ebtehaj proposal 2011

TWO DISTRIBUTION TACTICS FOR RETAILDEMAND FULFILLMENT

Milad Ebtehaj

University of Massachusetts Amherst

Proposal Defense

December 14, 2011

Milad Ebtehaj University of Massachusetts Amherst

Dissertation Proposal 1 of 48 Two Distribution Tactics

Introduction

I Global retailing has doubled from seven to fourteen trillion dollarsin the first decade of the twenty-first century.

I Customer expectations have increased significantly.I In-store availability of products and promised delivery of online

purchases, are identified as the two key success factors in thecurrent retail competition.

I Distribution makes product available to a customer.I Nimble distribution tactics are essential to furthering efficiencies of

retail delivery and replenishment operations.



Retail Competition

I Retail sales are expected to increase by three tofive percent in 2011.

I Retailer growth plans are estimated to be 40percent more than in 2010.

I Retail expansion plans specially in drugstores,discount retailing, and fast-casual restaurants areon the rise.

I Key competitive factors in retail marketplace:

I Product AvailabilityI Multi-channel Retailing



Product Availability

I Is product available to customers?

I Product in-DC availability is not equivalent to in-store availability.

I Product in-store availability is not equivalent to on-shelfavailability.

I Product on-shelf availability does not mean it is available.

I In online shopping, a purchased product is not yet available to thecustomer.

I It is the distribution system that makes the in-DC availableproduct, in-store available and an online purchased product,available to the customer.



Multi-Channel Retailing

I Consumers have different channel preferences.

I Multi-channel retailing satisfies the buying preferences of abroader range of customers.

I Increasing consumer desire for web-influenced smart shopping is aprimary motive for multi-channel retailing.

I Product characteristics define the suitability to a particularchannel.

o Customer’s point of view: for example, Is it important to see, feel,smell, or try an item?

o Retailer’s point of view: for example, what is the inventory value ofthe product?

I The efficiency of distribution system is essential to a successfulmulti-channel strategy.



Online RetailingI Online population of the world is growing fast from 1 billion people

in 2005 to 1.6 billion in 2009, and to 2.4 billion at the end of 2010.I The online market will enlarge quickly in the next decade.I According to Data Monitor, global on-line retail sales will increase

to 778.6 billion dollars by 2014, which is equivalent to an annualgrowth rate of 22.2 percent.

I From an entrepreneurial retail entries point of view, counterrecessionary aspects of online channel motivates its expansion.

Low operating cost

Saving time for customers

24-7 operating schedule

Cheaper for some products



Retail Distribution Management

I Retail distribution moves tens of thousands of products on aregular basis; A massive operation.

I Distribution efficiency of a retailer is a function of:

o Consolidation Decisiono Fulfillment Decision



Consolidation DecisionI A consolidation decision determines how to combine products into

shipment loads.o Transportation mode

Single mode

Multi mode

o Load consolidation optionFor example, in truck transport: TL, LTL, FP, SMP

I Tactical modifications of consolidation decisions can lead toincreased efficiencies.



Fulfillment DecisionI Demand fulfillment decisions determine how orders in all sales

channels are fulfilled.I Supplier push is forecast-driven, customer pull is demand-driven.

o Their interface is defined as the push-pull boundary, or thedecoupling point.

o Retailers attempt to move the push-pull boundary so as tominimize the dependency on forecast measures.

Amazon Fulfillment Center



Integrated Distribution Strategy

I The level of integration in a distribution strategy has beencategorized with respect to four factors:

o Location of distribution facilitieso Allocation of stores to each facilityo Inventory positioning across the supply networko Demand fulfillment policies

I Since the seminal work by Geoffrion and Graves in 1974, manyresearchers have attempted to devise exact or heuristic solutionmethodologies for multi-echelon distribution models.

