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Survey of recent advances on the interface betweenproduction system design and qualityRobert R. Inman a , Dennis E. Blumenfeld b , Ningjian Huang c , Jingshan Li d & Jing Li ea Operations Research Group, General Motors Global Research and Development , Warren ,MI , 48090 , USAb Department of Industrial and Operations Engineering , University of Michigan , Ann Arbor ,MI , 48109 , USAc Manufacturing Systems Research Lab, General Motors Global Research and Development ,Warren , MI , 48090 , USAd Department of Industrial and Systems Engineering , University of Wisconsin , Madison , WI ,53706 , USAe School of Computing, Informatics and Decision Systems Engineering , Arizona StateUniversity , AZ , 85287 , USAPublished online: 07 Mar 2013.

To cite this article: Robert R. Inman , Dennis E. Blumenfeld , Ningjian Huang , Jingshan Li & Jing Li (2013) Survey ofrecent advances on the interface between production system design and quality, IIE Transactions, 45:6, 557-574, DOI:10.1080/0740817X.2012.757680

To link to this article: http://dx.doi.org/10.1080/0740817X.2012.757680

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IIE Transactions (2013) 45, 557–574Copyright C© “IIE”ISSN: 0740-817X print / 1545-8830 onlineDOI: 10.1080/0740817X.2012.757680

Survey of recent advances on the interface betweenproduction system design and quality

ROBERT R. INMAN1,∗, DENNIS E. BLUMENFELD2, NINGJIAN HUANG3, JINGSHAN LI4 and JING LI5

1Operations Research Group, General Motors Global Research and Development, Warren, MI 48090, USAE-mail: robert.inman@gm.com2Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA3Manufacturing Systems Research Lab, General Motors Global Research and Development, Warren, MI 48090, USA4Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA5School of Computing, Informatics and Decision Systems Engineering, Arizona State University, AZ 85287, USA

Received September 2011 and accepted November 2012

Product design’s impact on quality is widely recognized. Less well recognized is the impact of production system design on quality. Asquality can be improved by integrating it with the design of the product, so it can be improved by integrating quality with the design ofthe production system. This article provides evidence of the production system’s influence on quality and surveys recent advances onthe interface between quality and production system design including the design of the production system’s quality control process.After mapping the literature, we identify opportunities for future research.

Keywords: Manufacturing, supply chain, tolerance, andon, inspection, statistical process control

1. Introduction

This article surveys the interface between production sys-tem design and quality. Production system design can im-pact quality. It is too much to ask of a production systemto guarantee good quality of a poorly designed product.However, a poorly designed production system can foulthe quality of even well-designed products. Also, produc-tion system design’s role in quality applies to a broad rangeof industries. For example, Table A1 in the Appendix citesan example where the production system is identified aseither the cause of a lapse in quality or an enabler to im-prove quality for each the U.S. Census Bureau’s 2007 NorthAmerican Industry Classification System’s (NAICS) sub-categories of manufacturing. It shows that a productionsystem’s impact on quality is pervasive; it touches virtuallyevery type of manufacturing. Although product design ad-mittedly plays a major role in quality, production systemdesign can also impact quality. Neglecting the interface be-tween production system design and quality can damageeven the best designed product’s reputation.

The examples in Table A1 (see Appendix) emphasize therelationship between the production system and quality.This article discusses production system design. The scopewe define as production system design covers the various

∗Corresponding author

stages needed to set up a complete system for manufactur-ing one or more products. These stages include the supplychain network, production planning to meet productand process specifications, system layout on the plantfloor, equipment selection and tooling, and productionmanagement to ensure efficient operations. While manyof the examples in Table A1 refer to operational lapses inproduction, three of the examples highlight the importanceof production system design on quality. First, in the textilemills category, the Market News Publishing (2008) reportshows that the production system design decision ofequipment upgrades can improve quality. Second, inthe leather and allied product manufacturing category,the Science Letter (2009) report indicates that applyingproduction system design optimization of the fatliquoringstage can improve quality. Third, in the primary metalmanufacturing category, the Modern Casting (2009) reportshows that the production system design tasks of improv-ing the process with new cleaning and degassing techniquescan improve quality. These examples demonstrate thatproduction system design can improve quality.

Despite the importance of production system designto quality, historically there has been relatively little re-search on the interface between production system designand quality. Figure 1 displays a version of a figure fromEarly and Coletti (1999) that categorizes the gaps betweencustomer expectations and perceived delivery. The qualitygap’s components are cumulative. Any of the gaps can lead

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Quality Gap

Understanding Gap

DesignGap

ProcessGap

Opera�onsGap

Percep�onGap

Understanding of Needs

Design of Product

Capability to Deliver Design

Actual Delivery

Customer Expecta�ons

Customer Percep�on of Delivery

Fig. 1. Components of the quality gap (color figure providedonline).

to perceived poor quality. Quality cannot be inspected intoa poorly designed product. However, even a perfectly con-ceived and perfectly designed product can leave customersdissatisfied if there are large process and operations gaps;all quality gap components are important. This article fo-cuses on the process and operations quality gaps. Both thedesign of effective production systems and the design ofquality control processes are essential to closing the overallquality gap.

Figure 2 displays research areas on a two-by-two matrixto put this article in context. The two rows display the twoseparate phases (design and operation) in the productionsystem development process. The two columns display two

Fig. 2. Filling the research gap at the interface of productionsystem design and quality (color figure provided online).

different objectives: productivity and quality. Historically,considerable research has been devoted to the areas in darkgray shading—production system design for productivity,production system operation for productivity, and qualitycontrol for operations. This article reviews recent researchcontributions that help close the research gap (depicted byan empty white square) of the interface between productionsystem design and quality. The horizontal arrow representsthe contributions that are extensions of production systemdesign for productivity research that consider quality as animportant factor. The vertical arrow represents contribu-tions that are extensions of quality control for operationsthat consider production system design issues.

We argue that quality can be improved by considering itduring production system design. Inman et al. (2003) sug-gest research areas to close the research gap on designingproduction systems for quality. This article reviews recentadvances in the literature that help incorporate quality inthe production system design process and explicitly ad-dresses the role of designing the quality control system.

Figure 3 displays a conceptual product developmentflow-chart showing the parallel processes of designing theproduction system and designing the quality control sys-tem. Production system design can generically be catego-rized into five stages: designing the supply chain, produc-tion planning, system layout, equipment selection, and pro-duction management. For expository purposes, we dividequality control design into quality function deployment,failure mode and effects analysis, inspection planning, de-sign of experiments, and statistical process control. Thesolid black lines connecting elements of production systemdesign with quality control system design indicate recog-nized linkages in the literature—but these linkages haveroom for more work. The red dashed lines indicate newopportunities for integrating quality control system designwith production system design. The existing connectionswill be discussed in the course of the literature review; theproposed connections will be discussed in the future re-search section.

