Lowerng Outbound Shppng Costs n an Onlne Retal Envronment by Makng Better Fulfllment and Replenshment Decsons by Jason Andrew Acmovc B.S. Physcs, Yale Unversty (1999) Submtted to the Sloan School of Management n partal fulfllment of the requrements for the degree of DOCTOR OF PHILOSOPHY IN OPERATIONS RESEARCH at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2012 2012 Massachusetts Insttute of Technology. All rghts reserved. Sgnature of Author Sloan School of Management August 2 nd, 2012 Certfed by.. Stephen Graves Abraham J. Segel Professor of Management Thess Supervsor Accepted by. Dmtrs Bertsmas Boeng Leaders for Global Operatons Professor Co-Drector, Operatons Research Center 1
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ABSTRACT Lowerng Outbound Shppng Costs n an Onlne Retal Envronment by Makng Better Fulfllment and Replenshment Decsons by Jason Andrew Acmovc Submtted to the Sloan School of Management on August 2, 2012 n partal fulfllment of the requrements for the degree of Doctor of Phlosophy n Operatons Research As onlne retalng or e-talng contnues to grow as more and more customers buy physcal goods on the nternet, fndng ways to reduce the cost and envronmental mpact of outbound shppng n ths sector wll become ncreasngly mportant. We nvestgate the mpact of makng poor fulfllment and replenshment decsons usng data obtaned from a large Amercan onlne retaler. Then, we propose mplementable.e., computatonally tractable and relatvely ntutve solutons for both the fulfllment and replenshment problems, both tested ether on actual data from our ndustral partner or on small but realstc models. We frst focus on the fulfllment problem, namely, decdng from whch warehouse(s) to fulfll a customer s order when several optons exst. We propose a heurstc that utlzes the dual values of a transportaton lnear program to estmate the opportunty cost of depletng nventory from a warehouse. Ths lnear program values nventory at a warehouse due to both ts geography and the sze of ts catalogue. After showng that ths lnear program s asymptotcally optmal usng concepts developed n arlne network revenue management we then test the heurstc on ndustry data, showng a 1% reducton n outbound shppng costs as compared to a myopc fulfllment polcy. The last part of the thess focuses on replenshment. Every perod, for each tem, the network places an order to restock all the warehouses. Complcatng ths decson are two factors. Frst, the orders wll not arrve mmedately, but rather requre a lead tme to be delvered. Durng ths tme a random number of customers wll place orders wth the network. Second, any customer s order may be flled from any warehouse, whch becomes mportant when warehouses stock out of an tem. Therefore, t s not trval to calculate the optmal nventory to order to each warehouse. We show that usng a standard replenshment polcy popular n practce can lead to dynamcs that result n ncreased outbound shppng costs. We propose a replenshment polcy heurstc that s ntutve and performs well on examples. Ths heurstc has two varants: a smpler one that assumes determnstc demand, and a more complcated one that accounts for stochastcty. Thess Supervsor: Ttle: Stephen Graves Abraham J. Segel Professor of Management 3
Acknowledgments People who were nvaluable n creatng ths thess: Aden Acmovc, Peggy Acmovc, Peter Acmovc, Rcha Agarwal, Russell Allgor, Cyntha Barnhart, Dmtrs Bertsmas, Ozlem Blgner, Jessca Acmovc Bond, Cathy Braasch, Andrew Carvalho, Tolga Cezk, Vvek Faras, Stephen Graves, Davd Merrll, Georga Peraks, Paul Raff, Laura Rose, and Andreas Schulz. I would also lke to thank the Sngapore-MIT Allance (SMA) for ther generous fnancal support of ths research. 4
Note for a qucker read Ths thess s farly comprehensve wth respect to research we conducted over the last few years. As such, some sectons may be skpped on the frst readng wthout loss of contnuty. These sectons are denoted wth a (*). 5
Abbrevatons ADP Approxmate dynamc programmng DP Dynamc programmng FC Fulfllment center LP Lnear program MYO Myopc PH Perfect hndsght SKU Stock keepng unt (usually dentfed by a unque bar code) TLP Transportaton lnear program 6
Table of Contents Acknowledgments... 4 Note for a qucker read... 5 Abbrevatons... 6 Table of Contents... 7 Table of Fgures... 11 Table of Tables... 14 Chapter 1 Introducton to onlne retalng supply chans and challenges... 17 1 Onlne retalng overvew... 17 2 Onlne retalng operatons... 18 2.1 Placng an order over the nternet... 18 2.2 Delay between order and depleton... 19 2.3 Assgnment of an order to fulfllment centers... 20 2.4 Internal fulfllment center operatons and outbound shppng... 20 2.5 Inventory replenshment... 21 2.6 Operatonal challenges... 22 3 Lterature revew and our contrbutons... 24 Chapter 2 Makng better fulfllment decsons: A heurstc... 27 1 Introducton... 27 2 Lterature revew... 29 2.1 Ratonng n the face of multple customer classes... 29 2.2 Emergency lateral transshpments among multple depots... 30 2.3 Dynamc and approxmate dynamc programmng... 32 2.4 Network arlne revenue management... 32 2.5 Opportuntes n the lterature... 33 3 Motvaton: Myopc fulfllment can ncur arbtrarly hgh costs*... 34 4 Dynamc program formulaton... 36 5 LP heurstc formulaton... 38 5.1 Transportaton problem formulaton... 38 5.2 An example of fulfllment usng the LP heurstc... 42 5.3 Utlzng the transportaton LP... 44 5.3.1 Makng better fulfllment decsons... 44 5.3.2 Makng better nventory placement decsons: a dscusson*... 44 6 Theoretcal propertes of transportaton lnear program... 44 6.1 Asymptotc convergence of transportaton problem obectve value... 45 7
6.2 LP heurstc may undervalue nventory postons f all unts are n one faclty*... 60 7 Two small models: smulaton results for dfferent polces*... 62 7.1 Descrpton of four fulfllment decson polces... 62 7.1.1 Myopc fulfllment polcy... 62 7.1.2 LP heurstc fulfllment polcy... 63 7.1.3 Dynamc program fulfllment polcy... 63 7.1.4 Approxmate dynamc program fulfllment polcy... 63 7.2 Example llustratng dfferences among polces... 65 7.3 Smulatng a 2-fulfllment center, 3-customer example... 68 7.4 Smulatng a 3-fulfllment center, 4-customer example... 69 7.5 Learnngs from smulatons... 72 8 Network revenue management equvalent: a dscusson*... 73 Chapter 3 Makng better fulfllment decsons: Applcaton to ndustry... 77 1 Introducton... 77 2 Analyss assumptons... 77 2.1 Dsaggregaton of SKU s n analyss... 77 2.2 Measurng proportonal mprovement n outbound shppng costs... 78 2.3 Mult-tem orders and cost accountng... 79 2.4 Orders shp as soon as they arrve... 81 3 Our ndustral partner and the dataset... 81 3.1 Takng a stratfed sample of SKU s... 82 3.2 Estmatng shppng costs... 83 3.2.1 Customer tme wndow choces and shppng optons... 83 3.2.2 Shppng costs... 84 3.2.3 Shppng opton feasblty... 85 4 Transportaton lnear program nput data*... 86 4.1 Parameter values... 86 4.2 Clusterng customer regons together to reduce sze of problem... 87 5 Comparng fulfllment polces... 89 5.1 Myopc and LP heurstc fulfllment polces... 89 5.2 Perfect hndsght polcy... 90 6 Transportaton LP as fulfllment decson makng tool: Smulatng performance on ndustry data... 91 6.1 Detals of the smulaton tself... 91 6.2 Overall smulaton results... 92 6.3 Characterstcs of SKU s that mprove the most... 93 6.4 Dstrbuton of mprovement... 95 8