I These models mostly combine two of above-mentioned factors.o Location-allocation, Location-inventory, Location-routing, Inventory-routing



Two New Distribution Tactics

I We envisage two new distribution tactics for retail demandfulfillment of two class of products:

o Full-pallet products for in-store saleso Online purchased products for in-store pickup

I Both tactics are studied using the framework of two-echelon mixedinteger multi-commodity network problems.

o First tactic addresses the location-allocation decisions.o Second tactic addresses the location-routing decisions.



First Tactic: Through-Store-Transshipment ofFull-pallet products in General Merchandising

I General Merchandisers are increasing thepractice of shipping full-pallet products to stores.

I From a distribution point of view, full-palletitems are referred as Pallet-in-Pallet-Out (PIPO)products.

I We examine the distribution of PIPO productsthrough stores instead of distribution centers.

I To identify the Through-Store-Transshipment(TST) channels, we employ a clustering andoptimization methodology.



TST Distribution Channel

Single DC Distribution Channel TST Distribution Channel

I TST circumvents the movement of PIPO products through DCs.



TST Network Configuration

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A Through-Store-Transshipment Plan for a network of 53 retail stores in Massachusetts.

I Central to TST is the identification of transfer stores, and theshipment routes.

I We develop a two-stage solution methodology to identify TSTchannels.



Two Stage Methodology

I First Stage: Multi-phase ClusteringI Cluster the retail stores into separate service areas.I Identify good potential transfer points simultaneously.

I Second Stage: Successive optimizationI Determine the minimum cost pallet movement plan for each service

area using a two-echelon mixed integer multi-commodity networkmodel.



First Stage: Multi-phase Clustering to Identify Service Areas

I Merge two clusters of stores for which the resulting cluster has theshortest diameter.

I The diameter is the maximum distance between two stores of acluster.

I The medoid of a cluster that has at least n̂ stores is a potentialtransfer point.

Multiphase Complete Linkage(τ, n̂, R,J)

Initialization α← 0; L← J; for l ∈ L do { Jl ← {j}; Kl ← ∅; }Phase for r = {1, ..., R} do

Current merger while α ≤ r(τ/R) do

Find l′, l′′∈ L for which l

′∪ l′′

has the minimum diameter β.

α← β; w.l.g Jl′ ← J

l′ ∪ J

l′′ ; K

l′ ← K

l′ ∪K

l′′ ; L← L \ {l

′′};

Augment transfer for l ∈ L do

if |Jl| ≥ n̂ then Kl ← Kl ∪ {medoid(Jl)};



Example

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A Prototypical 53-Store Distribution Region in Massachusetts.

I Application of the Multiphase Complete Linkage with adiameter of τ = 60 miles, n̂ = 4, and number of phases R = 4 for aprototypical distribution region with 53 stores in Massachusetts.



Example Continued

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Twenty-nine Clusters and No Transfer Points At the End of the First Phase.

I Of the twenty-nine clusters obtained at the end of the first phase,with a phase diameter of α = 15 miles, none has more than fourstores, and, consequently, no transfer point is identified.



Example Continued

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Twelve Clusters and Nine Transfer Points At the End of the Second Phase.

I Of the twelve clusters obtained at the end of the second phase,with a phase diameter of α = 30 miles, nine have more than fourstores for each of which a potential transfer point is identified.



Example Continued

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Nine Clusters and Eleven Transfer Points At the End of the Third Phase.

I Of the nine clusters obtained at the end of the third phase, with aphase diameter of α = 45 miles, seven have more than four storesfor each of which a potential transfer point is identified.



Example Continued

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Five Clusters and Fifteen Transfer Points At the End of the Fourth Phase.

I All five clusters obtained at the end of the fourth phase, with aphase diameter of α = 60 miles, have more than four stores foreach a potential transfer point is identified.



Second Stage: Optimal Pallet Movement Plan

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The Links in the Five Service Areas From Fifteen Potential Transfer Points to Stores.

I For regions a, and e the shipment plan is predetermined. For theother three regions, we successively optimize z(l). The minimumshipment cost of the system is simply, z = Min

∑l∈L z(l).



