


Type of e-supply chain Supply Flow management Logistics model LSPs involvement

Store-picking for Interflora(brick-and-mortar)

None(insourcing of

deliveries)Floral productsLimited

Only some SKU tens

Pull

Bunch built-to-order Warehouse-picking forAquarelle & Bebloom

(pure players)

Transportoutsourcing

(Chronopost…)

Editorial productsVery large

Several millions SKUs

Push

Large stocks,upstream by suppliers

Warehouse-picking

- insourced by Fnac(brick-and-mortar)

- outsourced by Alapage(pure player)

Transportoutsourcing

(Chronopost…) and

Storage outsourcingby Alapage

Store-picking forIntermarché, Système U,

and Leclerc

NoneSystème U and

Leclerc don’t practiceHDFood products

(e-grocery)

Large

Several thousand SKU

Push

Large stocks,downstream by

retailers(brick-and-mortar)

Warehouse-picking forCarrefour, Auchan, Cora,

and Télémarket

Adaptable Carrefour practices

outsourcing, whereasAuchan insources

Table 1Logistics practices observed by French cyber-storekeepers (Durand, 2007)

Interactions Between E-Grocery and City Logistics

As stated by Ségalou et al. (2004),the urban goods movement (UGM)is composed of several categoriesand subcategories. In this article,we are interested in two types ofmovements: last mile inter-establishment movements and end-consumer movements, whichare required to evolve with the development of e-grocery. Inter-establishment movementsrepresent about 40% to 45% of thetotal UGM in an urban area (Patier,2002). The last-mile flows ofretailing activities are estimated tobe 11% of total UGM (Routhier etal., 2009), whereas those related toonly grocery are about 9%.

End-consumer movements representabout 45% to 50% of the total UGM(Patier, 2002). Nowadays, most ofthese flows are tradition shoppingtrips, but the new forms ofdistribution need to be taken intoaccount from a global city logisticspoint of view. E-grocery currentlyrepresents less than 5% of totalshopping trips but could represent,according to Georget et al. (2008),more than 15% by 2020. Regardingtransport models from a citylogistics point of view, three mainstrategies are commonly seen inpractice: (1) HDs from a specificwarehouse, (2) HDs from a store,(3) OHDs through a store or adepot.

Home deliveries from a dedicatedwarehouse

To receive HDs from a specificwarehouse, orders are prepared bya warehouse-picking process.Important changes are then notedin the supply chain because thisnew and dedicated warehouse isnot located in a peripheral area.The ordered products are deliveredto the place of consumption usinglight goods vehicles, through anoptimized route. These trips aremade by delivery vehicles and canbe assimilated to traditional e-commerce HDs with morerestrictive constraints (Durand &Vlad, 2011).

Home deliveries from a traditional store

To receive HDs from a supermarket,orders are prepared by a picker inthe lanes and in the shelves of astore. This outlet, generally asupermarket of 2,000 squaremeters, is located on the outskirtsof the urban area, a few miles awayfrom the consumer home. Thereare no major changes in the supplyprocess of the store. Thepurchased products are eitherdirectly delivered to the home orpicked up by the consumer, mainlyby car, avoiding queues and waitingtimes. These trips can be thenassimilated to personal trips forshopping purposes (Gonzalez-Feliuet al., 2012).

Out-of-home deliveries via a storeor a depot

The main difference in the supplyprocess to make OHDs throughproximity pick-up points is newlocal depots. This time, indeed, theordered products are directlyprepared either in a depot (that is,in a new site) through a depot-picking process or in a store by aclassical store-picking process.These two different types of points,in which the products are finallypicked up by the final consumer,are both located near the place ofconsumption (Augereau & Dablanc,2008).

Finally, we would like to offer ashort overview of e-grocerydevelopment. If online sales affectalmost all business sectors, one hasto admit that e-grocery is still aniche market: its turnover was onlyabout 1.2 billion euros in 2009 inFrance. Additionally, currently onlyabout three million French Internetusers use online supermarkets.This type of sale is attractive firstfor reasons of practicality and oftime savings. Consumers want tosave time during food purchasing intwo ways: (1) reducing (or even byeliminating) their round trip traveltime to the store and also the timeof spent looking for a parking spaceand (2) eliminating waiting times atfood preparation counters and atcheckout. Internet users underlinethe practicality of online sales, also

in two ways: (1) online stores arecontinuously open, 24 hours a day -therefore this scenario allowstransactions at any time of the day- and (2) online orders can bedirectly delivered or dropped off atpick-up points. Consideration ofenvironmental problems alsoseems to push households toincrease their Internet purchases:they see a positive environmentalimpact because of the perceivedreduction of movements and ofGHG.

