Data-Driven Simulation-Enhanced Optimization of People-Based Print Production Service

Transcription

1 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice Sudhendu Rai Xeox Reseach Cente Webste, MS E, 800 Phillips Road, Webste, NY 14580, USA Abstact This pape descibes a systematic six-step data-diven simulation-based methodology fo optimizing people-based sevice systems on a lage distibuted scale that exhibit high vaiety and vaiability. The methodology is exemplified though its application within the pinting sevices industy whee it has been successfully deployed by Xeox Copoation acoss small, mid-sized and lage pint shops geneating ove $250 million in pofits acoss the custome value chain. Each step of the methodology consisting of innovative concepts co-development and testing in patneship with customes, development of softwae and hadwae tools to implement the innovative concepts, establishment of wok-pocess and pactices fo custome-engagement and sevice implementation, ceation of taining and infastuctue fo lage scale deployment, integation of the innovative offeing within the famewok of existing copoate offeings and lastly the monitoing and deployment of the financial and opeational metics fo estimating the etun-oninvestment and the continual enewal of the offeing ae descibed in detail. Keywods Sevice science, sevice systems, pocess optimization, people-based sevices, simulation, analytics 1 Intoduction Many industies ae tansitioning fom being manufactuing-focused to becoming moe sevice-oiented. The natue of sevice opeations can be equipment-based o people-based (Thomas 1978). People-based sevice businesses ely on unskilled labo, skilled labo o pofessionals fo thei sevice poduction. Equipment-based businesses ae futhe classified as being automated, monitoed by elatively unskilled opeatos, o opeated by skilled opeatos. The focus of this pape is on people-based sevice. H. Demikan et al. (eds.), Sevice Systems Implementation, Sevice Science: Reseach and Innovations in the Sevice Economy, DOI / _2, Spinge Science+Business Media, LLC

2 10 S. Rai It has been obseved that while the sevice business can lead to gowth in evenue, the goss magins ae lowe than the equipment business. At IBM the sevice business units accounted fo oughly 55% of the total evenues in 2005, while hadwae and softwae poducts accounted fo the est. In compaison, howeve, sevice business units contibuted only about one-thid of the company s total pofit (Wladaswky-Bege 2006). Sevices ae moe labo-intensive, less amenable to economies of scale, exhibit highe quality vaiations and ae geneally less poductive and pofitable compaed to the hadwae/softwae business. Impovement of the poductivity of the sevice business is theefoe a key impeative fo these industies to make the tansition successful. The geneal idea of impoving the poductivity of sevice business is not new. Leffingwell (1917) was one of the ealy eseaches who applied Taylo s Pinciples of Scientific Management (1911) to the activities of sevice industies such as banks, insuance companies, accounting fims and mail-ode fims. The goal of this effot was to set up outines that once leaned and emembeed could goven evey aspect of office life. Healthcae was anothe secto whee ideas of industial engineeing wee applied ealy on. Fo example, Banes Motion and Time Study (1937) descibes Opeating-oom setup showing tables fo instuments and supplies designed to facilitate the wok of the sugeon, his assistant and the nuses. Walt Disney Copoation has utilized industial engineeing techniques and pinciples of sevice opeations at thei theme paks. Chase and Apte (2007) discuss McDonald Copoation as one of the best-known examples whee successful application of scientific management to evey aspect of estauant opeation was the key facto undelying McDonald s success. The main pinciples embodied in McDonald s opeation include: (1) standadizing and educing vaiety of poducts; (2) simplification, standadization and automation of pocesses so that wokes with limited skills and taining can eliably poduce quality poducts and delive high quality sevice offeings; (3) monitoing and contol of pocess pefomance. Levitt (1972, 1976) descibes how companies could apply the poduction-line appoach to sevice business and futhe suggests that companies can substitute technology fo people and seendipity, and apply thee types of technologies had, soft, and hybid to industialize sevice offeings. Most attempts at industializing a sevice on a lage geogaphically distibuted scale emains focused on achieving standadization and developing cookie-cutte appoaches (e.g. McDonald s) o the notion of applying industial engineeing and opeations eseach techniques on a lage industial scale to impove sevice opeations (e.g. Disney). Unlike the McDonald s model, thee ae sevice opeations that ae geogaphically distibuted within an entepise but exhibit significant output vaiety acoss each opeations cente. An example comes fom document outsoucing business. A sevice povide such as Xeox Copoation can manage thousands of pint poduction facilities woldwide on custome pemises whee the output of one pint sevice cente can be significantly diffeent fom anothe. The coesponding sevice pocesses that delive this output ae also diffeent. The standadization that McDonald s has achieved is not possible because evey custome s document poduction needs ae unique and the sevice povide has to offe vaiety in ode to be competitive. At the

