Understanding the effects of outsourcing: unpacking the total factor productivity variable


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Research on why firms should outsource and how they should do it has proliferated in the past two decades, but few consistent findings have emerged concerning the benefits of outsourcing. We argue that this is in part due to the lack of an adequate framework for measuring the effects of outsourcing. To address this, we present such a framework based upon the Cobb–Douglas productivity function. We explain how our framework can be used to unpack one component of the Cobb–Douglas productivity function, the ‘total factor productivity’, which represents the other numerous sub-variables that affect outsourcing productivity, beyond the capital and labour expenditures. We also demonstrate the framework using a simple illustrative example.

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Understanding the effects of outsourcing: unpacking the total factor productivity variable

  1. 1. This article was downloaded by: [Simon Fraser University]On: 29 January 2013, At: 09:48Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UKProduction Planning & Control: The Management ofOperationsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tppc20Understanding the effects of outsourcing: unpackingthe total factor productivity variableBen Kitchera, Ian P. McCarthyb, Sam Turnera& Keith RidgwayaaAMRC with Boeing, Advanced Manufacturing Park, University of Sheffield, Wallis Way,Catcliffe, Rotherham S60 5TZ, UKbBeedie School of Business, Simon Fraser University, 500 Granville Street, Vancouver, BCV6C 1W6, CanadaVersion of record first published: 02 Feb 2012.To cite this article: Ben Kitcher , Ian P. McCarthy , Sam Turner & Keith Ridgway (2013): Understanding the effects ofoutsourcing: unpacking the total factor productivity variable, Production Planning & Control: The Management of Operations,24:4-5, 308-317To link to this article: http://dx.doi.org/10.1080/09537287.2011.648543PLEASE SCROLL DOWN FOR ARTICLEFull terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditionsThis article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses shouldbe independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims,proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly inconnection with or arising out of the use of this material.
  2. 2. Production Planning & ControlVol. 24, Nos. 4–5, April–May 2013, 308–317Understanding the effects of outsourcing: unpacking the total factor productivity variableBen Kitchera, Ian P. McCarthyb*, Sam Turneraand Keith RidgwayaaAMRC with Boeing, Advanced Manufacturing Park, University of Sheffield, Wallis Way, Catcliffe, Rotherham S60 5TZ,UK; bBeedie School of Business, Simon Fraser University, 500 Granville Street, Vancouver, BC V6C 1W6, Canada(Received in final form 10 November 2011)Research on why firms should outsource and how they should do it has proliferated in the past two decades, butfew consistent findings have emerged concerning the benefits of outsourcing. We argue that this is in part due tothe lack of an adequate framework for measuring the effects of outsourcing. To address this, we present such aframework based upon the Cobb–Douglas productivity function. We explain how our framework can be used tounpack one component of the Cobb–Douglas productivity function, the ‘total factor productivity’, whichrepresents the other numerous sub-variables that affect outsourcing productivity, beyond the capital and labourexpenditures. We also demonstrate the framework using a simple illustrative example.Keywords: outsourcing; Cobb–Douglas; total factor productivity; aerospace; manufacturing; Ishikawa diagram1. IntroductionThe practice of outsourcing is when one companycontracts out a part of its existing internal activity toanother company (Lei and Hitt 1995, McCarthy andAnagnostou 2004). It is an activity that has gainedprominence both in management practice and in theresearch literature. Particular attention has been paidby researchers to the labour market and the produc-tivity effects of outsourcing practices, where empiricalevidence is often employed to examine the effects ofoutsourcing. However, these and other studies presentconflicting findings and arguments over the effective-ness of outsourcing practices (Dabhilkar andBengtsson 2007). We suggest that these contradictoryresults can be explained in part due to the method usedto measure the effects of outsourcing (Horgos 2009).To help researchers and managers better under-stand the productivity (i.e. efficiency of production)implications arising from a decision to outsource, wepresent and illustrate a framework that relates businessinputs (capital and labour) to useful outputs (goods orservices). Central to our framework is theCobb–Douglas (1928) productivity function, a simpleand seminal algebraic expression relating productionoutput to production inputs. The function alsoemploys a third variable or factor, total factorproductivity (TFP), to crudely account for the