The boundaries in Fig. 3 are fluid and many researchareas cross boundaries; nonetheless, the figure providesa framework for organizing the literature. Figure 3 is aroadmap for this article where the numbers under eachstage refer to a subsection in this article’s literature review.Section 2 progresses through the production system designstages, and Section 3 describes the design of quality controlsystems. Section 4 concludes with suggestions for futureresearch.

2. Literature review of designing production systemsfor quality

This section surveys recent advances in production systemsdesign for quality. As shown in Fig. 3, we consider five maincategories: supply chain, production planning, system

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Fig. 3. Production and quality control system design’s role in the product development process (color figure provided online).

layout, equipment selection, and production management.These categories are arranged roughly in the coarse-to-fineorder that they are performed by a firm. The productionsystem design process begins with the overall supply chainnetwork (where to produce and where to source), followedin turn by production planning, system layout, equipmentselection and ending with production system managementonce the supply chain, planning, layout, and equipmentdecisions have been made.

2.1. Supply chain

A broad view of production system design includes thesupply chain. For recent survey articles regarding qualityin supply chain management, see, for example, Sila et al.(2006) and Talib et al. (2011). Li and Warfield (2011) pref-ace a special issue on quality coordination and assurancein supply chains. This article focuses on production systemdesign, and we now turn our attention to so supply chaindesign for quality.

Supply chain design begins with supplier selection. Qual-ity has long been a part of the supplier selection and con-tract specification and is supported by a rich literature. Forrecent literature reviews see Lo and Yeung (2006), Wu andWeng (2010), and Kumar et al. (2011). Another element ofsupply chain design is implementing advances in informa-tion technology. Xu (2011) argues that the supply chain’sinformation architecture can be designed to improve qual-ity and Tse and Tan (2011) argue that providing supplychain visibility mitigates the threat of poor quality.

Supply chain design also includes choosing the ship-ping mode and warehouse locations that can impact thequality of perishable goods. Blackburn and Scudder (2009)analyze supply chain design for perishable products andrecommend a responsive supply chain for the stage be-tween harvest and cooling and an efficient supply chain forthe stage from cooling to delivery. Dabbene et al. (2008)propose a method that trades off logistic costs with indicesmeasuring perceived product quality. Gong et al. (2007) op-timize the location of perishable food distribution centers.

2.2. Production planning

Production planning is an early step of production systemdesign that sets the boundary conditions (such as the re-quirements of perishable products that need the special con-sideration of time sensitivity) and first-order system design(such as tolerance analysis, process planning, and processcapability indices).

2.2.1. Perishable productsIn food and many other industries, products are perishable.When a product’s quality degrades with time, it must bedegraded or scrapped after a certain time span. Liberopou-los et al. (2007) develop a model for a failure-prone, buffer-less, paced, automatic transfer line, where the quality of thematerial trapped in the stopped workstations deteriorateswith time. If this material remains immobilized beyond acertain critical time, its quality becomes unacceptable andit must be scrapped. Similarly, Liberopoulos and Tsarouhas

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(2002) present a case study of increasing a croissant pro-duction line’s speed by inserting an in-process buffer in themiddle of the line to absorb some of the downtime. Huand Zong (2009) propose an extended product inspectionpolicy for a deteriorating production system, where prod-uct inspections are performed in the middle of a produc-tion cycle and, after the inspection, all products produceduntil the end of the production run are fully reworked.Wang, Hu, and Li (2010) develop a transient analysis tostudy the buffer capacity needed in dairy filling and packinglines.

Teunter and Flapper (2003) study a single-stage–single-product system where produced units may be non-defective,reworkable defective, or non-reworkable defective and thereworkable defectives are perishable or can become tech-nologically obsolete. Soman et al. (2004) consider make-to-order and make-to-stock production with limited prod-uct shelf life and sequence-dependent setups. Subbaiahet al. (2011) develop an inventory model for perish-able items with alternating production rates and randomperishability. Panda and Saha (2010) optimize the produc-tion rate and stopping time for a perishable seasonal prod-uct with increasing–steady–decreasing time-dependentdemand over the sales season.

2.2.2. Process planningProcess planning is the systematic determination of thesteps by which a product is manufactured. A key elementis setup planning, whose purpose is to arrange manufac-turing features in a sequence of setups that ensures qualityand productivity (see Huang and Liu (2003)). Researchin assuring quality in the setup planning process includesthe following. Zhang et al. (1996) discuss the importanceof setup planning to tolerance control and propose agraphical approach to generate optimal setup plans basedon tolerance specifications. Mantripragada and Whitney(1998) propose the “datum flow chain” concept to explic-itly relate datum logic to key product characteristics andassembly sequences and provide information for toleranceanalysis. Rong and Bai (1996) propose a machining accu-racy analysis for fixture design verification, considering thedependency of variations of multiple dimensions for betteridentification of machining errors. Song et al. (2005) usea Monte Carlo simulation method to analyze the qualityimpact of production planning. Xu and Huang (2006)present a setup plan evaluation system recognizing thatsetup planning is a multiple attributes problem associatedwith uncertainties and involves human inputs. For agiven setup plan, stream of variation methodologies (Shi,2006) and state-space modeling techniques model thedimensional variation propagation along different setups.Liu et al. (2009) propose a method to realize cost-effective,quality-assured setup planning for multistage manufactur-ing processes. Finally, Shi and Zhou (2009) survey researchin quality control for multistage systems.

2.2.3. Tolerance analysisWhile tolerance design and allocation (determining thetolerance for each product component) is an element ofproduct design, considering manufacturing’s tolerance isas an element of the production system design. Hong andChang (2002) review the broader literature on tolerancedesign. The following contributions integrate the man-ufacturing process selection with tolerance allocation.Robles and Roy (2004) incorporate the manufacturing costto attain a given tolerance in addition to manufacturingcapacity constraints, measurement errors, and processcapabilities. Etienne et al. (2008) allocate tolerance toprovide the best ratio between functional performance andmanufacturing cost following Boeing’s “Key Characteris-tics” approach. Sivakumar et al. (2011) consider both themanufacturing cost and quality loss for each candidatemanufacturing process in a multi-objective optimizationthat distributes tolerances among components. Abdul-Kader et al. (2010) optimize the cost of reworking orscrapping off-specification items and the cost of adjustingmanufacturing processes to reduce or eliminate rejectedpieces to find the best production specification.

2.2.4. Process capability indicesWhile process capability indices (see Stoumbos (2002) andAnis (2008) for recent reviews) attempt to measure a currentprocess’s capability, process capability indicators attemptto predict a proposed production system’s performance. Byidentifying key drivers of quality in the production system,these indicators can serve as guidelines for designingproduction systems for quality. Recent work in this areaincludes Nada et al. (2006), who develop a configurationcapability indicator to predict the quality performanceof manufacturing system designs. They use a hierarchicalfuzzy inference to relate manufacturing design parametersto quality. The design parameters are the number of flowpaths, the number of stations, overall process capability,level of mistake-proofing, inspection error, allocationof inspection stations, level of intelligent automation(Jidoka) implementation, and buffer size. The interme-diate predicted quality measures are the quality of theconfiguration, morphological structure, error detectionresponsiveness, defect prevention capability, and defectdetection capability, which they aggregate into an overallconfiguration capability indicator.