6.5 Senstvty analyss... 98 6.5.1 Tunng the LP heurstc*... 98 6.5.2 Forecast qualty*... 99 6.5.3 Scarcty*... 99 6.5.4 Dstrbuton of customer tme requests*... 100 6.5.5 Inventory mbalance... 101 6.5.6 Dsparty among fulfllment centers abltes to handle mult-tem orders... 103 6.5.7 Assumng all mult-tem orders were actually sngle-tem orders... 105 7 Transportaton LP as predctor of future outbound shppng costs... 105 7.1 Relatonshp of LP obectve value to actual ncurred costs: Smulaton results... 106 7.2 Usng LP obectve values to take manageral acton: A dscusson*... 109 7.3 Improvement versus transportaton LP obectve value*... 110 8 LP heurstc leaves nventory more balanced... 111 9 Fulfllng smarter to reduce nventory: A dscusson*... 112 Chapter 4 Makng better replenshment decsons... 115 1 Introducton... 115 2 Summary of contrbutons... 115 3 Lterature revew... 116 3.1 Lost sales... 116 3.2 Transshpments... 119 3.3 Reparable systems and contnuous revew polces... 123 3.4 Opportuntes n lterature... 124 4 A local base-stock replenshment polcy: Intutve but often sub-optmal... 125 4.1 Descrpton of the replenshment polcy at our ndustral partner... 125 4.2 Spllover: lateral nteractons... 129 4.3 Whplash: temporal nteractons... 130 4.4 Emprcal evdence of whplash... 137 5 Formulaton of replenshment problem n onlne retal*... 140 6 Descrpton of a heurstc replenshment polcy... 143 6.1 Current practce: Local base-stock polcy... 144 6.2 Heurstc: Proected base-stock polcy... 144 6.2.1 Overvew... 144 6.2.2 Estmatng on-hand nventory... 145 6.2.3 Settng safety stock levels... 146 6.2.4 Example of heurstc operatng on a smple determnstc system... 146 7 Two-FC model wth constant system demand, stochastc regonal demand*... 148 9
7.1 Model descrpton... 149 7.2 Fve replenshment polces... 151 7.2.1 Local base-stock... 151 7.2.2 Constant Order... 151 7.2.3 Proected base-stock... 151 7.2.4 Proected base-stock plus... 152 7.2.5 Dynamc program... 153 7.3 On-hand nventory on a revew day modeled as a Markov chan... 154 7.4 Computatons results for several nstances... 155 7.5 Learnngs from computatonal examples... 159 8 An n-fc model wth general stochastc demand... 160 8.1 Model descrpton... 161 8.2 Four orderng polces... 162 8.2.1 Local base-stock and constant order polces... 162 8.2.2 Proected base-stock... 163 8.2.3 Proected base-stock plus... 165 8.3 Computatonal experments... 167 8.3.1 Spllover prortes... 167 8.3.2 Detals of computatonal parameters... 168 8.3.3 Computatonal results... 169 8.3.4 Evdence of whplash... 173 8.3.5 Holdng less nventory by replenshng smarter... 174 8.3.6 Varyng the lead tme... 175 9 Concluson and next steps... 177 Chapter 5 Concludng remarks... 179 References... 181 Appendx A Stratfcaton methodology... 187 Appendx B Algebra showng dynamcs of two-fc determnstc system wth spllover... 189 Appendx C Estmate of savngs achevable wth a smarter replenshment polcy... 195 Appendx D Addtonal computatonal results for the 2-FC model... 197 10
Table of Fgures Fgure 1: Example of myopc fulfllment wth shppng costs... 28 Fgure 2: A smple network on whch a myopc polcy performs badly... 35 Fgure 3: Transportaton LP example... 43 Fgure 4: Network geography of three customers and two fulfllment centers along a lne segment... 62 Fgure 5: Partal effects plot of nventory and LP obectve value the approxmate dynamc programmng estmate of cost-to-go... 67 Fgure 6: Network geography for example wth three fulfllment centers and four customers... 70 Fgure 7: Approxmate locatons (because they are dsgused) of fulfllment centers of our ndustral partner... 82 Fgure 8: Demand densty for each Zp3 regon Color of a regon corresponds to proporton of demand n the regon dvded by square area... 82 Fgure 9: Shp cost data for a specfc shppng opton, and the ftted lnear functon... 84 Fgure 10: UPS tme-n-transt map: Shows tme to shp from anywhere n the Unted Stated to Washngton, DC va Ground... 85 Fgure 11: Regon clusters for dfferent numbers of clusters, from 25 to 885. We used 100 clusters for the heurstc n our smulaton.... 88 Fgure 12: LP heurstc performance vs. volume of sales, bucketed by sample strata... 93 Fgure 13: Fracton of perfect hndsght gap captured by LP heurstc... 94 Fgure 14: LP heurstc performance vs. nventory scarcty, bucketed nto vgntles (wth ratos hgher than 6 truncated from plot)... 95 Fgure 15: Dstrbuton of mprovement across all SKU s n the sample of the LP heurstc over myopc polcy (left closed ntervals)... 96 Fgure 16: Cumulatve proportonal contrbuton of SKU s n each velocty bucket to each polcy s total mprovement.... 97 Fgure 17: Overall mprovement vs. scarcty scenaro. Each data pont represents an entre smulaton run wth all SKU's (.e., they are not bucketed as n Fgure 14).... 100 Fgure 18: Improvement gap and LP heurstc performance vs. sales volume when nventory starts perfectly balanced (bucketed by sample strata)... 102 Fgure 19: Proporton of perfect hndsght gap closed vs. fulfllment center (FC) dsparty. Each data pont represents a full smulaton run on all SKU s.... 104 Fgure 20: Normalzed cost to shp orders for a SKU under a myopc polcy versus the obectve value of the transportaton lnear program, bucketed nto vgntles... 107 11