The Mathematical Statement of the Distribution Model

I Objective function:

It costs fIik to move a

trailer with capacity CI

from supply point i totransfer point k. It costs

fIIkj to meet the demand

at store j from transfer

point k using d∑

p Djp

CII etrailer(s) with capacity

CII .

z(l) = Min∑i∈I

∑k∈Kl

f Iikyik +∑k∈Kl

∑j∈Jl

f IIkjzkj

Subject to

wkjp = Djpzkj ∀ k ∈ Kl, j ∈ Jl, p ∈ P∑p∈Pi

xikp ≤ CIyik ∀ i ∈ I, k ∈ Kl∑i∈Ip

xikp =∑j∈Jl

wkjp ∀ k ∈ Kl, p ∈ P

∑k∈Kl

zkj = 1 ∀ j ∈ Jl

xikp, wkjp ≥ 0 ∀ i ∈ Ip, k ∈ Kl, j ∈ Jl, p ∈ P

yik ∈ Z+, zkj ∈ B ∀ i ∈ I, k ∈ Kl, j ∈ Jl




I The first constraintsset ensure that thedemand of product p ateach demand point j issatisfied.

z(l) = Min∑i∈I

∑k∈Kl

f Iikyik +∑k∈Kl

∑j∈Jl

f IIkj zkj

Subject to

wkjp = Djpzkj ∀ k ∈ Kl, j ∈ Jl,p ∈ P∑p∈Pi


xikp =∑j∈Jl


∑k∈Kl

zkj = 1 ∀ j ∈ Jl






I The secondconstraints set ensurethat echelon I trailercapacity is not violated.

z(l) = Min∑i∈I

∑k∈Kl

f Iikyik +∑k∈Kl

∑j∈Jl

f IIkj zkj

Subject to


xikp ≤ CIyik ∀ i ∈ I,k ∈ Kl∑i∈Ip

xikp =∑j∈Jl


∑k∈Kl

zkj = 1 ∀ j ∈ Jl






I The third constraintsset ensure the balance ofow of product at eachtransfer point.

z(l) = Min∑i∈I

∑k∈Kl

f Iikyik +∑k∈Kl

∑j∈Jl

f IIkj zkj

Subject to



xikp =∑j∈Jl

wkjp ∀ k ∈ Kl,p ∈ P

∑k∈Kl

zkj = 1 ∀ j ∈ Jl






I The forth constraintsset ensure that eachdemand point is servedby one transfer point.

z(l) = Min∑i∈I

∑k∈Kl

f Iikyik +∑k∈Kl

∑j∈Jl

f IIkj zkj

Subject to



xikp =∑j∈Jl


∑k∈Kl

zkj = 1 ∀ j ∈ Jl






I The last twoconstraints set specifyvariables. z(l) = Min

∑i∈I

∑k∈Kl

f Iikyik +∑k∈Kl

∑j∈Jl

f IIkj zkj

Subject to



xikp =∑j∈Jl


∑k∈Kl

zkj = 1 ∀ j ∈ Jl

xikp,wkjp ≥ 0 ∀ i ∈ Ip,k ∈ Kl, j ∈ Jl,p ∈ P

yik ∈ Z+, zkj ∈ B ∀ i ∈ I,k ∈ Kl, j ∈ Jl



Clustered Distribution Region

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The Links in the Five Service Areas From Fifteen Potential Transfer Points to Stores.

I The minimum number of first echelon trucks carrying product p totransfer points k ∈ Kl in each cluster l ∈ L must satisfy the totaldemand for the product in that cluster.∑

i∈Ip

∑k∈Kl

yik ≥⌈( ∑

j∈Jl

Djp

)/CI

⌉∀ p ∈ P



The Optimal Through-Store-Transshipment Plan

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The Optimal Through-Store-Transshipment Plan.

I The TST plan has a total cost of $38,059 per replenishment.



The Single DC Distribution Region

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The Single Distribution Center Distribution Region.

I The base distribution plan has a total cost of $39,077.

I We see a cost reduction of 2.65% per replenishment.



