Interactive choices in city logistics: innovative practices

Transcription

1 Interactive choices in city logistics: innovative practices Edoardo Marcucci 1, Romeo Danielis 2, Guido Paglione 3 University of Urbino, Institute of Economic Science, Via Saffi 42, University of Trieste, Economic and Statistics Science Department, Piazzale Europa, Institute for Transport Studies, University of Leeds, University Road LS2 9J

2 Motivation of the paper Past work on: Freight transport analysis Modal shift from road to intermodal in long distance shipments Shipper s evaluation of freight transport service attributes (value of time, value of reliability) Freight distribution in urban areas

3 Motivation of the paper The need to study the interaction (coordination, group choice, bargaining): between the shipper and the transport operator in point-to-point long distance freight transport between the producer, the distributor, the transport operator(s), the retailer (=the logistic chain) in an urban area goods distribution in this paper, the interaction between the shopkeeper and their customers for a Park&buy service in Pesaro (Marche).

4 Theoretical and methodological background Shipper-transport operator, negotiation by signing a contract Aim: analysis of the bargaining process (offer, counter-offer, concession, revision) D. Hensher and associates at ITLS, Sidney, various papers) End result observed, agreement or disagreement

5 Theoretical and methodological background IACE (Interactive Agency Conjoint Experiments): tasks (alternatives and levels) fixed by the modeller, S chooses, TO chooses, iterative questioning Pros: observes interaction in choice Cons: difficulty in identifying the pairs, costly, may not reach agreement, too few observations

6 Theoretical and methodological background MIGI (Minimum Information Group Inference) TR-B, 2007: Fixed starting point, inference on the end result via a choice coordination algorithm collecting information non only on the first choice, but also on the acceptability of the other options. Group utility function Pros: cheaper, estimate of the intial pass power model and concession model, estimate on the power of influence on specific parameters Cons: limited cases of agreement, fixed attributes

7 Theoretical and methodological background SEAL (Stated Endogenous Attribute Levels) offers and counter-offers Pros: observes the bargaining process, generates more data, not only agreement data, but the whole bargaining process Cons: costly

8 The case study (Historical) city centers have severe traffic restrictions, LTZ (limited traffic zones) Shopkeeper claim that traffic restrictions: hamper their business opportunities because customers cannot park their cars close to the shops (they need to carry their shopping bag to the parking lots located outside the city center) reduce the attractiveness of many commercial activities located within the city center to the advantage to surburban shopping malls with large parking facilities

9 The case study: a Park&Buy service in Pesaro Siena 2004, edrul pilot project - Park&Buy service Idea: Buy in a shop located in the LTZ. Have the parcels delivered to the parking lot (at home, at the hotel for tourists) by a logistics agency

10 Research questions: Will it work? Will it be economically sustainable? Who should pay for the service? Shopkeepers, customers or both and in which proportion? How quickly should the parcels bought in the shops be made available at the parking lot? Should the parcels be delivered on occasion to other destinations, e.g. home or hotel delivery? And who should pay for that extra-service? Should the service be organized using information technology or not?

11 Main features of the case-study Not existing service No bargaining. Shopkeepers organize and offer the service. Customers decide whether to use it or not. No counter-offer. (Ultimatum game) Both preferences of shopkeepers and customers count. What are the prospects for the P&B service? Is there any overlapping? Which trade-offs are they willing to accept?

12 The proposed methodology Starting values: attribute levels set by the shopkeeper (ultimatum game) Information on shopkeepers perception of their customers preferences Series of SC tasks based on the starting values Information on customers acceptance of shopkeepers choice