3 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 11 same time, the scale of opeations at each sevice cente is not lage enough to justify a business case fo local Disneyfication. The challenge is to develop a methodology that can impove the poductivity and pofitability of distibuted people-based sevice opeations on an ongoing basis while maintaining o impoving the vaiety of the sevice offeing to the custome. The impovement methodology should be sufficiently standadized and suppoted by automated (o semi-automated) softwae tools, platfoms and pocesses so that it can be deployed pofitably acoss a distibuted entepise. The wok should povide insights fo innovations acoss a boade aay of sevice offeings (Jong and Vemeulen 2003) as well as new sevice-oiented technology and management famewoks of the futue (Demikan et al. 2008). 2 Optimizing Sevice Opeations and Deliveing Business Results fo Locally Vaiable Opeations In this pape I descibe a methodology fo optimizing sevice opeations on a lage distibuted scale. By applying this methodology to the pinting industy, I demonstate how high business value can be geneated. The pinting industy eveals that the methodology can addess a high level of the local opeational vaiety (i.e. the optimized solution is tailoed to meet the needs of the specific custome), can be deployed pofitably acoss hundeds o possibly thousands of sevice opeations using a cost-effective and standadized pocess and can be adapted ove time to changing custome equiements. The focus of this impovement methodology is on impoving the actual dynamic actions associated with poviding the sevice offeing i.e. the povisioning of the offeing such that the custome has a bette sevice expeience in tems of faste cycle times, lowe cost and impoved quality. The maketing messages to customes have been einfoced with the impovements esulting fom the application of the methodology. This has esulted in seveal existing sevice contacts getting enewed and new business being secued. It is wothwhile to note that in most cases, the sevice contacts ae enewed o acquied not because new pinting technology (i.e. goods) is intoduced but because the design and execution of the existing sevice opeation is significantly impoved. This also suppots the dominate logic view fo maketing poposed by Vago and Lusch (2004), one in which sevice povision athe than goods is fundamental to economic exchange. This methodology is pesented as a six-step pocess, each step of which is descibed in a section of the pape. Section 3 descibes high-level chaacteistics of a specific sevice domain, the maket size and a categoization of the sevice business. Section 4 motivates the data-diven simulation-based methodology and descibes the key innovations embedded in the sevice optimization solution. Section 5 highlights the key human factos that have to be consideed in ode to ensue that the optimization solutions can be successfully deployed. Section 6 descibes the tools, taining

4 12 S. Rai and suppot infastuctue equied fo a lage scale ollout. In paticula this section will discuss a seamless, integated and automated simulation-based toolkit and a scalable pocess fo deployment of the sevice on a lage distibuted scale. Section 7 discusses the integation of pocess optimization solution within existing copoate pocesses to enable thei institutionalization. Section 8 descibes how business esults have been deliveed on a lage scale. The pape concludes with some emaks on a sevice innovation pocess whee eseaches and customes wok togethe to develop the innovation. 3 Step I: Identify a Sevice Opeations Domain and Scope the Oppotunity Entepises and businesses delive multiple sevice offeings and it is not clea at the outset which sevice opeations business has significant oppotunity. Befoe too much effot is put into developing a solution, it is impotant to develop an undestanding of the wokflows associated with the sevice opeations, scope out the maket size and develop a segmentation of the sevice opeations to undestand the types of solutions that will be equied to addess the entie oppotunity. In the pinting industy example, I led a team to optimize the poductivity of pint shops opeated by Xeox via a fou step pocedue: Fistly we modeled individual pint shops to convince ouselves that estuctuing the wok flow fom the taditional depatmental oganization to cellula configuations offeed the possibility of substantial poductivity impovement. Secondly, woking in patneship with the Xeox sevice delivey oganization, we tested and efined these models in a vaiety of diffeent pint shops to demonstate that the expected impovements wee achievable in pactice. We futhe used this oppotunity to pefect techniques fo maketing these tansfomational engagements to the vaious key audiences equied to implement them. These effots led us to maket segmentation and to poductivity esults that enabled us to establish the business value to Xeox of a copoate-wide oll out of the methodology. Thidly, in patneships with the appopiate Xeox sevice and engineeing oganizations we developed a oll out plan that included the development of the taining, tools, suppot-infastuctue and maketing collateals necessay fo Xeox sevice pesonnel to delive the tansfomational engagements to Xeox customes. Fouthly, we maketed this plan to appopiate management in the involved oganizations in ode to obtain the commitment and funding and authoization to implement it. By conceiving and implementing these fou steps ove a peiod of 3 4 yeas, we identified and scoped a highly pofitable sevice offeing fo Xeox, and secued authoization fo its implementation. Ou point hee is to emphasize that identifying the sevice oppotunity in some detail, pefoming enough exploatoy applications to establish its implementation and pofit paametes, and pepaing an actionable implementation plan fo copoate management ae indispensable initial steps in ceating a new pofitable sevice business based on wok pocess optimization. In the emaining Section 3.1

5 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 13 I descibe the wok flow chaacteization, maket size, and maket segmentation used in the oiginal implementation poposal to management. 3.1 Chaacteization of Wokflow in Pint-Sevice Cente Envionments Pint sevice cente can be classified into thee categoies based on the activity that they pefom tansaction pinting, on-demand publishing, o a combination of both. A tansaction-pinting envionment poduces documents such as checks, invoices, etc. Each document set is diffeent. Mail meteing and delivey ae pat of the wokflow. On-demand publishing envionments focus on poducing seveal copies of identical documents with moe finishing options such as cutting, punching and binding. Examples of such poducts include books, sales bochues and manuals. Othe envionments pefom both types of document poduction simultaneously with vaying emphasis on each one. The document poduction steps associated with pint jobs ae indicated in Fig. 1. Typically pint sevice centes have depatments that suppot individual steps of this wokflow. Each depatment suppots many diffeent types of intenal wokflows esulting fom the use of diffeent types of softwae tools, pinting machines types (e.g. offset, digital) and a vaiety of finishing equipment (such as cutting, binding, laminating, shink-wapping). Each of the six geneic steps in the pint poduction wokflow is associated with a depatment: Custome sevice and poduction planning depatment woks with the pint sevice cente customes to handle incoming equests, negotiate pice and due dates, povide tacking and notification, and wok with poduction depatment to plan and schedule delivey. Gaphics design depatment designs the content of the document. Pe-pess depatment pefoms tasks such as inspection of incoming pint jobs, editing jobs fo colo quality and accuacy, ceating poofs and woking with the custome sevice and pinting depatment to coodinate poduction. Pinting depatment pints the document. Fo offset pinting, these activities include pefoming setups on the offset (lithogaphic) pesses, loading Custome sevice Gaphics design Pe-pess Pinting Finishing Mailing Fig. 1. A pint poduction wokflow showing the vaious poduction opeations