otherinnumerate impacting variables (beyond labour andcapital) that also affect productivity. TheCobb–Douglas function was primarily developed todescribe productivity effects arising from the alterationof labour and capital, the two endogenous variablesthat can be controlled to some extent by managers.However, the Cobb–Douglas function has been exten-sively employed to gain a retrospective insight as towhether an outsourcing decision has made a positiveimpact upon the productivity of the firm by evaluatingthe amalgamated exogenous variables in the form ofTFP (e.g. Girma and Go¨ rg 2004, Olsen 2006, Gorget al. 2008). The outcomes of these investigationssuggest there is only a tenuous trend between out-sourcing intensity and productivity. Thus, we assertthat it is important to be able determine the TFP fordifferent outsourcing decisions ex ante by identifyingand evaluating the pertinent factors affecting thepotential TFP change.To this end, we present a framework that can beused to unpack the TFP variable into a number ofspecific sub-variables or categories. This follows otherstudies that closely examined the make-up of othercontingent factors such as industry uncertainty(Milliken 1987) and industry velocity (McCarthyet al. 2010). Our framework also complements otheroutsourcing frameworks (e.g. Fixler and Siegel 1998,Vining and Globerman 1999) and make-versus-buyframeworks (Platts et al. 2002), in that we argue thatoutsourcing performance is contingently dependent onhow TFP indicators combine with labour and capitalvariables to influence the success or failure of anoutsourcing decision.*Corresponding author. Email: imccarth@sfu.caISSN 0953–7287 print/ISSN 1366–5871 onlineß 2013 Her Majesty the Queen in Right of Canadahttp://dx.doi.org/10.1080/09537287.2011.648543http://www.tandfonline.comDownloadedby[SimonFraserUniversity]at09:4829January2013
  3. 3. We present our arguments in three sections. InSection 2, we review the research on the impact ofoutsourcing in the operations management and strat-egy literature focusing on the opportunities that thisstudy presents for developing the TFP framework. InSection 3, we present our framework by introducingthe TFP concept and then defining some categories ofsub-variables that could affect an outsourcing decision.Then, we illustrate the framework and method. We dothis by presenting a simple illustrative to show howpotential relationships among the TFP variable andoutsourcing performance categories can be unpacked.In Section 4, we explore the implications of ourframework for managers and scholars interested inunderstanding and measuring the impact ofoutsourcing.2. Research on the impact of outsourcingThere has been a profusion of management andeconomic research on outsourcing ever since thepractice gained prominence during the latter half ofthe twentieth century. Generally, much of this researchfocuses on two broad themes: labour market effects(Feenstra and Hanson 1999) and aggregate- and firm-level productivity and efficiency (Gilley and Rasheed2000, Raa and Wolff 2001). In terms of operationsmanagement, such effects have prompted researchersto examine the sourcing strategies of Belgian firms(Everaert et al. 2007) and how supply chains should bedesigned to operate in developing regions (Taps andSteger-Jensen 2007). Furthermore, McCarthy andAnagnostou (2004) show how outsourcing in the UKmanufacturing industry reduces the value of amanufacturing firm, which in turn represents a declinein the gross domestic product of the industry, asservices and other non-manufacturing activities areoutsourced.In a review of the outsourcing literature, Jiang andQureshi (2006) argued that outsourcing researchfocuses on three themes. Two of the themes thatdominate the literature are understanding the determi-nant factors that prompt the decision to outsource andexploring the methods by which a company imple-ments outsourcing. The third, less developed theme(accounting for less than 20% of papers reviewed) isthose studies that focus on the impact or results of anoutsourcing activity. One reason we suggest for thelimited attention to studying outsourcing results is thelack of a framework that guides researchers on how tocontingently measure the relationship between differ-ent outsourcing decisions and corresponding outputs.To address this and respond to Jiang and Qureshi’s(2006) call for researchers to pursue result-basedresearch on outsourcing, we present and illustrate aframework that focuses on unpacking the TFP vari-able. This allows researchers and managers todetermine and quantify some of the categories ofTFP sub-variables that affect outsourcing performanceand to identify some of the outsourcing opportunitieswhich best represent productivity growth opportuni-ties. We are motivated to develop this framework,because we believe the link between outsourcingintensity and productivity is too simplistic, i.e. asingle dimension is insufficient to account for thearray of influencing factors on TFP magnitude.Table 1 lists some of the major studies in manage-ment and economics that have