2.3. System layout

System layout is a production system design step that fol-lows production planning. Since the layout often impactsthe system’s flexibility and robustness, we discuss manufac-turing flexibility, production complexity and entropy, androbustness in this subsection.

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2.3.1. Manufacturing flexibilityManufacturing flexibility is the capability of building sev-eral different products in one system with no interruptionin production due to product differences. Flexibility en-ables mass customization and high manufacturing utiliza-tion. Most companies treat quality as a constraint and onlyincrease flexibility in cases where it will not harm quality(Hallgren et al., 2011). In other words, they are unwillingto sacrifice quality for flexibility (Rosenzweig and Eston,2010). Nevertheless, flexibility and quality can be related.

Several studies seem to indicate that flexibility canimprove quality. For example, Weber (2004) reports thatflexible modular assembly systems help achieve better prod-uct quality. Dangayach and Deshmukh (2005) surveyed122 Indian companies regarding advanced manufacturingtechnologies and found that flexibility and quality are pos-itively correlated with a Pearson’s correlation coefficientof 0.25. In Europe, BMW invested in additional robots toimprove both flexibility and quality (Kochan, 2005). Onthe other hand, Inman et al. (2003) suggest that flexibilitycould impact quality, but that the relationship has not beenthoroughly analyzed. Li and Huang (2007) use a Marko-vian model to investigate the relationship between machineflexibility and product quality. Pinker and Shumsky (2000)argue that cross-training workers to increase flexibility candegrade quality. Similarly, McDonald et al. (2009) modelworker cross-training and assignment and observe thatincreased cross-training reduces quality primarily becausea newly cross-trained worker will not perform as well asan experienced specialized worker.

2.3.2. Production complexity and entropyGlobalization and the demand for more product function-ality and variety are driving the incorporation of more com-plexity into production systems. Although the definitionof manufacturing complexity varies, it typically includesmixed product lines, a large number of materials at one lo-cation, and a complex supply network. In exploring com-plexity’s impact on quality, considerable attention is paidto the behavior of operators. Studies have found that op-erators tend to make more mistakes if their jobs involvemaking more choices, such as different parts, tools, or pro-cedures. Rao et al. (2010) use a neural network to analyzethe relationship between the error rate and the numberof choices. Abad et al. (2011) model “think time” (whenmaking choices), job complexity, and operator’s experience.Hinckley (1993) and Shibata (2002) present models to pre-dict defects based on the number of assembly operations inthe semiconductor industry. Su et al. (2010) apply a similarconcept to predict defects in copier manufacturing. Thesemodels all positively link the operator’s work complexity(number of job elements, task difficulty, think time, andso forth) with product defects. In addition to these studieson operators, Bozarth et al. (2009) explored supply chaincomplexity and found that internal manufacturing com-plexity (including the number of parts and products, the

types of processes, and the schedule stability) negativelyimpacts manufacturing performance measures (customersatisfaction and competitive performance). Huang and In-man (2010) analyze automotive plant build complexity’snegative impact on quality and investigate how complex-ity impacts assembly line work. In mixed-model assemblysystems, the station configurations may be highly complexbecause different product variants follow different produc-tion paths in the same assembly system. Several papersdevelop methods to link station configurations and prod-uct mix with quality; see, for example, Webbink and Hu(2005) and Abad and Jin (2011).

2.3.3. RobustnessRobust production system design is an important researchtopic. Fluctuations in operations can damage productquality, and robust production system design seeks tominimize this damage. The considerable literature on thetrade-offs between productivity, flexibility, and qualityincludes Son and Park (1987), Jacobs and Meerkov (1991),Bulgak (1992), and Han et al. (1998). There is less research,however, on quantifying the effects of random productionvariability on quality.

In the system design phase, many system parameters areeither uncertain or inaccurate. Li et al. (2008) introducethe notion of quality robustness in manufacturing systemdesign and analyze the impact of random variability inrepair and rework operations. The results are used to ensurerobustness by identifying which system parameters mostimpact quality.

Chincholkar and Herremann (2008) present a queueingnetwork model to estimate the manufacturing cycle timeand throughput in a production system with process drift,which will lead to producing defective parts before the driftcan be detected at the downstream station. Sensitivity anal-yses with respect to machine processing time and arrivalrate are carried out to provide insights. Such a model canbe used to evaluate system performance of alternative de-signs, such as inspection allocation and selecting equipmentwith different yields and drift rates.

2.4. Equipment selection

Equipment selection, which determines machine operat-ing characteristics and reliability, can impact a productionline’s quality. If machines have flexibility, lines can be run atdifferent speeds, resulting in trade-offs between productiv-ity and quality. Most machines are also subject to qualityfailures, which can lead to production loss and defectiveproducts. This subsection reviews the literature related tothe interface between equipment selection and quality.

2.4.1. Operating speedThe relationship between production line speed and qual-ity has been identified as an important consideration inproduction system design and operation (see Khouja et al.(1995), Mehrez et al. (1996), and Lin et al. (2001)). The line

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speed’s impact on overall productivity has also long beenrecognized (Buzacott, 1967, 1968; Dallery and Gershwin,1992). Increasing line speed can increase productivity interms of total jobs per hour but can harm quality if thespeed is too fast.

Taylor (1907) studied the effects of speed in metal-cuttingoperations. Taylor developed a formula that relates cuttingspeed to machine tool life (Taylor, 1907; Kalpakjian, 1995;Stephenson and Agapiou, 1997). An analysis of the trade-offs between quality and productivity for a production line,based on Taylor’s formula, is presented for different types ofmanufacturing operations in Owen and Blumenfeld (2008).Analytical models use a quality–speed relationship to quan-tify the effects of rework, repair, and scrap on throughputof quality jobs.

Sana (2010) introduces a model to determine the optimalproduction rate and product reliability to achieve the great-est total profit in an imperfect manufacturing process. Themodel includes a variable product reliability factor, vari-able unit production cost, and dynamic production ratefor time-varying demand. The Euler–Lagrange method isused to obtain the optimal product reliability parameterand dynamic production rate.

