SIMULATION VALIDATES DESIGN AND SCHEDULING OF A PRODUCTION LINE
Vidyasagar Murty | Neelesh A. Kale | Rohit Trivedi | Onur M. Ülgen | Edward J. Williams
PMC
University of Michigan – Dearborn 4901 Evergreen Road
Dearborn, Michigan 48128 U.S.A.
ABSTRACT
Discrete-event process simulation has historically enjoyed its earliest, most numerous, and many of its most conspicuous successes when applied to the design and/or the scheduling of production processes. In this paper, we describe an application of simulation to the design, layout, and scheduling policies of a production line in the automotive industry. Specifically, the production line in question was and is vital to the operations and profitability of a first-tier international automotive supplier. In addition to describing the process itself, the simulation model, and its results, we discuss some complex challenges of input data collection and interpretation.
INTRODUCTION
Discrete-event process simulation has a long pedigree of success in many fields of application; indeed, one of its earliest and still very frequent areas of application is in the manufacturing sector (Law and McComas 1998). The automotive industry, a major component of the manufacturing sector on most continents, has not only become increasingly competitive in recent years, but has developed longer and more complex supply chains. A chain is no stronger than its weakest link – all components of a supply chain must function reliably and efficiently to provide high consumer and shareholder value (Chopra and Meindl 2004). In this paper, we describe the application of simulation to the design, layout, and establishment of scheduling policies pertinent to a manufacturing line of a first-tier automotive supplier (i.e., a supplier who sells automotive components directly to a manufacturer of vehicles). Due to the increasing competitiveness throughout this industry, first-tier (not to mention second- tier, third-tier, etc.) automotive suppliers must constantly increase their efficiencies to withstand competitive pressures on price, timeliness of delivery, and flexibility (Walsh 2005). Given the extensive history of simulation successes in improving manufacturing processes and operations, an extensive simulation analysis was a logical weapon of counterattack against these pressures.
Representative examples of these successes appear in (Graupner, Bornhäuser, and Sihn 2004) relative to the processed-foods industry; (Steringer et al. 2003), who examined the logistics and material-handling strategies within diesel-engine assembly; and the application of simulation to scheduling interactions among raw material suppliers and an automotive stamping plant described by (Grabis and Vulfs 2003). Additionally, (Ülgen and Gunal 1998) discuss several applications of simulation in both automotive assembly plants and in plants which manufacture automotive components, taking care to note the extensive commonality of both concepts employed and benefits realized. In this paper, we provide an overview of the manufacturing process we analyzed collaboratively with the client, describe the construction, verification, and validation of the model, and present results and conclusions emerging from the study. We give particular attention to complexities arising from the collection and interpretation of input data. Whereas the newcomer to simulation methodology is likely to view the seemingly exotic step of model construction as most pivotal, experienced analysts know that “data collection is one of the initial and pivotal steps in successful input modeling” (Leemis 2004); note that the input is modeled.
PROCESS OVERVIEW
The first step of the simulation project, as in any simulation study, was defining the project objective. Once the objective was defined, the complete process was mapped and all relevant details were documented. The process description is as follows:
The production facility consists mainly of four compaction presses (P1, P2, P3 and P4), two assembly robots, and four sintering furnaces (F1, F2, F3 and F4), as shown in Figure 1 (last page). The facility produces four different types of metal powder precision components (“carriers”), denoted A, B, C, and D; and each component consists of two part types (symbolically [A1, A2], [B1, B2], [C1, C2], or [D1, D2]). The presses typically run in pairs; for example, if P1 is producing B1 part types, press P2 is producing B2 part types. Presses P3 and P4 cannot produce part types for carrier type C. Hence each press has two die sets. At any given time, a given press is using one die set while the other die set is being set offline for the next part type. (Accordingly, at any given time, the press compacts one kind of the part). After each die changeover on a press, there is a two-hour start up time for quality checks.