used TFP to examineoutsourcing results. They were selected using the termsTFP, outsourcing and ‘Total Factor Productivity’.Looking across these studies, we identify three themesthat support and motivate our study. First, as all thestudies listed in Table 1 employed TFP to justify a linkbetween productivity change and outsourcing, theyprovide arguments that support the need to betterestimate the impact of outsourcing on productivity.For example, Olsen (2006) provides a comprehensivereview of the prior research that used theCobb–Douglas function to identify a trend betweenoutsourcing intensity (i.e. ratio of purchased andcreated goods and/or services) and the productivityof the firm or industry. He argues that such studies areneeded because the impacts of outsourcing on produc-tivity have received only little attention. Similarly,Girma and Go¨ rg (2004) focus on three separate UKmanufacturing industries over the period 1982–1992and use TFP to examine whether outsourcing has apositive effect on productivity.Second, in no instance is an unequivocal linkbetween outsourcing intensity and productivityincrease proven. While a link is plausible based uponthe findings of the literature identified, the mechanismsby which this would operate have not been considered.Olsen (2006) concludes that using TFP growth ormagnitude as a measure, the reports studied indicatethat in 59% of cases an improvement in productivityhas been noticed and that in 41% of studies noimprovement is seen. He concludes that ‘there are noclear patterns as to how outsourcing affects produc-tivity’; moreover, productivity growth ‘much seems todepend on sector- and firm-specific characteristics’(Olsen 2006, p. 28). From this it seems that a weakrelationship between outsourcing intensity andimproved productivity exists; however, it also suggeststhat the act of outsourcing itself might not directly leadto increased productivity, reduced costs or improvedresource efficiency.Production Planning & Control 309Downloadedby[SimonFraserUniversity]at09:4829January2013
  4. 4. Third, we note that in the instances that changes inTFP have been measured, it is in conjunction with themeasurement of structural or environmental changes,such as radical changes in technological capability. Forexample, Feenstra and Hanson (1999) examined how aTFP increase can be attributed to specific changescarried out by the business. They observed thatoutsourcing accounted for a 15% increase in TFPmagnitude, while technological innovation was respon-sible for a 30% growth under the same conditions.These observations are the products of investigationswhich aim to identify the variables that predeterminethe success of an outsourcing decision. However, it isalso observed that no single variable offers a signal ofsufficient clarity to be distinguishable above thecombined magnitude of all other signals. From this,we focus in this article on understanding how multiplevariables could be considered to assess the impact ofoutsourcing.Together, the literature in Table 1 indicate that theoutsourcing of internal functions can have a significantimpact upon firm productivity, but the magnitude anddirection of that impact is not always favourable and isdifficult to predict without a more fine-grained analysisof the TFP term. This leads us to believe that there is asignificant gap in the understanding of how the contextof outsourcing decision affects outsourcing perfor-mance. That is, the data in the existing literature isconcerned with outsourcing and using aggregate TFPto assess success. Thus, it does not have the resolutionfor us to be able to differentiate the factors between anoutsourcing decision which leads to increased produc-tivity and one which does not, and the use of TFP untilnow should be categorised as ‘determinant’, asdescribed by Jiang and Qureshi (2006). Olsen’s (2006)statement that sector- and firm-specific characteristicsaffect the productivity growth supports our effort inthis article to divide together the variables whichdescribe the firms ‘situation’; the financial, geograph-ical and technical landscape in which the firm operates.To this end, we suggest some exogenous and endog-enous variables fitting into those financial, geograph-ical and technological categories which ultimatelyinfluence the magnitude of TFP growth the firmTable 1. Research on outsourcing and TFP.Authors Approach Industries Key findingsOlsen (2006) Survey of literature relatingto productivity effects ofoutsourcing, using TFPgrowth as a benchmarkindicatorVarious manufacturing,both firm and aggregatelevelThe link between outsour-cing and productivitybenefit is weak; somebenefit is noticed in smallmanufacturing firmsFeenstra and Hanson (1999) Compares TFP values forcompanies operating in a‘large country’ at aggre-gate level, seeking to dis-tinguish between thebenefits seen throughtechnology implementa-tion and through out-sourcing intermediateservicesSIC manufacturing indus-tries (aggregated)Increased technology isresponsible for 35%increase in the relativeincome of non-produc-tion