Retail supply chain management: a review of …...Retail business has been rapidly evolving in the...

Journal of Data, Information and Management (2019) 1:45–64https://doi.org/10.1007/s42488-019-00004-z

ORIGINAL ARTICLE

Retail supply chain management: a review of theories and practices

Deng Ge1 · Yi Pan1 · Zuo-Jun (Max) Shen1,2 ·Di Wu1 · Rong Yuan1 · Chao Zhang1

Received: 23 March 2019 / Accepted: 19 June 2019 / Published online: 18 July 2019© Springer Nature Switzerland AG 2019

AbstractRetail business has been rapidly evolving in the past decades with the boom of internet, mobile technologies and mostimportantly e-commerce. Supply chain management, as a core part of retail business, has also gone through significantchanges with new business scenarios and more advanced technologies in both algorithm design and computation power. Inthis review, we focus on several core components of supply chain management, i.e. vendor management, demand forecasting,inventory management and order fulfillment. We will discuss the key innovations from both academia and industry andhighlight the current trend and future challenges.

Keywords Supply chain management · Vendor management · Demand forecast · Inventory management · Order fulfillment

1 Introduction

From the first department store in 1852, the retail revo-lution has never stopped. Retail business has been rapidlyevolving in the past decades with the boom of internet,mobile technologies and most importantly e-commerce.More recently, new technologies are making it possible foreven more forms of retail business. Although the fundamen-tal problems of supply chain management for retail businessremains the same as what was seen decades ago as Forrester(1958) illustrated, the tools and methodologies have dramat-ically changed over the years. In this paper, we will high-light several key components in supply chain managementthat is particularly relevant to retail business, especiallytechnology-driven retail business, and provide an overviewof the evolution of supply chain management methodologiesand practices.

There are several key factors that differentiate a retailsupply chain from a manufacturer supply chain. Firstly,retailers generally face significantly more partners. A large-scale retailer could have tens of thousands of vendors tosupply its inventory compared to a much more manageable

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1 JD.com American Technologies Corporation, 675 EMiddlefield Rd, Mountain View, CA 94043, USA

2 College of Engineering, University of California, Berkeley,4143 Etcheverry Hall, Berkeley, CA 94709, USA

number for the manufactures. As a result, retail supply chainhas to be able to scale more efficiently and focus moreon supply chain coordination. Secondly, retailers face endconsumers in the number of hundreds of millions comparedto a limited number of wholesalers for manufacturers.Retailers need to invest more on understanding customerdemand in order to plan and adjust accordingly. Thirdly, thecost structure of a retailer is different from a manufacturer.The majority of retailers cost is inventory cost whilemanufacturer spends significantly more on equipment andproduct lines. As a result, inventory management is moreimportant for retailers. Manufacturers tend to focus moreon resource scheduling and planning. Lastly, with the ever-growing customer needs in service quality and deliveryspeed, retailers have to take fulfillment into considerationwhen building their supply chain networks. Although somemethodologies can also be applied to other supply chainsystems, in this review, we will primarily focus on thecomponents that are more relevant to retail supply chainsystems.

In retail, supply chain systems normally consist ofmany different components and have different set ups.In this paper, we only consider the related componentsthat the retailer as a decision maker has direct impacton. Traditionally, a typical supply chain in retail consistsof 4 main components: wholesalers (vendors), warehouses(distribution centers), stores and customers as shown inFig. 1. Other parties such as manufacturers and theirsuppliers that are further upstream of the supply chainsystem are normally beyond the scope from a retailer

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46 J. of Data, Inf. and Manag. (2019) 1:45–64

Fig. 1 Traditional retail supplychain

perspective. In a more modern setup, however, the supplychain system visible to the retailer becomes much morecomplex (as shown in Fig. 2). In additional to the increasingsize of the warehouse and store network, retailers now havemore options to satisfy their customers’ demand (e.g. fromstores, from warehouses, or directly from the suppliers,etc.). Further, more and more retailers are forming a deeperintegration with the manufactures and even raw materialsuppliers through the introduction of store brands or privatelabels (Chen 2018a, b). Retailers today still face the sameor similar supply chain management challenges such as thebullwhip effect (Lee et al. 1997). But the approaches andmethodologies used by retailers are continuously evolvingin order to further drive for lower cost, higher efficiency andultimately better customer experience.

One of the most distinguishable changes over thetime in supply chain management methodologies is theshift from “experience-driven” to “data-driven” approaches.An “experience-driven” supply chain management systemheavily relies on human experience to make the criticaldecisions such as inventory placement, fulfillment networkdesign, etc. Although there will still be usage of datain the process, assumptions based on experience andhuman judgment are usually pivotal to the success of suchmodels. For example, traditional inventory replenishmentmodel relies on the assumption of the shape of demanddistribution as well as vendor lead times in order to makean optimal decision (Covert and Philip 1973; Brahimiet al. 2006); Product assortment solutions (Kok et al.2008) also heavily depend on product similarities whichare mostly rated by experienced category managers. Onthe other hand, “data-driven” models rely more on thedata itself rather than human experience. Due to thegreatly increased data availability and the advancement indata mining and machine learning technologies, it is nowpossible for supply chain management systems to makedecisions on available data alone. For example, learningbased replenishment algorithms (Zhang et al. 2017) utilizeobserved demand data to help improve inventory decisions

over time. Machine learning based inventory algorithms(Shi et al. 2018; O’Neil et al. 2016) can directly take inthe historical sales and purchase order (PO) informationto produce optimal decisions without placing assumptionson demand estimation. Embedding technique (Shi 2018;Barkan and Koenigstein 2016) can also effectively learn theproduct similarities through historical customer order datawithout consulting to domain knowledge.

The growing interests in data-driven algorithms areuniversal across both industry and academia. At the sametime, we do observe promising outcomes from newerapproaches showing their ability to solve practical problemswith better empirical performance. With the ever-growingcomplexity of modern supply chain networks, data-drivenalgorithms are playing more and more important roles.

In this paper, we will primarily discuss several keycomponents of retail supply chain management systemsand present their corresponding evolution to the data era.Besides research literature, the review will also focuson industrial applications and demonstrate how theoriesare applied in the real-world. The structure of the paperis organized as follows: Section 2 discusses retailers’coordination and interaction with vendors; Section 3discusses topics related to demand forecasting, one of themost critical components in supply chain management;Section 4 reviews inventory management systems; Section 5follows on the fulfillment perspective of the supply chainsystem; Section 6 provides a summary for the futureresearch directions.

2 Vendor management

In a supply chain, vendor management is critical fora firm since it has a direct impact on product quality,service level and company profits. An effective vendormanagement requires information sharing between theretailer and vendors, such as sharing of demand forecast andinventory information through a vendor managed inventory

Fig. 2 Modern retail supplychain

J. of Data, Inf. and Manag. (2019) 1:45–64 47

(VMI) system in a marketplace with stationary and non-stationary demand. In this section, we will review the recentdevelopment of vendor management from both theoreticalresearch and industrial practice perspectives.

2.1 Introduction

Recent advances in information technology, particularly inthe e-business arena, are enabling firms to rethink theirvendor management strategies and explore new avenues forthe cooperation with vendors through information sharing.Sharing demand related information among supply chainmembers has achieved huge impact in practice. As pointedout by Stein and Sweat (1998), by exchanging information,such as Point of Sales (POS), forecasting data, inventorylevel and sales trends, many companies are reducing theircycle times, fulfilling orders more quickly, cutting outexcess inventory, and improving customer service.

Many companies not only share information with theirsupply chain partners, but also jointly make decisions toimprove supply chain performance by using CollaborativePlanning, Forecasting and Replenishment (CPFR). Accord-ing to Panahifar et al. (2015), CPFR is a technologicalinnovation tool that was first registered as a trademark bythe Voluntary Inter-industry Commerce Standards (VICS)in 1998 and is defined by VICS as a collection of new busi-ness practices that leverage the Internet and EDI (electronicdata interchange) in order to achieve two goals: radicallyreduce inventories and expenses while improving customerservice.

Verity (1996) reported in Business Week that Wal-Martand Warner Lambert attained significant improvements ofin-stock positions while reducing inventory through CPFR.CPFR is one of a series of supply chain initiatives likeJIT (Just-In-Time), ECR (Efficient Customer Response) andVMI (Sheffi 2002) driven by organizations to make theirsupply chains more responsive and keep all the supply chainmembers in tune with the end customer demand, both interms of the product and its volumes.