The carrier parts are then routed from the press to the buffer with negligible travel time. The assembly robots pick parts from the buffer, assemble them to form a carrier and place them on the furnace conveyor using a round-robin discipline. The part picking is done using an “oldest individual part” discipline. For example, suppose an A1 part has waited 10 minutes, an A2 part has waited 1 minute, a B1 part has waited 20 minutes, there is no B2 part in the buffer, a D1 part has waited 9 minutes, and a D2 part has waited 8 minutes. Then, since assembly of a B carrier is at the moment impossible, an assembly robot will pick the A1 and A2 parts for assembly next. The robots assemble carriers and feed the furnace conveyors, which run continuously through the furnaces, as long as there are parts in the buffer. By policy, the fourth furnace is fed only when all the other three are full; indeed, the client wished to examine the possibility of “mothballing” (entirely abandoning use of) the fourth furnace. Carriers are sintered (i.e., the powdered mixture of metal they comprise is heated to just below the fusing point of the most easily fused ingredient, causing coalescence into a strong component (El Wakil 1998)) as they travel through the furnaces, and upon exit are ready to move to the finished goods storage area. The entire process is fully automated. Scrap rates for the presses and furnaces are assumed to be 2% and 1% respectively. Additional modeling assumptions, discussed with and approved by the client, and documented, were:
Raw material is always available
Operators are modeled as resources that are always
The production is fully automated and
Robots have 5% of downtime with one hour as mean time to repair (MTTR)
Travel time between the buffer and furnace conveyor is zero.
There is no blocking of parts upon exiting the furnace
Die setup takes eight hours and startup takes two Both add to a total of ten hours for the changeover of die.
The robot assembles the parts on a FIFO [first-in, first-out]
There is no delay when production is switched from presses P3 and P4 to presses P1 and P2, provided P3 and P4 have been running for more than 12 hours.
MODEL CONSTRUCTION, VERIFICATION, AND VALIDATION
The analysts and clients agreed upon the use of the SIMUL8® software for this project. This software is relatively easy to use. In addition to provision of standard constructs such as Work Entry Points, Storages (queues or buffers), Work Centers, Resources, and Work Exit points, SIMUL8® allows construction of the simulation model logic and its animation to proceed concurrently. Additionally, SIMUL8® provides features such as Schedules for Resources, plus the ability to “profile” a model to discover where most of the model execution time is spent (Hauge and Paige 2004). To improve model run- time performance, the analysts then concentrated their efforts on those portions of the model logic consuming the largest percentages of execution time.
To aid in model verification, the complete model was built in two stages. One model contained the presses; the other, the robots and furnaces. After verifying each of these models, the analysts linked them into one larger model, hence using the principle of modular design well known to software engineers and practitioners (Deitel and Deitel 2003). Additionally, these originally separate models confirmed that the presses (not the assembly robots, nor the furnaces) were the system bottleneck. Since the client already firmly believed this, its early corroboration by the study increased the credibility of the analysis.
A significant step in model construction and validation was distribution fitting for the raw downtime data. Downtime data included repair time (TTR) and time between failures (TBF) for four types of downtime (mechanical, electrical, hydraulic and miscellaneous) for each of the four presses. The client provided TTR and TBF data for a year, and remarked “each press is down about 25% of the time.” Since SIMUL8® considers MTTR and MTTF as input, the given TBF data was converted to TTF by subtracting TTR from TBF for each downtime event. Distributions were fitted to each MTTR and MTTF using the Stat::Fit® distribution-fitting tool. The fitted distributions were analyzed with Kolmogorov-Smirnov and Anderson-Darling goodness-of-fit tests, with greater reliance placed upon the Kolmogorov-Smirnov test results (the test which, due to familiarity, the client found more credible). Use of the best fitting MTTR and MTTF distributions in a preliminary test run (these distributions were gamma and Weibull with parameters implying long tails) produced results implying the machines would be down more than 50% of the time, a severe mismatch with direct observation. The analysts next discussed this problem with the clients at length. The discussion revealed that the original data set of TTFs and TTRs contained very long downtimes because if, for example, a repair began just before quitting time on a Friday, and was completed the following Monday morning after a weekend hiatus, the entire weekend was wrongly included in the downtime (Williams 1994). After cleansing the data, Stat::Fit® was rerun and the new distributions obtained (exponential) yielded test data closely matching the client’s newly gained understanding of TTF and TTR.
After the above data cleansing was completed, model verification and validation were successfully undertaken using generally recognized techniques such as checking hand calculations against deterministic runs, examination of traces and of the animation, structured walkthroughs of the model logic, and Turing tests undertaken cooperatively with the client (Sargent 2004).