6 14 S. Rai pape and ink, pefoming untime colo coections, offloading pinted mateial and tanspoting it to the finishing depatment. Fo digital pinting, the input to pintes is an electonic pint steam and the output consists of pinted documents. Digital pinting is used fo shot-unlength jobs and when the vaiable content is high. Digital pinting technology is diffeentiated by low setup, simple intefaces and smalle equipment size. The job input is a digital data steam ( digital mastes ) athe than had-copy mastes ( mechanicals ). The geneation of these data steams ceates majo changes in the wok content of the depatments that pecede the pinting step in the oveall wokflow. Finishing depatment takes as input pinted mateial and pefoms a vaiety of finishing opeations such as folding, cutting, saddle-stitching, binding and packaging. Mailing depatment packs and labels the finished goods and ships them to customes. Offset pinting is the dominant pinting technology used today (US Census Bueau 2008). Moe than 98% of pint poduction evenue is associated with offset and offset-like technology. Nevetheless, custome demand fo moe pesonalized documents, quicke tunaound time, lowe ovehead and set-up costs, and geogaphically distibuted pinting has led to the migation of offset wokflows to ondemand digital pinting wokflows fo monochome pinting. As colo digital systems that poduce pint quality equivalent to o bette than offset pint quality at competitive costs ae developed, the same migation is expected to occu fo colo documents. Fo the foeseeable futue both of these wokflows ae expected to coexist within the pinting industy. 3.2 Maket Size The pinting industy is lage and fagmented. The Noth Ameican Industial Classification System (NAICS) code fo pinting and elated suppot activities is 323. In 2005, the total value of pint shipments coesponding to code 323 was $ billion with an annual payoll of $ billion (U.S. Census Bueau 2008). The industy employed 642,300 employees with the payoll pe employee of $38,753. An estimate of $100,000 in annual sales pe employee is emakably accuate in detemining a commecial pinte s annual sales (The Industy Measue 2007). Changes ove time in the numbes of small (1 9 employees), medium (10 49 employees), and lage (50+ employees) establishments povide a measue of industy dynamics. Figue 2 shows the numbe of pint sevice cente gouped by the numbe of employees. The incease in the numbe of lage establishments and decline in the numbe of small and medium sevice cente eveals that business is moving fom small and medium sized sevice cente to lage sevice cente.

7 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 15 Fig. 2. Commecial, quick pintes by employee size, 2002 vs 2004 vs 2006 (The Industy Measue 2007) 3.3 Pint Poduction Sevice Categoization Thee have been vaious taxonomies poposed fo classifying sevice systems. Hayes and Wheelwight (1979) poposed taxonomy based on two dimensions, poduct o maket vaiety (anging fom high to low) and type of poduction system (anging fom job shop though batch poduction, flow line to continuous pocess). Chase (1978) poposed a classification based on the extent of custome contact in sevice ceation. Shostak (1987) poposes a taxonomy that uses two dimensions: degee of complexity of the sevice delivey stuctue and degee of divegence allowed at each pocess step. Wemmelov (1990) poposes a simila taxonomy using two dimensions namely, degee of divegence and degee of custome contact. Schmenne (1986) also poposes a taxonomy using two dimensions: degee of labo intensity and degee of customization o inteaction. Buzacott (2000) developed a categoization of sevice system stuctues based on an analysis of thei elative pefomance and how this pefomance is affected by the natue of the tasks that have to be pefomed. Because of the geat divesities in pint sevice centes, a categoization scheme has been developed that allows the development of optimization tools and techniques fo vaious segments of the pint sevice cente maket. Based on the expeience fom ealy engagements, the pint sevice cente maket segmentation matix

8 16 S. Rai II. FM business, IV. Lage complex commecial opeations Vaiability pintes, CRDs I. Mom and pop shops III. Document factoies o book pintes Utilization Fig. 3. A segmentation of pint sevice centes within the pinting industy (FM designates facilities management. CRD designates copoate epogaphics depatment) shown in Fig. 3 was developed. Pint sevice centes ae chaacteized based on the job vaiability (chaacteized by job mix, job size distibution and job inte-aival time distibution) and esouce (equipment and manpowe) utilization levels. Segment I encompasses sevice centes that opeate individually to poduce a few types of poducts on as-needed basis (e.g., stoe-font pint sevice centes fo convenience document poduction). Segment II encompasses sevice centes that ae modeately sized (e.g., copoate epogaphics depatments), poduce seveal diffeent types of job types (typically less than 40), and ae challenged with deliveing high quality of sevice such as tunaound time and pint quality at competitive costs. Segment III encompasses sevice centes that typically specialize in a few diffeent types of wokflows to ceate poducts that ae manufactued in high-volumes (e.g., lage book manufactues). Typically these sevice centes ae found to opeate at highe levels of esouce utilization than pint sevice centes found in segments I and II. Segment IV contains sevice centes that manufactue a wide aay of documents often within a specific industy segment such as financial o healthcae, ae lage in size (e.g., ove $50 million of annual evenue), and exhibit leveaged economies of scale in poduction pocesses to achieve bette utilization of esouces than the sevice centes in segments I and II. This classification enabled the development of customized solutions that addess pint shops in each of these segments.

9 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 17 4 Step II: Chaacteize, Model and Optimize the Sevice Envionment This section discusses the specific chaacteistics of a sevice envionment that exhibits high vaiety and multiple souces of vaiability and motivates the need fo a simulation-based methodology. It futhe motivates the stuctue of the solution and the opeating policies. 4.1 Chaacteization of the Sevice Envionment Pint sevice centes expeience many souces of vaiability. Segment II and IV especially exhibit high levels of task size and outing complexity that makes them had to optimize (Fig. 3). These sevice centes ae pimaily make-to-ode sevice systems that cate to specific equests of each incoming custome. The incoming sevice equests have andom aival and due-date equiements that vay fom job to job and often exhibits vaiability within the same job-type. The size of the jobs is often chaacteized by highly non-nomal distibutions as shown in Fig. 4 and sometimes fat-tail distibutions (Rai 2008). Fig. 4. A histogam of the job size distibution in pint poduction sevice cente can exhibit highly non-nomal and long-tail chaacteistics