By ensuring end-to-end information sharing, the occur-rence of the bullwhip effect is reduced thus lowering inven-tory levels across the chain. It also allows the partners ina supply chain to visualize a bigger picture in terms of theentire supply chain rather than their individual enterprise.

As partner collaboration is initiated from the planning tothe replenishment stage, the supply chain can better respondto the exceptional circumstances so as to make it a moreproactive system rather than a reactive one. On a moreabstract level, CPFR aims at creating an environment oftrust between trading partners where the benefits of sharinginformation are realized. The role of CPFR in various stagesof supply chain activity is aptly represented in Fig. 3.Within an efficient and integrated CPFR environment wherefirms share promotion plan, sales data and retail analyticsand vendors share inventory and shipment information, thesupply chain is more responsive to the external businesschange and all the partners can benefit from the informationsharing.

In the vendor management, however, an incompleteunderstanding of the value of information sharing and phys-ical flow coordination may hinder the efforts that promotethe efficiency and responsiveness of a supply chain. Weattempts to better understand the information sharing andflow coordination by reviewing and categorizing the recentresearch and practice in this area.

2.2 Literature review

Relevant literature consistently recognizes that inventoryreduction can be achieved by implementing initiatives suchas information sharing, continuous replenishment programand VMI. In the field of these initiatives, there are severalstreams of the related literature: literature on (1) inter-organizational systems (IOS); (2) quantitative models ininformation sharing; and (3) implementation of informationsharing through VMI and CPFR (Yao and Dresner 2008).

The first stream of research has revealed the businessvalue of IOS by studying the supply chain management

Fig. 3 The CPFR in a supplychain


initiatives, such as information sharing, continuous replen-ishment programs and VMI. Research has shown that IOS,as a link between suppliers and buyers, can improve a firm’sperformance and bring it competitive advantages (Sethiet al. 1993; Palmer and Markus 2000; Premkumar 2000;Srinivasan et al. 1994).

The second stream of research has quantitativelyexamined the value of information sharing in supply chains.The consequences of the bullwhip effect, for example, canbe minimized through information sharing (Lee et al. 1997;Lee and Whang 1999; Lee et al. 2000; Cachon and Fisher2000; Chen et al. 2000).

Some research has found that policies such as VMIcan decrease the bullwhip effect, thereby improving supplychain efficiency, such as by lowering inventory levels andreducing cycle time (Cachon and Zipkin 1999; Kulp et al.2004; Mishra and Raghunathan 2004). Angulo et al. (2004)use simulation to find demand information sharing is asignificant part of VMI implementation and can improvethe fill rate by up to 42%. Cetinkaya and Lee (2000)develop an analytical model for coordinating inventoryand transportation decisions with VMI systems. Lee andWhang (1999) present empirical evidence to confirm thevalue of supply chain coordination. These authors show thatinventory turns and stock-outs have been improved afterthe implementation of continuous replenishment programs,using data collected from 31 grocery retail chains.

The third stream of research has widely discussed thecollaboration and integration in the context of supply chainsin recent years, especially in the area of CPFR. Panahifaret al. (2015) review the scope and value of CPFR using adevised state-of-the-art taxonomy for the classification ofselected references related to CPFR. In the paper, basedon a total of 93 papers published from 1998 to 2013 onCPFR, the authors attempt to seek answers to the questionof what are the main constructs and efficient frameworkfor successful implementation of CPFR. The key findingsof the paper is that four main constructs for successfulimplementation of CPFR have been identified: 1. CPFRenablers; 2. CPFR barriers; 3. trading partner selection;4. incentive alignment. The findings indicate that there isa need for better understanding of the amount and levelof information sharing as an important function of CPFRimplementation.

This paper also categorizes the CPFR implementationbenefits for companies, which consists of three main dimen-sions: Information, Service and Finance. The informa-tion dimension encompasses improvement of forecastingaccuracy, reducing the amount of exchanged informationand reducing the bullwhip effect. The second dimensioninvolves more criteria including increased responsiveness,enhanced customer service quality, improved inventory

management, improved product offering, operational effi-ciency, product availability assurance, decreased replenish-ment lead time, increased customization capability. Thefinancial dimension is the most important objective forfirms implementing CPFR. This covers several criteriareported in the previous studies such as increased rev-enues and earnings, increased margins, increasing EVA(Economic Value Added), increasing shareholder wealth,decreasing cost of production, planning and deployment,maximum efficiency of members, a reduction of inventoryin the supply chain, decreasing working capital, reductionin production and inventory costs, reduced overall costs,increasing the sales of products and reduction in stock-outs.

2.3 Industry practice of information sharing

Potential economic benefits of information sharing throughCPFR are well-recognized and have been publicized inpractice by successful retail businesses such as Wal-Mart.

In its CPFR partnership with P&G, Wal-Mart’s market-ing information is integrated with P&G’s manufacturingsystems to make better consumer-based decisions acrosstheir firm-level boundaries. For example, Wal-Mart’s POSdata show the transaction-level information about con-sumer’s choices, thus providing the actual demand informa-tion on what is selling and the selling price. P&G’s productsare then developed, manufactured and delivered to meetthose customer needs in a timely manner. CPFR pilot withP&G provided a structured contractual platform for jointforecasting and planning activities between Wal-Mart andits vendors that ultimately drive the replenishment processthrough the entire supply chain. As pointed out by Andraskiand Haedicke (2003), by 2003 Wal-Mart has establishedover 600 trading partners through CPFR to reduce its oper-ating expenses to the lowest in the industry. Successful col-laboration with CPFR partners allowed Wal-Mart to priceits products 10% below most of the competitors.

Kim and Mahoney (2010) provided a detailed case studyof the CPFR arrangement between Wal-Mart and P&G. Thecase study reveals that the successful implementation ofCPFR depends not only on extensive information sharingbut also on mutual learning as well as commitments tothe dedicated partners from the repeated interactions. Itgrows out of first gaining an awareness of its contractualpartners’ business needs by asking: 1. what is competitiveadvantage of your partners; 2. what is the competitiveadvantage to you if you combine them with yours; 3.what kind of business relationship does that create. Thus,successful implementation of CPFR requires higher levelsof communication including the exchange of strategies andobjectives between partners at the beginning of a planningperiod.


As e-commerce retail industry in China has beenbooming recently, the information sharing through CPFRhas been widely implemented by Chinese retailers. Tobuild an efficient and responsive supply chain that providesfast delivery to customers, JD.com, China’s largest retailer,online or offline, has invested in the collaboration withits vendors through CPFR by continuously applyingthe advanced information technology in supply chaininnovation. Below we will illustrate several successful casesfrom JD.com, where the value of information sharing in thevendor management is fully realized.

More efficient vendor management with Vendor A Onepractical example of information sharing is the EDI systemcoordination between JD.com and Vendor A, who is a Chi-nese leading electrical appliance manufacturer. Vendor A isone of the top vendors for JD.com by providing electricalappliances, such as small kitchen appliances, laundry, largecooking appliances, and refrigeration appliances.

The original information exchange between JD.com andVendor A such as replenishment purchase orders was simplyby emails and telephone, which resulted in informationdelays and inaccuracies, and orders being intractable. Toovercome this challenge, JD.com and the vendor cooperatedin building an EDI system, allowing the informationexchange in real-time, being transparent and traceable.

The information shared between JD.com and Vendor Aincludes the JD.com’s sales plan, monthly demand forecastby region, purchase orders, and vendor confirmationresponding to the orders. The business benefits of thisinformation sharing through EDI and the integrated demandforecasting is significant: 1. JD.com has better estimationfor the Vendor A’s inventory and production capacity,increasing the prediction time window from one month tothree months. 2. With JD.com’s sharing demand predictionfor longer periods, Vendor A can make a better arrangementof raw material procurement, production capacity andworkers schedule. The production plan of Vendor A is 95%alignment with JD.com’s purchase plan. 3. The time delaybetween JD.com’s purchase orders and Vendor A’s responseand confirmation is significantly reduced by 70%.

