Thetitle of the thesis I researched is “Execution quality: An analysis offulfillment errors at a retail distribution center”, which was published in theJournal of Operation Management, one of the SSCI journals. The analysis can bedivided into two parts. In the first part , I analyze the structure ofliterature review. In the second part, I give some tips about the writing ofliterature review based on previous analysis.
The first part
The structure ofliterature review
1、previous studies about the research
First of all, writers reviewed previousempirical studies of execution quality in supply chains. At the same time, theyillustrated the difference between their own studies and previous studies.Instead of examining shipping discrepancies among suppliers to an automanufacturer and demonstrate the importance of electronic data interchange,they studied the link between a retailer and its suppliers and examined thetypes of fulfillment errors.
2、the expansion of previous studies
From this part, we can learn that writersproposed an inventory model which extends previous research regardingfulfillment errors. Instead of assuming that the portion of defective itemsproduced is constant with respect to the lot size or that defective items arethe result of a production process that goes out of control with a fixedprobability per unit produced, they found that the incidence of fulfillmenterrors decreases with order quantity.
3、the innovation based on previous studies
In this part, writers examined thecharge-back incentives used by retailers to penalize suppliers for fulfillmenterrors. Writers did not study contracting for product quality except the costof lapses in execution quality. In addition, they also evaluated whetherobserved charge-backs align supply chain incentives by capturing the cost offulfillment errors or not.
The second part
Tips:
1、When you write the literature review, you should becritical but objective.
2、You should introduce and review the results ofprevious researches concisely.(around 30 words)
3、You’d better list your research questions orhypotheses.
Executionquality: An analysis of fulfillment errors at a retail distribution center
Nathan Craiga, , Nicole DeHoratiusb, Yan Jiangc, DiegoKlabjanc
a Fisher College of Business, The Ohio StateUniversity, United States
b Booth School of Business, University of Chicago,United States
c Department of Industrial Engineering and ManagementSciences, Northwestern University, United States
Abstract
Purchase orders specify many aspects of afulfillment process, including item quantity, delivery time, carton labeling,bar coding, electronic data interchange, retail ticketing, and others. Thesefulfillment terms are instrumental for highly optimized retail supply chainsemploying automation and techniques such as pack-by-store. When fulfilling apurchase order, a supplier may commit a fulfillment error, i.e., the suppliermay fail to adhere to the terms specified by the retailer. The retailer maythen penalize the supplier for the fulfillment error via a chargebackdeduction, which reduces the supplier's revenue. We present a study of thefulfillment errors and chargebacks that occur in practice using data collectedfrom a major retailer's distribution center. While fulfillment errors involvingincorrect product quantities and delivery times have received the mostattention in the literature, we find that the majority of fulfillment errors inthe context we study involve documentation, bar coding, and retail ticketing.We refer to these as correctable fulfillment errors, since they are amended atthe retailer's distribution center through rework. We develop a model ofinventory management with correctable fulfillment errors and use the retailer'sdata to assess the cost of these correctable fulfillment errors to theretailer's inventory system. Our research provides guidance to managers inidentifying products and suppliers that impose large fulfillment error costs aswell as in setting appropriate chargebacks for fulfillment errors.
1. Introduction
Retailers replenish inventory through supplynetworks comprising suppliers, retail distribution centers (DCs), and retailstores. Suppliers typically ship products ordered by retailers to DCs, whichbreak bulk and distribute items to retail stores. Inventory management at DCsis complicated and challenging (de Koster and Balk, 2008), not only because ofproduct variety and long lead times, but also due to supplier shipments that donot conform to the terms specified by retailers’ purchaseorders (POs). These terms stipulate item quantities, delivery times, cartonlabeling, electronic data interchange, item packaging, bar coding, retailticketing, and other aspects of the fulfillment process. If the supplier failsto adhere to these terms, the supplier commits a fulfillment error.
Retail supply chains are increasingly relianton fulfillment that complies with the terms specified by POs. Supply chaintechniques such as pack-by-store and cross-docking require product packaging,retail ticketing, and carton labeling that conform to specifications.Automation, such as automated storage and retrieval systems and the robotsproduced by Kiva Systems, is unable to function without appropriate bar codingand electronic data interchange. Lapses in execution quality, such as thefulfillment errors identified above, impair the operation of highly optimizedsupply chains and undermine firms’investments in technology.