Holistic Approach

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The Links in the 0.1% Optimal Shipment Plan for the Holistic Model after 12 hours of computation time.

I After 12 hours, the incumbent has an objective value of $38,017.

I The cost is slightly lower than TST ($42), but it violates the 2ndechelon distance limitation.



Computational Tests

I Two prototypical chains of retail stores with presence in two U.S.census regions: New England (NE); and Middle Atlantic (MA).

Four basic networks for two prototypical retailers

RegionNumber of ZIPCodes

Aggregated Minimum |J| |L| |K|ZIP Codes Market Size

NE1 1,716 824 20,000 213 18 55MA1 6,062 2567 35,000 454 44 101NE2 1,716 824 25,000 186 16 46MA2 6,062 2567 45,000 404 45 104

I We use the four regions to generate 32 problem instances.

I We test two product groupings for each retailer’s scenario, eachwith two demand variations, and two levels of demand intensityare examined.



Computational Tests: Continued

I Supply points are in the states of Massachusetts, New Jersey,Maryland, Pennsylvania, and Florida.

I For each instance we obtain three plans:I A single DC plan OI A TST plan SI A distance constrained plan (≤ 60 miles) T

I All models are solved using IBM ILOG CPLEX ConcertTechnology for JAVA on a 64-bit Linux machine with an IntelCore2 CPU Q9300, and 3.8 GiB of memory.

I Termination criteria: Computation time limit = 3600 seconds;Duality gap of 0.1%.



Computation Results: First Retailer

InstanceName

Base (O) Cluster-based (S) Saving Distance-constrained (T)

zO zSComp.

|K∗|1− zS

zOzT

Comp.|K∗|time time

($) ($) (sec.) (%) ($) (sec.)

NE1/2H/3 151,892 143,987 309.22 24 5.20 145,201 3600.00 42

NE1/2H/5 161,675 153,321 621.35 29 5.17 154,212 3600.00 42NE1/2L/3 158,505 148,553 304.11 36 6.28 148,849 3600.00 44NE1/2L/5 171,321 159,957 120.59 28 6.63 160,954 3600.00 44NE1/4H/3 195,974 188,294 5.90 18 3.92 190,919 3600.00 19NE1/4H/5 217,271 207,740 8.28 18 4.39 212,510 3600.00 21NE1/4L/3 214,985 203,553 3.50 22 5.32 206,171 3600.00 26NE1/4L/5 232,612 220,328 15.46 20 5.28 223,533 3600.00 25

MA1/2H/3 421,401 374,239 622.04 56 11.19 378,664 3600.00 80MA1/2H/5 431,756 381,863 336.31 57 11.56 387,320 3600.00 84MA1/2L/3 396,069 351,705 309.44 59 11.20 355,171 3600.00 79MA1/2L/5 405,962 358,969 170.59 55 11.58 361,895 3600.00 76MA1/4H/3 459,191 420,758 3.72 44 8.37 432,432 3600.00 51MA1/4H/5 473,126 432,814 5.79 45 8.52 444,832 3600.00 51MA1/4L/3 467,861 425,661 90.06 48 9.02 446,976 3600.00 62MA1/4L/5 484,006 439,266 5.32 47 9.24 455,257 3600.00 56



Computation Results: Second Retailer

InstanceName

Base (O) Cluster-based (S) Saving Distance-constrained (T)

zO zSComp.

|K∗|1− zS

zOzT

Comp.|K∗|time time

($) ($) (sec.) (%) ($) (sec.)