RESULTS AND CONCLUSIONS
The client’s primary performance metric was the “makespan of a production cycle.” In the client’s terminology, a “production cycle” is the production of all carrier varieties in the amounts demanded by the marketplace in one week and its “makespan” is the time required for that production. Hence, the basic target makespan is 7.0 days or one calendar week. The client was particularly interested in comparing the merits of sequential scheduling (involving production of parts at only two presses, and hence producing only one type of carrier at any given time) versus batch scheduling (in which presses P1 and P2 run throughout the week [unless down] and presses P3 and P4 run as needed). Therefore, model experimentation focused upon (a) comparison of these scheduling disciplines, (b) assessing the sensitivity of system throughput to downtime, and (c) assessing the sensitivity of system throughput to buffer sizes. Accordingly, five scenarios were explored in detail, as summarized in Table 1 (last page). All five scenarios were run seven days a week, three shifts per day, for seventy weeks (ten-week warm up time and sixty-week run length). The 95% confidence intervals for the makespan performance metric are based on six replications. In this table, downtime data set 1 represents expected downtime of the presses, whereas downtime data set 2 represents severe downtime (“worst-case analysis”). The “overall buffer capacity” represents a physical constraint on the buffer immediately downstream from the presses, whereas the “buffer limit per part type” represents an operational constraint on the number of any one part type allowed to reside in the buffer at any given time. As indicated by the table, a configuration using batch scheduling, a 24,000- capacity buffer permitting 6000 parts of one type to reside therein, and simultaneous use of three furnaces meets the makespan target even under robustly – even under the stress of very pessimistic downtime assumptions (scenarios 4 and 5).
In addition to the clear superiority of this alternative (which permitted the client to achieve operational savings by using one fewer furnace than anticipated), other significant insights gleaned from this simulation study were:
Increased press downtime leads to increased press blockage because when a press goes down more frequently its paired press, which is compacting the corresponding part, fills its share of the buffer and becomes blocked more Concurrently, increased press downtime increases the system sensitivity to the buffer limit per part.
The expenses of increased buffer size (these expenses include capital investment, use of floor space, and increased work in process) are justified not only to achieve the required makespan, but also to improve press
Neither robot can begin assembling a carrier unless min(X1 parts available, X2 parts available) [X e
{A,B,C,D}] = y; currently y = 1. Increasing the value of y will improve furnace utilization, and evaluating various plausible values of y will be the object of further study.
Batch scheduling is significantly superior to sequential
ACKNOWLEDGMENTS
All five authors take pleasure in commending anonymous referees for their valuable suggestions to improve the organization and clarity of this paper.
REFERENCES
Chopra, Sunil, and Peter Meindl. 2004. Supply Chain Management: Strategy, Planning, and Operation, 2nd ed. Upper Saddle River, New Jersey: Pearson Education, Incorporated.
Deitel, H. M., and P. J. Deitel. 2003. C++ How to Program, 4th
Upper Saddle River, New Jersey: Pearson Education, Incorporated.
El Wakil, Sherif D. 1998. Processes and Design for Manufacturing, 2nd ed. Boston, Massachusetts: PWS Publishing Company.
Grabis, Janis, and Girts Vulfs. 2003. Simulation and Knowledge Based Scheduling of Manufacturing Operations. In Proceedings of the International Workshop on Harbour, Maritime and Multimodal Logistics Modelling & Simulation, eds. Yuri Merkuryev, Agostina G. Bruzzone, Galina Merkuryeva, Leonid Novitsky, and Edward Williams, 177- 182.
Graupner, Tom-David, Matthias Bornhäuser, and Wilfried Sihn. 2004. Backward Simulation in Food Industry for Facility Planning and Daily Scheduling. In Proceedings of the 16th European Simulation Symposium, eds. György Lipovszki and István Molnár, 47-52.
Hauge, Jaret W., and Kerrie N. Paige. 2004. Learning SIMUL8: The Complete Guide, 2nd edition. Bellingham, Washington: PlainVu Publishers.
Law, Averill M., and W. David Kelton. 2000. Simulation Modeling and Analysis, 3rd edition. Boston, Massachusetts: The McGraw-Hill Companies, Incorporated.
Law, Averill M., and Michael G. McComas. 1998. “Simulation of Manufacturing Systems.” In Proceedings of the 1998 Winter Simulation Conference, eds. D. J. Medeiros, Edward F. Watson, John S. Carson, and Mani S. Manivannan, 49-52.