workers, whereasoutsourcing is responsi-ble for around 15%.Girma and Go¨ rg (2004) Uses firm-level (SIC) datato analyse TPF growthUK chemical, mechanical,instrument and electronicmanufacturingIntensive outsourcing isdirectly proportional tofirm-level TFP growthGorg et al. (2008) Firm-level data is used todistinguish between theeffects of materials andservice outsourcing inmanufacturing firmsIrish manufacturing com-panies, domestically andinternationally ownedServices outsourcing haslittle effect on productiv-ity for firms who do notexport, but has notice-able effect for thoseexportingRaa and Wolff (2001) A consolidation frameworkis used to distinguishbetween goods and ser-vices outsourcing andtheir respective effectsupon productivityUS manufacturing firms TFP growth in themanufacturing industryduring the period1977–1987 was aided bythe outsourcing of inter-mediate service functions310 B. Kitcher et al.Downloadedby[SimonFraserUniversity]at09:4829January2013
  5. 5. should expect as a result of the particular outsourcingdecision. A set of such parameters was outlined in thereport by Jiang and Qureshi (2006, p. 48) in theirframework for outsourcing results analysis, andcategorised into those parameters which have availabledata to be measured and those which are not supportedand therefore immeasurable. In that particularinstance, however, due to the retrospective natureof the study, only data which happened to be recordedat the time were available. In this article, we argue thatthese fiscal, physical and technological parameters canbe addressed as key metrics, critical to the outcome ofthe productivity evaluation.3. Outsourcing: a TFP frameworkIn this section, we define and discuss the TFP variable.We then describe a framework that can be used toexamine how the composition of this variable wouldimpact an outsourcing decision. To do this, we presentan illustrative outsourcing example and show how thecontext of the example can be analysed using theframework so as to offer insight and benefit tomanagers.3.1. Total factor productivityTFP is a factor in the Cobb–Douglas (1928) produc-tion function. The now seminal function was intro-duced as a means to evaluate firm-level and aggregatedproduction magnitudes. The typical two input produc-tivity function describes productivity in terms of thecapital and labour applied to a product (Durrand 1937,Aigner and Chu 1968). Capital and labour are subjectto an efficiency variable, which is known as the TFP.The classic two-input Cobb–Douglass function isstated in the following form:y ¼ ALK
  6. 6. where Y is the process output, the magnitude ofproductivity. Typically, this would be measured interms of quantities of good produced, or revenuegenerated through sales or exports (if considering anaggregated national product). In this article, weconsider the process output to be directly proportionalto TFP, where capital and labour inputs areconstraints.A is the TFP; the other numerous sub-variables(beyond that explicitly accounted for capital andlabour) that affect the productivity. The aim of thisarticle is to present a framework for identifying,grouping and evaluating the implicit sub-variablesthat might constitute the TFP for different outsourcingdecisions.L is the labour input, the hours of labour investedper product. This is assumed to be constant andexclusive from TFP. In reality, however, a change inTFP might have a subsequent affect upon hoursrequired, but to preserve simplicity for the presentexamination, the labour hours required per product isbenchmarked by the in-house process.K is the capital input, the capital investmentrequired to facilitate production, for both recurringand non-recurring expenditure. As with labour invest-ment, this variable is considered constant for ourexamination despite fair assumption that some TFPinputs might affect the amount of capital required tocarry out production. and
  7. 7. are the elasticity constants; these exponentsaccount for diminishing returns, i.e. the values of and
  8. 8. should each equal less than 1, such that the marginalproduct diminishes as capital and labour inputs areincreased. The values of and
  9. 9. are derived by means ofbest fit to the existing data. Cobb and Douglass (1928)themselves employed an value of 0.75 (and conse-quently
  10. 10. ¼ 0.25, assuming that þ
  11. 11. ¼ 1) whichachieved a suitable fit to their 1899–1922 productiondata. Subsequent studies (using the Durrand (1937)independent exponent form) have tended to agree withvalues of these magnitudes. For example, Meeusen andvan Den Broeck (1977) analysed the 1962 Census ofManufacturing Industries, demonstrating values for and
  12. 12. ranging from 0.71 to 0.84 and 0.15 to 0.34,respectively, with a combined figure closely approxi-mating 1. The values of and
  13. 13. are circumstantial; theyare specific to a particular industry, geographic locationand time period and like TFP are products of theenvironment in which the company operates. Again likeTFP, the elasticity constraints are defined by humanactions and typically quantified at an aggregate level;however, these do not vary significantly over time orbetween economic or social conditions. As such, for thisexamination, they are assumed to be fixed.Furthermore, for the studies mentioned in Table 1, and