Fgure 21: Normalzed cost to shp orders for a SKU under the perfect hndsght optmzaton versus the obectve value of the transportaton lnear program, bucketed nto vgntles... 107 Fgure 22: Normalzed cost to shp orders for a SKU under myopc fulfllment versus per-unt obectve values (cf. Fgure 20), wth regresson lne... 108 Fgure 23: Perfect hndsght gap and LP heurstc performance vs. obectve value. Each plot shows a dfferent sales volume range, as denoted n the ttles.... 111 Fgure 24: Normalzed outbound shppng cost vs. scarcty scenaro. Each data pont represents a smulaton run of all SKU's.... 113 Fgure 25: Inventory levels over tme for two-fulfllment example wth spllover when startng nventory levels are 12 and 18 respectvely... 133 Fgure 26: Inventory levels over tme for two-fulfllment center example wth spllover when startng nventory levels are 20 and 10 respectvely... 134 Fgure 27: Inventory levels over tme for two-fulfllment center example wth spllover when lead tmes dffer. The lead tmes are 3 and 5 days, and startng nventores are 12 and 18, respectvely.... 135 Fgure 28: Spllover patterns over tme for sx scenaros, each of whch has a dfferent startng nventory but dentcal parameters: n = 2, r = 7, L = 35... 136 Fgure 29: Relatonshp between a fulfllment center's devaton n one perod wth the devaton n ether the next perod (left) or two perods (rght). Black dots are ndvdual observatons, the lne s drawn from the regresson slope and ntercept n Table 26, and large blue crcles are bucketed averages of devaton.... 138 Fgure 30: Dfference n fracton ordered between the second and thrd revew perods plotted aganst the devaton n the frst perod. The devaton n the frst perod s broken nto 30 buckets, wth each pont represents the mean dfference wthn each groupng.... 139 Fgure 31: Dynamcs of two- fulfllment center system usng proected base-stock polcy... 148 Fgure 32: Markov chan state transton probabltes under a local base-stock polcy where a state s defned as the on-hand nventory n fulfllment center 1 on a revew day... 155 Fgure 33: Order amounts and statonary dstrbuton plots for a scenaro wth parameters as gven n the upper rght table. The expected spllover for each polcy s n the lower rght table. (Plots have been slghtly perturbed n order to more easly see the dfference among the polces.)... 156 Fgure 34: Order amounts and statonary dstrbuton plots for a scenaro wth 4 unts of system safety stock... 157 Fgure 35: Order amounts and statonary dstrbuton plots for a scenaro wth λ 1 equal to 0.1 and two unts of system safety stock... 158 Fgure 36: Expected proporton of spllover plotted versus SS SYS. λ 1 =0.5, L=4, r=7, and d SYS ={5,10} respectvely.... 159 12
Fgure 37: Randomly generated locatons of fulfllment centers for computatonal results used to calculate for n = {2, 5, 10, 15}... 168 METRIC Fgure 38: Unweghted spllover for the Fgure 39: Weghted spllover for the Fgure 40: Unweghted spllover for the Fgure 41: Weghted spllover for the CYCLE scenaro... 169 CYCLE scenaro... 170 METRIC scenaro... 170 METRIC scenaro... 171 Fgure 42: Unweghted spllover for the METRIC scenaro wth SYS 100... 172 Fgure 43: Weghted spllover for the METRIC scenaro wth SYS 100... 173 Fgure 44: Fracton of perods a fulfllment center experenced out-spll when the target servce level s METRIC 0.95 and n=15 for the scenaro. Data s shown for the uncondtonal case and the case condtonal on n-spll durng the prevous perod.... 174 Fgure 45: Effect on spllover of ether addng extra stock or replenshng smarter. Here, mplementng the proected base-stock polcy has the same effect as doublng the safety stock.... 175 Fgure 46: Fracton of sales splled over plotted versus lead tme for low (SL = 0.85) and hgh (SL = 0.98) safety stock scenaros... 176 Fgure 47: Order amounts and statonary dstrbuton plots for a scenaro wth λ 1 equal to 0.2, daly system demand of 5, and two unts of system safety stock... 197 Fgure 48: Order amounts and statonary dstrbuton plots for a scenaro wth one unt of system safety stock... 198 Fgure 49: Order amounts and statonary dstrbuton plots for a scenaro wth λ 1 equal to 0.1 and two unts of system safety stock... 198 13
Table of Tables Table 1: Decson varable optmal values... 43 Table 2: Cost-to-go estmates for fulfllment example... 66 Table 3: Bass functon and parameter values for the approxmate dynamc program example... 66 Table 4: Regresson statstcs for approxmate dynamc program example... 66 Table 5: Cost-to-go estmates for fulfllment example (complete: updated from Table 2)... 68 Table 6: Cost matrx from each fulfllment center (FC) to each customer n example... 68 Table 7: Average ncurred per-tem fulfllment costs for dfferent fulfllment polces... 69 Table 8: Costs from each fulfllment center (FC) to each customer... 70 Table 9: Parameter values for the approxmate dynamc program... 70 Table 10: Regresson statstcs for approxmate dynamc program... 70 Table 11: Average ncurred per-tem fulfllment costs for dfferent fulfllment polces... 71 Table 12: Z k values for example... 80 Table 13: Characterstcs of our sample of SKU s... 83 Table 14: Mnmum delvery tme requred for dfferent shp mode optons and dstance ranges... 86 Table 15: Proportonal reducton n outbound shppng costs of perfect hndsght and LP heurstc polces over a myopc one... 93 Table 16: Proportonal mprovement of perfect hndsght and transportaton LP heurstc polces wth and wthout a perfect forecast... 99 Table 17: Reducton n shppng costs for perfect hndsght and LP heurstc polces for 3 dfferent tme request scenaros (Performed on a small sample of SKU s)... 101 Table 18: Improvement gap and LP heurstc performance for a perfectly balanced scenaro... 102 Table 19: Example of ρ s for dfferent dsparty scenaros... 103 Table 20: Perfect hndsght and LP heurstc performance compared to myopc when mult-tem orders are gnored... 105 Table 21: Coeffcents for lnear regresson model used to predct actual ncurred costs usng myopc fulfllment polcy... 108 Table 22: Summary statstcs lnear regresson model used to predct actual ncurred costs usng myopc fulfllment polcy... 108 Table 23: Replenshment polces compared by Zpkn (2008a)... 119 14
Table 24: Parameters for example... 132 Table 25: Sample data and resultng devaton calculaton... 137 Table 26: Regresson statstcs for model relatng order devatons over tme. The ** sgnfes a p-value at or below 0.001... 138 15
