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Smart Business Process Framework
- 1. Smart Business Process - Driving breakthrough productivity improvements in service operations Sudhendu Rai
- 2. • Introduction – Xerox Services Capabilities Overview – Introducing the notion of Smart Business Process – The need for Smart Business Process • Vision – Strategic vision – Examples of science of process optimization • LDP Lean Document Production- An instantiation of SBP within the print production services domain – Overview of print production process – LDP Solution – LDP Case Studies & Business Impact • Current focus – Expanding SBP to transaction processing services • Concluding Remarks PARC | 2 Agenda
- 3. PARC | 3 Xerox Services Capabilities Overview
- 4. PARC | 4 What is Smart Business Process? A business process that is continually optimized and operated using data-driven simulation optimization and real-time productivity analytics encapsulated in standalone and web- based software solutions Data collection using IT systems & customized technologies (RFID, handheld devices) Data analysis & simulation optimization framework Workflow models using simulations Real-time operational techniques)],([)( ),(min xYExgwhere xxg = Smart Business Process
- 5. PARC | 5 The need for Smart Business Process Service operations are constantly challenged with designing efficient processes and to operate them profitably Transaction print and mail operations was paying $3M in financial penalties due to changes in regulatory compliance laws Margin improvement Margin and client sat improvement TP capability standardization, margin improvement, service integration Service processes are getting data-rich through evolution of underlying process management platforms IBM Filenet, SPC controller Regulatory compliance requires fine- grained data collection OOM Prisma MCP2 MCP3 Cheap data collection sensing technology offers opportunity for process data collection on multiple dimensions Wireless scanner for manual process event data logging RFID technology Video cameras Mobile phone apps Process modeling & optimization can enable breakthrough process productivity gains Theoretical models using simplified abstractions useful to gain insights but do not scale to real world problems Data driven simulation modeling and optimization of processes can be the answer. Additional innovation needed • Process characterization and abstraction • Data collection, cleansing and fusing • Service structure optimization • Real-time management techniques Global Bank Global Bank Service Bureau Service Bureau
- 6. Advance the science of data-driven domain specific service process simulation optimization, instantiate the solutions through software tools and platforms and demonstrate significant business results through their deployment PARC | 6 Strategic Vision Handheld(s) DCClient Scan Barcodes AccessPoint Data Collection PC DCServer Job Tracking DB Customer Data Job Ticket Operator Ticket Machine Ticket Task ID Job TicketJob Ticket Shop Definition Job Ticket Printing 3'-0" 6'-0" Desk Customer Counter DocuTech 6135 Specialty-paper Shelving F r o n t StateofUtahCopyCenter1foot= 10feet= F r o n t Closet Filing Cabinet Job File Trays Supplies Booklet Maker F r o n t F r o n t Boxing & Finishing Table DocuColor 6060 DocuColor 6060 F r o n t Desk Fridge & Microwave Desk DC470 Table Table Supplies Supplies Supplies Filing Cabinet Work Station Scan Station Work Station Work Station Scan Station M20i M20i Trac Super- Sealer The Educa tor Lamin- ator Tabl etop Cutte r Sic kin ger CL 12 Coil Roller Power Pole Power Poles Paper Pallet DocuColor 5252 Supplies Paper Pallet Paper Pallet Paper Pallet Mobile Finishing Table Mobile Finishing Table Mobile Finishing Table VeloBind 323 Ibico EP-28 Power Pole DocuTech 6135 DocuTech 6135 DocuTech 6135 Power Pole Power Pole GBC 16DB2 & 111PM-3 GBC Magna- punch FF1 DigiB Mail Merge FF? Digi? FF? Digi? Power Pole