Leemis, Lawrence M. 2004. Building Credible Input Models. In Proceedings of the 2004 Winter Simulation Conference, Volume 1, eds. Ricki G. Ingalls, Manuel D. Rossetti, Jeffrey
Smith, and Brett A. Peters, 29-40.
Sargent, Robert G. 2004. Validation and Verification of Simulation Models. In Proceedings of the 2004 Winter Simulation Conference, Volume 1, eds. Ricki G. Ingalls, Manuel D. Rossetti, Jeffrey S. Smith, and Brett A. Peters, 17- 28.
Steringer, Robert, Martin Schickmair, Johann Prenninger, and Maximilian Bürstmayr. 2003. Simulation of Large Standard Stillage Placement on a Diesel-Engine Assembly. In Proceedings of the 15th European Simulation Symposium, eds. Alexander Verbraeck and Vlatka Hlupic, 425-429.
Ülgen, Onur, and Ali Gunal. 1998. Simulation in the Automotive Industry. In Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, ed. Jerry Banks, New York, New York: John Wiley & Sons, Incorporated, 547-570.
Walsh, Tom. 2005. For Pinched Parts Suppliers, It’s Life on the Edge. The Detroit Free Press 174(272):1A, 9A (3 February 2005).
Williams, Edward J. 1994. Downtime Data — its Collection,
Analysis, and Importance. In Proceedings of the 1994 Winter Simulation Conference, eds. Jeffrey D. Tew, Mani S. Manivannan, Deborah A. Sadowski, and Andrew F. Seila, 1040-1043.
AUTHOR BIOGRAPHIES
VIDYASAGAR MURTY holds a bachelor’s degree in Mechanical Engineering (Jawaharlal Nehru Technological University, India, 2000) and a master’s degree in Industrial Engineering (Un iversity of Cincinnati, 2003). He joined Production Modeling Corporation in 2003 as an Applications Engineer. He mainly uses Enterprise Dynamics®, WITNESS®, SIMUL8® simulation packages and manages simulation projects. He is a member of the Institute of Industrial Engineers [IIE] and has served as Vice President of Administration on the IIE – Greater Detroit Chapter board since 2004.
NEELESH A. KALE received a Bachelor of Engineering degree in Production Engineering from the University of Pune, India (2000) and an M.S. degree in Industrial Engineering from Oklahoma State University, USA (2003) with a concentration in operations research and statistics. Currently he is working as a junior simulation analyst with Production Modeling Corporation, Dearborn, Michigan. His interest areas are simulation modeling and analysis, and traditional industrial engineering techniques for performance improvement. He frequently uses Enterprise Dynamics®, Simul8®, and WITNESS® simulation packages for modeling and analysis.
ROHIT TRIVEDI earned his bachelor’s degree in the field of Mechanical Engineering (Maharaja Sayajirao University of Baroda, Gujarat, India, 2001) and completed his master degree program in Industrial Engineering with concentration in the field of Engineering Management Program (Wayne State University, Detroit, Michigan, USA). He is currently pursuing his master degree program in the field of Business Administration (Wayne State University, Detroit, Michigan, USA). He is working as an Engineering Consultant with primary focus in the areas of Process Management, Simulation, Lean Manufacturing and traditional Industrial Engineering. He enjoys teaching as an external faculty member for University of Michigan — Dearborn. He was awarded the Graduate Professional Scholarship from Wayne State University Graduate School, 2004-2005. He received second prize at the national level for Technical Paper Presentation Contest. (TKIET, Warananagar, Maharashtra, India, 2000). He was a member of ISTE (Indian Society for Technical Education, 1997-2001).
ONUR M. ÜLGEN is the president and founder of Production Modeling Corporation (PMC), a Dearborn, Michigan, based industrial engineering and software services company as well as a Professor of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn. He received his Ph.D. degree in Industrial Engineering from Texas Tech University in 1979. His present consulting and research interests include simulation and scheduling applications, applications of lean techniques in manufacturing and service industries, supply chain optimization, and product portfolio management. He has published or presented more that 100 papers in his consulting and research areas.
Under his leadership PMC has grown to be the largest independent productivity services company in North America in the use of industrial and operations engineering tools in an integrated fashion. PMC has successfully completed more than 3000 productivity improvement projects for different size companies including General Motors, Ford, DaimlerChrysler, Sara Lee, Johnson Controls, and Whirlpool. The scientific and professional societies of which he is a member include American Production and Inventory Control Society (APICS) and Institute of Industrial Engineers (IIE). He is also a founding member of the MSUG (Michigan Simulation User Group).