NE2/3H/3 156,646 147,188 16.2 18 6.04 150,292 3600.00 26

NE2/3H/5 170,938 160,420 310.47 18 6.15 163,925 3600.00 25NE2/3L/3 180,698 167,559 108.63 18 7.27 172,573 3600.00 24NE2/3L/5 193,804 180,182 64.46 19 7.03 183,066 3600.00 20NE2/5H/3 143,693 135,690 307.38 17 5.57 136,949 3600.00 24NE2/5H/5 155,788 146,668 11.52 18 5.85 147,528 3600.00 24NE2/5L/3 193,945 183,576 5.16 16 5.35 188,947 3600.00 17NE2/5L/5 212,129 200,551 3.72 17 5.46 205,609 3600.00 20

MA2/3H/3 385,721 344,165 6.71 50 10.77 349,025 3600.00 59MA2/3H/5 394,875 351,521 6.58 51 10.98 359,643 3600.00 64MA2/3L/3 387,420 346,873 11.11 49 10.47 360,655 3600.00 68MA2/3L/5 392,516 351,833 8.93 49 10.36 362,048 3600.00 65MA2/5H/3 341,422 307,610 302.57 46 9.90 318,822 3600.00 57MA2/5H/5 351,794 315,501 3.79 47 10.32 327,357 3600.00 55MA2/5L/3 405,723 372,750 1.87 45 8.13 400,513 3600.00 55MA2/5L/5 419,491 384,679 3.44 45 8.30 400,273 3600.00 55



Computation Results: Summary

I Computations reveal that, on average, TST reduces thetransportation costs by 7.8% when compared with the single DCscenario.

o The percent is saving from 3.9% to 11.6%.

I The larger the distribution network, the more significant thedistance savings.

I The segmentation of a distribution region into service areas forTST does not significantly impact the total shipment cost.



Concluding Remarks

I It is beneficial to ship PIPO products throughstores.

I The proposed methodology is computationallyviable.

o Instances are solved in at most 622 seconds.

I Implementation of TST should be phased-ingradually so as to avoid the possibility ofstart-up inefficiency.



Second Tactic: A Distribution Model for OnlineOrder Fulfillment

I In a ‘buy online, pick up at store’ sales model, customers placeorders online and receive them at nearby stores.

o Walmart, Sears, and Target

I A long delivery time frustrates the shopper.

I To avoid the negative impacts of lost sale:o Limit the online channel to in-store available products

- Lowe’s

o Establish a fast-moving distribution channel

I The distribution tactic that we propose offers a fast-movingdistribution channel for such commit-to-delivery products that arenot stocked at stores.



Current Channel

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I The accumulated demand volume of such orders in the stores, which arededicated to a single DC, is not enough to justify an immediate shipment.

I The retailer should wait to pool the demand across time, and thenpiggyback them on the regular shipments.



Proposed Channel

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I Using a subset of distribution centers as fulfillment centers (FC), theretailer fulfills online orders via a separate channel.

I Products move from suppliers to FCs. Then, each store is served via ajoint FC-to-store delivery route to maximize truck utilizations.



Full-SKU versus Partial-SKU Channels

I From an operational strategy point of view, there are two aspectsto this problem.

I As the demand increases, the issue is that should we employfull-SKU or partial-SKU fulfillment centers?

o Full-SKU provides better FC-to-store shipment consolidation.

o Partial-SKU provides better Supplier-to-FC shipment consolidation.



Full-SKU fulfillment

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2324

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4142

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1112

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2324

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4142

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6768

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o A full-SKU FC can fill the store’s demand in one single jointshipment.

o A joint delivery route that moves all SKUs from the FC to stores,achieves a better FC-to-store shipment consolidation.



Partial-SKU Fulfillment

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2324

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4142

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6768

70

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1112

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2324

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4142

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o A partial-SKU FC cannot fill the store’s demand in one singleshipment. Therefore, split shipment to stores would be necessary.

o A partial-SKU fulfillment strategy pools a larger number of storesto achieve a better supplier-to-FC shipment consolidation.




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1112

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2324

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2829

30

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34

35

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4142

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47

48

49

50

51

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54

55

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57

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61

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63

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6768

70

1

2

3

4

5

6

7

9

10

1112

14

15

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17

18

19

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21

22

2324

25

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2829

30

31

32

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34

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39

4142

43

44

45

46

47

48

49

50

51

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63

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65

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6768

70

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4





Thank you



ebtehaj proposal 2011

Documents