Shelves Mail Horizon PF-P330 Folder Baumfolder 714 PDI HD4170 Triumph 4850 A Cutter Ibico HB-24 Coil Roller Comb Binder Comb Binder & Punch Coil Punch Comb Punch GBC USP13 Profold Elite Folder Interlake S3A Stitcher F r o n t Paddy Wagon Drill Inputs Red: fax Orange: customer walk-up Yellow: email Pink: Links Green: courier Print Blue: B&W Purple: color Finishing Teal: stitching Dark red: cutting Dark orange: folding Dark green: comb & coil Dark blue: drill Desk 3'-0" 6'-0" Specialty-paper Shelving StateofUtahCopyCenter1foot= 10feet= Closet Boxing & Finishing Table Work Table Table Table Work Station Scan Station M20i Horizon PF-P330 Folder GBC USP13 Paddy Wagon Challenge EH3A Drill DC470 Mobile Finishing Table Chicago Screws Supplies Supplies Supplies Supplies Paper PalletPaper Pallet Paper Pallet Paper Pallet Work Station Scan Station CustomerCounter Desk Filing Cabinet Filing Cabinet M20i Profold Elite Folder Triumph4850A Cutter Clamco Shrinkwrapper GBC Magna- punch Sickinger CL12 PDI HD4170 Ibico EP-28 Ibico HB- 24 GBC 16DB2 & 111PM-3Trac Super- Sealer VeloBind 323 Fridge & Microwave The Educa tor Lamin- ator MobileFinishingTable Mobile Finishing Table MobileFinishingTable Job File Trays Inputs Red: fax Orange: customer walk-up Yellow: email Pink: Links Green: courier Print Blue: B&W Purple: color Finishing Teal: stitching Dark red: cutting Dark orange: folding Dark green: comb & coil Dark blue: drill Interlake S3A Stitcher Baumfolder 714 DocuColor 5252 DocuTech 6135B DocuTech 6135C DocuTech 6135D Booklet Maker DocuColor 6060B Table- top Cutter $$$ Cell Routing Algorithm xij: Portion of job Ji to be manufactured by cell Cj. tij: Estimated time for cell Cj to finish 100% of job Ji. (tij=0 if Ji cannot be finished in Cj) minimize F(x11,x12,…, xnm) subject to xij >=0, for all i,j x11+x12+…+x1m=1, …,xn1+xn2+…+xnm=1 e.g.F=Gj(x11, x12,…, xnm) =x1j t1j+x2j t2j+…+xnj tnj. (F= Time that a given cell j isbusy) minimize max {L1G1(x11, …, xnm),…, LmGm(x11, …, xnm)} subject to xij >=0, for all i,j x11+x12+…+x1m=1, …, xn1+xn2+…+xnm=1 Ljsare nonnegative constantsselected to expressour preferences among the costs e.g. Take L1 >>L2,…, L1 >>Lm, to emphasize the busy time of the first cell over the others. Take L1 =L2 = … =Lm, to minimize the time to finish all jobs. Optimized Process Continuous Improvement
- 7. Recognizing the new trend in Operations Research: From Problem-driven OR to Data-Driven OR Problem driven OR – The starting point is a problem identified by an academic or an industry professional, and the challenge is to find answers by developing new theory or new insights about the problem at hand Data driven OR – The starting point is not a specific problem, but rather a large data set that allowed us to identify new opportunities References Simchi-Levi, D. “OM Research: From Problem-Driven to Data-Driven Research”, Manufacturing & Service Operations Management Vol. 16, No. 1, Winter 2014, pp 2-10 PARC | 7
- 8. Examples of Science of Process Optimization • Data-driven simulation optimization – Optimization of non-linear stochastic processes where the objective function is evaluated using simulation models driven by actual process data – A very rich area of research with a regular full-track at WSC – a premier conference in the field of stochastic discrete-event simulation • Process flexibility – Cellular vs departmental workflows – Flexibility allocation in a service enterprise • Inter-process buffer optimization • Workflow design and scheduling in the presence of heavy-tail job size distribution PARC | 8
- 9. Simulation Optimization – Discrete/Continuous Optimization via simulation where g(x) is the single objective represented as the expected value of a random variable where represents the randomness. denotes the d-dimensional vectors with integer components is the continuous parameter space The distribution of is unknown function of the decision variable x but can be determined via simulation models Why is this hard? • Simulation is computationally demanding • The randomness makes it hard to compare and evaluate outcomes during the optimization iterations and provide guarantees on optimality. )],([)( ),(min xYExgwhere xxg = ),( xY ),( xY d Z d R PARC | 9