EDWARD J. WILLIAMS holds bachelor’s and master’s degrees in mathematics (Michigan State University, 1967; University of Wisconsin, 1968). From 1969 to 1971, he did statistical programming and analysis of biomedical data at Walter Reed Army Hospital, Washington, D.C. He joined Ford Motor Company in 1972, where he worked until retirement in December 2001 as a computer software analyst supporting statistical and simulation software. After retirement from Ford, he joined Production Modeling Corporation, Dearborn, Michigan, as a senior simulation analyst. Also, since 1980, he has taught evening classes at the University of Michigan, including both undergraduate and graduate simulation classes using GPSS/HÔ, SLAM IIÔ, SIMANÔ, ProModelÒ, SIMUL8Ò, or Arena®. He is a member of the Institute of Industrial Engineers [IIE], the Society for Computer Simulation International [SCS], and the Michigan Simulation Users’ Group [MSUG]. He serves on the editorial board of the International Journal of Industrial Engineering – Applications and Practice. During the last several years, he has given invited plenary addresses on simulation and statistics at conferences in Monterrey, México; Istanbul, Turkey; Genova, Italy; and Riga, Latvia. He has just served as Program Chair of the 2004 Summer Computer Simulation Conference, and is serving as Program Chair for the 2005 IIE Simulation Conference and the 2005 Summer Computer Simulation Conference.
SIMULATION IMPROVES MANUFACTURE AND MATERIAL HANDLING OF FORGED METAL COMPONENTS
Teresa Lang | Edward J. Williams | Onur M. Ülgen
Industrial & Manufacturing Systems Engineering Department
College of Engineering, Engineering Complex, University of Michigan – Dearborn 4901 Evergreen Road
Dearborn, MI 48128 U.S.A.
ABSTRACT
As competitive pressures increase within the manufacturing sectors of economies worldwide, and especially within the automotive sub-sector, the importance of achieving operational efficiencies to reduce costs and thence to increase profits while keeping and attracting customers steadily increases. Simulation, time studies, and value stream mapping have long been key allies of the industrial engineer assigned to find and progress along the often difficult and challenging road leading to such efficiencies. The presentation here, and undertaken collaboratively between the university and the company involved, concentrates primarily on the use and achievements of discrete-event process simulation in improving the manufacture and material handling of forged metal components sold in the automotive and industrial manufacturing marketplace.
INTRODUCTION
Historically, the first major application area of discrete-event process simulation was the manufacturing sector of the economy (Miller and Pegden 2000). With the passage of time, simulation has become more closely allied with other industrial engineering techniques such as time and motion studies, value stream mapping, ergonomics studies, and “5S” examinations used concurrently to improve manufacturing operations (Groover 2007). Illustrative examples of simulation applications to manufacturing and industry appearing in the literature are: analysis of pig iron allocation to blast furnaces (Díaz et al. 2007), construction of a decision support system for shipbuilding (Otamendi 2005), and layout of mixed- model assembly lines for the production of diesel engines (Steringer and Prenninger 2003) In the application documented here, simulation was applied to reduce manufacturing lead times and inventory, increase productivity, and reduce floor space requirements. The client company was and is a provider of forged metal components to the automotive light vehicle, heavy lorry [truck], and industrial marketplace in North America. The company has six facilities in the Upper Midwest region of the United States which collectively employ over 800 workers. Of these six facilities, the one here studied in detail specializes in internally splined (having longitudinal gearlike ridges along their interior or exterior surfaces to transmit rotational motion along their axes (Parker 1994)) shafts for industrial markets. The facility also prepares steel for further processing by the other five facilities. Components supplied to the external marketplaces are generally forged metal components; i.e., compressively shaped by non-steady-state bulk deformation under high pressure and (sometimes) high temperature (El Wakil 1998). In this context, the components are “cold- forged” (forged at room temperature), which limits the amount of re-forming possible, but as compensation provides precise dimensional control and a surface finish of higher quality.