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Chapter 1 Introducton to onlne retalng supply chans and challenges 1 Onlne retalng overvew In 2010, sales of retal tems over the nternet n the Unted States brought n revenues of $142 bllon (comscore, Inc. 2011). Ths number represents a 10% ncrease n sales over the prevous year, and s expected to grow to $248.7 bllon n 2014, representng 8% of US retal sales (Forrester 2010a). Growth rates are smlar n Western Europe (Forrester 2010b). In Chna, the growth s much faster; onlne sales n 2010 were $520 bllon Yuan ($80 bllon USD), whch s more than double the onlne retal sales seen the prevous year (Bloomberg News 2011). An onlne retal busness (or e-tal busness) operates very dfferently from brck-and-mortar retalng, and requres a new set of tools to run effcently. As the onlne sector contnues to grow, learnng these dfferences and how to better manage onlne operatons wll become ncreasngly mportant. One mportant aspect of onlne retalng s fulfllment: pckng, packng, and shppng orders to customers. Based on 10k statements of several onlne retalers (Amazon.com, Inc. 2009, 2010; Bluefly, Inc. 2008; Overstock.com, Inc. 2009), outbound shppng and handlng can account for 5-7% of revenues, equatng to $12-$17 bllon n the Unted States n 2014 based on the above numbers. Outbound shppng costs of hybrd companes that operate brck-and-mortar stores as well as onlne retal webstes are not avalable because they are not requred to separate ther reportng between the two channels. Because proft margns of large onlne retalers and conventonal retalers are on the order of 2-5% (Seekng Alpha 2010), reducng outbound shppng costs by even a lttle can have a large mpact on proftablty. In ths thess, we study the mpact of smarter, forward-lookng fulfllment decsons as well as forward-lookng replenshment polces on outbound shppng costs n an onlne retal envronment. In the tradtonal retal supply chan, vendors typcally supply dstrbuton centers whch n turn supply retal stores. Customers pck the retal store to vst, and buy tems from the nventory on the shelf at the tme of ther vst. All customers are served mmedately, namely, as they check out wth ther new purchase. In general, assortment s lmted by the physcal space of the store tself. Onlne retalng supply chans, on the other hand, may appear smlar to the customer, but actually dffer from conventonal retalng n several key areas. Instead of a storefront wth a backroom, onlne retalers keep ther nventory n fulfllment centers. Wthn these centers, orders are pcked off of shelves, aggregated, packaged, and shpped to customers. Addtonally, although multple dstrbuton echelons stll exst, the structure s not strctly herarchcal. The dstrbuton network may consst of large 17
fulfllment centers desgned to hold a wde varety of stock keepng unts (SKU s), as well as small fulfllment centers desgned to maxmze geographcal coverage for the most popular SKU s. Any of these fulfllment centers can serve any customer, and they can even replensh each other. Besdes the structure of the dstrbuton network, onlne retal supply chans dffer from brck-and-mortar supply chans n four other ways. Frst, n onlne retal supply chans, the onlne retaler decdes from where to fulfll an order, not the customer. Second, there always exsts a tme delay between the placng of an order and the fulfllment n onlne retal supply chans. Thrd, n onlne retal supply chans, customers usually have an opton to choose ther servce or delvery tmes, e.g., next day versus next week, dependng on what they are wllng to pay. Lastly, the onlne retal customer often has access to any tem that exsts n the network. If the buldng nearest the consumer s out of a partcular tem, the order wll be sent from a farther locaton at an addtonal cost to the seller, but often at no addtonal cost to the buyer. Ths s n contrast to brck-and-mortar retalers, for whom a stock-out n a partcular buldng leads to ether a lost sale or a backorder. Onlne retalers can provde customers access to a large collecton of low-volume products,.e., products whose demand may be on the order of a few unts per year. If the onlne retaler decdes to hold only a few unts of such tems, the number of fulfllment centers mght exceed the number of on-hand unts of a partcular SKU, makng t necessary to decde whch stes wll and wll not carry the tem Decdng what to hold where s not an easy problem. But even f one decded optmally what to hold where, mplementng that soluton s another matter. Due to operatonal consderatons dscussed below, the actual nventory poston can quckly grow far apart from the deal. When ths occurs, s a myopc fulfllment polcy (one whch fulflls demand from the cheapest fulfllment center) adequate? Is a smple local base-stock perodc revew polcy adequate for replenshng spent nventory? If not, what s the best way to fulfll customer demand and replensh nventory? In ths thess, we nvestgate these questons, nspred by the real problems faced by a large Amercan onlne retaler. Throughout the thess, we assume that what to stock where and how much has already been decded. Instead, we focus on operatonally reactng to these a pror decsons through fulfllment and replenshment strateges n ways that mnmze outbound shppng costs. 2 Onlne retalng operatons 2.1 Placng an order over the nternet Onlne retalers mantan webstes that lst all products avalable for purchase. Oftentmes, every product that has postve nventory n the system wll show up as avalable to the consumer, regardless of where 18
ths nventory actually s. Ths s a key dfference between onlne and conventonal retal: that the customer s choces are not lmted by what s on the shelves n a sngle faclty. The customer places tems n hs vrtual shoppng cart, and checks out. When the customer checks out, he also decdes when he would lke hs tems. There s a trend n onlne retalng to charge a fxed prce for a specfc delvery tme, regardless of the actual fulfllment cost. The customer may choose between havng hs tems n 2 days or 8 days, each of whch mght ncur a specfc fxed prce: 2 day shppng may cost $10, and 8 day shppng may be free. At the tme of checkout, the onlne retaler provdes the consumer wth estmated shppng tmes as well as shppng costs. Ths nformaton s provded n real tme so that the customer may make nformed decsons before commttng to an order. 2.2 Delay between order and depleton One mportant dfference between brck-and-mortar and onlne retal s the exstence of a delay between when an tem s requested and when an tem s depleted from nventory. In tradtonal brock and mortar retalng, nventory s depleted as soon as a customer pulls an tem off of a shelf. In onlne retalng, the customer s makng a request to have an tem at hs doorstep at some due date n the future. Between the request day and the due date, the onlne retaler has a tme wndow wthn whch t can: A. Calculate optmal future strateges: Make calculatons t cannot perform n real tme that help to make optmal choces B. Wat for nventory t knows s n transt to arrve: If nventory n a fulfllment center near the customer wll arrve (for nstance) n a few hours, the onlne retaler can delay the shpment untl then C. Move tems between fulfllment centers: In general, t may be cheaper to move 1000 tems by a tractor traler halfway across the country to a fulfllment center that s geographcally close to a set of customers, aggregate the tems nto 500 shpments representng customer orders, then shp these 500 packages by cheaper modes from ths fulfllment center, as opposed to shppng them n 1000 packages from further away. If used n the three ways ust descrbed, ths tme wndow can provde a sgnfcant beneft to the onlne retaler. In ths thess, we do not consder the benefts assocated wth ths delay; we nstead focus on real tme decson makng that can be performed on the fly as orders are receved. Nevertheless, leveragng ths delay could be a frutful avenue for future research. 19