Supply lead time reduction with Vendor B Vendor B is amajor beverage and food supplier for JD.com. Since 2014,JD.com set a goal to provide fresh products and high-quality customer service in the beverage market. Knowingthe potential benefits of supply chain coordination, theexecutive managements of both JD.com and Vendor Breached a strategic agreement that would strengthen thecollaboration in information sharing. Since then, threekey steps have been implemented: 1. Demand forecastand promotion plan sharing; 2. Vendor B direct inventory

replenishment to JD.com warehouses; 3. Using Vendor B’swarehouses to fulfill JD.com customer orders. In 2016,with the CPFR practice and information sharing efforts, thesupply lead time from Vendor B has decreased from 8 daysto 3 days. The in-stock rate increases from 73% to 96%, andGMV increased by 5%.

Higher in-stock rate for peak promotion with Vendor C Inthe important promotional and holiday events, such asThanksgiving Black Friday event in US, 618 and Single Dayevents in China, the sales could account for 30% or higherof the annual sales, and retailers usually need to prepareinventory 1-2 months in advance to meet the peak demand,especially for the top-selling products. Through the CPFR,firms can book the vendors inventory and improve the in-stock rate during the promotional events. Another successcase of information sharing is the implementation of CPFRbetween JD.com and Vendor C. During the 618 promotionalevent in 2017, Vendor C fulfilled 99% of JD.com’sreplenishment orders. The promotional information sharingresulted in Vendor C’s products in-stock rate improved by3%, and lead time reduced by 20%.

3 Demand forecasting

Demand forecasting is one of the most important compo-nents in supply chain management. In fact, demand fore-casting results are key inputs for many decision-makingprocesses in retail such as inventory management, networkplanning, pricing and revenue management, marketing, etc.Over the recent years, many retail companies, especiallye-commerce companies have significantly increased theirinvestments in improving demand forecasting performance.Further, with the increased data availability and the devel-opment of more sophisticated machine learning algorithms,we observe many new developments in this space in recentyears. We will review both theoretical research and indus-trial practices of demand forecasting in this section.


Demand forecasting is critical to the success of a retailcompany. Particularly in supply chain management, anaccurate and practical demand forecasting system can be asignificant source of competitive advantage by improvingcustomer service levels and by reducing costs related tosupply-demand mismatches (Snyder and Shen 2011). Asa result, the forecasting problem has a long researchhistory in the field of statistics and recently in machinelearning. Demand forecasting is a practical domain of timeseries modeling and forecasting (Hamilton 1995). Many


important models have been proposed in the literaturefor improving the accuracy and efficiency of time seriesmodeling and forecasting. In the section below, we describethree important classes of time series models that arecommonly used in the practice of demand forecasting,i.e. the stochastic time series, machine learning and deeplearning models, together with their inherent forecastingstrengths and weaknesses. Further, we will also pointout several specific forecasting scenarios that are closelyassociated with retail industry.

3.1.1 Stochastic time series models

Stochastic time series models are the most popularmethods used in demand forecasting. There are twomain classes of stochastic time series models: linearand non-linear models. Linear model includes the twomost widely used stochastic time series approaches, i.e.the Holt-Winters method (Holt 2004; Winters 1960) andthe Autoregressive Integrated Moving Average (ARIMA)model (Box et al. 2015). ARIMA model has subclassesof other models, such as the Autoregressive (AR),Moving Average (MA), Autoregressive Moving Average(ARMA), and Seasonal ARIMA (SARIMA) (Hamzacebi2008) models. Although these models can capture trendand seasonality, they are ineffective in modeling highlynonlinear time series. To overcome this drawback, variousnon-linear stochastic models have been proposed inthe literature, such as the Autoregressive ConditionalHeteroskedasticity (ARCH) (Engle 1982), and GeneralizedARCH (GARCH) (Bollerslev 1986) models; however, theimplementations are not as straight-forward and simple asthe linear models.

The strengths of stochastic time series models are themodel interpretability and computational efficiency. It iswell received by practitioners due to the easiness toimplement and the intuitive results. However, the mod-els have relatively strong assumptions in terms of theunderlying stochastic processes, which are normally oversimplifying the practical situation. Recently, with theadvancements in big data and machine learning, researchershave found Machine Learning and Deep Learning algo-rithms to be able to provide better forecasting performance.

3.1.2 Forecasting with machine learning techniques

In the last few decades, machine learning techniques arewidely used in the field of forecasting, such as Decision Tree(Breiman 2017), K-Nearest Neighbor Regression (KNN)(Hastie and Tibshirani 1996), Support Vector Regression(SVR) (Drucker et al. 1997), and Gaussian Processes (GP)(Williams and Rasmussen 1996). Comparing to stochastictime-series models which are more model-driven, the

machine learning methods are more data-driven; in general,these machine learning methods are exploited to improvetime series predictions by minimizing a loss function.Ahmed et al. (2010) compares the accuracy and timeconsumption of these machine learning models.

Zheng and Su (2014) proposed a two-step enhancedKNN method, and the method consistently improved theforecasting accuracy in short-term forecasting. Recurrentleast-square SVR (Suykens and Vandewalle 2000) anddynamic least-square SVR (Fan et al. 2006) are two popularSVR models for time series forecasting. Girard et al.(2003) proposed to use the non-parametric Gaussian processmodel for multi-step ahead time series prediction, so thatthe uncertainty about intermediate regressor values can beincorporated, thus the uncertainty on the current predictioncan be updated. Decision Tree provides a foundationfor various tree-based ensemble algorithms including thetwo most widely used machine leaning techniques, i.e.,Random Forest (RF) (Kam 1995) and Gradient BoostingDecision Trees (GBDT) (Chen and Guestrin 2016; Keet al. 2017; Prokhorenkova et al. 2017). RF has beenused for electricity load forecasting (Nedellec et al. 2014;Dudek 2015), and it is shown that the RF model providesas accurate forecasts as artificial neural networks (ANN)and outperformed the ARIMA and Decision Tree models(Dudek 2015). RF has been proven to be competent in one-step-ahead time series forecasting, and it is also shown thata low number of recent lagged variables performs better,highlighting the importance of the training set’s length(Tyralis and Papacharalampous 2017). Similar to RF, GBDThas exhibited competent performance comparing to otherstochastic time series and machine learning methods on timeseries forecasting (Kusiak et al. 2009). However, there issome limitation as tree-based models cannot extrapolate, i.e.cannot predict value bigger or smaller than the value in thetraining set, so they are not suitable for data with a trendin time series. The solution is feature engineering, which isto remove the trend first by constructing many time seriesfeatures (e.g. day of week, holiday, season, moving average,and lag) (Kusiak et al. 2009). Another weakness of machinelearning models is that they require more computationcomparing to stochastic time series models, which increasesthe time complexity of the solutions.

3.1.3 Forecasting with deep learning techniques

Recently, artificial neural networks (ANNs) have attractedincreasing attentions in the domain of time series forecast-ing (Zhang 2003; Kihoro et al. 2004; Kamruzzaman andSarker 2006). The excellent feature of ANNs, when appliedto time series forecasting problems is their inherent capabil-ity of non-linear modeling, without any presumption aboutthe statistical distribution followed by the observations.


Recurrent Neural Networks (RNNs) are a special type ofANNs which contain self-connections. Unlike feedforwardNNs, the hidden states of RNNs serve as memory to helpmap both current time inputs and previous time internalstates to new desired outputs. This allows RNNs to capturetemporal information in sequence data. Long Short-TermMemory (LSTM) (Hochreiter and Schmidhuber 1997) isan important class of RNN variants, which has additionalmemory-control gates and memory cells to selectively storehistorical information and keep long term information.RNNs and LSTMs have been successfully applied to timeseries data forecasting problems. Langkvist et al. (2014)reviewed recent research in unsupervised feature learningand sequence modeling with deep learning methods forvarious time series data such as video frames, speechsignals, and stock market prices. Bianchi et al. (2017)compared different RNN variants and showed that LSTMsoutperformed others on highly non-linear sequences withsharp spikes thanks to the quick memory cell modificationmechanism. Cinar et al. (2017) proposed using an LSTMencoder-decoder with position-based attention model tocapture patterns of pseudo-periods in sequence data. Theyapplied the attention mechanism (Bahdanau et al. 2014) toexplore similar local patterns in historical data for futureprediction. However, it is impractical to look into the fullhistory of time series and the selection of which part of thehistory to attend to relies on human knowledge. Taieb andAtiya (2016) analyzed the performance of different multi-horizon forecasting strategies on synthetic datasets withdifferent factors, such as length of time series and numberof horizons. Flunkert et al. (2017) proposed a techniquecalled DeepAR to make probabilistic forecasts by assumingan underlying distribution for time series data. DeepARcould produce the probability density functions for targetvariables by estimating the distribution parameters on eachtime point with multi-layer perceptions (MLPs). However,the distributional assumption is often too strong to apply toreal-world datasets.