OVERVIEW OF PROCEDURES AT THE FORGING FACILITY
As mentioned, the facility examined in this study specializes in internally splined shafts for one dedicated customer in the industrial marketplace, and in steel preparation processes for two colleague plants within the same company. Therefore, this particular plant has exactly three distinct customers. The figure below shows a typical forging produced here:
The major production equipment used at this facility comprises:
Eight hydraulic presses (150-750 tons, single station, manually fed)
Eleven tank coating lines with five traveling tumblers
Two saws
One “wheelabrator™” (trademark name of equipment used for shot blasting)
Eight small and two large heat treatment areas, with five bell furnaces.
Having three dedicated customers, this facility produces parts in three distinct families, each with its own process routing. Parts of family #1 go first to shot blast (a cleaning process to remove surface scale and dust from the parts or billets) at the “Wheelabrator,” a manually operated machine; then to lubrication at the coating line, and then to the outgoing dock for weighing and shipping. Families #2 and #3 have longer itineraries, summarized in the following tables:
Table 1. Process Routing for Production Family #2
Operation
Workcenters
Saw cutting
Saw 1 and Saw 2
Shot blasting
“Wheelabrator”
Annealing
Heat treat
Lubricating 1
Coating line
Weighing and shipping
Outgoing dock
Table 2. Process Routing for Production Family #3
Operation
Workcenters
Saw cutting
Saw 1 and Saw 2
Shot blast
“Wheelabrator”
Annealing
Heat treat
Lubricating 1
Coating line
Cold Hit 1/Inspect
390T, 490-2T,
500T
Stress relief
Heat treat
Lubricating 2
Coating line
Cold Hit 2/Inspect
150T, 490-1T,
490-2T
Final audit
Final audit
Weighing and shipping
Outgoing dock
At the saw cutting process, bar stock is received in 5-ton bundles 30 feet long. A bundle is loaded onto the saw using a crane; only then is the bundle broken open and fed into the saw. Although the saw routinely cuts every piece to an exact length (vital), it is more difficult, and equally vital, to control the weight of the billet (bar after cutting). The two saws share, and are run by, one operator.
Two varieties of heat treating are used. Spherodize annealing converts strands of carbon in the steel to spheroids before forging, rendering the steel more formable and hence capable of being forged at room temperature. Stress relieving, done after forging, relieves the stresses accumulated in the steel during forging, thereby permitting distortion-free carburizing of the internal splines. This carburizing is done at customers’ sites. These two heat-treat operations share one operator, who is responsible for loading the parts into “heat treat pots” (Figure 2 below) to be placed in the furnace and unloading the parts afterwards. Since the parts expand during heat treat, the unloading times are 50% longer and also have triple the standard deviation of the loading times.
After final heat treat, the parts are coated in a zinc- phosphate and soap lubricant; this requires that they be dumped into tumblers (Figure 3 below) which can be rotated and submerged in the lubricant, and then lifted and rotated again to drip excess solution. This work also requires operator intervention.
After lubrication, those parts destined for either of the two corporate downstream plants are ready for final inspection, weighing, and shipment thereto; the lubrication prepares them for further cold-forging there. Parts destined for the external customer are cold-forged locally subsequent to inspection, weighing, and shipment to the customer.
DATA COLLECTION AND INPUT ANALYSIS
As usual, data collection consumed a significant percentage (about 35%) of time invested in this process improvement study (Carson 2004); educators must gently explain to students that simulation studies are unlike Exercise 4 in the textbook, with “givens” such as “the machine cycle time is gamma distributed with parameters….” Much of the data collection work simultaneously supported both the value stream mapping and the simulation analyses. Historical data on the arrival times and quantities of raw material, which occurred approximately daily at 9am by truck, was readily available. The quantities of raw material delivered were approximately normally distributed, as verified by the Anderson-Darling goodness-of-fit test available in the Minitab® statistical software package (Ryan, Joiner, and Cryer 2005) and the Input Analyzer of the Arena® simulation software. Machine cycles, such as the lubricant immersion time, the shot blast time, or the required length of heat-treat time, were well known, but operator intervention times, such as time to load or unload the heat-treat pots or the tumblers, had to be collected by traditional time-&-motion study stopwatch measurements (Mundel and Danner 1994). The stopwatches made the workers uneasy at first, raising the specter of the Hawthorne effect; data collection needed to be as quiet and unobtrusive as possible (Czech, Witkowski, and Williams 2006). Two significant aids in this data gathering were: (1) it occurred across all manually assisted operations – hence no one operator or group of operators felt threatened by special vigilance, and (2) labor-management relations at the company were and are historically favorable. Downtime frequency of occurrence, downtime duration, and scrap rate data were conveniently available from historical records, a commendable situation described vividly in (Weiss and Piłacińska 2005).