2.3 Assgnment of an order to fulfllment centers An addtonal dfference between tradtonal brck-and-mortar and conventonal retalng s that n the latter, the seller decdes from where to deplete nventory. In tradtonal brck-and-mortar retalng, t s the customer who decdes from where to deplete nventory mplctly by decdng whch store to walk nto and by takng nventory from the shelf n that store. In onlne retalng, the customer provdes a set of tems, an address, and a due date: t s up to the onlne retaler to decde from where to shp that set of tems. If the order s for multple tems, the onlne retaler may try to send the order from a sngle fulfllment center f that s possble. If no fulfllment center holds all the tems from that order, the onlne retaler must decde how to splt up the order nto multple shpments across multple fulfllment centers. In ether case, a myopc polcy may not always be optmal. Shppng a customer s order from the nearest fulfllment center today may lead to hgh outbound shppng costs for tomorrow s customers orders. These future shppng costs may be hgh because: A. Packages need to be shpped further B. Packages need to be shpped by more expensve modes of transportaton, such as arplanes C. Orders for multple tems may need to be splt nto multple packages. How to make fulfllment center assgnment decsons n real tme n a way that mnmzes long term average outbound shppng costs s one man focus of ths work, and s nvestgated n chapters 2 and 3 of ths thess. 2.4 Internal fulfllment center operatons and outbound shppng Once an order (or a porton of an order) s assgned to a fulfllment center, t must be processed there. Fulfllment centers are the operatonal heart and soul of companes that sell tems over the nternet. The tasks that take place n a typcal fulfllment center can be dvded nto the followng categores: recevng, stowng, pckng, sortng, packng, and shppng. Recevng Goods usually arrve on pallets by truck to an nbound area of the warehouse. Cases may be broken nto ndvdual tems, kept as a case, or even kept all together on the pallet they arrved on, dependng on where the tems are destned for wthn the buldng. Stowng Items that are broken out of cases nto ndvdual unts are placed nto pck areas. These shelves may not look much dfferent from shelves at a brck-and-mortar retal store, except that tem densty s usually hgher, and each segment of shelf may be barcoded so that the warehouse management 20
system can keep track of each tem s exact locaton n the fulfllment center, whch may be as large as a mllon square feet. Cases that are not broken nto ndvdual unts are put n reserve storage, for nstance, on pallet racks accessble by cranes or forklfts. When an tem s nventory level gets low n the pck area, cases are moved from the reserve areas, broken nto ndvdual unts, and stowed on pck shelves. Pckng As orders come nto the fulfllment center from customers, they are assgned to pck lsts. Ths process s n tself a complcated problem outsde the scope of ths thess: t s not easy to determne the rght mx of pck densty (mnmal walkng between tems on a pck lst), and overall fulfllment center effcency (not watng too long to release orders to be pcked). Each pck lst s assgned to a pcker. A sngle order for multple tems may be splt nto multple pck lsts to ncrease the effcency of the pckers. These orders wll need to be aggregated later on before shppng (see Sortng below). Even n ths advent of automaton, many fulfllment centers stll fnd t most effcent to use human pckers to walk up and down the asles of shelves. Human labor s more effcent to scale up durng growth and busy tmes, and humans are far superor at pckng up many dfferent knds of tems (from teddy bears to books to water bottles to pencls). The pcker places the tems on hs lst nto a bn or cart. Ths bn or cart s then transported to the sorter, and the pcker receves a new pck-lst. Sortng Orders for multple tems may have been splt among pckers to ncrease effcency n pckng. These mult-tem orders need to be reassembled before packng them nto the shppng box. All bns of pcked tems arrve to a sortaton area. Here, the tems are sorted back nto ndvdual orders. Oftentmes, each order s assgned a small area (n a chute or on a shelf). Items from the arrvng pckng bns are placed n the correct area correspondng to ther respectve orders. Once all the tems for an order are aggregated, the tems n that order move to packng. Packng Here, the tems n an order are placed n cardboard boxes wth approprate packng materals (Styrofoam peanuts or ar pllows, for nstance). The boxes are gven shppng labels and sent to the outbound shppng area. Shppng The ndvdual boxes that come from the packng area are sorted by carrer (for nstance, FedEx, Unted Parcel Servce, local courer), and placed onto the approprate outbound trucks. 2.5 Inventory replenshment The onlne retaler must also decde how to replensh nventory. The state of the system s represented by the amount of nventory n each fulfllment center and the amount of nventory n transt. Based on ths system state, the dstrbuton of lead tmes from vendors to fulfllment centers, the probablty dstrbuton of regonal demand n the near future, and a customer servce level target, the onlne retaler wll decde 21