2.4.2. Production lines with quality failuresBoth operational and quality failures exist in productionprocesses. Operational failures refers to machine break-downs, and quality failures refers to production of defectiveparts. These two types of failures have different character-istics and result in different system behaviors. For exam-ple, increasing the production rate or adding buffers canhelp overcome operational failures—but may make qual-ity failures worse. A quality failure may lead to continuedproduction of defective parts before the problem is solved(such as drilling with a worn tool); in this case, increas-ing processing speed or buffer capacity may be detrimentalto the overall production of good quality parts. Therefore,an integrated model explicitly considering both types offailures is needed. Kim and Gershwin (2005) develop atwo-machine Markov process model for a machine sub-ject to both quality and operational failures. They identifycases in which the effective production rate increases withlarger buffers and also cases in which the effective produc-tion rate decreases with larger buffers. Kim and Gershwin(2008) extend this two-machine line model by developingapproximation methods for analyzing long lines that haveboth quality and operational failures and discuss inspectionallocation and remote quality information feedback. Simi-larly, Korugan and Ates (2007) model a two-machine–one-buffer line subject to both operational and quality failuresto study the quality impact of manufacturing new parts andremanufacturing returned parts on the same machines. Tanand Gershwin (2011) introduce a methodology to analyzea general Markovian continuous-flow system with a finitebuffer. The method applies to a range of general models,such as systems with phase-type distributions, multiple up

and down states, and multiple unreliable machines in seriesor parallel in each stage, and can also be used to modelsystems with quality–quantity relationships.

2.5. Production management

This subsection addresses the continuous improvement ofproduct quality by managing production. It includes qual-ity improvements that can be achieved by identifying andmitigating quality bottlenecks, implementing an andon sys-tem to correct defects online, batching products to reducethe negative impact of changeovers, managing the work-force to reduce variations caused by absentee workers, andeffective planning of preventive maintenance to mitigatemachine deterioration.

2.5.1. Quality bottleneckThroughput bottleneck identification and elimination havebeen used as an effective way to improve throughput. Anal-ogous to productivity bottlenecks, quality bottlenecks alsodeserve study. From a system’s point of view, a quality bot-tleneck is the factor (operation, ratio, sequence, inspectionor other performance metric) that most impedes productquality. Improving the bottleneck factor will lead to thelargest improvement in product quality compared with im-proving all other factors. From this perspective, Wang andLi (2009) investigate the impact of product sequencing onquality, introducing the notion of a quality bottleneck se-quence, which is the sequence that impedes quality in thestrongest manner. Wang, Li, Arinez, and Biller (2010a) ex-tend this work by defining and analyzing the quality bottle-neck transition, which is the transition whose improvementwill lead to the largest improvement in quality.

Meerkov and Zhang (2010) consider serial productionlines consisting of producing and inspection machines thatfollow Bernoulli reliability and quality assumptions to gaininsight into the nature of both production and quality bot-tlenecks. Meerkov and Zhang (2011) extend the study toserial production lines with quality–quantity coupling ma-chines, where product quality is interrelated with machineefficiency. Using this methodology, Arinez et al. (2010) de-scribe a continuous improvement project to improve pro-duction quality and throughput at an automotive paintshop. By identifying the quality bottleneck, the operationspeed can be reduced while maintaining capacity by addinga parallel operation leading to a significant improvementin the throughput of non-defective jobs.

2.5.2. Andon production systemsDerived from the Japanese word for “paper lantern,”andon is a term for a visual control device for monitoringassembly line quality. An andon cord along the assemblyline allows workers to signal a request for help. When aworker pulls the andon cord, it triggers a light as a call forhelp, and the line can be stopped if needed to correct theproblem (Monden, 1997; Liker, 2004). Originating with

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the Toyota Production System, it has been used in manymanufacturing plants worldwide as an effective approachto improve quality (Mayne et al., 2001; Strozniak, 2001;Inman et al., 2003; Tierney, 2004).

In a quantitative analysis, Li and Blumenfeld (2006)examine the benefits of andon systems and determine con-ditions for successful use. The study develops analyticalmodels to quantify the production rate of quality jobs fordifferent types of transfer production lines. It derives prac-tical rules to guide operations management on the factoryfloor. Subramaniam et al. (2009) show how andon displaydata can be used to improved production performance.They present a production monitoring system that auto-mates data collection to provide reliable performance in-formation.

2.5.3. Production batchingMany multiple-product manufacturing systems use batch-ing to reduce changeover time and cost and improve qual-ity. Most studies addressing batching focus on improvingproductivity (e.g., by minimizing setups) and only a fewconsider quality. However, production batching may im-pact quality. Cao et al. (2009) account for the productionrun length’s impact on quality in their model of splittinglots into alternative routes in a cellular manufacturing envi-ronment. They suggest that if a production route is subjectto deterioration, then shortening the route by lot splittingwill improve quality. Also, if a longer production run im-proves quality, then merging sub-lots can lengthen produc-tion runs to improve quality.

Wang, Li, Arinez, Biller, and Huang (2010) analyze thequality of a manufacturing system with batch operations.Using a Markov chain model, they derive a closed formulato evaluate the probability of producing a good quality part.They discover situations where behavior is non-monotonic,which highlights the importance of careful analysis whendetermining batching policies to maintain quality. Wang,Li, Arinez, and Biller (2010b) extend this work by investi-gating the impact of product sequencing and batch policieson quality and providing insights into policies that supporthigh quality.

2.5.4. Maintenance planningPlanning for maintenance can improve productivity andquality. One type of planning is preventive maintenance.Most preventive maintenance research is focused on pro-ductivity but some incorporates the impact of quality. Forexample, Radhoui et al. (2009) introduce a joint qualitycontrol and preventive maintenance policy for a manufac-turing system with random failures and non-conformingproduction. The maintenance action will be taken if theproportion of non-conforming units in each lot exceeds athreshold value. A buffer stock is built up to ensure the con-tinuous supply during maintenance. A mathematical modelcombined with simulation is developed to determine theoptimal threshold value and buffer size simultaneously to

minimize the total cost due to maintenance, quality, andinventory.

Colledani and Tolio (2012) provide a general theoryencompassing preventive maintenance to analyze a pro-duction system with progressively deteriorating machines.They show that by a joint analysis and design of functionsof quality, maintenance, and production control at the sys-tem level in a multistage manufacturing system, the systemperformance can be improved. The industrial benefits areshown through application of the method to a real manu-facturing context.

ElMaraghy and Meselhy (2009) present a frameworkto investigate the complex relationship between qual-ity and maintainability in reconfigurable manufacturing.They show that manufacturing system changeability af-fects product quality in two respects: manufacturing systemdesign and maintenance. Maintainability is an importantconcern in choosing the manufacturing system parameters.This article presents a maintainability strategy using therelationships between the manufacturing system parame-ters and the multi-objectives for optimizing quality, cost,and availability, which makes the maintenance systems lesscomplex and adaptive to manufacturing changes.

2.5.5. AbsenteeismAn assembly line’s operation depends on all workers beingpresent. When any are absent, the line does not functionoptimally and this can impact product quality. Absen-teeism’s effects on productivity and quality have been notedby several authors (Gatchell, 1979; Mefford, 1986; Wom-ack et al., 1991; Oliver et al., 1994; Conti, 1996; Connelly,2003; Chang, 2004; Mayne and Clanton, 2004; Terlep,2007). In a case study of lean manufacturing, Brondoand Baba (2010) analyze attendance data for an assemblyplant. They point out that lean production systems areteam based and explain how absenteeism affects the workenvironment for the team leader and other team members.