CONSTRUCTION, VERIFICATION, AND VALIDATION OF THE SIMULATION MODEL
Owing to ready availability within both academic and industrial contexts, and ample software power to both simulate and animate the production processes in question, the Arena® simulation modeling software (Kelton, Sadowski, and Sturrock 2007) was used. The animation was basic and, given the time limitations of this study, only two-dimensional, but these limitations were of little importance to the client management. Arena® provides direct access to concepts of process flow logic, queuing disciplines (e.g., FIFO), modeling of processes which may be automated, manual, or semi- automated, use of Resources (here, the various machines and their operators), definition of shift schedules, constant or variable transit times between various parts of the model, extensibility (in its Professional Edition) via user-defined modules (Bapat and Sturrock 2003), and an Input Analyzer (used as discussed in the previous section to verify distributions).
Verification and validation techniques used included a variety of methods such as tracking one entity through the model, initially removing all randomness from the model for easier desk-checking, structured walkthroughs among the team members, step-by-step examination of the animation, and confirming reasonableness of the preliminary results of the model with the client manager by use of Turing tests (Sargent 2004). For the “one-entity” tests, an entity of each product type for each of the three customers was used in succession. Since the facility has maintained accurate and complete inventory data over a lengthy period of time, the inventory and work-in-process levels predicted by the model furnished an excellent “test bed” for validation. Comparison of localized performance data pertinent to each work center (e.g., machine utilization and length of queue preceding the machine) with model results was also helpful to the validation effort. Validation of the first model built – the “current operations model” was considered complete by both the analysts and the client when machine utilizations, operator utilizations, inventory levels, and throughput all correctly matched recent historical data to within 6%.
RESULTS AND OPERATIONAL CONCLUSIONS
The simulation model representing current operations was specified to be terminating, not steady- state, because this manufacturing process, unlike most, “empties itself” each night (here, at the last of three shifts) and resumes work the next day with the delivery of new raw material (Altiok and Melamed 2001). Therefore, warm-up time was always zero. Results and comparisons between the current and proposed systems were based on ten replications of the current-state model and on thirty replications of the proposed-state model (described next, and of higher intrinsic variability) each of length five working days (one typical work week). The number and duration of replications were chosen based on the helpful Arena® capability of predicting confidence interval widths for performance metrics on their standard deviations among replications run.
The initial model vividly exposed the inefficiencies in material handling already suspected of existing in the production system. Each time parts are dumped into or out of any container, they are at risk of dings and dents. The dumping that occurs in the coating line (into and out of the tumblers) is necessary – these tumblers are attached directly to the coating line, are made of stainless steel to withstand the caustic chemicals used in this operation, and have mechanisms permitting their rotation to “spin-dry” the parts as mentioned above. Therefore, the tumblers, costing about $60,000 each, represent a significant capital investment. On the other hand, the dumping into and out of containers – the heat- treat pots – seemed wasteful. Certainly the parts must be stacked in containers to be heat-treated, but the processes immediately upstream (shot blast and/or forging) and downstream (coating) from heat-treat presume the parts to be in some type of container already. Therefore, a second model was built in which these material handling operations were revised under the hypothesis that parts would be put in heat treat pots instead of other containers for all operations up to (but not including) the actual coating process. Under this new scenario, day-to-day operations would certainly need more heat-treat pots, and this second model was used primarily to answer the question “How many more heat-treat pots would be needed to avoid excessive work-in-process inventory and delays?”
Point estimates and confidence intervals built at the 95% level, using the Student-t distribution (since population standard deviation was estimated from sample standard deviation) for the current system predicted the following:
Mean number of heat-treat pots in use in the current system is 93 during any one work
Maximum number of heat-treat pots in use in the current system at any time during any one work week is
In the proposed system (material-handling revision) the mean number of heat-treat pots in use is between 308 and 316 with 95%
In the proposed system (material-handling revision) the maximum number of heat-treat pots in use is between 422 and 435 with 95%
Hence the simulation results were summarized for management as a recommendation to buy 225 heat-treat pots (there being currently 204 heat-treat pots on hand). The disadvantage: this recommendation entails a capital expenditure of $225,000 ($1,000 per pot). The advantages are:
One heat-treat dumping operator on each of the three shifts is no longer needed (annual savings $132,000).