13 The SC experiment Table 1 - An example of choice task submitted In your view a Park-and-Buy project (delivering parcels to the parking lot) would make sense for your business? If, yes, what characteristics should have? Attributes A* B* None of the two is convenient to me Cost per parcel to be charged to the shopkeeper Cost per parcel to be charged to the customer Minutes within which the parcel should be available at the parking lot With the use of information technology? Yes or no? Yes no - Destinations other than the parking lot: No \ Yes, charging the extra cost to the customer \ Yes, charging the extra cost to the shopkeeper No Yes, charging the extra cost to the customer Preferred alternative by the shopkeeper Preferred alternative by the customer with no knowledge on shopkeeper s preference Does the client accept the alternative chosen by the shopkeeper? *In the first task A reads as follows: This is the optimal solution for my business and B as follows: This is the optimal solution for my customers -

14 Sample Case study: Pesaro (Marche) Italy 21 shops, 19 customers

15 Estimated models Choice model of the shopkeeper Choice model of the customer Choice model of the group based on an additive linear group utility function Initial pass power model with both agreement and disagreement cases Initial pass power model with agreement cases only Choice model of the group based on a multilinear additive group utility function

16 Results Descriptive Econometric Simulative

17 Descriptive results: the bargaining area retailer cost customer cost Interview Number

18 Descriptive results as proposed by retailers clients preferences as perceived by retailers as accepted by retailers as accepted by clients as accepted by both Cost to be paid by retailers Cost to be paid by clients Minutes w ithin w hich to despatch goods

19 Econometric results Shopk. Cust. Full PM Re. PM Variable β t-stat β t-stat τ t-stat τ t-stat Cost to be charged to the shopkeeper Cost to be charged to the customer Minutes within which the parcel should be available at the parking lot Use of information technology Extra-cost to be charged to the shopkeeper for other destinations Extra-cost to be charged to customers for other destinations

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21 Simulation Attributes Alternative A Preferred by shopkeepers Alternative B Preferred by customers Cost to be charged to the shopkeeper Cost to be charged to the customer Minutes within which the parcel should be available at the parking lot Use of information technology 1 1 Extra-cost to be charged to the shopkeeper for 1 0 other destinations Extra-cost to be charged to customers for other destinations 0 1 Models: P(A) P(B) Shopkeepers choice model 79% 21% Customers choice model 0% 100% Complete intitial pass power model 1% 99% Agreement only intitial pass power model 0% 100%

22 Illustrazione grafica A Frontiera delle possibilità B

23 Conclusions Extending the traditional single agent, fixedattribute level SC experiments to analyze group choice or agents interaction is important in freight and logistics analysis Both the models and the methodology of data collection is not yet established Interactions could be very specific and require ad hoc methodologies Park&buy service: there is interest for the service, agreement is possible, customers preferences are likely to prevail

24 Thanksforyourattention!

25 Linear group utility function U n = wu g i i i= 1 Ug=individual utility W=weight Ui=individual utility ( ) ( 1 )( ) j = τ β j + τ β j + ε j g sk sk k sk ck k U X X Power Systematic utility

26 Multi-linear gruop utility function n n n ( ) U = wu + w u u +... w uu... u g i i ii 12 i1 i2 i n 1 2 n i= 1 i = 1 i > i Interaction terms

27 Initial Pass Power Model ( ) ( 1 ) ( 1 ) ( 1 ) ( 1 ) ( 1 ) ( 1 ) ( 1 ) ( 1 ) ( ) ( 1 ) U = α + τ β x + τ β x + ε sk sk 1k sk ck 1k 11 ( ) ( ) U = α + τ β x + τ β x + ε sk sk 1k sk ck 2k 12 ( ) ( ) U = α + τ β x + τ β x + ε sk sk 1k sk ck 3k 13 ( ) ( ) U = α + τ β x + τ β x + ε sk sk 2k sk ck 1k 21 ( ) ( ) U = α + τ β x + τ β x + ε U sk sk 2k sk ck 2k = α + ( τ β ) x + ( τ ) β x + ε 23 sk sk 2k sk ck 3k 23 ( ) ( ) U = α + τ β x + τ β x + ε sk sk 3k sk ck 1k 11 ( ) ( ) U = α + τ β x + τ β x + ε sk sk 3k sk ck 2k 22 ( ) ( ) U = α + τ β x + τ β x + ε sk sk 3k sk ck 3k 33