10 18 S. Rai Job vaiety is also high as shown in Fig. 5 and many diffeent poduct types (i.e. multiple outings) may simultaneously be in the sevice cente at any given time. Depending on the day of the week o month of the yea, the demand is diffeent leading to high demand vaiability as shown in Fig. 6. The simultaneous existence of these multiple souces of vaiability makes it difficult to model, pedict and optimize the pefomance and cost stuctue of these Fig. 5. Pint poduction sevice centes can have seveal equests that equie multiple outings fo fulfillment at any given time Fig. 6. Pint sevice centes can exhibit vey high fluctuation in the volume of incoming pint equests

11 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 19 sevice centes. Discete-event simulation models can be constucted but they equie time and expensive esouces and equie data collection and validation. Since the conditions of the sevice cente change with time (non-stationay pocesses), the models need to be updated with time adding additional complexity and cost. Compaed with a standadized McDonald s type model, the modeling complexity can be quite high. An entepise can take the McDonalds appoach and limit the type of document sevice offeings it povides and ceate a cookie-cutte opeation. While this appoach may be applicable to some sevice centes, the document sevice vaiety is detemined by the equiements of the document poduction needs of the entepise which vaies widely acoss industies and even within industies. The capability to effectively offe document poduction sevice offeings equested by the entepise is one of the key equiements fo gaining maket-shae within the outsoucing business. The ability to handle vaiety cost-effectively is a key diffeentiato. Supplies and vendos that ae seen as incapable of offeing this vaiety get excluded fom outsoucing consideation. 4.2 Data-Diven Modeling of Sevice Opeations Thee ae thee appoaches to model and analyze sevice pocesses namely, analytical modeling, diect expeiments and simulations Analytical Models These ae the best class of models if they can be developed to descibe the p ocess at hand. They ae usually computationally fast and give good insight into the pocess. They can also be used to undestand how the model vaiables affect the outcome(s) being studied and pefom fast sensitivity and optimization analyses. Analytical models ae built on abstactions of the eal wold pocess and often make assumptions to get analytical solutions. If the pocess being modeled is sufficiently complex (which often is the case fo eal-wold sevice opeations), the assumptions that ae made to develop the analytical models often make them less useful as pedictos of actual system behavio Diect Expeiments A second appoach is to deploy data collection tools within the sevice pocess and then develop analytical tools to analyze and optimize the pocess. While this is a useful appoach, it is limited by the ability to collect data fom the pocesses in an unobtusive manne without affecting system behavio. Data collection can also be expensive and is often viewed as a non-value added activity unless the data

12 20 S. Rai a nalytics can demonstate additional value. Howeve, if data can be collected inexpensively on an ongoing-basis, this appoach can be effective in assessing and optimizing the pefomance of sevice opeations. The use of this appoach in automated sevice offeings (ones that equie minimal o no people involvement) is well-known. Companies such as Amazon, Google, Yahoo, E-Bay and many othes egulaly collect sevice data and use analytics to impove thei sevice offeings Simulations Simulations ae used when the pocess being modeled is complex and not easily amenable to analytical modeling o diect expeimentation. It enables the study of the inteactions within lage systems to get insights into the citical factos that affect desied pefomance. It can be used to impove and optimize these systems though analysis of numeous what-if scenaios. It can also be used to tain pesonnel without disupting the actual opeations. In many instances simulations ae used to establish the validity of analytical models. Simulations ae also used with diect expeiments and data collection to make them significantly moe poweful and useful (Banks et al. 2004). Advancements in simulation methodologies and incease in available computational powe has inceased its usage in pefoming lage systems and pocess analysis. Even though simulation appeas to be the most geneal methodology to model and optimize complex sevice opeations it has two dawbacks namely: Simulation models can take a long time to build especially as the pocess complexity inceases. The skill level, time and cost equied to build simulation models fo complex and vaying sevice pocesses is sufficiently high to inhibit wide-scale deployments of this methodology. Thee ae many classes of sevice offeings whose oveall opeating famewok can be epesented within a genealized famewok but whose specific instantiations have sufficient vaiability that a single paameteized model is not sufficient to captue the oveall complexity. Fo example, a copoate epogaphics depatment poviding pint poduction sevice can all be descibed at a high level by a geneal wokflow as shown in Fig. 1. Jobs aive at the custome sevice depatment, ae moved to the pint aea, then to the finishing aea and finally to the delivey aea. Howeve, when one looks moe deeply at the specific instances of these pint sevice centes, one can expeience a wide vaiety. Fo example, one sevice cente may accept jobs electonically, anothe may eceived had-copy pape documents; the pinting aea can use digital equipment and/o offset equipment; the finishing aea can have automated and/o manual finishes with diffeing labo equiement. Labo may have diffeent chaacteistics such as those chaacteized by skill vaiations o by thei employment categoy (e.g. pemanent o