  14. 14. have also been assumed static, such thatproductivity changes can be expressed as a product ofTFP only.A criticism of the Cobb–Douglass function con-cerns its applicability and validity. Labini (1995), forinstance, argues that the static values assigned to and
  15. 15. are insufficient to be able to account for thedistribution of production shares, and that a dynamicvalue (i.e. one that accounts for fluctuations in the rateof change of marginal product) should be implementedin their place. Also, Barnett (2004) points out that thedimensions of the constant TFP are raised to the powerof the exponents of labour and capital. Should theseexponents each equal unity, the dimensions areProduction Planning Control 311Downloadedby[SimonFraserUniversity]at09:4829January2013
  16. 16. acceptable and meaningful, but we have alreadyestablished non-integer values 1 are typical, inwhich case the dimensions of TFP are meaningless.Such criticisms indicate that TFP, while appropriatefor examining the link between outsourcing andperformance, has been treated as a ‘dumb’ number.This constrains the TFP to a measurable but unin-telligible variable unable to help decision makers assessa current, unique set of outsourcing circumstances.With the following framework, we present a method bywhich managers can evaluate the circumstances inwhich they are exposed to, the effect these decisionsmight have on the TFP value they should expect toachieve through implementation of an outsourcingstrategy.3.2. The frameworkThe framework we present is used to unpack anddefine TFP for different outsourcing decisions.It involves two major steps.In the first step, we employ the problem-solvingtool, the Ishikawa (1990) diagram, to unpack TFPvariable into a number of sub-variables. We identifyTFP as the observed effect or symptom in the Ishikawadiagram, from which point we can begin to draw outthe sub-variables which influence it, and furthermorethe subsequent variables influencing them. Each levelof variable is broken down until we have variables thatcan be individually measured. As a problem-solvingmethod, the Ishikawa diagram assumes that each‘branch’ or sub-variable (Figure 1) is of equal impor-tance to its counterparts at the same hierarchical level.We note, however, that some of the branches willultimately have a greater influence on the TFPmagnitude than others, but for now the diagramserves the purpose of allowing us to illustrate thecauses behind TFP magnitude changes. Furthermore,while the Ishikawa diagram traditionally has fourbranches or the 4 Ms – materials, machines, manpowerand methods – that unpack the causes behind internalmanufacturing process problems, the branches of theFigure 1. Example of how the Ishikawa diagram can be used to unpack TFP.312 B. Kitcher et al.Downloadedby[SimonFraserUniversity]at09:4829January2013
  17. 17. diagram can be altered to focus on variables pertinentto different situations. We employ the four perspec-tives, customer, financial, internal business process andlearning and growth, from the strategic performancemanagement tool, the balanced scorecard (BSC)(Kaplan and Norton 1992). These perspectives providea coherent link between a corporate mission statementand the behaviours and outputs of firms and thus canbe used as sub-variables to unpack and make thecauses of TFP growth intelligible. The first of thesesub-variables is the financial perspective, which isintended to describe the performance of the businessfrom the perspective of the shareholder. Second, thecustomer perspective is a sub-variable for how out-sourcing decisions affect the perception of the cus-tomer of the business. Further, the internal businessprocess perspective is the sub-variable for how out-sourcing impacts the internal processes by which thebusiness creates value. Finally, the learning and growthperspective is the sub-variable that measures how theoutsourcing decision will extend, diminish or shiftexisting knowledge gaps and present growthopportunities.The second step in our framework involves takingthe ‘measurable’ variables defined in the Ishikawadiagram, and creating a comparison table to examinehow each variable might vary for differentoutsourcing options. To do this, the existing in-house(non-outsourcing option) is scored zero, and thevariables for each outsourcing option are allocatedeither a positive or negative score. The scores of thecomparison table are then summed to assess therelative positive and negative effects each outsourcingoption has on productivity.3.3. Framework illustrationBy means of example, we proffer a hypotheticalsituation whereby two alternative external suppliersare being considered by an North American ownedand located manufacturing company to carry out anon-core manufacturing process. The particular mea-sures we use for this illustrative example were derivedfrom the