Fulfillment errors also negatively affectsuppliers. Retailers penalize fulfillment errors through chargeback deductions,or chargebacks, which reduce supplier revenue. These penalties represent asignificant cost for suppliers, reducing overall supplier revenue by 2–10% (Zieger, 2003). Moreover, retailers and suppliersregularly contest the cost of fulfillment errors through the disputes andnegotiations that surround chargebacks (Chain Store Age, 2002).
The first goal of our research is to build onexisting studies of execution quality by describing the fulfillment errors andchargebacks that occur in practice within a retail supply chain. A recentsurvey of 42 retailers found that an average of 13% of shipments received bythe retailers had inaccurate advance ship notices, or ASNs (Retail, 2010). Wecollected data on purchase orders and fulfillment errors from a major retailer,Omega.1 These data include audit reports that record the type of fulfillmenterror that occurred, the chargeback levied against the supplier for the error,and the time required for Omega to perform rework on items affected by theerror, if applicable. We find that 7% of Omega's POs experienced a fulfillmenterror. Quantity shortages, ticket errors, and ASN errors are the most commontypes, accounting for 52% of all fulfillment errors.
Moreover, we observe that fulfillment errorswithin retail supply chains can be classified as either correctable ornon-correctable, depending on whether they are amended through rework by theretailer. For example, quantity shortages and late shipments are notcorrectable by the retailer. On the other hand, many fulfillment errors can becorrected by the retailer alone through on-site rework. If the supplier failsto document a shipment properly (e.g., with a packing list) or transmit an ASN,the retailer's employees can manually inspect the shipment to identify itscontents. Similarly, if the supplier attaches extraneous packaging or ticketswith an incorrect bar code to products, employees of the retailer amend sucherrors.
Correctable errors represent a substantialportion—56%—of thefulfillment errors we observed. Industry reports make similar observations.Three of the five most common fulfillment errors identified by a logisticsprovider are correctable: invalid ASNs, incorrect Uniform Commercial Code (UCC)128 labels, and incorrect tickets (Ma, 2013). In contrast to non-correctablefulfillment errors, which have been studied as random yields (Yano and Lee,1995) and lead times (Bagchi et al., 1986 and Eppen and Martin, 1988), researchon the cost of correctable fulfillment errors is limited. Correctablefulfillment errors impose both direct and indirect costs on retailers. Considerthe case of a ticket error. The retailer faces a direct labor cost and a decreasein labor productivity since employees remove incorrect tickets and affix theappropriate tickets. In addition, the error increases lead time and lead timevariability, which can cause damaging stockouts at the retailer's stores. Whilecertain aspects of the cost of fulfillment errors, such as labor, arestraightforward to calculate, many retailers, including Omega, do not know theoverall cost of correctable fulfillment errors to their inventory systems(Retail, 2010).
The second goal of our research is to extendprior research on the cost of fulfillment errors, which focuses onnon-correctable errors, by proposing a stochastic (Q, R) model thatincorporates correctable fulfillment errors and associated rework. We studythis model via numerical experiments using parameters estimated from Omega'sdata on 13,500 replenishment stock keeping units (SKUs). 2 We find thatcorrectable fulfillment errors for the SKUs we study impose a substantial coston inventory management at Omega, namely, the cost of these errors is between1% and 4% of the operating budget of the DC we studied.
Managers have multiple approaches foraddressing fulfillment errors. First, since fulfillment errors make theinventory supply process less efficient by imposing unnecessary costs onretailers and suppliers alike, managers can work with suppliers toward theelimination of fulfillment errors (Wang et al., 2014). Kulp et al. (2007)describe the various methods retailers use to collaborate with suppliers toreduce fulfillment errors. Second, in the absence of collaboration, retailerscan use incentives, such as chargebacks, to pass the cost of fulfillment errorsto suppliers. Chargebacks should reflect the cost of vendor non-compliance toretailers (Aron, 1998). Third, retailers can adjust their inventory policies toaccommodate fulfillment errors. These approaches are not mutually exclusive.
Understanding the cost of fulfillment errorsin detail allows retailers collaborating with suppliers to communicate thiscost and to prioritize products and vendors that will benefit most from areduction in fulfillment errors. Further, understanding the cost of fulfillmenterrors helps retailers ensure that their chargebacks are appropriate. We findthat Omega's chargebacks are too low in many cases and potentially too high inothers, which suggests misaligned incentives with respect to execution qualityin Omega's supply chain (Narayanan and Raman, 2004). Finally, retailers thatelect to adopt an adjusted inventory policy often incur a per-SKU cost whenmodifying an existing inventory system. For example, under thesoftware-as-a-service business model, inventory management software vendorslike Predictix charge per SKU to modify their standard inventory system.Retailers may thus elect to modify their inventory policy for SKUs that willbenefit most from the change.