13 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 21 t empoay with coesponding wage diffeences). When one looks at the pemutations of these diffeences, many diffeent pocess instantiations ae possible. While each one can be modeled using any one of the discete-event simulation packages available in the maket, the cost and time associated with building these models can be pohibitive to enable thei wide-scale and ongoing use to analyze and optimize these sevice opeations. Many industies delive sevice offeings whee people ae an intinsic pat of the delivey pocess. Well known examples include gocey stoes, depatmental stoes, pint sevice outlets, etail banking outlets, estauants, hospitals, govenment offices, epai shops and many othes. It is not had to convince ouselves that we live in a pimaily sevice economy if we look at the numeous sevice industies that suound us and ae an integal pat of daily life. Ownes of these sevice businesses have long tied to diffeentiate themselves based on content and quality of the output. Howeve, vey few but extemely lage fanchise ownes have applied simulation and modeling tools to optimize the way they delive these sevice offeings. Some lage fanchises (e.g. McDonald s) have tied to achieve pocess efficiencies by standadization of pocesses and ceating an optimized instantiation of these standadized models. But that also limits thei ability to offe wide vaiety to thei customes. On the othe hand, specialized estauants may offe a wide vaiety of options but cannot typically match the pice points of the fanchise ownes. Within the pint poduction sevice space, entepise clients often demand wide vaiety in pint poduction that is typical to the document needs of the entepise. Thus to be a pefeed on-site sevice povide, the pint sevice cente has to delive the equied document vaiety but has to do so at a cost that makes it pofitable fo them as well. The tade-off between output vaiety and pofitability is a difficult one to manage and if not done effectively can lead to unhappy customes o unpofitable opeations. Since a simulation model is built using abstactions of eal-wold pocesses, a key issue to esolve is to detemine how much effot is spent in captuing the pocess details within the model and the accuacy of the pediction that is equied. Focusing too much on non-essential details can add unnecessay complexity to the simulation without impoving the value it povides while ignoing citical elements can advesely affect its utility and insights it gives. The pupose of the simulation models discussed in this pape is twofold; the fist is to povide guidance to make changes to the stuctue of cuent state of sevice opeations to impove seveal poductivity metics and second is to validate that the model-based pedictions of the impovements ae obseved in the e-stuctued sevice opeations. The emphasis is on captuing the essential elements of the sevice pocesses (elated to both stuctual and contol aspects) that povide good diectional and quantitative guidance on what needs to be changed and how. The simulations models discussed in this pape ae diectly diven by data collected fom the sevice opeations. The cuent state metics ae assessed and then changes ae made to the stuctue and contol policies within the simulation model to evaluate how poductivity metics can be impoved. The impovements ae futhe validated

14 22 S. Rai though data collected fom the actual e-stuctued sevice opeations and compaed to oiginal state metics to validate that the model-based changes actually deliveed eal-life impovements. The combination of data-diven simulation-based modeling and empiical validation of tangible poductivity impovements though eal-life data collection einfoces the powe of this methodology fo optimizing sevice opeations. The appoach pesented in this pape consists of two distinct components. The fist is the development of an optimization toolkit based on the apid modeling and simulation of a sevice opeations cente. These models can then be utilized though a seies of what-if analysis scenaios (using automated and semi-automated appoaches) by elatively low-skilled pesonnel to optimize the specific opeation. The second is the development of a pocess to deploy this solution cost-effectively on a lage distibuted scale to multiple sevice centes within the industy. This pape pesents the solution within the context of the pint poduction sevice domain. While the domain of application is specific to the pinting industy, the solution poposed and leaning fom this endeavo can be genealized to a wide ange of people-based sevice offeings. 4.3 Sevice Stuctue and Pocess Optimization The cost and pefomance of pint poduction sevice is a function of both the p ocess stuctue (labo, equipment, facilities layout) and opeating policies. To optimize the poductivity of the systems it is always necessay to make tadeoffs in system design between the effectiveness in coping with intenal and extenal vaiability and the efficiency, speed and cost of poviding the sevice. Most taditional pint sevice centes ae functionally oganized. All equipment that pefoms one type of function is located in one aea. Fo example, all pintes ae located in a pint oom. Finishing devices such as insetes ae located in a sepaate oom. These centes ae moe akin to job shops that have high levels of flexibility in tems of using equipment fo diffeent jobs. Incoming wok flows fom one depatment to anothe until the sevice equest if fulfilled in its entiety. In the next section, a stuctue fo edesigning the taditional sevice centes into moe efficient and cost-effective opeations is poposed. 4.4 Stuctue Design of Pint Sevice Centes Using Autonomous Cells and Hieachical Scheduling Business pocess innovation o eengineeing eceived much attention in ealy 1990s. Two books witten fo business audience (Hamme and Champy 1993; Davenpot 1993) attacted wide inteest. Hamme and Champy poposed a set of Commonalities in Reengineeed Business Pocesses shown in Table 1.

15 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 23 Table 1. Commonalities in eengineeed business pocesses (Based on Hamme and Champy 1993, Chapte 3) 1. Seveal tasks ae combined into one 2. Wokes make decisions 3. The steps in the pocess ae pefomed in a natual ode 4. Pocesses have multiple vesions 5. Wok is pefomed whee it makes the most sense 6. Checks and contols ae educed 7. Reconciliation is minimized 8. A case manage povides a single point of contact 9. Hybid centalized/decentalized opeations ae pesent Buzacott (1996) has evaluated the stuctue of eengineeed (and pimaily tansaction-pocessing) systems using fomal system models fom queuing theoy to develop insights about the conditions unde which such adical changes of system stuctue ae likely to be appopiate. The basis of compaison ae pefomance measues that can only be evaluated using stochastic models, such as the level of wok-in-pocess o the time a tansaction spends in the system (whee Little s Law L = lw means that it is only necessay to explicitly conside the level of wok-in-pocess). Two systems ae compaed namely, Seies systems: Hee the total wok content is subdivided among m facilities aanged in seies with each facility dedicated to a single task. Paallel systems: The paallel system has n identical facilities at any of which all equied tasks can be pefomed on a job one afte the othe without inteuption and with little changeove o setups between task. Using queuing models developed by Haison and Nguyen (1990) and Buzacott and Shantikuma (1993), he concludes that moving fom a seies (flow-line) type stuctue with division of labo to paallel systems, depends citically on the pocessing time vaiability. If the tasks ae such that they ae the same fo all customes and have elatively low complexity, the seies sevice stuctue can be quite effective. Howeve, high task pocessing time vaiability (esulting fom task size o task complexity vaiation) makes it attactive to move to paallel systems. In addition, diffeent types of allocation stategies such as andom allocation, cyclic allocation and single queue ae exploed. To addess the complexity of opeations associated with the pint poduction pocesses, the sevice cente esouces ae oganized in autonomous cells (Rai et al. 2000). As a esult, the most common jobs can be finished autonomously inside (at least) one of these cells. Figue 7 shows how taditional pint sevice centes ae oganized based on a depatmental stuctue opeated by specialized wokes and