The cost of this service, however,seems to constitute the majorobstacle to e-grocery developmentbecause, in the mind of manyFrench people, online shopping ismore expensive: either the price ofproducts sold on Internet is higherbecause it integrates the cost ofbasket picking and delivery costsor the price of articles is the sameas in the store but online shoppingis added to the logistic servicecosts. Less sensitive to this costthan the other SPCs (socio-professional categories), the SPC+(upper SCP) is also, at the moment,the category most attracted by e-grocery: more than half of theirfood income is already being madein cyber-markets, where the offer isparticularly reduced with only7,000 SKUs compared to 40,000 in atraditional supermarket.

Simulation as an EvaluationTool For E-Logistics

In this section, we provide anassessment of three distributionscenarios adopted by French e-grocers: (1) one that allows onlywarehouse-picking, which istranslated into HD services only;(2) one based on store-picking andthat combines HD services with in-store, pick-up shopping trips; and(3) one that conversely offers onlya pick-up service from a nearbydepot.

The proposed scenarios

In order to isolate the effects of e-commerce from other effects, suchas population growth or changes in retailing demography, wepropose several hypotheses basedon changing only the end-consumer


supply organizational schemas(with the respective inter-establishment changes ifapplicable):

• S0: A reference situation corresponding to those of theurban area of Lyon in 2005-2006(Gonzalez-Feliu et al., 2011).

• S1: A “warehouse-picking and HD” scenario. This hypothesissupposes that the onlydistribution channel for e-groceryservices is that of HDs using awarehouse-picking strategy. Thissupposes the use of a regionaldepot and then simulates HDroutes from this depot. Thisscenario supposes that only largee-grocery groups are proposingthese services.

• S2: A “store-picking and HD” scenario based on theassumption that all householdsasking for e-commerce servicesare served by a store within theirurban area. This scenariosupposes two types of retailingactivities: small retailers who willcover small routes from alllocations within the urban areaand big stores that will useperipheral stores as the startingpoint of longer routes.

• S3: A “depot-picking and OHD” scenario based on theassumption that only depot-picking can be used byinhabitants for e-commercepurposes. These depots arelocated in areas that already havea supermarket in order to obtaina realistic set of depots.

For each hypothesis, a quota of 10%to 50% of e-commerce users issupposed. Moreover, bothwarehouse-picking and store-picking strategies will be simulatedeach time.

Simulation procedure

The simulation procedure chart isshown in Figure 3. We assume thatall strategies follow a store-pickinginventory schema because this isnowadays the most interesting interms of environmental and socialimpact (Durand, 2010). For this

reason, only B2C flows will besimulated.

The simulation procedure is anadaptation of Gonzalez-Feliu et al.'smethod (2012), defined as follows.First, following each scenario'sassumptions and hypotheses, theinput data files are generated. Notethat the simulation needs, as input,an e-commerce user rate defined asthe percentage of the total numberof households using e-commerce.From these different rates, weestimate the number of shoppingtrips to be substituted, using thesubstitution procedures defined byGonzalez-Feliu et al. (2012). Then,we use a catchment area model(Gonzalez-Feliu et al., 2011) toidentify each pair of origin-destination, that is, to find, for apotential delivery, its origin (astore, a warehouse, or a depot) andits destination (a household).Following Alligier's (2007) andDurand & Vlad's (2011)considerations, we define twotypes of HD routes, one for store-picking deliveries and one forwarehouse-picking last-mile trips.Then, we estimate the routes usinga procedure adapted from Gendronand Semet (2009) and Routhier etal. (2009). This procedure works asfollows: given a starting point and a

set of possible destinations of aroute, we assign a number ofdestinations to the route in order tominimize the total transport cost,respecting two main constraints:(1) the total number of drivinghours including the driver's breaksis less than nine (legal value) and(2) the vehicle capacity is notreached. The time constraintsrelated to the customer'spreferences are taken into accountfollowing an empirical method(Gonzalez-Feliu, 2008).

The B2B flows delivering thedifferent retailing activities of a cityare extracted from a generaldiagnosis using the Freturb model(Routhier & Toilier, 2007). In orderto evaluate the real impact of B2Cflows on urban freight transport,we take into account all the urbanB2B flows, where last-mile retailingactivities represent less than 10% interms of total road occupancyrates. These flows are given byFreturb's generation module usingan establishment file and ageographic division of thesimulation area as inputs. Then wesubstitute the flows correspondingto the new B2C activities (i.e.,warehouse-picking HDs and depot-picking HDs, whose impact on B2Bflows is positive [decrease of thenumber of last-mile deliveries]).