authors’ experience in the aerospacemanufacturing sector. The primary branches ofFigure 1 were defined by the BSC perspectives –financial, customer, internal business process andlearning and growth – and provide a universal startingpoint for considering the potential implications of anoutsourcing decision.Two potential supplying companies are considered.They manufacture a typical static gas turbine enginecomponent. One is a long established and reputablelocal supplier and the other is a recent internationallylocated entrant to the market. The suppliers arecharacterised by the criteria shown in Table 2. Toscore and compare the suppliers using the TFPsub-variables shown in Figure 1, we present Table 3.It indicates that the benchmark (in-house) methodwould yield no difference in TFP, supplier 1 wouldresult in an increase in TFP on the basis of a score ofþ5 and supplier 2 would offer further benefit over boththe benchmark and supplier 1 with a score of þ9. Wedeconstruct these scores to develop general commentsabout the categories of sub-variables as follows.3.3.1. CustomerSupplier 1 offers an overall negative effect upon TFPmagnitude due to not being able to control deliveryschedules. Supplier 2 offers overall benefit despitenegative connotations of offshore outsourcing: sup-plier brand affiliation and the attractiveness to newcustomers both suffer due to increased perception ofquality escapes caused by outsourcing.3.3.2. FinancialThe example indicates supplier 1 to be the mostpreferred source in terms of cost, centring aroundTable 2. Description of potential suppliers.Characteristic Supplier 1 Supplier 2Geographic proximity Based in a similar geographic location to thepurchasing companyGeographically distant from the purchasingcompanyGrowth approach Long established business based on traditionalworking methodsBusiness model based around rapid growthLabour force Skilled labour with higher wages Low labour costCapabilities Similar capabilities to in-house manufacture Maintains and implements cutting edgetechnologyProcess Small batch sizes Large batch sizesProduction Planning Control 313Downloadedby[SimonFraserUniversity]at09:4829January2013
  18. 18. a benefit in reduced capital expenditure. Supplier 2 alsooffers negligible benefit according to the example.3.3.3. Internal business processesBoth suppliers 1 and 2 appear to offer similar benefit tothe purchasing company through the liberation ofhours to be spend on core competences.3.3.4. Learning and growthBoth suppliers 1 and 2 offer great benefit to thepurchasing company by opening knowledge diffusionroutes.Analysis of this illustrative scoring example (parti-cularly the cost-based variables) highlights a limitationin our relatively simplistic comparison method. By notassigning importance to each sub-variable, and simi-larly not calculating magnitudes of differentiation forour measurable variables, we allow what might beinsignificant factors such as transportation/logisticalexpenses to take an equal leverage upon overall scoreas a primary aspiration such as processing costreduction. This manifests itself as a reduced magnitudefor the total financial benefit, despite the intention ofour demonstrative scoring to show that supplier 2would yield a significant cost saving.A question regarding data being populated into thestudy is also presented; how do we confirm the qualityof the data upon which our decision is based? If thedata are not of sufficient quality, does the output of theexercise offer us any greater insight than a comparisonof recurring cost data (comparison of quotations)alone? It is clear that if we are to either increase thenumber of metrics we are considering, or increase thevolume of empirical data supporting each metric,the reliability of the result increases.In sum, the example simply shows how the frame-work can be used to deduce the most beneficialoutcome (i.e. supplier 2). It maps and makes transpar-ent our assumptions about benefits in cost reduction aswell as increased customer satisfaction (thoughincreased product quality) and internal ‘Knowledgeand Learning’ by knowledge diffusion and increasedTable 3. Comparison of suppliers using the metrics in Figure 1.Category Metrics In house Supplier 1 Supplier 2Customer Product quality 0 0 1Delivery 0 À1 1Value 0 À1 1Back up 0 À1 1Attractiveness to new customers 0 À1 À1Supplier brand affiliation 0 1 À1Total 0 À3 2Financial Materials 0 0 1Processing 0 1 1Currency exchange 0 0 À1Contract negotiations 0 À1 À1Distance 0 0 À1Transportation modes 0 0 À1Competition 0 0 À1Delivery 0 0 1Quality 0 0 1TRL 0 1 1Equipment ownership 0 1 1Total 0 2 1Internal business process Labour hours on core productivity 0 1 1Value of core processes 0 1 1Labour hours on non-core productivity 0 0 0Cost of non-core activities 0 0 0Total 0 2 2Learning and growth Production volume 0 1 1Access to new market 0 1 1Knowledge transfer 0 1 1Access to technology 0 1 1Total 0 4 4314 B. Kitcher et al.Downloadedby[SimonFraserUniversity]at09:4829January2013