how much nventory to order. An onlne retaler s network structure may be flat n whch case the nventory s ordered drectly from vendors to the fulfllment centers or t may have multple-echelons n whch case the nventory may be ordered nto one or more central dstrbuton centers before beng shpped to ndvdual fulfllment centers. Because solvng these replenshment problems optmally can be dffcult due to the curse of dmensonalty assocated wth dynamc programs and due to the fact that smple polces are not guaranteed to be optmal onlne retalers often resort to heurstcs to dctate replenshment decsons. A gven replenshment polcy may have an effect on the followng ncurred costs: Holdng costs: The drect and ndrect cost of holdng a unt of nventory n a specfc fulfllment center. For nstance, a dollar used to purchase an tem sttng on a shelf somewhere s a dollar that must be borrowed or a dollar that cannot be nvested elsewhere. Addtonal costs are those assocated wth warehouse operatons (the cost of the warehouse, heatng and coolng, securty, etc.) as well as rsk (theft, obsolescence, etc.). Backorder and lost sales: One way to combat hgh holdng costs s to hold a small amount of nventory. However, there s also a cost to not havng nventory on-hand that a customer wants. The customer mght decde to shop elsewhere and the sale s lost (along wth the assocated proft), or the tem s backordered because the customer agrees to wat untl the tem s n stock agan. If an tem s backordered, the onlne retaler may ncur drect costs (such as gvng a dscount) and ndrect costs (such as dmnshed customer loyalty n the form of lost future sales). Much of the nventory management lterature examnes how to optmally balance holdng and shortage costs, both from backorders and from lost sales. Outbound shppng costs: Even f a polcy s found that optmzes holdng costs, backorder costs, and lost sales costs, ths polcy may have a detrmental effect on outbound shppng costs. For nstance, the fulfllment center wth the cheapest holdng cost may be far from many customers. Storng all nventory at ths faclty could lead to very hgh outbound shppng costs. In addton to decdng what to hold where, the replenshment polcy tself may have a large effect on outbound shppng costs. In ths thess, we assume constant holdng costs among fulfllment centers and that the decson of what to hold where has already been decded. We nvestgate nstead n chapter 4 how dfferent replenshment polces may affect outbound shppng costs. 2.6 Operatonal challenges Operatng an onlne retal supply chan under the best of condtons would be qute a challenge. Unfortunately, onlne retalers do not have ths luxury. Every day, operatonal challenges create less than 22
desrable condtons under whch these companes must operate as best they can. These challenges may lead to mbalanced nventory postons and the need to shp packages longer dstances than planned. In ths thess, we acknowledge that onlne retalers are not operatng under deal condtons. We present fndngs based on stylzed models as well as fndngs based on actual data from an onlne retaler. Ths data tself reflects the state of the system as t actually was when the data was pulled. Namely, nventory was not always balanced; that s, nventory was not always n the rght places n the rght quanttes. When nventory s out of balance, t s even more mportant to make smart fulfllment and replenshment decsons. Operatons consderatons that may lead to mbalanced nventory nclude the followng: Fulfllment center physcal capacty Each fulfllment center can hold only a lmted number of cubc feet of product. If a replenshment polcy tres to order product nto a fulfllment center that s full, those tems wll need to be sent elsewhere n the network. Ths may cause nventory mbalance. Fulfllment center flow capacty Customer orders are constantly arrvng nto the system. It s possble that an deal assgnment of orders to fulfllment centers may be nfeasble due to flow capacty constrants. If all the orders are assgned to one fulfllment center, those orders may overload bascally every operaton that takes place n a fulfllment center: there mght not be enough pckers to pck the tems, there mght not be enough sortaton capacty to sort the orders, there mght be too many orders to pack wthn the allotted tme, and there mght not be enough trucks to take all the packages to ther destnatons. In order to balance the orders and workload among fulfllment centers, orders may be assgned to buldngs from whch t s more expensve to shp than the optmal buldng. Ths balancng may lead to mbalanced nventory as well as hgher outbound shppng costs. Vendor varablty Actual vendor lead tmes may greatly dffer from quoted lead tmes. Addtonally, vendors may delver product from a sngle order to dfferent fulfllment centers at dfferent tmes. If a product s fully depleted n the system, and the vendor frst delvers t to the east coast, then all of the orders n the near future n the entre country wll be shpped from the east coast. By the tme the vendor delvers the product to the west coast, all of the east coast fulfllment centers are empty whle the west coast s holdng all of the remanng nventory. Thus n ths way, nventory may also become mbalanced. Unforeseeable hgh mpact events Ice storms, floods, power losses, blzzards, and earthquakes may cause fulfllment centers n some part of the country to temporarly shut down. Durng ths perod, all demand for that fulfllment center wll spll over to other (possbly less optmal) fulfllment centers. By the tme the center comes back onlne, the damage has been done, and nventory may be mbalanced. 23
Mnmum order quanttes For many low demand tems, the onlne retaler may order only a few tems from a vendor nto each fulfllment center. If the vendor has a mnmum order quantty and s not wllng to splt a case, the onlne retaler may order the entre product (a case) nto a sngle fulfllment center. In these ways, t s easy to see how nventory can become mbalanced for even the best operatng onlne retalers. It s mportant for fulfllment and replenshment polces to acknowledge ths, and be able to make good decsons even when nventory s not where t should be. 3 Lterature revew and our contrbutons Whle we revew most of the lterature specfc to partcular areas n later chapters, we brefly menton work nvestgatng onlne retalng supply chans n general. Due to the fact that most of the companes sellng tems onlne (but not most of the volume of onlne sales) are brck-and-mortar frms expandng nto onlne retalng hopng not to get left behnd, much lterature has focused on dual channel supply chans,.e., supply chans whch servce both a tradtonal brck-and-mortar channel as well as an onlne retal channel. See for nstance Cattan et al. (2006), Sefert et al. (2006a, 2006b), Chen et al. (2008), and a revew by Agatz et al. (2008). Several of these papers focus on specfc ndustres, such as Sefert et al. who work wth HP and examne personal computer sales. Herer et al. (2006) menton the French company FNAC whch operates brck-and-mortar as well as onlne stores. By usng transshpments among ts warehouses, FNAC has been able to ncrease the sze of ts onlne portfolo threefold