16 S. Rai Seve Room Mailing Aea PB 8 Seies P i n t e 4 Cell 3 Seale Mooe Pinte Roll System Cell 4 P i n t e 3 Pilla Cell 2 Insete SQA DESK PB 8 Seies PB 8 Seies Insete H I L I T E P R I T E R PB 8 Seies Input P i n t e 3 Shink Wappe P i n t e 2 Insete Pilla Bell and Howell P i n t e 4 Pinte Insete Insete Roll System P i n t e 3 P i n t e 1 PB 8 Seies Shink Wappe Shink Wappe Pinte Roll System Cage PB 8 Seies Cutte Mooe SQA DESK Seale P i n t e 1 LOADING Cutte P i n t e 2 Mailing Aea Cell 1 LOADING Insete Seve Room Insete 24 Input H I L I T E P R I T E R Insete Room Fig. 7. Figue showing how a depatmental configuation of a pint sevice cente is tansfomed into a stuctue utilizing autonomous cells compaes it to the edesigned opeational famewok based on autonomous cells whee divese pieces of equipment ae collocated and opeated by coss-tained wokes. This oganization into autonomous cells is a key concept of the poposed solution to optimize these pint centes, offeing the advantages of loweing poduct tanspotation times, educing complexity associated with inteaction of lage numbe of job types, easing quality and poduction contol while managing esouces moe effectively and avoiding congestion. Inspied by the goal of lean poduction (Womack and Jones 2003) to achieve a waste-fee highly efficient document poduction sevice cente, Xeox coined and tademaked the solution as LDP Lean Document Poduction. To ochestate the flow and contol of jobs though the paallel hieachical cell stuctue, the Lean Document Poduction Contolle (LDPC) uses a 2-level achitectue (Rai and Viassolo 2001) shown in Fig. 8 fo poduction management. The LDPC has: A sevice cente contolle module (Sevice centecm) high-level contolle, in chage of global sevice cente management Seveal cell contolle modules (CellCMs) low-level contolles, in chage of local management inside cells Detailed desciptions of these contolles and the algoithms can be found in the Rai et al. (2009). The solution equies stuctual design of efficient autonomous cells (ight type, numbe and configuation of equipment, people and layout) as well as detemination of efficient outing and scheduling policies that achieve significantly impoved pefomance ove the cuent state. This is accomplished via the use of disceteevent simulation methodology fo evaluating the design and opeational efficiency of the estuctued pocess.

17 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 25 Fig. 8. Two-level achitectue fo the Lean Document Poduction Contolle 5 Step III: Develop Effective Human Factos Pactices One of the most impotant and often-ignoed aspects of people-based sevice opeations ae people who opeate and manage them. While data analysis and simulations can povide useful insights into edesigning efficient sevice opeations, ignoing the human factos can pove costly and in some instances nullify the effect of the optimization effot. Fom this standpoint, it is necessay to conside both management whose pimay focus is to make sue the financial viability and pofitability of the opeations and the opeational staff who ae involved in executing and deliveing the sevice on a day-to-day basis. Management needs to be convinced that the simulationbased estuctuing will impove pofitability and gow evenue and the opeational staff needs to believe that this effot will help them delive bette sevice and impove thei futue pofessional pospects. Conducting expeiments on the sevice opeations by estuctuing sample jobs based on simulation models and showing befoe-and-afte metics can be utilized to instill confidence that the simulation pedictions can dive tangible benefits to the opeations. This can be done pio to pefoming full-scale simulation and optimization of the entie opeations. Testimonials should be gatheed fom successful engagements and then utilized to build confidence duing the couse of futue engagements. Since the e-stuctuing of the opeations often equies changes to the facility (such as layout and electical) that implies cost, management has to be convinced that it is wothwhile to make these investments. A etun-on-investment analysis associated with the cost of pefoming these e-design and the benefits achieved is

18 26 S. Rai also citical to getting buy-in. Instituting ewad policies that ecognizes impovements in opeato poductivity as a esult of e-stuctuing is also impotant fo the success of the oveall optimization effot. It also needs to be emphasized that the e-design effot is not teated as a one-time effot but that the opeations needs to continually (o the vey least, peiodically) assess and impove the stuctue and contol pocesses. 6 Step IV: Develop Tools, Taining and Suppot Infastuctue fo a Lage Scale Rollout This section discusses how the simulation tools wee automated and a pocess developed fo lage scale ollout of the optimization sevice. 6.1 Automation of the Simulation Softwae Toolkit The benefits of simulation automation ae twofold both technological and e conomical. Automation enables faste, less eo-pone simulations and exploation of a lage design space leading to bette solutions. It also allows deployment of this sevice by less skilled and less expensive pesonnel impoving the economics of the deployment pocess. Discete-event simulation is an established methodology fo studying the behavio of a system as it evolves ove time. It is fequently used fo pocess modeling in a wide vaiety of sevice offeings and industies when diect expeimentation o analytical modeling is impactical. Many discete event simulation softwae tools (Aena Simulation Softwae 2010; PoModel 2010) ae available in the maket. These tools povide a (gaphical) pogamming inteface and basic pimitive constucts fo model building. The taditional appoach to constuct a discete event simulation model is to employ a highly skilled modele who would typically take significant amount of time (days, weeks o months depending on the size and complexity of the opeations) to gathe pocess infomation data, constuct the model and iteate though the design. Howeve, scaling this methodology to a lage scale using available discete event simulation tools equies majo cost investment, infastuctue development, taining and peiodic efesh to account fo changing conditions. Thee is often sufficient vaiability in the models that depends on the modele theeby ceating challenges in standadization. This issue can be addessed if a solution can be developed that has a genealized modeling famewok that captues essential chaacteistics of the class of sevice opeations being modeled but has enough flexibility to captue the vaiation inheent in the specific instantiations within the paticula industy. If this poblem can be solved, then a modeling solution can be