Comparing to stochastic time series and machinelearning models, the strengths of deep learning models arethe model capability to incorporate a variety of information,no need for feature engineering, and generally higherforecast accuracy. The weakness of deep learning models isthat when the training size is large, it requires an enormousamount of computation, and the model interpretability is thelowest among the three classes.

3.1.4 Retail specific forecasting scenarios

Retail demand forecast has its own unique challenges thatmakes the problem more complex. Firstly, retail demandforecasting normally have to deal with censored demandwhere observed demand data are affected by out of stock

inventory (Jain et al. 2014). Empirically, out of stock has ahigh correlation with promotion activities in retail business.So, the forecasting algorithm needs to deal with the missingdata very carefully in order to produce accurate results.The expectation-maximization (EM) algorithm (Dempsteret al. 1977) is one of the approaches that widely adoptedfor this need (Anupindi et al. 1998; Talluri and Van Ryzin2004; Vulcano et al. 2010; Conlon and Mortimer 2013).Secondly, with the ever-increasing product selection, newproduct forecast is becoming a more and more importanttopic. Since the publication of the Bass model in 1969(Bass 1969), research has been made to make diffusionmodels theoretically sounder and practically more effectivefor new product forecast (Ismail and Abu 2013; Lee et al.2014; Kahn 2014). Thirdly, product complementarity andsubstitutability plays an important role in the actual sales ofproducts, and the demand for a product can depend directlyand indirectly on other products in different categories(Shocker et al. 2004; Duan et al. 2015). Fourthly, a retailer,especially e-commerce retailer generally has a large productselection with highly diversified products and with differentproduct characteristics, such as fashion, fresh groceries, etc.For fashion products, demand uncertainty, lack of historicaldata and seasonal trends usually coexist and make demandforecasting more challenging (Nenni et al. 2013). For freshgroceries, successful demand forecasting is very criticalbecause of the short shelf-life of fresh products and theimportance of the product quality (Doganis et al. 2006;Shukla and Jharkharia 2013). For long-tail products, theproblem has proved to be more challenging due to thesparsity of the sales data that limits the degree to whichtraditional analytics can be deployed. Pitkin et al. (2018)developed a Bayesian hierarchical model to forecast thistype of demands.

3.2 Industry practice

As mentioned in the earlier section, demand forecastingis a heavily invested domain in the industry, especially inretail. There are many existing commercial solutions on themarket. At the same time, many companies have establisheddedicated teams on demand forecasting to keep pushing forthe boundary of the forecasting accuracy.

3.2.1 Commercial forecasting tools

Demand forecasting is a key part of traditional supplychain management software. Leading companies in thisspace include JDA, IBM, SAS, Oracle and more. Theprediction methods they use are mostly stochastic timeseries algorithms that emphasize the need for planningin software functions, such as assuming simulations andartificial adjustments to requirements. Although these


companies lack sophisticated predictive algorithms, theyhave a high market share in large and medium-sizedenterprises because of their long history, wide productcoverage, and good integration with ERP (EnterpriseResource Planning) systems.

With the popularity of machine learning in time seriesprediction, in recent years, there have been many productswith machine learning and prediction accuracy as themain selling points. Such products can use machinelearning algorithms to make more accurate forecasting ofdemand than stochastic time series methods, leveraginginternal and external data such as product features, storelocations, weather, geography and economic indicators.Representative companies in this field include LLamasoftand Blue Yonder. It is worth noting that JDA acquiredBlue Yonder in 2018, and then launched the machine-learning-based cloud prediction product Luminate DemandEdge in October of that year, marking JDA’s transitionto cloud services and artificial intelligence (JDA Software2018). Besides, Amazon recently launched its time seriesforecasting cloud service “Amazon Forecast” based onmachine learning and deep learning techniques (e.g.DeepAR (Flunkert et al. 2017) and MQ-RNN (Wen et al.2017)), and no machine learning experience is required touse this service.

In the last two years, a number of common machinelearning platforms have emerged, such as H2O, DataRobotand Ali PAI. This type of product service is targeted atdata scientists, allowing them to perform modeling throughvisual operations without programming. In this trend, thereis a branch that is a general predictive software that does notneed to be modeled by itself. The most representative oneis H2O Driverless AI, which has many advanced algorithmsbuilt in, and has the ability to automatically target any set ofdata by the machine to find the optimal algorithm.

3.2.2 Probabilistic demand forecasting system

Many practical decision-making scenarios require richerinformation provided by probabilistic forecasting thatreturns the full conditional distribution, rather than pointforecasting that predicts the conditional mean. For real-valued time series, this is traditionally achieved by assum-ing an error distribution on the residual series. However,an exact parametric distribution is often not directly rel-evant in applications as mentioned above. Instead, par-ticular quantiles of the forecast distribution are useful inmaking optimal decisions, both to quantify risks and mini-mize losses (e.g. risk management, operation optimization).Probabilistic forecasting is a key enabler for forecastingdemand and optimizing business processes in retail busi-nesses.

Many researches focus on generating quantile estima-tions for target variables by formulating forecasting prob-lems as quantile regressions (Koenker and Bassett 1978).Zheng (2010) proposed to minimize objective functions forquantile regressions with high dimensional predictors bygradient boosting (Friedman 2001). Xu et al. (2016) pro-posed a quantile autoregressive model which can outputmulti-horizon quantile predictions by sequentially feedingpredictions of previous steps into the same NN for currentprediction. Their NN-based autoregressive model is differ-ent from RNN as prediction values, instead of hidden states,are fed recursively. For the above methods, separate modelshave to be trained for different quantiles for multi-quantileforecasting tasks, which is inefficient in practice.

Recent forecasting researches from e-commerce com-panies show strong interests for probabilistic forecasting,such as DeepAR (Flunkert et al. 2017) and MQ-RNN (Wenet al. 2017) from Amazon and the multi-horizon fore-casting algorithm (Fan et al. 2019) developed by JD.com.MQ-RNN (Wen et al. 2017) generates multiple quantileforecasts for multiple time horizons. It uses an LSTM toencode the history of time series into one hidden vector, anduses two MLPs to summarize this hidden vector, togetherwith all future inputs, into one global context feature andhorizon-specific context features for all horizons. However,this global context feature may be too general to captureshort-term patterns.

Fan et al. (2019) presented an end-to-end deep learningframework for multi-horizon probabilistic demand forecast-ing. This LSTM based system improves the considerationfor temporal relationship within the forecast result by allow-ing propagation of information both forward and backwardin the LSTM decoder (shown in Fig. 4). This approach out-performs current state-of-the-art models on the GOC20181

Sales Forecasting dataset (Yuan and Jing 2018).

4 Inventorymanagement

Inventory management is to make informed decisions aboutthe quantity and placement of stocked goods. As a com-plex multi-layer system, it is required at various loca-tions/facilities across the inventory network. It covers theentire inventory control process including the monitoring ofgoods moved into and out of warehouses/distribution cen-ters and the reconciling of the inventory balances. In thissection, we discuss the state-of-the-art regarding three keyaspects of inventory management, namely, replenishment,transshipment, and placement.

1https://jdata.joybuy.com/en/html/detail.html?id=4

https://jdata.joybuy.com/en/html/detail.html?id=4


Fig. 4 Structure of deeplearning probabilisticforecasting framework


Inventory management is an extensively studied field inthe literature. Due to space limitation, we only covermain methodologies and invite readers to refer to thecited reference for more comprehensive introductions andreviews. In the following subsections, three key aspectsof inventory management are covered, i.e. replenishment,transshipment and placement. These are the foundations formany practical inventory management systems in practiceand still see many new developments recently.