Blumenfeld and Inman (2009) develop an analyticalmodel of an assembly line’s operation to quantify howabsenteeism can affect product quality. The model incor-porates the increased demand for assistance from a teamleader when absent workers must be replaced with less ex-perienced substitutes. A related model that includes teamsizing delineates how the throughput of defect-free jobsdecreases with team size and absenteeism (Inman andBlumenfeld, 2010). Specific cross-training strategies for ad-dressing assembly line absenteeism are examined in Inmanet al. (2004). Their workforce reliability model shows howcross-training can help maintain quality and productiv-ity under absenteeism. Other approaches to reducing ab-senteeism include attendance bonuses, warning letters, andcounseling (Barmby, 2002; Connelly, 2003; Garsten, 2005;Terlep, 2007). There is also evidence that new and sophis-ticated automation may eliminate adverse effects of absen-teeism on assembly line production and quality (Mateo,2008).

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3. Literature review of designing quality control systemsfor production

Section 2 reviewed recent advances in designing produc-tion systems for quality. This section views the interfacebetween production system design and quality from theperspective of quality control system design. In the qualitycontrol research area, the existing research work falls intotwo general categories: quality control for product qualityimprovement and quality control for production systems.The former topic has been extensively studied, the latterless so. This section surveys research on quality control forproduction systems. To parallel the approximate chrono-logical order in Section 2, we organize this section’s topicsroughly in the sequence (planning, design, and finally con-trol) that they would be encountered by a firm. We nowreview recent advances in the quality control system designtopics displayed in Fig. 3.

3.1. Quality function deployment

Quality function deployment (QFD) translates customerrequirements into product attributes (to inform product de-sign), which can then be translated to production processrequirements (to inform the design of the production pro-cess and the design of the quality control process). Xu et al.(2010) provide a comprehensive review of recent QFD de-velopments. Recent work includes Chen et al. (2006), whouse a fuzzy logic approach to apply QFD to the design ofa flexible manufacturing system. As depicted by the solid(PP-QFD) line in Fig. 3, QFD has been applied to produc-tion planning. For example, Lowe et al. (2000) develop atool based on QFD to rapidly evaluate the effectiveness ofthe thixoforming process as a manufacturing stage. Hassanet al. (2010) apply QFD to select the process alternativesand then apply Failure Mode and Effects Analysis (FMEA)based on the resulting product and quality characteristicsto identify the process with the highest quality-to-cost ratio.

3.2. FMEA

Often performed in conjunction with QFD, a step inprocess quality planning is to identify failure modes andanalyze their effects. Based on a survey of Australianmanufacturers, Karim et al. (2008) conclude that FMEAis an important tool for improving product quality. Alaaet al. (2008) present a process planning approach incor-porating QFD and FMEA to improve the quality-to-costratio. Chin et al. (2003) incorporate QFD and FMEA intheir rough quality planning process to help identify thebest manufacturing process candidates. Almannai et al.(2008) incorporate QFD (to identify the most suitablemanufacturing alternatives) and FMEA (to assess the riskof each) in a decision support tool. Padiyar et al. (2006)describe a supply chain information system based onthe FMEA framework to reduce non-conforming parts.

Belmansour and Nourelfath (2010) present an aggregationmethod to evaluate the throughput of tandem productionlines. They treat quality failure as an additional systemstate in a multi-state machine model.

The discussed studies show that FMEA plays an im-portant role in Production Planning (PP) and ProductionManagement (PM) to improve quality and throughput. Asindicated in Fig. 3, the PP-FMEA and PM-FMEA links arerecognized connections in the literature. Integrating FMEAwith PP and PM can help improve both a production sys-tem’s quality and productivity.

3.3. Inspection planning

A popular research area is quality inspection in productionsystems. There has been significant research integrating In-spection Planning (IP) with both System Layout (SL) andPM, as depicted by the solid black (SL-IP, PM-IP) linksin Fig. 3. Example applications include designing the num-ber and locations of inspection stations, designing inspec-tion plans (e.g., full inspection or sampling), finding thebest action among several options (e.g., rework, repair, orscrapping), dealing with different types of constraints (e.g.,inspection time, average outgoing quality limit, or budgetlimit), dealing with different kinds of production systems,and developing quantitative problem-solving tools. See Raz(1986) and Mandroli et al. (2006) for nice reviews.

Determining the number and location of inspection sta-tions is at the intersection of designing the production sys-tem and designing the quality control system. The practicalimportance of this research area is evidenced by Greimel(2011), who report that Hyundai Motor Company in-creased the number of inspection stations on its assemblylines to improve quality. Below are some of the recent re-search advances regarding inspection.

Gershwin and Schick (2007) describe and classify theprincipal issues that arise in the context of quality/quantityinteractions including quality failures, quality inspection,the actions that may be taken in response to inspection, andpertinent measures of system performance. Penn and Raviv(2007) develop a profit model of unreliable serial produc-tion lines that depends on both production rates and theallocation of quality control stations. This model enablesthem to explicitly consider quality in the production systemdesign. They maximize profit using a branch-and-boundapproach with a dynamic programming algorithm to findthe best combination of production rates and allocation ofquality control stations.

Mhada et al. (2011) develop a fluid model of an unre-liable production line consisting of production machinesand inspection stations that reject non-conforming parts.They apply a decomposition/aggregation method to min-imize the average long-term combined storage and short-age costs, while accounting for part quality and specifyingthe location of quality inspection stations. Korugan andHancer (2007) study a serial production line with rework

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and information feedback. They use an overlapping de-composition method to decompose the system into seriallines and analyze quality information feedback in differentscenarios.

3.4. Design of experiments

Design Of Experiments (DOE) is related to the design ofquality control systems for a production process. In a typ-ical DOE, physical experiments are carried out in a pro-duction process and the experimental data are analyzed toidentify key process parameters influencing quality output,which can be subsequently optimized to achieve a qualitytarget. DOE is a very large research area conventionally ap-plied to product quality improvement. One application ofDOE is to robust production system design. Since the focusof this article is designing production systems for quality,we limit our DOE discussion to robust design because un-like other DOE topics, it is an area within DOE that doeshave recent work that explicitly considers production sys-tems. We feel that the many other areas of DOE, while veryimportant to the broader topic of quality, would be outsidethe scope of this article. Taguchi (1986) introduced robustdesign with the goal of controlling (design) parameters orfactor settings to optimize quality and make output insensi-tive to uncontrollable (noise) factor variation. Some recentwork has applied Taguchi’s methods for production systemdesign. For example, Dubey and Yadava (2007) optimizemultiple quality characteristics. Wazed et al. (2011) ap-ply the Taguchi DOE approach to a multistage productionsystem with machine breakdowns and quality variations.Sukthomya and Tannock (2005) apply Taguchi experimen-tal design with both historic data and a neural networkmodel to manufacturing process optimization. These twoworks correspond to the linkage between PM (batch sizingand process optimization, respectively) and DOE (Taguchimethods in particular) in Fig. 3. In both works, to avoidproduction disruptions, Taguchi methods are not appliedby conducting real experiments but through simulation andexisting data, respectively.