Inter-establishment flow module

Traditional purchasing flow

module

GHG emissions (in tons of CO2-eq.)

Estimation of trips to substitute

Substitution procedure

Substitution of downstream

Figure 3Integrated simulation procedure chart

(Adapted from Gonzalez-Feliu et al., 2012)

Finally, the results are aggregatedto estimate the total travelleddistances by type of vehicle. Threeindicators are proposed: (1) theoverall distance, in kilometers, foreach category of flows; (2) the roadoccupancy rates, in PCU1; and (3)GHG emissions rates, in tons ofequivalent CO2, estimated usingthe IMPACT ADEME software(ADEME, 2003). The area ofapplication is the urban communityof Lyon, the second largest urbanarea in France in terms ofpopulation, Paris metropolitanregion being the first. This choicewas made mainly because of dataavailability (all the required datafiles are complete and have beenpreviously processed for modelingand simulation tests). The Lyonurban area consists of about2,000,000 inhabitants and 800,000households. We used a databasethat derives from the 2006household trip survey of the Lyonurban area (Grand Lyon, 2011) andthe 2005 establishment of acensorial database (SIRENE2).

Simulation results

We are thus able to establish anumber of results from which wecould develop a comparativeanalysis of the three systemsstudied. These results areexpressed in kilometers (see Table2), which are directly linked to thetransportation costs identified bythe e-grocery and by calculatingthe road occupancy rates (in km-PCU) and then the GHG emissions(in tons of CO2 equivalent units).Note that the reference scenarioproduces nearly 2,300 billionkilometers per year in Lyon's urbanarea. Moreover, the downstreamdelivery flows in 2006 wereconsidered negligible.

From them, we can observe thatscenarios 1 and 2 lead to an overalldistance increase. In all cases, pick-up and delivery flows, whose costsare assumed by retailers proposingproximity delivery services, are notnegligible. Thus, we can state thatthese costs have to be taken intoaccount not only in theoptimization process but also inpricing and tariff developmentsbecause they have an effect on the

company benefit margins. Moreprecisely, the use of peripheralwarehouses for only HD for e-grocery leads to large HD distances(each route is about 150 to 250 km,according to Durand and Vlad[2011], and delivers 35 to 50households). This scenario (1) isthe less favorable in terms oftravelled distances. Scenario 2,which mixes HD and pick-upservices, follows the same trend,but with much lower impacts. Inboth cases, the travelled distancesof HD are bigger than the gainsobserved on both last-mile B2Bflows and shopping trips. This canbe explained by the fact that peopleindividually optimize theirshopping trips, sometimes bymaking work-shopping-householdtrips, which lead to a distanceincrease of only 2 to 5 km per trip(Gonzalez-Feliu et al., 2011), muchlower than the associateddistances of a delivery route.Scenario 3, which uses nearbydepots, presents a decreasing trendof distances. This is due to the factthat depot delivery routes arebetter optimized than HD routes.This scenario is then the mostfavorable but the gains are

contained: about 5% of overalldistance reduction, mainly due tosupermarket and hypermarketshopping trips decreasing.

Regarding road occupancy rates,we can observe that scenario 1,which uses specific peripheralwarehouses with only HD for e-grocery, and scenario 2, whichmixes HD and pick-up services, areless favorable in terms of roadoccupancy rates than scenario 3,which uses nearby depots. In thetwo first scenarios, the decrease inindividual movements related topurchasing do not efficientlycompensate the increase due to theuse of commercial vehicles for HDservices, which does not seem tobe optimized. In scenario 1, almostall the gains made in terms ofshopping trips are neutralized bylong and sub-optimized HD routes.In scenario 2, these routes are


1. Road occupancy rates are estimated in pri-vate car units (PCU), defined as follows: 1 pri-vate car = 1 PCU, 1 light goods vehicle = 1.5PCU, 1 simple truck = 2 PCU, and 1 semi-articu-lated = 2.5 PCU.2. The SIRENE files are produced by the FrenchInstitute of Statistics (INSEE).

Table 2Simulation results in total travelled distances in the Lyon urban area (km/year)

(IEM: inter-establishment movements, PD: proximity delivery movements, ST: shopping trips)

better optimized (the starting pointis in general inside the urban zoneor in the first periphery) but, ingeneral, car pick-up generates moredistance and, thus, more roadoccupancy rates than traditionalshopping. Scenario 3, the “pick-upeverything” is more favorable, butthe gains in road occupancy ratesremain small: less than 9% gain intons of CO2-eq. when the use rate is50%.