4.1.1 Replenishment

If the customer demand and the vendor lead-times areappropriately predicted, one would be able to vary stocksaccordingly to accommodate their needs. However, bothdemand and lead-times are difficult to forecast to asatisfying accuracy level due to the stochastic nature ofthese quantities (e.g., variability in demand and lead-time).In addition, items may be required faster than the supply canprovide it. Among all different types of incurred inventorycosts, stock-out cost is usually the most significant part,which may happen in cases such as fluctuating customerdemand, forecast inaccuracy, and variability in lead-times,etc. To compensate for these, one can otherwise holdsufficient stock to cope with unexpected or excess demandso as to prevent stock-outs. The minimum stock levelmaintained is referred to as safety stock and this inventorymanagement strategy is called safety stock policy. Besides,safety stock can also compensate for the uncertainty in thevendor lead-time prediction. Arguably, safety stock is thebasis for the broader problem: inventory replenishment.

The safety stock idea has been used to devise a fewclassic replenishment models such as the continuous reviewinventory policy (s, Q) and the periodic review inventory

policy (R, s, S) or (R, S) (Snyder and Shen 2011). Theformer uses a replenishment quantity of Q which is usuallydetermined using the idea of economic order quantity(EOQ) (Harris 1915), while the latter typically assumes amore realistic stochastic demand and takes a similar form asthe safety stock derived above.

The strategic safety stocks as described above canhelp to manage the risk of stock-out, which usuallyoutweighs other relevant costs. A comprehensive reviewof safety stock techniques is available in Guide andSrivastava (2000). Various safety stock strategies have beendeveloped, for instance, Salameh (1997) and Brandoleseand Cigolini (1999). Estes (1973) developed a re-orderingpoint inventory model that accounts for demand andleadtime variability by assuming that the demand duringthe leadtime follows a normal distribution. Later on, Ruiz-Torres and Mahmoodi (2010) determined safety stock byfocusing on historical data without making any particulardistributional assumptions of demand and leadtime. Theoptimal placement of safety stocks also has been extensivelystudied with seminal work including Clark and Scarf (1960)and Lagodimos and Anderson (1993). Graves and Willems(2000) formulated the safety stock placement problems asa network optimization problem and developed a dynamicprogramming algorithm as solution procedure. Lesnaia(2004) formulated the safety stock problem in supply chainsas a general network problem, which is shown to be NP-hard.

The inventory system in practice may consist of multiplelayers of warehouses such as regional distribution center(RDC) and forward distribution center (FDC). At each levelof the supply chain, stock levels of various SKUs needto be determined. Multi-echelon inventory managementdeals with multiple layers of inventory simultaneously.Specifically, a multi-echelon system can be categorizedinto a few types: a serial chain, a divergent system and a


convergent system. A divergent system consists of a singlecentral stage and several successors. A convergent systemhas a one-end stage with several predecessors. A serial chainis a special case of the divergent systems that have onesingle successor.

Many real-world supply chains are multi-echelon sys-tems consisting of several stages of procurement, manu-facturing, and transportation. The theory of multi-echeloninventory management was initialized by Clark and Scarf(1960), in which a basic model for a supply chain consist-ing of multiple stages with a serial structure is considered.The authors proved the optimal base stock levels can beobtained by the minimization of one-dimensional convexcost functions. Another seminal work on this topic is Sher-brooke (1968). Later on, the work of Clark and Scarf(1960) was extended in terms of many aspects, such assystems with a pure assembly/convergent structure, fixedbatch sizes or fixed replenishment intervals, and advanceddemand information, etc. For instance, the optimality ofbase stock policies and the decomposition result (Clarkand Scarf 1960) was based on a stochastic dynamic pro-gram in a finite-horizon setting, which was then extendedto the infinite-horizon case (Federgruen and Zipkin 1984).The optimal inventory policy for a serial system assumingMarkov-modulated demand was derived by Chen and Song(2001).

There are various solution procedures proposed inliterature for the inventory problem of multi-echelonsystems, including both heuristics, approximate methods,and exact methods. The optimal policy and optimal costsof a multi-echelon, serial system have been computed byboth the approximate method based on two-moment fits andexact method in van Houtum and Zijm (1991). Shang andSong (2003) proposed a simple heuristic-based inventorypolicy for the multi-echelon, serial system with linear costsand stationary random demands through the lower andupper bound subsystems.

4.1.2 Transshipment

Transshipment problem deals with the problem of managinggoods between distribution centers, facilitates inventoryre-balancing, bulk inventory receiving, and customerorder fulfillment (Herer et al. 2002). Different fromreplenishment problem, transshipment problem handles thenecessary movements of goods between warehouses withinan echelon, e.g. within the retailer’s network. Throughtransshipment, the overall inventory level can be reducedwhilst maintaining the required service levels.

This topic has been studied extensively since the late1950’s (Allen 1958, 1961) focusing on varying number ofitems, warehouse echelons, warehouses, ordering policies,and cost analysis. Two main types of transshipment exist,

i.e. proactive and reactive transshipment, distinguished bythe timing of the decision-making process with respectto demand realization. Proactive transshipment redistributeinventory between warehouses at predetermined momentsin advance of any potential customer orders while reactivetransshipment, as it is suggested by the name, reacts to realdemands and act accordingly. Proactive transshipment mod-els, e.g. Allen (1958) and Karmarkar (1981), range from asimple single-period, single-transshipment with no networkinventory replenishment model to more complex modelconsidering multi-period, multi-transshipment with networkinventory replenishment. Research on reactive transship-ment can be split between periodic review (Krishnan andRao 1965; Tagaras 1989) versus continuous review (Lee1987; Wong et al. 2006) models, which can be furtherdetailed into single-echelon, multi-echelon and decentral-ized systems. A comprehensive review of lateral transship-ment models can be found in Paterson et al. (2011). Itis worth noting that with e-commerce retailers, there is agrowing emphasize on reactive transshipment not only toimprove product availability but also to reduce order splits(Zhang et al. 2018). As last mile delivery is one of the largestcomponents of fulfilment cost (Xu et al. 2009), an efficientreactive transshipment algorithm can significantly reduceoverall fulfillment cost.

Recent research further increases the intricacy of thesystem but primarily in a focused area. For instance, atwo-item, two-warehouse periodic-review inventory modelwith transshipment was examined in Ramakrishna et al.(2015). Noham and Tzur (2014) implemented a fixedtransshipment cost to improve on previous methods ofmodeling single and multi-item transshipment problems.Torabi et al. (2015) modeled the transshipment problem ine-commerce as a mixed integer linear programming (MILP)and solved optimally to minimize the cost of logistics, whilenot consider the effects of shipment and leadtime.

4.1.3 Inventory placement

Inventory placement is the problem of determining thechoice of fulfillment centers in which to place each SKU.The inventory placement problem is specifically relevantin the era of e-commerce due to the greatly increaseditem selection available to the retailers. The retailer hasto strategically store the items in order to better balanceshipping speed and inventory cost. As an optimizationproblem (e.g., mixed integer linear programming), it aimsto find a placement plan for a given planning horizonthat minimizes the total costs (i.e., shipping, overhead)and satisfies the expected geographical demand andrespects fulfillment center capacity constraints. It coverstopics on facility (e.g., warehouses) location and productplacement.


Inventory placement problem can be viewed as anextension of the facility location problem and a special caseof the multi-commodity capacitated fixed-charge networkflow problem. The problem varies by the number ofcommodities (i.e., single-commodity, multi-commodity),capacity (i.e., uncapacitated, node-capacitated or arc-capacitated), fixed charge (i.e., charge on nodes, edgesor both), and network structure (e.g., two-echelon, multi-echelon).

There has been extensive research on topics relatedto inventory placement. Earlier work includes the single-commodity uncapacitated facility location problem (singleplant location problem or SPLP) (Jakob and Pruzan 1983;Cornnejols et al. 1977), and the multi-commodity locationproblem (Warszawski and Peer 1973; Karkazis and Boffey1981; Revelle and Laporte 1996) which was extended basedon SPLP. The capacity constraints, on nodes, edges or both,impose additional challenges for the solution procedures.Various techniques have been used for solving the problemsuch as greedy heuristics, dual ascent method, and Bendersdecomposition. Sridharan (1995) provides a survey on thesesolution techniques. Melkote and Daskin (2001) proposedthe capacitated facility location/network design problem tobridge the gap between the problems of facility locationand network design. The multi-commodity capacitatedfixed-charge network flow problem is a more generalizedformulation compared to the aforementioned approaches.Gendron (2011), Gendron and Larose (2014), and Meloet al. (2009) provide comprehensive reviews on the exactsolution approaches, including cutting plane methods,Benders decomposition, and Lagrangian relaxation, andrecent advances to this problem. To capture other practicalconsiderations, researchers have been making efforts inconsidering multi-period dynamics (Melo et al. 2006),stochasticity of demand or costs (Snyder 2006), andnonlinear cost structures.