3.5. Statistical process control

With advances in sensing technologies, automatic data ac-quisition has become common in production systems. Withabundant online measurement data, statistical methodscan be used to monitor product quality and detect changes.Statistical Process Control (SPC) is a well-known researcharea in which various control charts have been developed tomonitor product quality and detect changes. The literatureon SPC is quite large. Please refer to Woodall and Mont-gomery (1999) for a nice review. Design of an SPC controlchart includes designing the chart’s parameters, such assample size, sampling frequency, and control limits. Controlcharts can be used to monitor various quality aspects suchas single quality characteristics, multiple correlated quality

characteristics, mean shifts and changes in variances andcovariance, small versus large changes, and quality char-acteristics that follow particular probability (or possiblynon-parametric) distributions. An interesting recent areaof SPC research is profile monitoring (see Woodall et al.(2004) and Woodall (2007)). Cherif et al. (2008) apply goalprogramming to quality control system design by maximiz-ing satisfaction through the setting of specification limits.

Typically, the effectiveness of an SPC design has beenevaluated based on quality metrics such as the average runlength; i.e., how long it takes the control chart to detect thequality change after it occurs. Little consideration has beengiven to productivity performance metrics. However, it isnot surprising that SPC design impacts not only qualitybut also productivity. For example, once a control chartgenerates an out-of-control signal, normal production maybe interrupted to allow for checking whether the signal isa false alarm or an indication of a real quality problem. Inthe latter case, production may be further delayed to allowfor fixing the quality problem.

Nevertheless, some recent studies do analyze the impactof SPC on productivity. Colledani and Tolio (2006, 2011)propose analytic methods to incorporate SPC inspectionstations and design parameters into the evaluation ofproduction system performance. Their studies focus onSPC with online and 100% inspection policies. Colledaniand Tolio (2009) focus on SPC with off-line and samplinginspection policies. Their studies extend existing knowledgeregarding system productivity by incorporating SPC in theproduction system model. For example, one new findingis that larger buffers may not lead to higher throughput ofconforming products, which differs from the prior under-standing that conforming product throughput increasesmonotonically with buffer capacities. This result opens thepossibility of identifying a buffer size that maximizesthe throughput of good parts. This work corresponds tothe linkage between PM (buffer capacity optimization)and SPC (for non-conforming products) in Fig. 3. Borghet al. (2007) extend the work by Colledani and Tolio (2006)and analyze production lines with on-line SPC and reworkof defective parts. The probability of rework is not fixedbut depends on the quality control system parameters andthe probability that machines to go out of control.

Rahim and Ohta (2005) introduce a generalized eco-nomic model to integrate inventory and quality control.By taking into account the changes in both process meanand variance and using the joint R chart and X chart tocontrol the production process, the economic productionquantity and inspection schedule can be determined. Theproduction process can shift from an in-control state toan out-of-control state due to an assignable cause. Thesignal of shifting triggers a search for the cause within apre-specified time, and the process is brought back to anin-control state by repair.

Cheng and Chou (2008) integrate the ARMA controlchart to monitor market demand and an individual control

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chart to monitor the inventory level. They use simulationto investigate the effects of demand change and autocor-relation on the inventory decisions. They use a WesternElectric handbook to define decision rules to detect non-random patterns on control charts.

Some studies focus on specific production systems.Colledani and Yemane (2010) integrate SPC into produc-tion logistics modeling for closed-loop production systemssuch as CONWIP (CONstant Work In Progress) lines.Shanoun et al. (2010) focus on a simplified model of semi-conductor production consisting of one production tool,one buffer, and one control device. Their study shows thatwhen buffer behaviors are considered in planning the pro-cess control, the number of controls may be reduced, lead-ing to better productivity. A test of their proposed methodover a 300-mm wafer fabrication data set showed that 35%of controls can be skipped. This work also reflects the link-age between PM (buffer behaviors in particular) and SPCprocess control. Baud-Lavign et al. (2010) point out thatin the semiconductor industry, key and costly investiga-tions are made to reduce scrap, which should be taken intoaccount in the design of SPC. Specifically, they propose asimulation model to infer SPC parameters, such as the sam-ple size and sampling interval, by considering reuse of SPCdata for scrap investigation and associated costs. Totondoet al. (2009) develop a prediction model using simulationfor both the number of non-sampled items between twosuccessive samples and the time between two successivesamples in a multi-product, multistage parallel manufac-turing system subject to sequence-disorder and multiple-stream effects. In addition to predicting the averageperformance, the analysis provides the shape of the numberof non-sampled item distribution. Finally, Hajji et al. (2010)study a joint production control and product specificationsdecision-making problem in an unreliable manufacturingsystem.

4. Future research opportunities

This section presents future research opportunities at theinterface between production system design and quality.We discuss integrating different aspects of system designand quality, extensions of existing work, and new applica-tions that would address specific links between quality andproduction system design.

4.1. Integrating production and quality control systemdesign elements

There is a broad opportunity in integrating the design ofthe production system with the design of its quality controlsystem. Instead of designing the quality control system af-ter the production system is already designed, there is anopportunity for designing both simultaneously. For exam-

ple, Chowdhury (2005) describes the following sequentialseven-step process.

1. Understand who the customers are.2. Capture and analyze the voice of the customer.3. Translate the voice of the customer into performance

requirements.4. Choose the best design concept to meet the performance

requirements.5. Translate the performance requirements into product/

service design parameters.6. Translate the product parameters into manufacturing

conditions (this step does not apply to a service).7. Determine activities required to maintain manufactur-

ing conditions or service process parameters.

There may be opportunities for integrating steps (6) and (7)instead of performing them sequentially.

Though there have been significant efforts to try to in-corporate productivity consideration into the design of aquality control system as reviewed in Section 3, there arestill a large number of subareas in quality control that havenot fully taken productivity into consideration. Examplesof these subareas include root cause diagnosis of faults,tolerance allocation and synthesis, sensor selection and al-location, and reliability and maintenance scheduling. Con-sidering productivity in traditional quality control systemdesign topics identifies several new research directions.

Another way to view the integration of production andquality control system design is to consider the linkages be-tween the design of production and quality control systemsas shown by the dotted lines in Fig. 3. Both the solid line(recognized) and dotted line (new) integrations in Fig. 3provide opportunities for future research. Even the areasthat have been previously recognized would benefit frommore research. Here we elaborate on the new connections.

Applying QFD to supply chain designThe proposed link between QFD and supply chain rep-resents the opportunity of extending QFD into supplierselection, outsourcing decisions, and logistics planning byresearching how to align both internal and external suppliercapabilities and shipping capabilities with their impact onthe most important customer requirements.