- 10. Areas of simulation optimization research • Discrete optimization via simulation • Ranking and selection • Efficient simulation budget allocation • Continuous optimization via simulation • Random search methods • Response surface methodology • Stochastic gradient estimation • Stochastic approximation • Sample average approximation • Stochastic constraints • Variance reduction techniques • Model-based stochastic search methods • Markov decision processes US Government funding agencies • National Science Foundation • Air Force Office of Scientific Research • Department of Energy • NIH • Office of Naval Research Many of these have been awarded within the last 5 years PARC | 10
- 11. Exploring Process Flexibility for Service Process Design • Flexibility is expensive • How much flexibility is enough and how to allocate it optimally is still an active area of research Seminal work on process flexibility Jordan, W.C, Graves, S.C. “Principles on the Benefits of Manufacturing Process Flexibility”. Management Science Vol. 41, No. 4, April 1995. Multi-stage supply chain Graves SC, Tomlin BT (2003) Process flexibility in supply chains. Management Sci. 49(7):907–919. Queuing networks Iravani SM, Van Oyen MP, Sims KT (2005) Structural flexibility: A new perspective on the design of manufacturing and service operations. Management Sci. 51(2):151–166. Call centers Wallace RB, Whitt W (2005) A staffing algorithm for call centers with skill- based routing. Manufacturing Service Oper. Management 7(4):276–294. Proof of optimality of closed chains Chou MC, Chua GA, Teo C-P, Zheng H (2010b) Design for process flexibility: Efficiency of the long chain and sparse structure. Oper. Res. 58(1):43–58. Simchi-Levi D., Wei, Y. “Understanding the performance of the long and sparse designs in process flexibility”. Operations research. Vol. 60 No. 5, Sep-Oct 2012 pp 1125-1141 Open Issues In Flexibility Research •For example: With asymmetric demand, closed chain may not be optimal •Legros B., Jouini O., Dallery Y., “A flexible architecture for call centers with skill-based routing” International Journal of Production Economics 159 (2015), 192-207 • “The most well-known architectures with limited flexibility such as chaining fail against such symmetry. We propose a new architecture referred to as single pooling with only two skills per agent and demonstrate its efficiency” AAdam BBob CCarol DDianne EEdith FFred AAdam BBob CCarol DDianne EEdith FFred Limited Flexibility Closed Chain Network Inflexible network PARC | 11
- 12. Buffer Optimization: Inter-machine buffers and production uncertainty System Efficiency Buffer Size B Gershwin, S.B., “Manufacturing Systems Engineering”. Prentice Hall. PARC | 12
- 13. Developing solutions to deal with high levels of task size variability in service processes • Let X be a random variable with cdf F(x) = P[X≤ x] and complementary cdf (ccdf) Fc(x) = P[X>x]. We say here that a distribution F(x) is fat-tailed if Fc (x) ~ cx-a 0<a<2 In the limit of x->∞ α dlogx (x)dLogF lim x c -= → • Size-based binning policies are more effective when task size distribution is heavy-tailed in distributed server processing • Methods have to be adapted to take into account production characteristics such as setups, job arrival patterns, multiple job types etc. h xxdF h M xxdFxxdFxxdF p k px x x x x kx h h ===== = = - )( )(...)()( 1 2 1 1 0 x0=k xh=px1 x2 xi 1.0 F(x) = Pr{ X ≤ x } 1 1 )( 1 1 11 = + - = - -- a a a aa if if k p k p h i k h ih x h ii k: smallest job size p: largets job size a: Exponent in the Bounded Pareto distribution M: Mean h: Number of cutoff points Bounded Pareto Distribution f(x) = (akax-a-1)/(1-(k/p)a) pxk pdf job size • On Choosing a Task Assignment policy for a distributed server system: M. Harchol-Balter, M.E. Crovella, C.D Murta, Lecture Notes in Computer Science, Springer Berlin/Heidelberg. • Size-independent vs. size-dependent policies in scheduling heavy-tailed distributions. Nham, J. (MS Thesis, MIT) PARC | 13