Another related topic on inventory placement of broadand current interest focuses on the placement of specifictypes of goods named product placement. It is to determinethe set of SKUs (i.e., products) to store in a specific location(i.e., distribution center) at a time point so as to betterservice customers. It can also be viewed as an assortmentproblem. The assortment planning problem has beenextensively studied and is still an emerging research area.Reviews of the state-of-the-art on assortment planning areprovided in Mahajan and van Ryzin (1999) and Kok et al.(2008). The latter also summarizes the current approachesused in the industry practice for inventory placement. Atypical approach in literature to the assortment problem isto formulate it as an optimization problem with which toselect a subset of products maximizing the revenue capturedat a single store/location. To take the interaction betweenassortment and demand, a demand model, such as the

multinomial logit model, exogenous demand, or locationalchoice models, is utilized to capture consumer behaviors.For uncapacitated assortment problems, algorithms rankingproducts by margin under the multinomial logit (MNL)discrete choice model are available in Talluri and VanRyzin (2004), Gallego et al. (2004), and Liu and VanRyzin (2008). Later, Schon (2010) and Wang (2012) studiedthe assortment problem with capacity constraint imposed.In contrast to the static view of the assortment planningproblem, Rusmevichientong et al. (2010) and Caro andGallien (2007) addressed the dynamic assortment problemthat revises or changes assortment selection as time elapses.


Inventory management has long been the center piece forretailers. In this section, we present industry approaches tothe inventory management problem regarding the followingaspects. As will be seen, these companies take significantlydifferent approaches and emphasize different aspects of theinventory management problem.

4.2.1 Replenishment

The idea of safety stock has been popular for decades inindustry as inventory replenishment method. Starting in1994, IBM started to develop the Asset Management Tool(AMT) aiming to achieve quick responsiveness to customerswith minimal inventory (Lin et al. 2000). In addition, IBMhas developed a software tool named the IBM Supply ChainSimulator (SCS) to help with making strategic businessdecisions about the design and operation of its supply chain,which include a function for determining safety stocks forthe modeling of distribution (Bagchi et al. 1998).

Intel uses a periodical review model in its multi-echeloninventory optimization (MEIO) system, where inventorytargets are reviewed weekly with up-to-date sales andinventory information. The inventory targets are set basedon a weeks-of-inventory (WOI) policy by product family.The inventory target is then fed into an Intel-developedadvanced-planning and scheduling (APS) optimizer thatminimizes production costs, lost-sales costs, and costs fordeviating from the inventory targets (Manary and Willems2008).

Walmart has been collaborating with P&G to deploy acontinuous review replenishment policy in which they con-tinuously monitor the inventory levels by RFID technologyand automatically replenish their inventory when levels gobelow the safety stock level (Kosasi et al. 2014). Similarly,Amazon.com tracks its inventory position in real-time basedon warehouse receipts and shipments and places purchaseorders to vendors based on the forecasted amount neededsubtracting the current on-hand inventory in the warehouse


(Zeppieri 2004). Chiles and Dau (2005) provide a detaileddescription of the replenishment process at Walmart andAmazon.

Multi-echelon inventory management is increasinglyincorporated into the practice of supply chain management.For instance, ideas along this line have been successfullyapplied in practice such as IBM (Lin et al. 2000), EastmanKodak (Graves and Willems 2000), and Philips Electronics(de Kok et al. 2005).

At JD.com, a sophisticated inventory control systemis implemented with a system of inventory replenishmentmodels. The system selects the appropriated models basedon product characteristics as well as business requirements.For example, products with low daily sales may be treatedusing a dedicated model to account for the higher relativevariability while high volume items are generally drivenby continuous or periodical review models. In general,the replenishment models are formulated as optimizationproblem minimizing total cost including inventory holdingcost, and stock-out cost.

JD.com utilizes a fulfillment network structure thatresembles multi-echelon inventory system with a RDC-FDC design. Recently, approximation algorithms have beenproposed for solving the replenishment (to RDCs) andallocation problems (from RDCs to FDCs) jointly (Qi et al.2018). This work is extended based on the seminal workof Levi et al. (2007), which proposed the dual-balancingpolicies. The algorithm is validated with actual JD.comoperation data and provides high computational efficiencyby leveraging from two novel techniques: marginal costaccounting and cost balancing.

4.2.2 Transshipment

Transshipment is a vital and growing part in the inventorymanagement in practice. Transshipment allows for moreefficient use of network resources and reduces costsof fulfillment and replenishment throughout the entireinventory network. Amazon uses a transshipment methodcalled lateral transshipment which deals with the movementof goods within the same echelon of the supply chain. Threetransshipment types exist in Amazon, i.e. Customer Order(reactive), Network Inventory Re-balancing (proactive) andDomestic Cross-dock (proactive). Reactive transshipment isprimarily used to reduce fulfillment cost while proactivetransshipment is used for cost savings on replenishmentto fulfillment centers. Young (2016) details the keysteps of transshipment process in Amazon, which includeinventory, transshipment pick, merge/palletize, outbounddock, transportation, inbound dock, receive, and stow,etc. The same process covers both reactive and transfer(proactive) transshipment.

JD.com is well known for its super-fast delivery thanks tothe smart inventory allocation mechanism. It features sameand next day delivery as a standard, allowing customersto receive same-day delivery for orders placed before11am and next-day delivery by 3pm for those placedbefore 11pm. More than 90% of orders on JD.com aredelivered same-day or next-day. Due to the fast deliveryspeed requirement, reactive transshipment, although stillexists in JD.com network, takes a smaller portion of theoverall transshipment volume. In contrast to Amazon’slateral transshipment, JD.com owns a more complexdistribution network and uses a multi-step inventoryallocation algorithm. Specifically, they calculate the targetinventory level for each product and then select a subsetof products to transship via optimization. This innovativeallocation strategy has led visible improvement regardingstock-out rate at the distribution centers and the proportionof orders meeting their same- and next-day deliverystandards.

4.2.3 Inventory placement

Compared to the more operational and detail-orientedapproaches in academia, industry uses a more strategic andholistic approach for assortment planning.

As reviewed by Kok et al. (2008), in retailing industry,one common approach to assortment planning is forcorporate headquarters to decide on a single commonassortment that is carried by all stores of the chain (e.g.,Best Buy), except that in smaller stores, the breadth ofthe assortment may be reduced by removing some of theleast important SKUs. On the contrary, a few retailers (e.g.,Bed Bath & Beyond) allow their store managers to managethe assortment on their own. One example that followsa hierarchical approach to assortment planning is AlbertHeijn, BV, a leading supermarket chain in the Netherlands.Specifically, they first divide SKUs into categories, andstore space is allocated to each category accordingly.Subsequently, they carry out product selection and facingallocation to products as detailed in Kok et al. (2008).

In contrast to academic studies, product placement forindustry practitioners is an even more intricate decision thatrequires many more practical issues to be considered and/orcustomized to their specific business needs. For instance,as a strategic necessity, Best Buy would carry a rarelydemanded product just to maintain consumer perceptionof it as offering the latest lines of technological products.Retailers are also known to frequently carry unprofitableproducts in order to strategically drive for growth or overallprofitability of a whole category. Besides, Best Buy alsoconsiders various other supply chain considerations, suchas vendor relations, vendor performance and the scope of


products offered by a vendor, to develop the assortmentplans (Kok et al. 2008).