Applying FMEA to the supply chainAnalyzing the failure modes by which supply chains candamage quality (for example, sourcing from suppliers with-out demonstrated quality control, dual-sourcing’s impacton product variation, long lead times that lead to corro-sion, variable lead times that allow out-of-sequence de-liveries that break the first-in–first-out component usagediscipline) can lead to more effective supply chain design.

Applying FMEA to equipment selectionThe proposed link between failure mode and effects anal-ysis and equipment selection represents an opportunity to

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consider different types of equipment (for example, roboticversus manual welding) on failure modes.

Incorporating inspection during PPThe proposed link between IP and PP represents an oppor-tunity to explore how existing inspection research results(such as optimal number and location of inspection sta-tions) can be integrated with the PP process during theinitial design phase.

Applying DOE to SLThe proposed link between DOE and SL represents anopportunity to apply DOE to different layout alternatives,to identify which layouts work best under various operatingcriteria.

Applying SPC to SLThe proposed link between SPC and SL represents an op-portunity to apply SPC to parallel, cellular, or job-shoplayouts.

4.2. Specific research opportunities

Figure 3 helps uncover the proposed research opportuni-ties. There are, however, many other research opportuni-ties in addition to those identified as new areas in Fig. 3.Additional opportunities can be divided into two broadcategories:

• extensions of existing work (robust scheduling, humanfactors, propagation of quality metrics, supply chain,and competing objectives); and

• new applications (energy efficiency, product usage, prod-uct launches, and digital manufacturing)

4.2.1. Extensions of existing workRobust schedulingAnother research opportunity is robust scheduling of mul-tiple products to ensure quality and productivity. Thescheduling policies should be robust to changes or vari-ations in production environment, and both quality andproductivity should be considered.

Human factorsInvestigating the human element’s impact on quality in pro-duction systems is another opportunity for future research.First of all, quality is often associated with culture. Cultureimpacts execution and decision making and undoubtedlyimpacts quality. However, since culture is difficult to defineand model, there remains a considerable opportunity forfuture research in understanding the relationship betweenculture and quality. Another human factor that impactsquality is ergonomics. Ergonomics is an element of produc-tion system design and its role in productivity and workerhealth and safety is widely studied. However, there is anopportunity to further investigate the impact of a produc-

tion system’s ergonomics on quality. A related productionsystem design decision is whether or not to automate anoperation. This issue is especially important in developingcounties where the labor cost relative to automation is lowand the decision is typically made by balancing throughputand cost. However, the quality impact of this decision is notwell understood and is an opportunity for future research.

Propagation of quality metrics throughthe production systemAs discussed in Section 2, there is a substantial litera-ture on how dimensional quality propagates through theproduction system. Still, there are opportunities for fu-ture research, including investigating the propagation ofother quality metrics (i.e., other than dimensional varia-tion) through the production system and integrating thatknowledge of quality propagation with production systemdesign.

Supply chainAnother opportunity for future research is supply chaindesign for quality. For example, the quality implications ofthe supply chain design decision to single-, dual-, or multi-source is a possible research area. While multi-sourcing ismore resilient to production disruptions, it makes qualitycontrol more challenging because the components sourcedfrom different plants will invariably differ. As many produc-tion systems involve worldwide operations, another areafor future research would be to explore the effects of globalsourcing and supply chain flexibility on system design ob-jectives. In global networks, complex routing and long leadtimes may result in special issues that need to be addressedwith regard to production system design and quality.Additionally, extended global supply chains may increasesupply chain supply risk and make it more difficult to main-tain quality. Future work could extend the research morebroadly to logistics networks in order to ensure that qualitystandards carry throughout an entire supply chain. Someof the topics mentioned in Section 2.1.1 may serve as build-ing blocks for extensions to large complex supply chainnetworks.

Competing objectivesAnother broad area of opportunity is to address the prob-lem of competing objectives (such as cost, safety, through-put, flexibility, and quality) in production system design. Amajor challenge is to establish basic design principles thatensure top quality while accounting for these competingobjectives. Finally, looking into the future, production sys-tems may change drastically. Trends such as globalization,increasing fuel cost, technological advances, and environ-mental initiatives could certainly reshape production enter-prises. The future research challenge will be to understandhow to maintain and improve quality in the design of theproduction systems of the future.

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4.2.2. New applicationsEnergy efficiencyOne competing objective seldom considered in the design ofproduction systems for quality is energy efficiency. Insteadof focusing solely on productivity and quality, there is anopportunity for integrated models of productivity, quality,and energy efficiency. The goal is for production systemsto be productive, ensure quality, and be energy efficient aswell.

Digital manufacturingDigital manufacturing can refer narrowly to a process ofadditive manufacturing or three-dimensional (3D) printingwhere a part is made directly from a digital file by layingsuccessive layers of material or, more generally, to the useof a computer-based system of analytics, simulation, and3D visualization to create product and manufacturing pro-cess definitions simultaneously. Sometimes referred to asthe third industrial revolution, the digitization of manu-facturing will drastically affect how things are made. Theaccelerating use of digital manufacturing opens new oppor-tunities for better understanding how to design productionand quality control systems for new digital manufacturingprocesses.

New product launchesNew product launches often suffer quality lapses, yet thereis little research regarding designing the production andquality control systems for product launches. Should thenew product production start in multiple manufacturingsites simultaneously or sequentially? Should new productsbe launched from plants building similar products or havetheir own plant?

Product usageQFD incorporates customer requirements in product de-sign, process planning, and process control. However, thisunidirectional process can benefit from adding a feed-back loop from actual customer usage. Hence, anotherresearch opportunity is to understand the relationship be-tween product usage and the production and quality con-trol systems. For example, if the product’s usage varies evenslightly from the original customer requirements, it may bepossible to improve customer satisfaction and excitementby modifying manufacturing tolerances, routings, and in-spection policies.

4.3. Research challenge

Research in these areas is challenging because of the con-founding effects. The impacts on quality of the variousstages of production system design and quality control sys-tem design confound each other and often can only be mea-sured at the end of all these interacting processes. Despiteits challenging nature, research in this area has substantial

leverage. Once a production system design is in place, it be-comes difficult and expensive to modify to improve qualityand productivity. By considering quality during productionsystem design, expensive renovation and remediation canbe avoided. Furthering our understanding of the interfacebetween production system design and quality will enablehigher quality products as well as cost-efficient production.

Acknowledgement

We gratefully acknowledge the contributions of threeanonymous reviewers.