- 14. • Simulation optimization enables us to optimize complex service processes in the presence of variability and uncertainty • Optimal allocation of flexibility in service operations can deliver almost the same benefit as full flexibility • Buffer optimization can enable systems to delivery high throughput in the presence of failures and downtime • Novel scheduling strategies are utilized for dealing with extremely high levels of variability in task size distributions PARC | 14 Benefits of a scientific approach to business process optimization )],([)( ),(min xYExgwhere xxg = System Efficiency Buffer Size x0=k xh=px1 x2 xi pdf job size AAdam BBob CCarol DDianne EEdith FFred AAdam BBob CCarol DDianne EEdith FFred
- 15. PARC | 15 Lean Document Production: An instance of SBP for Print Production Services Domain Data collection using IT systems & customized technologies (RFID, handheld devices) Data analysis & simulation optimization framework Workflow models using simulations Real-time operational techniques Smart Business Process SO techniques • Ranking & selection • Greedy algorithms • Simulated annealing Data collection tools • Wireless handheld event data collection tool • Job/shop templates • Standardized questionnaires
- 16. Diverse Types of Print Shops BellandHowell Inserter Inserter Inserter PB 8 Series PB 8 Series PB 8 Series Inserter Cage Inserter Room Desk Desk Desk Desk Desk Desk LOADING Server Room Mailing Area Input Desk P r i n t e r 1 Cutter P r i n t e r 2 P r i n t e r 4 P r i n t e r 3 Desk SQA DESK Moore Sealer Desk Roll System Printer Desk H I L I T E P R I T E R Desk ShrinkWrapper Pillar DeskDesk Desk Desk P r i n t e r 3 Desk ShrinkWrapper Roll System Printer 55' - 4 1/4" 2' - 4 7/8" 18'-12" 62' - 1 1/8" 12'-0" PAPER PAPER SKRINK WRAP CUTTER DRILL DOCUTECH # 2DOCUTECH # 1 D O C U T EC H # 3 53905 1 0 0 D O C 4 0 B DOC 40 A DC 265 A DC 265B FAX 55' - 4 1/4" Transaction Print Shop Copy Shop Offset Print ShopCombination of Transaction & Copy Shop PARC | 16
- 17. Print production services workflow overview Collater Cutter Binder Postage Meter Shipping Electronic Submission Color Printer Black & White Printer Large continuous feed printer Paper cart WIP Jobs Customer Walk-in Paper cart WIP Finishing Mailing Graphics design Pre-press Customer service Printing PARC | 17
- 18. 360003000024000180001200060000 Median Mean 4003002001000 A nderson-Darling Normality Test V ariance 2034062.0 Skew ness 16.142 Kurtosis 383.015 N 1692 Minimum 1.0 A -Squared 1st Q uartile 6.0 Median 28.0 3rd Q uartile 152.0 Maximum 39802.0 95% C onfidence Interv al for Mean 267.4 425.32 403.4 95% C onfidence Interv al for Median 25.0 33.0 95% C onfidence Interv al for StDev 1379.7 1476.0 P-V alue < 0.005 Mean 335.4 StDev 1426.2 95% Confidence Intervals Job Size (Page Count) distribution Challenges in optimizing production processes in a services business Production is done in customer premises-Not a controlled factory environment Multiple sources of variability- analytical modeling impractical • Job – arrival and due dates – sizes – types (routings) – Volume fluctuation • Equipment – Random machine failure and repair – Processing rate variability • Personnel – Labor skill differences – Flexible work schedules Day Volume 39035131227323419515611778391 6000000 5000000 4000000 3000000 2000000 1000000 0 _ X=2220922 UCL=5074045 LB=0 3_5 4_5 5_5 6_5 7_5 8_5 9_5 10_511_512_51_6 2_6 3_6 111 Daily Production Volume Failure Repair PARC | 18