19 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 27 developed that can be scaled and eplicated at multiple sevice cente opeations. Futhe, if the pocess of model building can be automated, it can educe the time equied to build the model and also educe the eos and associated vaiations inheent in constucting these models. To automate a pocess, it is necessay to undestand it in sufficient detail and develop abstactions which ae complete enough so that specific instances of the pint sevice opeations can be modeled using it. The automated modeling solution should also integate vaious aspects of the model building pocess (e.g. data collection, definition of vaious elements, pocess design, planning, scheduling and simulation) to poduce a seamless expeience fo the use. Futhe, the softwae toolkit should be embedded in a pocess impovement famewok so that the use can systematically execute the tool effectively and in a eplicable manne fo optimizing the sevice centes. A document poduction sevice entepise may consist of thousands of small onsite opeations. Each opeation may have few pieces of equipment (e.g. less than 30 machines including computes and pinting and finishing equipment) opeated by less than 10 sevice cente pesonnel. The annual evenue being geneated fom one of these sites may ange fom $250,000/yea to $5 million/yea. A sevice that assesses and optimizes these opeations has to be cost-effective to be widely deployable so that it does not negatively impact the pofit magins of the opeations. Yet the optimization sevice should be capable of modeling vaiety and vaiability discussed ealie. The appoach poposed in this pape to make the simulation capability easie to use is to develop a stuctue fo building the simulation model and automate the time-consuming steps. Instead of woking with geneal pupose pimitives available in the simulation model, the sevice envionment is abstacted. Objects and pocesses that wee unique to the document poduction sevice industy ae modeled as constucts that ae ecognizable by the industy pesonnel. An easy-to-use softwae inteface is ceated to allow the popeties and capabilities of the sevice cente to be defined within the simulation tool using these high-level constucts. A ange of opeating policies, patented pocess algoithms (e.g. split lage jobs into small optimized batches) can be selected and applied to the model. Once the model is defined using a declaative inteface using high-level constucts, it is checked fo accuacy. Subsequent to that a simulation model is constucted automatically which is then used to optimize the cente. The high-level constucts petinent to a document poduction sevice cente consists of equipment, opeatos, shop schedule, autonomous cell, opeating and sequencing policies and sevice equests. These objects have to be paameteized in a sufficiently geneic manne so that specific unique instances can be ealized. Fo example equipment can be descibed though the specification of its capabilities, setup chaacteistics, speed, failue and epai time distibutions and opeato equiements to opeate it. While this is a geneal specification of pinting equipment, diffeent values and data sets associated with the individual paametes can eadily captue the specific equipments in the shop. Table 2 shows the vaious objects that ae used to chaacteize the pint poduction sevice cente.

20 28 S. Rai Table 2. A list of objects and thei attibutes that ae equied to define a pint poduction sevice cente opeation Objects Sevice cente Components and attibutes Autonomous cells, equipment, opeatos, schedule, opeating policies Autonomous cell Opeatos, equipment, schedules, cell opeating policies Opeato Skills, schedule Equipment Function capabilities, schedule Function capability Speed, setup equiements, vaiability, opeato equiements, status (up o down) Job Job stuctue, quantities at each node, aival, due, completion and intemediate events (e.g. stat, stop at individual steps) It is useful to emak hee that the development of a complete set of these abstactions is a pe-cuso to automation of the modeling pocess. The specific objects descibed in Table 2 ae specific to the pint poduction sevice business and will equie adaptation fo extensions to othe industies. Once a complete set of abstactions to model the sevice opeations has been developed, the next step is to develop the algoithms that will be used to optimize the cente. Depending on the appoach, these algoithms could be focused on optimizing the stuctue of the opeation and/o the opeational contols used to impove the efficiency. Fo optimizing the pint poduction sevice centes, thee classes of algoithms wee developed, namely methods to design autonomous cells, scheduling policies fo outing incoming equests to the cells and opeating policies fo splitting lage jobs and pocessing them within the cells. Thee may be many algoithms that can be effective fo each of these classes. The automation goal is to allow the use to select them and automatically build simulation models with those algoithms imposed on the models (Jackson and Rai 2000). This automation vastly impoves the speed of modeling and educes the numbe of eos in the model. Othe aspects of simulation automation equie steamlining the vaious steps of the optimizing pocess such as data collection, data analysis and epoting, simulation and modeling, scheduling and monitoing so that the use can easily exchange data fom one phase of the analysis to anothe. Analysis and optimization of such opeations involves a lage numbe of choices that manifest themselves as discete categoical, continuous vaiables and constaints that ae quite complex and equies many tade-offs to be made. The automation of the simulation pocess of modeling the autonomous cell achitectue with hieachical scheduling enables an exhaustive seach of the design space and allows the use to iteate though multiple scenaios. The genealized famewok also allows the tool to captue the vaiety and vaiability inheent in these sevice

21 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 29 p ocesses and suggest ways to optimize the opeations without necessaily educing the sevice capability. Fo moe details on the algoithms and modules of the softwae tool, the eade is efeed to Rai et al. (2009). 6.2 Pocess fo Solution Deployment The pocess of optimizing the pint sevice cente using the solution and tools developed above is boken down into multiple steps as shown in Fig. 9. These ae peengagement pocess, assessment, ecommendations, implementation and monitoing. Pe-Engagement: The sevice cente management team is engaged and though the use of demonstations and pesentations, the solution methodology is explained to the sevice cente pesonnel. The goal of this step is get concuence fom management to initiate the optimization engagement. Assessment: The assessment phase begins with a suvey wheeby infomation elating to opeational issues is collected using standadized templates. An appoach that has poven useful is to demonstate some of the key aspects of the optimized solution (such as small batch cellula pocessing and scheduling) on some sample sevice equests and measue Lean Document Poduction Pocess Pe - engagement Pocess Recommendations Assessment Pesent to Site Management & Shop Floo Team Conduct Site Inteviews & walkthoughs Install Data Collection System Obtain Senio Management appoval (go-ahead) Validate the LDP Site Suvey Tain the Opeato in the Data Collection Pocess Demo LDP Wokflow Collect the Data Sanitize the Data Collected Note: The LDP pocess begins afte the LDP site suvey has been completed, and a Xeox team membe has deemed the site as a valid LDP oppotunity Define Cuent State Metics Shae infomation with site management Analyze The Data Cellulaize Run Modeling Simulations Identify Aeas of Pefomance Impovement Pepae Recommendations & Multiple Impovement Scenaios Implementation Shop Layout Change? No Pesent Recommendations Yes Obtain time line fo layout changes Monito Veify Layout Change Continue to Monito Shop Pefomance Tain Opeatos on new wokflow Implement Scheduling Policies Obtain Concuence on the selected implementation scenaio Collect New Shop Metics Demonstate Achieved Pefomance Results Fig. 9. A multi-step view of how the simulation-based optimization solution is deployed in pint sevice centes