If we convert these results into GHGemissions rates, we observe thanthe variations are similar (thedifferences between roadoccupancy rates and GHGemissions present differences ofabout 0.2% to 0.4%). Note that GHGemissions rates have beenestimated assuming the currentfleet distribution, that is, almost allvehicles are diesel, and the ages ofthese vehicles are distributed usingADEME's (2006) ratios. For thesereasons, road occupancy and GHGemissions rates almost match.Making further assumptions, forexample, transferring freight from

high-polluting schemes (becausevehicles used are more polluting)to B2C schemes, using methane orelectric vehicles, will increasethese gains (in terms of GHGemissions) without altering theothers (travelled distances androad occupancy rates). Thishighlights an interesting question:what kind of action has to beconsidered to create CO2reduction? Technology does notseem to change the organization ofa supply chain, and onlyorganizational changes seem to befundamental for an efficient GHGreduction (Routhier et al., 2009).Indeed, only organizationalchanges (as shown in our scenariosimulation) have an impact oncongestion. However, it isimportant to support the choices ofpublic authorities and privateactors (Gonzalez-Feliu & Morana,2010) to help them to find aconvergence between individualcost-reduction targets and acollective vision of congestionreduction and environmental-friendly practices.

Conclusion

In this article, we have given anoverview on the latestdevelopments in e-grocerydistribution and presented ascenario analysis using anempirical simulation approach.Three scenarios, each of themrelated to a new form of B2Cservices (HDs, shopping trips in acar, and proximity pick-up points)have been presented andsimulated. We can observe thatscenario 1, “all-HD,” and scenario 2,which mixes HD and pick-upservices, appear to be lessfavorable than scenario 3. Although the individual purchasemovements decrease, the use ofcommercial vehicles for all-HD doesnot seem to be optimized in thisconfiguration. The resulting gain inGHG emissions is respectivelyabout 4.3% and 4.1% when the userate is 50%. Scenario 3, the “pick-upeverything” would apparently bemost favorable: almost a 9% GHGemission reduction when the userate is 50%. This reflects a sharpdecline in motorized shoppingtrips, the assumption being madethat the depots are located near theheart of residential neighborhoodsand the density of these points issufficient to lead to changes in userbehavior, including the use of theircar. Finally, through the externalimpacts of household supplies, weshow that consolidation of HDs andproximity reception points (wheremost trips are made on foot) canlead to significant savings.

The remaining question concernsthe managerial implications of thethree scenarios. The first scenario,in which 40% of e-shoppers opt foran in-store pick-up service, raisesthe key question of the nature ofthe operator who must support theHD services. Does the e-tailerassume this role? Shouldn't an LSPassume that role? This secondalternative would be to fine-tunethe prospect of consolidating andsharing online order processing onurban platforms to reduce thenumber of HDs per household.

Regarding scenario 2, the “all-HD”choice, the internalization of HDappears relevant because it


Table 3Simulation results in road occupancy rates (km-PCU/year) and in tons of CO2-eq./year in the Lyon urban area

(IEM: inter-establishment movements, PD: proximity delivery movements, ST: shopping trips)

generates transport cost savings.However, LSPs specialized in thefield in France, starting with Star'sService, for example, also seemable to offer quality services at avery reasonable price. Finally, thelocal depot option is the mostinteresting in terms of reducingCO2 emissions but also the mostcostly and takes the longest toimplement. The deployment oflocal depots requires significantinvestment (Augereau & Dablanc,2008), which inevitably leads tohigher management costs. Apooling of these infrastructuresthrough urban platforms couldthen be the best solution to theurban delivery problem (Paché,2010), although this strategyremains long and arduous(Gonzalez-Feliu & Morana, 2010).

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About the authors

Bruno Durand is a lecturer in management sciences at the University of Nantes, where he co-manages the International Logistic fifth-year university program in the Language Faculty(Department of Applied Foreign Languages). As ASLOG administrator (French Logistics'Association), he pursues his research work within the LEMNA (Nantes-Atlantic Laboratory ofEconomy and Management), most particularly in the field of e-logistics and city logistics.

Jesus Gonzalez-Feliu is a research engineer at the French National Centre of Scientific Research(CNRS) and member of the Laboratory of Economy in Transportation (LET). He obtained his PhDin information and systems engineering in 2008 at Politecnico di Torino (Turin, Italy) in the subjectof operations research as applied to urban freight distribution solutions and two-echelon vehiclerouting problems. His research interests include city logistics planning and policy, decision supportsystems, shopping trip modeling, and the effects of e-commerce on urban mobility, transportationsystem optimization, and performance and collaborative transportation.


impacts of proximity deliveries on e-grocery trips

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