- 19. Traditional Print Shop Operation Frameworks BellandHowell Inserter Inserter Inserter PB 8 Series PB 8 Series PB 8 Series Inserter Cage Inserter Room Desk Desk Desk Desk Desk Desk LOADING Server Room Mailing Area Input Desk P r i n t e r 1 Cutter P r i n t e r 2 P r i n t e r 4 P r i n t e r 3 Desk SQA DESK Moore Sealer Desk Roll System Printer Desk H I L I T E P R I T E R Desk ShrinkWrapper Pillar DeskDesk Desk Desk P r i n t e r 3 Desk ShrinkWrapper Roll System Printer •High equipment flexibility •Low labor flexibility •Classical job-shop scheduling Job Shops Inline or Flow Shops Print Shrinkwrap •Automated inline systems •Dedicated line (inflexible) Mail Print Insert Ship PressureSeal Shrinkwrap Fulfillment PARC | 19
- 20. LDP Lean Document Production Solution – The Notion of Autonomous Cells BellandHowell Inserter Inserter Inserter PB 8 Series PB 8 Series PB 8 Series Inserter Cage Inserter Room Desk Desk Desk Desk Desk Desk LOADING Server Room Mailing Area Input Desk P r i n t e r 1 Cutter P r i n t e r 2 P r i n t e r 4 P r i n t e r 3 Desk SQA DESK Moore Sealer Desk Roll System Printer Desk H I L I T E P R I T E R Desk ShrinkWrapper Pillar DeskDesk Desk Desk P r i n t e r 3 Desk ShrinkWrapper Roll System Printer LOADING Server Room Mailing Area Input Desk P r i n t e r 1 Cutter Inserter PB8 Series Inserter PB8 Series P r i n t e r 2 P r i n t e r 4 P r i n t e r 3 Desk Desk Desk SQA DESK Inserter PB8 Series Moore Sealer Desk Cell 4 Roll System Printer Desk Desk Cell 2 H I L I T E P R I T E R Desk Shrink Wrapper Pillar Cell 3 Cell 1 An autonomous cell has all the resources (equipment and labor) to create a few different types of finished products PARC | 20
- 21. Routing Sequencing and Release Control Batch-Splitting • Job routing to cells occurs at jobs queued at the shop level • Sequencing and release control occurs at the jobs queued at the cell interface • Optimal batch-splitting occurs within the cell LDP Lean Document Production Solution- Hierarchical Scheduling PARC | 21
- 22. Modeling, analysis and optimization algorithms Cell Routing Algorithm xij: Portion of job Ji to be manufactured by cell Cj. tij: Estimated time for cell Cj to finish 100% of job Ji. (tij=0 if Ji cannot be finished in Cj) minimize F(x11,x12,…, xnm) subject to xij >= 0, for all i,j x11+x12+…+x1m=1, …, xn1+xn2+…+xnm=1 e.g. F = Gj(x11, x12,…, xnm) = x1j t1j+x2j t2j+…+xnj tnj. (F = Time that a given cell j is busy) minimize max {L1G1(x11, …, xnm), …, LmGm(x11, …, xnm)} subject to xij >= 0, for all i,j x11+x12+…+x1m=1, …, xn1+xn2+…+xnm=1 Ljs are nonnegative constants selected to express our preferences among the costs e.g. Take L1 >> L2, …, L1 >> Lm, to emphasize the busy time of the first cell over the others. Take L1 = L2 = … = Lm, to minimize the time to finish all jobs. Batch Splitting Algorithm T(b) = s1 + (r1+r2+…+rn) b + (N/b –1) max{s1+r1b, s2+r2b, …, sn+rnb}. • Compute the set of integers bs that divide N exactly. • Evaluate T(b) for all the bs in this set, and store these values in a vector. • Select the minimum component of this vector. The b corresponding to this component is the optimal batch size. Print Black & White Pages Print Color Pages Collate & Trim Fold Stitch Mail Print Black & White Pages Print Color Pages Collate & Trim Fold Stitch Mail Print shop independent job description language Production workflow Signature booklet with black and white pages Automated workflow mapping Creation of discrete event simulation models from declarative specification of shop, job and algorithms PARC | 22
- 23. Automated modeling and simulation Inputs •Shop, cell, equipment and operator configuration and schedule •Scheduling policy parameters Automated process models incorporating scheduling, batching, dispatching rules and operator assignment Output Analysis Iterative Design, Analysis and Optimization PARC | 23
- 24. PARC | 24 LDP Process Optimization Toolkit
- 25. Examples of Simulation Optimization within LDP Equipment optimization Operator optimization Rai, Gross, Ettam, “Simulation-based optimization using greedy techniques and simulated annealing for optimal equipment selection within print production environments” PARC | 25