  19. 19. time spent on core competencies. Largely, this isportrayed by the example and the framework wouldsuggest that supplier 2 is, all things considered, themost favourable option for production of the compo-nent. In the context of TFP, the selection of supplier 2would facilitate higher production volumes if the samecapital and labour resources are to be committed, orthat capital and/or labour inputs may be decreased toachieve the current level of productivity. Finally, it isimportant to note that in practice evidence would bepresented to support the assumptions and indicate theimpacts of the variables in the framework.4. Discussion and conclusionsThe central contribution of our study is the develop-ment and illustration of a framework for unpackingthe outsourcing decision using the TFP, a variable inthe Cobb–Douglass function. With this framework, weargue that it is possible to forecast how TFP variancefor different outsourcing options would impact out-sourcing performance. This allows managers andresearchers to identify the variables which affect thecontingent benefit realised by an outsourcing decision.We now discuss several implications of the framework,of relevance to both management practice and futureempirical research.First, where several outsourcing options might allrepresent an opportunity for benefit, our frameworkprovides a reliable and sanitised method of quantifyingthe magnitude of benefit. However, as argued earlier inthis article, the simplistic comparison method wepresent is only an illustration; it is not sufficientlyaccurate to prescribe the level of benefit an organisa-tion should expect to see. Therefore, we suggest thatevaluation and comparison methods for other cognatedecision-making problems could be applied to TFPforecasting. Currently, there is a considerable gap inour capability for defining measurable variables (thesecondary branches of the Ishikawa diagram), how weassign importance to each branch and how we subse-quently score and evaluate the data. This problem issuited to multi-criteria decision analysis (MCDA),which develops methodologies for practitioners toapply in order to overcome complex decision problems(Figueira et al. 2005). A commonality between MCDAtheories is the way in which qualitative/quantitative,explicit/implicit, empirical/derived data is broughttogether; dimensionally incompatible informationfrom disparate sources is handled in nearly allMCDA problems. It is recognised that MCDA’sability to combine these data in a logical fashion,which replicates the complex evaluation methods wetacitly employ when faced with such problems, makesit especially suitable for use in outsourcing decisionproblems where we wish to use TFP forecasting.Second, our literature review highlights how TFPhas already been used with reference to outsourcingdecision problems, furnishing us with data andaccounts of the effects some outsourcing decisionshave already made. Provided we can obtain therelevant supporting information (as defined by thepreferred MCDA method, above), we suggest applica-tion of the TFP forecasting method to the pre-existingstudies mentioned in Table 1 and propose how the TFPforecasting method would suggest that the overallproductivity change would occur compared to themeasured value in each study. As well as being apertinent example of the framework worthy of discus-sion in itself, this study would allow us to begin togenerate weighting factors for the metrics used, whichcould be used as baselines in subsequent studies.Third, our examination might suggest that the TFPvariable itself is a function of the measurable variableswe identified. Although the measurable variablesidentified are undeniably linked, significant data arerequired to solve an algorithm with so many variables,so as to generate an explicit function for TFP.However, application of artificial neural networks(ANN) may meet the same objective. ANNs are seenas particularly useful when considering how manydisparate inputs can have impact upon perceivablyunrelated outputs; ‘it is often easier to have data thanto have good theoretical guesses about the underlyinglaws governing the systems’ (Zhang et al. 1998, p. 35).Of course, the accuracy of the TFP magnitude outputwould be directly related to the appropriate selection ofinput parameters; those inputs would be identifiedthrough testing of the model and alignment with thedata which is both already available to train thenetwork and would be easily available for the user tomeasure.The framework’s greatest impact remains withmanagers faced with an outsourcing decision. Withan ability to identify those factors that affect thesuccess of outsourcing, managers not only shall bebetter informed of the overall impact of the decision,they will also be aware of those factors which have thegreatest bearing on success. This allows them toinvestigate how to affect those factors to best enablea positive result. The metrics used in the TFPforecasting method would be expected to vary betweenfirms, and due to the size of some multinational firmssome discrepancy in forecasting metrics may be evidentwithin the same organisation also. To investigate this,and to capture universal opinion on the drivers of TFP,we suggest that a Delphi Analysis be carried out toProduction Planning Control 315Downloadedby[SimonFraserUniversity]at09:4829January2013