22 30 S. Rai impovements. It is impotant to addess the skepticism of employees at this stage of the engagement and the demonstation execise is intended to dispel these concens and pepae them fo the step of data collection. Special pupose data collection systems (as discussed in Rai et al. 2009) ae installed to collect infomation. The sevice cente pesonnel ae tained in the use of the data collection tools. The data that is acquied in this phase is peiodically checked fo quality and consistency and appopiate feedback is povided to the pesonnel as needed. The data is analyzed to geneate cuent state metics of the opeations and establish a baseline. These include sevice equest lateness, opeato and equipment utilization, types of sevice equests, wok-in-pocess, demand ate pofiles and financial metics (e.g. cost and magins). Subsequent to developing an undestanding of the cuent baseline metics, the sevice cente is edesigned into autonomous cells. The softwae toolkit is used to develop the stuctue of the cell (equipment, people and thei espective skills, layout) by iteating though multiple configuations and scheduling policies. The automation embedded in the modeling and simulation toolkit enables a quick seach and iteation ove a lage numbe of scenaios as shown in Fig. 10 to develop solutions that demonstate significant quantitative poductivity impovements ove the cuent state. The iteations ae pefomed ove both discete categoical as well as continuous Fig. 10. The figue shows the thee steps of optimizing the sevice cente by iteating though defining the sevice cente, automated pocess modeling and simulations and output analysis

23 Data-Diven Simulation-Enhanced Optimization of People-Based Pint Poduction Sevice 31 vaiables such as multiple cell configuations, machine speeds, scheduling policies, skill mix and opeations schedule (e.g. one-shift o two-shift opeations), vaiations in equest mix and quantity and the like. The innovation and automation embedded in the softwae enables a much iche and data-diven quantitative analysis and optimization of the pocess than moe qualitative o focused pocess impovement appoaches that ae successful in opeations with lowe vaiety, vaiability and uncetainty. Pesentation of Recommendations: The esults of the simulation and optimization studies ae pesented to sevice cente management. A key element is a etun-on-investment (ROI) analysis to insue that the appoach has business justification. If management accepts the solution, a tansition plan fo implementing the solution to the new cellula configuation is developed. Implementation: In this phase, the sevice cente migates to the new cellula layout. The opeatos ae tained in the new wokflows including new scheduling policies. New opeational data is collected to fine-tune the opeations and demonstate impovement with espect to the baseline state. Monitoing: The sevice cente poductivity metics ae tacked on an ongoing basis to ensue that the gains continue to be ealized fom the eengineeed pocess. 7 Step V: Integate with Related Copoate Pocesses 7.1 DMAIC and Simulation-Based Optimization Seveal companies ae utilizing the DMAIC famewok (Geoge 2002) fo s evice pocess impovement. While the DMAIC pocess is a poweful famewok fo pocess impovement, it lacks the use of igoous discete-event simulation and optimization tools. Thus if the sevice opeations have significant vaiety and complexity associated with job mix, non-nomal distibutions, andom failues and epais, the cuent set of Lean Six Sigma tools cannot adequately model and analyze the consequences of these inteactions. Howeve, institutionalization of the DMAIC pocess within an oganization leads to a lage numbe of geen belt and black belt pesonnel focused on pocess impovement initiatives. By taining these pesonnel in the use of the modeling and simulation toolkit and pocess descibed ealie, thei capabilities ae significantly enhanced. These black belts can utilize these tools to optimize the sevice centes. Within Xeox Copoation, a significant numbe of black belt pesonnel wee tained in this methodology. This led to a wide-scale deployment of the pocess optimization methodology (Fig. 11) and toolkit within the pinting business.

24 32 S. Rai Fig. 11. A mapping of the vaious tools and analysis steps of the LDP Lean Document Poduction solution to the DMAIC pocess 8 Step VI: Delive Business Results on a Lage Scale Quantitative measues of impovements esulting fom the utilization of the poductivity optimization solution aveaged ove 17 sampled sevice centes, include labo savings of 20%, poductivity (measued as evenue/cost) impovement by 40%, cycle time eduction of 80%, evenue incease of 17%, and annual pofit incease of 20% of the evenue of the shop pio to the LDP implementation. Since 2003 it has been widely implemented in the field via the Xeox Copoate Lean Six Sigma (LSS) Black Belt pogam utilizing the DMAIC pocess outlined above. A sample of ove 80 sevice centes yields aveage cycle time impovement of 50% and on-time-pefomance impovement of 11%. Ove 80 consultants have undegone taining in the use of these tools and ove 100 pint poduction sevice centes have been optimized using this methodology. The cumulative value deliveed by these engagements is ove $250 million acoss the Xeox custome value chain. In addition, the highe quality and faste delivey enabled by the LDP solution povide stategic competitive advantages to pint sevice centes. Xeox also has filed 64 patents applications elated to this solution and 15 have been ganted so fa. The LDP innovation was a unne-up at the 2008 Fanz Edelman competition (Edelman 2008) sponsoed by INFORMS.