- 26. LDP Lean Document Production Assessment Process Current State Analysis Job Types Capacity Analysis Implementation Bar-coded Job Ticket Control Logic Tracking Database Internet Server Site Survey & Data Collection Finishing Room Print Room Cell Design & Floor Plan Studies Autonomous Cells Simulation results Iterate over multiple scenarios PARC | 26
- 27. Change Management Tools Project Definition Roles Shared need Shape vision and Communication Mobilize commitment Systems & structures Executed SOW Force Field Analysis More of / Less of In/out frame Assess Recommend Move & Install Ramp UP Steady State Virtual Pilot Calendar Test Management Roles Stakeholder analysis Threat vs. Opportunity Matrix “North Star” Communication Plan / Lean Education Part I and II Elevator Speech Stakeholder analysis Lean Metrics/ Pre and post transformation PARC | 27
- 28. Benefits to Operations from deployment of LDP Produce More Jobs Reduce Your Costs Grow Your Business Delight Your Customers • Improve job turnaround time by more than 20% • Improve quality (fewer defects and late jobs) • Manage customer demands more effectively • Improve ability to respond to rush orders • Improve productivity by more than 20% • Improve capacity by more than 10% • Simplify job management (fewer touch points) • Do more with fewer resources • Reduce labor cost by more than 12% • Save more than 15% in floor space • More effectively utilize/deploy equipment • Reduce storage and obsolescence costs • Increase revenue by expanding capacity of existing capital and labor • Increase profits and cash flow, giving you the opportunity to INVEST and GROW PARC | 28
- 29. LDP Impact on Profits (2003-2008) $0 $10,000,000 $20,000,000 $30,000,000 $40,000,000 $50,000,000 $60,000,000 2003 2004 2005 2006 2007 2008 Profit Financial impact on Xerox from the deployment of LDP • Creates sustainable competitive advantage • Improves cost productivities • Increases revenue opportunities • Increases customer satisfaction levels 2008 Edelman Finalist PARC | 29
- 30. Lean Document Production: Success Stories Global Financial Services Firm Challenge • Facing regulatory compliance pressures to deliver more information in less time Solution • Applied Lean Document Production to consolidate two statement-processing centers • Streamlined operations to reduce labor costs, floor space, and maintenance expenses • Reconfigured and updated printing and insertion equipment Results • First-year savings of $2.5M • Reduced footprint by 46%, leading to $1M in cost savings • Optimized staffing allocation, increased color output quality “Using Xerox Lean Document Production, we’re able to produce customer statements more efficiently, while reducing costs significantly.” Senior Executive, Global Financial Services Firm PARC | 30
- 31. Printer Room Inserter Room Insert Warehouse Print Forms WarehouseCreditcard Productionarea Large transaction print and mail facility– before LDP PARC | 31
- 32. Large transaction print and mail facility– after LDP Key Results: ❑ 46% savings in floor space (Emptied the printer room) ❑ RPC consolidated in the freed up space, additional cost avoidance of $1M Printers & Inserters Combined in to cells Freed up Space Freed up space PARC | 32
- 33. Concluding Remarks • The notion of Smart Business Process (SBP) was introduced – A business process that is (continually) optimized and operated using data- driven simulation optimization and real-time productivity analytics encapsulated in standalone and web-based software solutions • Some examples of underlying science behind SBP were presented • An instantiation of SBP in the domain of print production services namely Lean Document Production was discussed in detail to demonstrate breakthrough operational productivity improvements • SBP is being expanded to other areas: • Transaction processing • Healthcare/Hospitals • Customer care/ call centers • Transportation services • … PARC | 33