  20. 20. determine the company’s overall TFP forecastingcriteria and their rankings.Notes on contributorsBen Kitcher is a Lead Engineer formachining processes at the Universityof Sheffield’s AdvancedManufacturing Research Centre(AMRC). Ben has worked at theCentre for 5 years, developing deci-sion support tools for the selection ofmanufacturing processes, researchprojects and product developments.The tools developed include elements of the Trade Studymethod, the Analytical Hierarchy Process and the BalancedScorecard. Combination of these elements has yielded a toolset and process template which has particular relevance tomid-to-high TRL projects, having already been proven inindustrial projects for Rolls-Royce, Messier-Dowty, Boeing,BAe Systems and to research projects sponsored by TSB andEU FP7.Professor Ian McCarthy is the CanadaResearch Chair in Technology andOperations Management in theFaculty of Business Administrationat Simon Fraser University. Hereceived his MSc and PhD degreesfrom the University of Sheffield, andprior to joining Simon FraserUniversity he was a Faculty Memberat the Universities of Warwick and Sheffield. Focusing ontechnology-based firms, Professor McCarthy is well knownfor his work on how firms should be designed and managed,in terms of their operations, so as to succeed in differentindustries. He uses complex systems theory, evolutionarytheory and classification methods to identify and map thedifferent operational designs that firms must adopt. Inparticular, he is interested in how firms differ in their newproduct development processes, RD management controlsystems, outsourcing practices and collaborative networks.Dr Sam Turner is the Head of theProcess Technology Group at theAdvanced Manufacturing ResearchCentre (AMRC) and has workedwith the AMRC since its foundationin 2001 on machining technologies,growing the group to 50 strong. Sincethis time, he has undertaken researchin machining dynamics, titanium andnickel alloy machining, machine tool adaptive control,cutting tool design and machining strategies. The research,sponsored through EPSRC, TSB, EU FP6, FP7 and indus-try, has been recognised in industry as delivering step changetechnologies. He has over 10 peer reviewed journal andconference papers on machining technology and has exten-sive experience of applying this research to industrial projectsand uses this experience to focus on the significant gapswithin the current fields of research that will have step changeimpact on future production processes.Professor Keith Ridgway is theExecutive Dean of the University ofSheffield Advanced ManufacturingInstitute comprising the AdvancedManufacturing Research Centre withBoeing (AMRC) and the NuclearAMRC. The AMRC was establishedin 2001 to carry out research inmanufacturing technologies directlyrelated to the aerospace industry. In addition to Boeing, theAMRC has more than 60 sponsors, including BAe Systems,Rolls-Royce and Messier Dowty. Application of the researchdeveloped has led to a step change in the manufacturingcapability of many of the industrial sponsors and encouragedtheir on-going commitment and support. He is a Fellow ofthe Royal Academy of Engineering, the Institution ofMechanical Engineers, Royal Institute of Naval Architectsand Royal Aeronautical Society and was awarded the OBEfor services to UK manufacturing industry in June 2005.He has over 160 publications including 57 journals articles.ReferencesAigner, D.J. and Chu, S.F., 1968. On estimating the industryproduction function. The American Economic Review,58 (4), 826–839.Barnet, W., 2004. Dimensions and economics: some prob-lems. Quarterly Journal of Austrian Economics, 7 (1),95–104.Cobb, C.W. and Douglas, P.H., 1928. A theory of produc-tion. The American Economic Review, 18 (1), 139–165.Dabhilkar, M. and Bengtsson, L., 2007. Invest or divest?On the relative improvement potential in outsourcingmanufacturing. Production Planning Control, 19 (3),212–228.Durrand, D., 1937. Some thoughts on marginal productivity,with special reference to professor Douglas’ analysis.The Journal of Political Economy, 45 (6), 740–758.Everaert, P., Savens, G., and Rommel, J., 2007. Sourcingstrategy of Belgian SMEs: empirical evidence for theaccounting services. Production Planning Control, 18 (8),716–725.Feenstra, R.C. and Hanson, G.H., 1999. The impact ofoutsourcing and high-technology capital on wages: esti-mates for the United States, 1979–1990. The QuarterlyJournal of Economics, 114 (3), 907–940.Figueira, J., Greco, S., and Ehrgott M., eds., 2005. Multiplecriteria decision analysis: state of the art surveys.Dordrecht: Kluwer Academic.Fixler, D.J. and Siegel, D., 1998. Outsourcing and produc-tivity growth in services. Structural Change and EconomicDynamics, 10 (1), 177–194.Gilley, K.M. and Rasheed, A., 2000. 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