wthout ncreasng the assocated total level of stock. There has also been some work on supply chans n pure onlne retalng envronments. Chhaochhra (2007) and Xu (2005) examne nventory polces for low demand tems n an onlne retal envronment. Xu (2005) and Xu et al. (2009) nvestgate both fulfllment center operatons as well as the best way to bundle mult-tem orders together. Merram (2007) dscusses network confguraton and ts relaton to outbound shppng costs n an onlne retal envronment. We buld on the lterature n the followng ways. In chapter 2, we defne the fulfllment problem n an onlne retal envronment: when a customer places an order for one or more tems, from whch fulfllment center or set of fulfllment centers should the onlne retaler fulfll ths order? We descrbe both the obectve of the onlne retaler as well as the constrants t faces (both hard constrants and computatonal ones). We develop a heurstc to make fulfllment decsons that s computatonally tractable and has desrable asymptotc propertes. Ths heurstc s tested on a small example and compared aganst a myopc fulfllment polcy, an optmal fulfllment polcy, as well as an approxmate dynamc programmng heurstc. 24
We test ths fulfllment decson heurstc on actual data from a large Amercan onlne retaler n chapter 3. In ths chapter, we descrbe how we dsaggregate the problem to make the analyss tractable, as well as other approxmatons. We compare the myopc, heurstc, and perfect hndsght polces by smulatng them on a sample of ths data. We then nvestgate the types of tems that mprove the most from usng forward-thnkng fulfllment polces. A comprehensve senstvty analyss s also performed, to understand the mpact of changes n the system. We end ths chapter by focusng on addtonal benefts of the heurstc, namely, that nventory s left n a more balanced poston at the end of the smulaton. In chapter 4, we turn to the replenshment problem. We show that hgh outbound shppng costs due to nteractons among fulfllment centers may result when a popular replenshment polcy a local basestock polcy that orders up to a pre-specfed amount n each fulfllment center each perod s used to make orderng decsons. We then analyze a new replenshment polcy a proected base-stock polcy that performs well n smulatons wth respect to outbound shppng costs. We compare a local base-stock polcy, a naïve constant order polcy that s system unaware, a proected base-stock polcy, a more sophstcated varant of the proected base-stock polcy that accounts for demand stochastcty, and an optmal polcy on a stylzed model. Through numercal analyss, we show that the proected base-stock polcy s near optmal on many examples. These polces except for the optmal polcy are also smulated on a more realstc model, wth smlarly postve results. In order to mplement the basc and sophstcated versons of the proected base-stock polcy on the more realstc model, we propose a method for estmatng one of the nput parameters to the heurstc: the on-hand nventory ust before the replenshment order arrves after the lead tme. We suggest a lnear programmng approach that matches supply to ether expected or sampled demand, and then measures the remanng nventory n the supply nodes. Fnally, we summarze our results and dscuss future research opportuntes n chapter 5. 25
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Chapter 2 Makng better fulfllment decsons: A heurstc 1 Introducton Ths research grew out of a partnershp wth a large Amercan-based retaler that sells a large catalog of physcal tems onlne and operates a network of fulfllment centers around the Unted States. Ther stock vares n cost and popularty, wth some tems sellng thousands of unts n a week, and others sellng a dozen unts over the course of a year. Our ndustral partner, lke many onlne retalers, makes ts fulfllment decsons n real tme, both for operatonal reasons as well as to provde shpment optons and delvery commtments to the customer n a tmely manner. We assume that our ndustral partner makes these decsons myopcally: the onlne retaler fulflls each order the cheapest way possble based on ts current nventory poston, wthout accountng for any cost mplcatons for fulfllng future orders. In chapters 2 and 3 of ths thess, we nvestgate the extent to whch we mght mprove the performance of the myopc polcy wth an mplementable heurstc. By mplementable we mply both computatonally tractable and ntutve to the extent necessary both to wrte flexble code and to sell the dea to busness managers. We assess the beneft from and feasblty of makng decsons that mnmze the sum of the current outbound shppng cost plus an estmate of future expected outbound shppng costs ncurred as a result of the new nventory poston. What follows s an llustratve example outlnng the possble ptfalls of a myopc polcy. Imagne two fulfllment centers (FC s): one n Los Angeles and one n Nashvlle. The Los Angeles faclty has 3 textbooks left n stock, whle the Nashvlle faclty has one textbook and 9 CD's n stock. Over the course of the next day, two customers wll arrve who each wants hs order delvered wthn three days: one n Dallas wantng a textbook, and one n Washngton, DC wantng a textbook and a CD (although the system s unaware of these customers at the outset of the day). Fgure 1 shows the costs of shppng each tem or combnaton of tems from each faclty to each customer. These costs were retreved from www.ups.com on March 8, 2011. They represent the cost to send a one pound package to a resdental address wthn a 3-day wndow. The $12.12 fgure represents the cost to send a two pound package from Nashvlle to Washngton, DC. 27
Fgure 1: Example of myopc fulfllment wth shppng costs Qualtatvely, havng the textbook n Nashvlle s more valuable than Los Angeles for two reasons. Frst, Nashvlle s close to the geographc center of mass of the US populaton, and thus closer to the average customer than Los Angeles. Second, the faclty n Nashvlle carres a wder assortment of tems, ncreasng the possbltes of bundlng mult-tem orders nto a sngle shpment. If the Dallas customer arrves frst, the onlne retaler (actng myopcally) wll shp the textbook from Nashvlle rather than Los Angeles, savng $11.93 - $11.03 = $0.90. Ths depletes the textbook nventory at Nashvlle, and t has only nne CD's remanng. Then the Washngton, DC customer arrves, wantng a textbook and a CD. Nashvlle no longer has the text book; hence, the text book must shp from Los Angeles, and the CD must shp from Nashvlle, for a total cost of $21.65 + $11.03 = $32.68. The total fulfllment cost for the myopc fulfllment polcy (MYO) s $11.03 + $32.68 = $43.71.. If the onlne retaler could have seen the future, t would have fulflled the Dallas customer's order from Los Angeles and the Washngton, DC customer's order from Nashvlle, at a total cost of: $11.93 + $12.12 = $24.05, a lttle over half the cost of the myopc cost. We call ths the perfect hndsght polcy (PH). As mentoned n chapter 1, n the onlne retalng world, customers may be wllng to pay a premum to receve ther tems more quckly. Ths fee depends on the level of servce, and not the cost of the actual fulfllment. In the above example, both the Dallas and Washngton, DC customers pad a premum to receve ther orders wthn 3 days. These premums dd not depend on the cost ncurred by the onlne 28