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Appendix

Table A1. Examples of the impact of the production system on quality by industry category

Industry category (NAICS US Code) Evidence of production system impact on quality

Food Manufacturing (311) Daily The Pak Banker (2009) reports that a manufacturer used tainted fish whenproducing fish crackers, leading to a cholera outbreak in Malaysia

Beverage and Tobacco ProductManufacturing (312)

The Associated Press (2001) reports that a major international soft drink companyrecalled thousands of bottles of beverage because of a manufacturing defect caused bybroken glass in the bottling process

Textile Mills (313) Market News Publishing (2008) reports that a textile manufacturer adopted new thermalinsulation and corrosion protection for its dyeing machines which increased productquality

Textile Product Mills (314) The Economic Times (2005) reports that the Indian textile industry is working onreducing its manufacturing defect rate from thousands per million to four per million

Apparel Manufacturing (315) Business Wire (1995) reports that a major sport shoe company identified a manufacturingdefect and offered replacements for defective shoes

Leather and Allied ProductManufacturing (316)

Science Letter (2009) reports that leather quality can be improved by using experimentaldesign of the fatliquoring stage of the production process

Wood Product Manufacturing (321) Lloyd (2004) reports that a major furniture manufacturer identified a manufacturingdefect in one of its wood furniture product lines that led to a significant decline in itsoperating margin

Printing and Related Support Activities(323)

Transportation and Distribution (1998) reports that a book printer improved quality byreducing the handling damage to paper rolls and by providing computer-aidedinspection

Petroleum and Coal ProductsManufacturing (324)

Yang (2010) reports that a major gasoline refiner apologized for defective gasolineresulting from lax supervision and ineffective quality control

Chemical Manufacturing (325) Chase (2004) reports of flu vaccine being contaminated with dangerous bacteria duringproduction

Plastics and Rubber ProductsManufacturing (326)

Reed and Whitmire (2000) report that a major automotive tire manufacturer recalledthousands of tires due to a manufacturing defect

Nonmetallic Mineral ProductManufacturing (327)

Rogers (2007) reports a faulty concrete mixture used in a highway bridge

Primary Metal Manufacturing (331) Modern Casting (2009) observes that new cleaning and degassing techniques can reducethe manufacturing defects in metal castings

Fabricated Metal ProductManufacturing (332)

Calcott (2000) reports that faulty heat treatment caused tie-rod end and steering jointassemblies to fail prematurely

Machinery Manufacturing (333) The Esmerk Danish News (2008) reports that a wind turbine lost one of its blades due toa manufacturing defect

Computer and Electronic ProductManufacturing (334)

Clark (2004) reports that a major semiconductor producer recalled computer chipsbecause of a manufacturing defect

Electrical Equipment, Appliance, andComponent Manufacturing (335)

The Desert News (2002) reports that a major camera maker recalled 75 000 camerasbecause of a manufacturing defect

Transportation EquipmentManufacturing (336)

Koenig and Freed (2011) report that a major aircraft maker identified misaligned rivetsdue to poor workmanship as the cause of a hole being created in an airline

Furniture and Related ProductManufacturing (337)

Paradis (2005) reports that a manufacturing defect can cause swing seats to fall

Miscellaneous Manufacturing (339) The Drug Industry Daily (2011) reports a medical device maker recalling surgical suturesbecause the packaging may not have been properly sealed, allowing some sutures tobecome non-sterile

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Biographies

Robert R. Inman is a Staff Research Engineer at General Motors.He received his B.S. in Engineering from the University of Illinois inChampaign–Urbana and received an M.S. and Ph.D. from NorthwesternUniversity in Industrial Engineering and Management Sciences. Beforejoining General Motors, he was an Assistant Professor of Industrial En-gineering at Auburn University. He has served as an Associate Editor forIIE Transactions on Scheduling and Logistics and is a registered Profes-sional Engineer. His research interests include supply chain, productionand inventory control, manufacturing systems, customer experience, andquality.

Dennis E. Blumenfeld is an Adjunct Assistant Professor in the De-partment of Industrial and Operations Engineering at the University ofMichigan in Ann Arbor. He spent most of his career as a Staff ResearchScientist at the General Motors Research and Development Center, fo-cusing on design and operations of manufacturing systems and logisticsnetworks. Before joining General Motors, he held faculty positions atPrinceton University and University College London. He received a B.Sc.in Mathematics and M.Sc. in Statistics and Operations Research fromImperial College London and a Ph.D. in Civil Engineering from Uni-versity College London. He is a Member of the Institute for OperationsResearch and the Management Sciences and a Fellow of the Royal Statis-tical Society. He is the author of the book Operations Research Calcula-tions Handbook (CRC Press, 2010), an operations engineering referencevolume in its second edition. He has published articles on transporta-tion modeling, traffic flow and queueing, logistics, inventory control, andproduction systems and received the IIE Transactions Best ApplicationPaper Award in Design and Manufacturing in 2009. He has also servedon the editorial advisory board of the journal Transportation Research.

Ningjian Huang is a Staff Research Engineer at Manufacturing Sys-tems Research Lab, Global R&D Center, General Motors Company. Hereceived his Ph.D. degree in Systems Engineering from Oakland Univer-sity in 1991. He joined GM the same year and has worked on numer-ous research projects in manufacturing-related areas. His research inter-

ests include manufacturing system modeling, simulation, analysis, andoptimization.

Jingshan Li is an Associate Professor in the Department of Industrial andSystems Engineering,University of Wisconsin at Madison. He was withGeneral Motors Research & Development Center, Warren, Michigan,from 2000 to 2006 and the University of Kentucky, Lexington, Ken-tucky, from 2006 to 2010. He received B.S., M.S., and Ph.D. degrees fromTsinghua University, Chinese Academy of Sciences, and University ofMichigan, Ann Arbor, Michigan, in 1989, 1992, and 2000, respectively.He is the co-author of the textbook Production Systems Engineering(Springer, 2009; Chinese edition by BIT Press, 2012). He has publishedabout 140 peer-reviewed journal articles and conference proceedings. Heis a Department Editor of IIE Transactions and an Associate Editor ofIEEE Transactions on Automation Science and Engineering and Interna-tional Journal of Production Research. He has served as the Lead GuestEditor of several special issues in IEEE and IIE Transactions. He hasreceived an NSF CAREER Award, Best Paper Awards from IIE Trans-actions and IEEE Transactions on Automation Science and Engineering,an IEEE Early Industry/Government Career Award in Robotics andAutomation, and he has been a finalist in the best paper competitionsat several international conferences. His primary research interests are indesign, analysis, and improvement of production and health care systems.

Jing Li is an Assistant Professor in Industrial Engineering at ArizonaState University. She received an M.A. in Statistics and a Ph.D. in In-dustrial and Operations Engineering from the University of Michigan in2005 and 2007, respectively. Her research interests are modeling, analysis,and control of manufacturing systems interfacing with computational in-formatics and statistical machine learning. She has also been working onproblems in health care and system biology. Her research is sponsoredby the NSF, NIH, DOD, and Arizona State. She is an NSF CAREERAwardee. She is also a recipient of the Best Paper Award from the In-dustrial Engineering Research Conference (twice). She is currently theChair of the Data Mining Subdivision of INFORMS, an Associate Ed-itor for the Journal of Chinese Institute of Industrial Engineers, and aGuest Editor for IIE Transactions.

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