In JD.com, there are two types of warehouses ordistribution centers, namely regional distribution centers(RDC) and forward distribution centers (FDC). The formerhave larger capacities and tend to store comprehensiveSKUs, including slow moving products, and also serve asthe suppliers for the latter. On the other hand, the latter havelimited capacities, are geographically closer to customers,and usually store mostly high-selling SKUs in order tofulfill orders in a timely manner. As a primary feature thatcustomers pay attention to, delivery time is an importantmetric for e-commerce and e-retailers are thus seekingto cut their delivery time in order to be more attractiveto customers. As a result, JD.com need to maximize thetotal number of orders that are shipped from FDCs soas to benefit from fast shipping. In case an order cannotbe satisfied by an FDC (e.g., one product is missing inthe FDC), it may be routed to the closest RDC. As aconsequence, the delivery is subject to delay. To makethings more complicated, it is normally preferable to havean order with multiple items delivered together. As a result,simply placing the best sellers in FDC may not be ableto best utilize the capacities. Since a significant portionof orders contain more than 2 products, it is necessaryto group items with similar attributes that sell togetherin the same FDC to avoid splitting orders into multiplepackages.

Shi (2018) presented one example of the solutions usedfor inventory replacement in JD.com. To fully leveragethe fast delivery from FDCs, they aim to maximize thenumber of orders that can be fulfilled entirely from theFDCs and minimize the occurrences of order splits. Asa large-scale optimization problem, a practical solutionprocedure to this is to use heuristics that rank orders bytheir popularity. Specifically, they use SKU2Vec algorithm,which is motivated by Google’s Word2Vec (Mikolov et al.2013) to compute a latent vector for each SKU which is thenused to model the closeness to another SKU. An end-to-endneural network framework is then used to make inventoryassortment decisions by directly capturing the co-purchaserelationship between products reducing order split by about2% (i.e., 2 million less split packages per year) compared toa benchmark Greedy Ranked algorithm.

Alternatively, they also propose a data-driven graph-based algorithm at JD.com that outputs subsets of productsto be placed at FDCs. This is achieved by samplingbatches of orders and aggregating the solutions of aselection problem solved with parametric cut minimization.This innovative approach is demonstrated to have superiorperformance than the former inventory placement strategy(Jehl et al. 2018).

5 Order fulfillment

Order fulfillment is the process of accepting, processing,and delivering customer orders. Order fulfillment is acrucial component contributing to the customer experience,as well as an important aspect of cost control in supplychain. For a large retailer like JD.com, the order fulfillmentdecision is among the most difficult problems given thatthere are billions of products in its large-scale and multi-stage inventory network.


Order fulfillment has become a popular research topic sincee-commerce entered the main stage of retail business. Inthe era of e-commerce, the packages placed online can bedelivered to the customers within a few days, sometimeseven the same day. The scale and complexity of the onlineorder fulfillment system gives rise to a broad range ofresearch opportunities. In this section, we review threemain technical aspects of the order fulfillment process,namely Fulfillment Optimization, Order Prioritization, andFulfillment Flexibility.

Fulfillment Optimization is the problem in the center ofthe order fulfillment. When a customer placed an orderonline, the retailer needs to decide where to ship theproducts to the customer destination. Different from thetraditional retailing, customers do not select where theproducts should be shipped from as the logistic process ofan order fulfillment is hidden from the customer after theorder has been placed. This allows for greater flexibilityin selecting the best location and transportation methodto ship the products to meet the customer requirementswhile minimizing the fulfillment cost. Acimovic andGraves (2017) consider the fulfillment problem for alarge distribution network of minimizing shipping costsfor a single SKU, and propose an LP-based policy thatincorporates the forecast of future orders. Xu et al. (2009)consider a batch optimization policy where each singleorder fulfillment decision is delayed until solving thefulfillment for a batch of orders together using an integerprogramming. Jasin and Sinha (2015) consider a problemsimilar to Xu et al. (2009) that incorporates different costsof bundled shipments.

Besides solving the fulfillment optimization problemfor a given level of customer requirement, another streamof research works focuses on investigating policies thatcan improve fulfillment performance by differentiatingcustomer requirements, or Order Prioritization. The ideaof this can be traced back to the inventory ration policiesthat use limited inventory to fulfill multiple classes of


customer demand discussed in Topkis (1968), Kaplan(1969), Veinott (1965), and Nahmias and Demmy (1981).In this stream of literature, customers are prioritized, andthe inventory system is allowed to either back-order orlose demand for low priority customers to fulfill futuredemand for high priority customers. In the context ofe-commerce, it is not easy to differentiate customersdirectly as usually all customers purchase products froma single website. However, differentiating customer ordersby different levels of desired delivery-time standard isfeasible. Cattani and Souza (2002) investigate rationingin a direct marketing environment (similar to onlineretailing), where customers may pay a higher fulfillmentfee to reduce their delivery times. The integration of e-commerce and omni-channel retailing also brings newresearch opportunities. As offline customers usually havea priority in purchasing the products than the onlinecustomers, Karp (2017) discusses a protection level policyto ensure the right amount of inventory is reserved forthe offline customers. Govindarajan et al. (2018) discussesa joint decision for the inventory and fulfillment policiesthat optimize the total costs. Agatz et al. (2008) providean excellent review on the other related omni-channelresearches.

Fulfillment Flexibility strategies approach the order fulfill-ment from a network design point of view. Inventories ofthe same product are stored in multiple storage locations inthe network so as to provide “flexibility” for order fulfill-ment. Higher degree of flexibility allows for more options insatisfying customer orders which eventually leads to lowerorder fulfillment costs due to less order split and inventorystock-out at nearby storage locations. Designing a flexiblefulfillment network is related to the concept of process flex-ibility, which is developed by Jordan and Graves (1995) in1995. DeValve et al. (2018) extend the flexibility conceptto online retailing by investigating a threshold policy withthe structure which is motivated by the protection levelsin revenue management originated from Littlewood (1972).Acimovic and Graves (2017) show how traditional decen-tralized allocation policies may perform sub-optimally andinduce dynamics (whiplash) that result in costly spillover.The literature of fulfillment flexibility also shares somesimilar features with models from inventory transshipmentliterature. Paterson et al. (2011) show flexibility can becreated both by transshipping inventory among the facili-ties proactively and reactively. Axsater (2003) develops adecision rule dictating whether to transship, or whether toincur the back-order costs. Yang and Qin (2007) discussa model that utilizes virtual lateral transshipment betweentwo factories. Archibald et al. (2009) develop a trans-shipment heuristic for a realistic multi-location inventorysystem.


With a fast growth in the past ten years, large e-commerceretailers like Amazon.com and JD.com have establishedhighly efficient order fulfillment processes. We introducethe framework of a general order fulfillment engine and thenetwork structure it has been built upon in this section.

5.2.1 Order fulfillment engine

The order fulfillment process usually starts with OrderApproval. A fraud detection procedure is conducted toidentify potential risks. This is typically done witha classification algorithm. In the case of a high-risktransaction, a human verification procedure may beinvolved to further analyze the risk. After the order isverified, the order will be dispatched to the warehousesfor processing. This step is usually controlled by an OrderManagement System (OMS). The fulfillment optimizationalgorithms play a key role in this step. The algorithmneeds to take in all the realistic constraints such asinventory availability, delivery cut-off times, warehouseprocessing capacity, outbound transportation capacity, andthe specific features of the order and then minimizesthe overall fulfillment cost. For various reasons, an orderconsisting with multiple products may be split into multiplewarehouses for processing. As a result, the customermay receive multiple packages for an order placed. ThisOrder Dispatching step usually takes a short time and isprocessed without human intervention. The next key processis Picking, Assembling, and Packing at the warehouses.This step is usually controlled by a Warehouse ManagementSystem (WMS) which ensures the orders dispatched to thewarehouse can be completed before the outbound due timesand at the same time maximizes the operational efficiency.The packed orders are then loaded onto trucks, waiting tobe shipped. Depending on the distance from the warehouseto the customer, different transportation methods may beinvolved in the shipment, ranging from air to motorcycles.The order fulfillment completes with the success of the finalmile delivery of the package to the customer.

Product Return is an important aspect of customer experi-ence. Most companies provide a convenient return policywith no or small additional return shipment fees. To providemore flexibility to the customer, many companies also applya free Cancellation Window policy which allows customersto cancel the purchase before a cut-off time. After the can-cellation window, customers who want to cancel orders canonly do so by returning goods after they have arrived andby mailing the package back. To further increase customerexperience, e-commerce retailers are trying to extend thecancellation windows until the delivery of a package. For


example, JD.com uses a concept called Cool-Down Period(Wu 2018) to reduce the cost for order cancellation. For eachorder, a cancellation probability is calculated based on thenature of the order and who is buying. An order may be puton hold for a cool-down period before dispatching ordersto the warehouses for picking. The cool-down period willbe longer if the order is associated with a high cancellationprobability.