retaler, so that t was n the retaler's best nterest to fulfll the orders on tme as cheaply as possble. Any savngs n shppng costs go straght to the bottom lne. We assume that customers have optons wth respect to how fast they want ther tems, wth shorter delvery tmes correspondng to hgher shppng fees (regardless of the actual fulfllment cost). The onlne retaler has several optons wth respect to how to actually shp tems to customers. Faster shppng modes ncur hgher shppng costs on the part of the onlne retaler. We note that the onlne retaler need not use a fast shppng mode to serve a customer who requests a short delvery wndow. If the tems n a customer s order are n a faclty nearby, the onlne retaler may use a relatvely cheap shp mode, even f the customer requests the tems very quckly. Thus, a large savngs can be realzed not only by shppng tems shorter dstances, but also by usng cheaper modes of transportaton, namely, choosng trucks over arplanes whenever possble. The obectve of the onlne retaler s to choose fulfllment centers to serve each customer s request n such a way that mnmzes long term average outbound shppng costs. Our contrbuton n ths chapter s to develop an order-fulfllment heurstc and demonstrate that t has desrable theoretcal propertes. The heurstc s based on a transportaton lnear program, and has the potental to run quckly and be mplemented n real-tme decson makng systems. We show prelmnary results of the heurstc on a few small examples, comparng ts performance to optmal solutons and more complcated heurstcs. In the next chapter, we show how the heurstc performs on real data, and characterze for whch types of SKU s the heurstc works best. 2 Lterature revew The relevant lterature to ths problem of makng fulfllment decsons on the fly can be broken nto four categores, none of whch s specfcally related to onlne retalng: ratonng for multple customer classes, emergency lateral transshpments among multple depots, dynamc and approxmate dynamc programng, and arlne network revenue management. 2.1 Ratonng n the face of multple customer classes One way n whch to vew the problem of makng better fulfllment decsons s as a ratonng problem. Say a fulfllment center serves dfferent classes of customer n ts vcnty, where class s defned by the tme wndow that customers request. If nventory n ths fulfllment center s low, and other nearby fulfllment centers have many tems n nventory, the system can raton nventory from ths fulfllment center towards customers of specfc classes, namely, customers who are not tme senstve. One way to do ths would be to set an nventory threshold such that f the on-hand nventory at a fulfllment center falls below that level, a specfc customer class would be dened servce at ths faclty. Ths mght be 29
especally valuable due to the dfferent customer classes seen n onlne retalng, from those who want ther orders delvered quckly to those who are wllng to wat for ther orders. There s a rch lterature on ratonng nventory n the presence of multple customer classes, albet mostly for a sngle warehouse node. In these cases, customer classes are defned by ther prorty levels, and each level has a desred fll rate, or servce level. For each class, a support level s set, such that when the total nventory drops below a customer class support level, all demand for that class s backordered. The characterstcs of ths system are explored n Nahmas and Demmy (1981), buldng on prevous work by Kaplan (1969) and Venott (1965). In Arslan et al. (2007), the authors fnd nventory levels for a multple demand class system by equatng the classes to nodes n a seral supply system. Muckstadt (2005) has examned the mult-class customer settng n spare parts supply chans, where (s-1, s) replenshment polces are assumed. Ths work, bult on Caggano et al. (2001), defnes customer classes by servce levels as well as tme requrements, and backorders are allowed. In all the prevous references, though, customers are prortzed, and t s allowed to ether backorder or lose demand for low prorty customers n order to fulfll future demand for hgh prorty customers. In our stuaton, however, classes are defned by tme wndow requests, and all demand must be satsfed wthn a requested tme wndow f there s nventory n the system. Otherwse, sales are lost. Addtonally, even f good ratonng polces could be set for a specfc nstance of nventory postons n a network of fulfllment centers, ths polcy would most lkely change sgnfcantly f the nventory postons n the system changed. To be useful and accurate, the threshold levels at a partcular fulfllment center would need to depend on the nventory levels of all the other fulfllment centers n the network. Lastly, demand s usually very slow for the above systems and ntegralty s mportant, makng the technques suboptmal for an envronment where demand for tems may range anywhere from very slow to very fast. 2.2 Emergency lateral transshpments among multple depots When a fulfllment center (called A) serves a customer who lves nearer to a dfferent faclty (called B), ths may be modeled as an emergency lateral transshpment. It s smlar mathematcally to the stuaton where an tem s shpped from A laterally to B (lateral because they are on the same echelon level, emergency because t s reactve rather than proactve), then shpped from B to the customer. The cost dfferental between shppng from A to the customer and shppng from B to the customer may be ncluded as a sort of transfer cost from A to B n the transshpment model. Much of the transshpment lterature s also relevant to replenshment polces, specfcally when the transshpments are proactve or when the retaler s allowed to make transfers at the end of a revew perod once demand s known. Lterature focusng on these aspects s mentoned n more depth n chapter 4, whch dscusses 30