In sum, our research characterizes the typesand prevalence of fulfillment errors and associated chargebacks that occur inpractice at a representative retailer, identifies correctable fulfillmenterrors in retail supply chains that are distinct from traditional random yieldsand lead times, and conducts numerical experiments using empirical data tounderstand the impact of such correctable fulfillment errors on inventorysystem cost. The rest of this paper is organized as follows. In Section 2, wereview related literature and position our work. We present an exploratorystudy of fulfillment errors and rework time in Section 3. In Section 4, weintroduce an inventory management model that incorporates correctablefulfillment errors. We present an empirical analysis of fulfillment errors andrework time in Section 5. In Section 6, we report the results of numericalexperiments conducted on the inventory model using parameters estimated fromOmega's data. Further, we propose procedures for identifying SKUs with highcosts of fulfillment errors. In Section 7, we discuss the findings of our studyand suggest directions for future research.
2 Literaturereview
Our research builds on empirical research onsupply chain execution. In a related study, Srinivasan et al. (1994) examineshipping discrepancies among suppliers to an auto manufacturer and demonstratethe importance of electronic data interchange in a just-in-time setting. Incontrast, we study the link between a retailer and its suppliers, and weexamine the types of fulfillment errors that occur, the rework necessary toaddress errors, and the costs and penalties imposed due to errors. Relatedissues involving execution quality in retailing include misplaced inventory(Camdereli and Swaminathan, 2010), inventory record inaccuracy (Heese, 2007 andDeHoratius et al., 2008), and backroom inventory (Gaukler et al., 2007 andEroglu et al., 2012). In addition, our research is related to studies of theimpact of execution quality on the relationships between firms and theirsuppliers (Wang et al., 2014).
The inventory model we propose extends priorresearch regarding fulfillment errors. The errors we classify asnon-correctable have been studied as random yields (Yano and Lee, 1995) andrandom lead times (Bagchi et al., 1986 and Eppen and Martin, 1988). Whileresearchers have studied non-correctable fulfillment errors extensively,correctable fulfillment errors have received little attention in the context ofretail supply chains. Correctable fulfillment errors are related to productionprocess errors that can be corrected through rework, but fulfillment errorsdiffer from errors in the production context. Prior research generally assumesthat the portion of defective items produced is constant with respect to thelot size (Peters et al., 1988, Zhang and Gerchak, 1990, So and Tang, 1995,Jamal et al., 2004 and Eroglu and Ozdemir, 2007) or that defective items arethe result of a production process that goes out of control with a fixedprobability per unit produced (Porteus, 1986 and Lee, 1992). In contrast, wefind that the incidence of fulfillment errors decreases with order quantity(see Section 5). We would expect this relationship if, for example, suppliersare more likely to inspect larger orders. To treat the errors we observe in thefulfillment context, our model generalizes the relationship between theincidence of errors and order quantity. Moreover, in contrast to priorresearch, the numerical experiments we present utilize parameters derived fromdata supplied by a major retailer.
This paper also builds on research thatassumes that lead time depends on characteristics of an order. In our model,the time required to perform rework on fulfillment errors drives therelationship between order quantity and lead time. Rework time is a function ofthe incidence of fulfillment errors, which in turn depends on order quantity.Moinzadeh and Lee (1987) study a related inventory system in which a firmreorders defective items, and the number of defective items depends on orderquantity. Moinzadeh and Lee (1989) examine an inventory system where thesupplier delivers an order in two shipments, and the time between the twoshipments does not depend on order quantity. Our model also relates to modelsof lot size dependent lead time (see, for example, Kim and Benton (1995) andHariga (1999)), which treat increases in lead time due to the time required toproduce larger quantities.
Finally, we examine the chargeback incentivesused by retailers to penalize suppliers for fulfillment errors, which are atype of supply chain incentive. Research in this area focuses on selectingprices and other transfers that realize the supply chain optimal production quantity(Cachon, 2003). In this context, chargebacks are studied as a mechanism forcompensating retailers for markdowns and unsold inventory (Tsay, 2001 and Leeand Rhee, 2008). In addition, prior research has studied contracting forproduct quality (Reyniers and Tapiero, 1995, Baiman et al., 2000, Lim, 2001 andJin et al., 2014). The cost of lapses in product quality is due to defects—in particular, to warranties and scrap. In contrast,we study the cost of lapses in execution quality, and we evaluate whetherobserved chargebacks align supply chain incentives by capturing the cost offulfillment errors.
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