The new trend of the integration of online and omni-channel retailing raises new opportunities to the orderfulfillment. On one hand, with the omni-channel storeshaving the inventory of the same ideal product when thecustomer placed the order online, the order may be shippedto customer directly from the store which reduces thedelivery time. On the other hand, with multiple omni-channel stores as potential sources for order fulfillment,it increases the number of choices for order dispatchingand thus increases the complexity of the fulfillmentalgorithm. For example, at JD.com, with its expansionto the omni-channel retailing including JD Home for 3Cand home appliance, 7Fresh for food and groceries, andthe collaboration with Walmart Stores (Shan 2018), it hasbuilt a fulfillment system to select the most cost-efficientfulfillment methods to meet customer requirements byleveraging the power of online and offline.

5.2.2 Order fulfillment network

The design of the order fulfillment network can be generallycategorized as Fully-Connected Distribution Network andHierarchical Distribution Network. A fully-connecteddistribution network refers to a fulfillment system whereeach inventory storage facility (a warehouse or a depot) canbe used to fulfill the order from any place in the network.This distribution network is exemplified by Amazon.comand Walmart.com, the two largest e-commerce retailersin the US. With a fully-connected distribution network,as the inventory of a product is stored in many storagefacilities, the order fulfillment decision can be very flexibleas there will be many ways to fulfill an order fromdifferent storage facilities. While this flexibility guaranteesthe required product can be fulfilled as long as there isinventory somewhere in the entire network, it increases thetransportation cost as orders are more likely to be shippedin longer distance and even with air methods in order tomeet the delivery promise. To avoid high transportationcost, the fulfillment decision should not only look at thedirect fulfillment cost (usually composed of transportationand processing cost) but also consider the future demandfrom the customers in order to fulfill orders strategically.Acimovic and Graves (2017) provide a good review ofthe current industry practice from an academic point ofview.

Different from a fully-connected distribution network, ahierarchical distribution network is composed of multiplelevels of inventory storage facilities. When a product isout-of-stock at the lower level storage facility, the demandis fulfilled by a higher-level storage facility, which isusually much farther from the customer. This hierarchicaldistribution network is typified by JD.com. Products needto be carefully placed (Shi 2018) at each tier of the networkin order to maximize the utilization of the storage capacity.The risk of inventory stock-out at the lower level storagefacility needs to be carefully managed by frequent inventorytransshipment (Ma et al. 2018) from the upper level storagefacility. The advantage of a hierarchical distribution networkis its high efficiency. As the lower-level storage facility isusually close to the customers, it enables ultra-fast deliveryto the customers. A hierarchical system is also easier tomanage as the inventory transshipment flow among thestorage facilities is usually unidirectional.

6 Future challenges

In this section, we discuss several challenges that retailersare still facing today in the area of supply chainmanagement. As the retail business evolves overtime, we dosee new challenges as well as new methodologies emergingin practice which could provide some insights to the futuresupply chain management research directions.

6.1 From supply chain to supply network

Supply chain is originally named to describe the chain ofactivities involved in moving a product or service fromsupplier to customer. However, as technology and businessscenario evolves, the supply “chain” is also evolving toa supply “network” where the different parties within thesystem forms a tighter interconnection.

As described in Section 5, the real fulfillment networksused by large scale retailers have grown to very complexstructures (JD operates more than 550 warehouses nation-wide in China (JD.com 2018b); Amazon is operating 75 ful-fillment centers within North America (Amazon.com 2018);Walmart and Sam’s Club combined have 175 fulfillmentcenters in United States (MWPVL International Inc. 2018)).A modern fulfillment network may include tiered networkstructure similar to the classic multi-echelon design. How-ever, the network topology could also be subject to addi-tional constraints such as availability of transship links orcompatibility of product types. For example, JD.com’s ful-fillment network is a superset of 6 networks, e.g. small-to-medium sized warehousing, oversized warehousing, crossborder, cold chain delivery, frozen and chilled warehousing,B2B and crowdsourcing logistics (JD.com 2018a), each has


its own capability and capacity. These networks can sharethe same transportation or fulfillment facilities in certainsections of the supply chain process while requires special-ized channel for some other sections. For example, someproducts in the cold chain delivery can be delivered usingthe regular delivery channel (shared with other networks)using a specialized packaging, but these products must bestored in refrigerated warehouses. At the same time, over-sized products have their own last mile delivery mechanismbut can share the same or similar storage facility as small-to-medium sized items. How to efficiently utilize the complexnetworks by efficiently sharing the facilities and capacitiesstill requires extensive research.

Crowd sourcing has also grown to be a viable approachto supplement or even act as the backbone of the trans-portation and fulfillment system (Rouges and Montreuil2014). Companies such as Amazon.com, Walmart has allestablished certain type of crowd-sourced delivery solu-tions within the last several years. JD.com and Walmartrecently invested $500 Million into Dada-JD Daojia, whichis a crowd-sourcing delivery platform in China with oper-ations in more than 400 major cities (Choudhury 2018).Using such delivery approach provides better scalability tothe supply chain system, but also brings in additional uncer-tainty in both reliability and cost implication. There are stilla lot of questions to be answered, especially when in-housefulfillment capacity is used alongside the crowd-sourcingabilities.

Furthermore, following recent pushes of omni-channelretailing (Verhoef et al. 2015), it is more and more com-mon for retailers to use physical store’s shelf inventoryto fulfill online orders as well. Some issues and exist-ing research are summarized by Hubner et al. (2016) andMelacini et al. (2018). With the recent collaboration withWalmart (Shan 2018), JD.com gains the ability to accessWalmart’s local stores’ inventory. This provides better cus-tomer experience for both better selection and faster deliv-ery. However, this creates complexities in inventory man-agement and fulfillment decisions as well for both JD.comand Walmart. How to efficiently manage inventory acrossthe fulfillment network is still a challenging problem to allpractitioners.

6.2 Frommanaging supply tomanaging demand

Supply Chain management is now not just about managingsupply but also on managing demand. Recent advancesin search and recommendation algorithm has made itpossible for managing demand and supply simultaneouslyto maximize retailer’s revenue and/or profit (Bernstein et al.2015; Chen et al. 2016). Inventory markdowns (Lazear1986) has long been a common practice for managingproducts by retailers, especially for seasonal products.

But the gap between revenue management and inventorymanagement still exists despite efforts by researchers(Elmaghraby and Keskinocak 2003).

The rise of group purchase (Liang 2018) in Chinarecently also creates new challenges in supply chainmanagement where massive demand is created within avery short period, creating pressure on both inventorymanagement and logistics. With the help of social networks,the group purchase frequently establish highly localizedpattern creating geographic hot spots for the productdemand. As the group purchase products are normallylimited time promotions, these hot spots exist in both spatialand temporal space, creating ever increasing difficulty inmanaging inventory and fulfillment.

6.3 Big data, small data

With the growth of online marketplace as well asdigitization in offline stores, retailers now have extensivedata to work with in order to figure out the best managementstrategy. With the rise of data availability, there is aclear trend toward data-driven approaches in supply chainmanagement research. However, how to effectively utilizesuch data in providing better supply chain managementdecision is still not clear to many retailers. Further, thedata size available to supply chain management is stillconsiderably smaller than other more popular AI areassuch as image or natural language processing, etc. Forexample, current effort on reinforcement learning basedreplenishment algorithm (Sui et al. 2010; Chaharsooghiet al. 2008) still need a relatively large number of iterationsfor the algorithm to converge to optimal which may beunrealistic for practical implementation. Although thereare studies (Shi et al. 2018; Zhang and Gao 2017;Oroojlooyjadid et al. 2016) on using deep learning to solveinventory problems in supply chain, few has been able todemonstrate performance on practical set ups.

Besides the “big data” issue, e-commerce marketplaceshave greatly improved the number of small players in theretail industry, who only manufacturer or manage a smallset of SKUs or tailored to a particular customer base. Suchretailers face a huge disadvantage in the era of big data.How to effectively utilize the “small data” available to thesmall players to help them make effective decisions is alsoan exciting problem to solve.

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