Less material handling (dumping parts into and out of pots) entails less risk of quality problems (dings and dents).
The work to be eliminated is difficult, strenuous, and susceptible to significant ergonomic
Hence, from a financial viewpoint, the alternative investigated with this simulation study has a payback period just under 1¾ years, plus “soft” but significant benefits.
INDICATED FURTHER WORK
Further work to be investigated next via simulation involves balancing the schedule so that parts do not, as they do now, “flood” into either the heat treatment or the coating departments. The saw cuts one job at a time, and the order in which those jobs are run is discretionary. Saw cycle time is highly variable (from one to seven hours) based on the number of workpieces per box fed to a saw. Simulation may be able to prove that having all short jobs run on one saw and all long jobs run on the other saw will smooth the flow of parts downstream. If so, the gap between mean and maximum number of heat-treat pots in use can perhaps be narrowed with detriment to neither work-in-process inventory nor work-in-process time. Then the number of pots to be purchased will decrease and the payback period will likewise decrease, thereby making the operational alternative suggested by the simulation study even more attractive.
OVERALL CONCLUSIONS AND IMPLICATIONS
Taking a longer view, the benefits of this study extend beyond the improvement of manufacturing and material handling in one facility of one moderate-sized company in the automotive sector. Publicity accorded to the study by the senior professor in charge of the simulation course (as is routinely done for many “senior projects” or “capstone projects”) has drawn beneficial local attention to the ability of simulation (and by implication, other analytical methods [e.g., the value- stream mapping used here] within the discipline of industrial engineering) to help local companies increase their competitiveness. Such help is particularly pertinent to the beleaguered automotive and manufacturing industry, especially in Michigan, which is currently the 50th of the 50 United States economically (Morath 2007). Additionally, the success of this study has increased the willingness of local business and management leaders to welcome and provide project opportunities for advanced undergraduate students. This willingness stems partly from the short-term attraction of having useful industrial-engineering work done, and partly from the long-term attraction of making an investment in the experience level of students who will shortly be entering the labor market as industrial engineers (Black and Chick 1996). A student who, within the auspices of this simulation course, understands the “connection between the physical activities and the consequential financial flows” (Ståhl 2007) is well prepared to make both technically sound and financially valuable contributions at his or her place(s) of career employment.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the cogent and explicit criticisms of Karthik Vasudevan, Applications Engineer, PMC, Dearborn, Michigan as being very beneficial to the clarity and presentation of this paper. Comments from an anonymous reviewer likewise further enhanced the presentation of the paper.
REFERENCES
Altiok, Tayfur, and Benjamin Melamed. 2001. Simulation Modeling and Analysis with Arena. Piscataway, New Jersey: Cyber Research, Incorporated, and Enterprise Technology Solutions, Incorporated.
Bapat, Vivek, and David T. Sturrock. 2003. The Arena Product Family: Enterprise Modeling Solutions. In Proceedings of the 2003 Winter Simulation Conference, Volume 1, eds. Stephen E. Chick, Paul J. Sánchez, David Ferrin, and Douglas J. Morrice, 210-217.
Black, John J., and Stephen E. Chick. 1996. Michigan Simulation User Group Panel Discussion: How Do We Educate Future Discrete Event Simulationists.
International Journal of Industrial Engineering – Applications and Practice 3(4):223-232.
Carson II, John S. 2004. Introduction to Modeling and Simulation. In Proceedings of the 2004 Winter Simulation Conference, Volume 1, eds. Ricki G. Ingalls, Manuel D. Rossetti, Jeffrey S. Smith, and Brett A. Peters, 9-16.
Czech, Matthew, Michael Witkowski, and Edward J. Williams. 2007. Simulation Improves Patient Flow and Productivity at a Dental Clinic. In Proceedings of the 21st European Conference on Modelling and Simulation, eds. Ivan Zelinka, Zuzana Oplatková, and Alessandra Orsoni, 25-29.
Díaz, Diego, Francisco J. Lago, M. Teresa Rodríguez, and Sergio Rodríguez. 2007. Knowledge Based Extendable Simulation of Pig Iron Allocation. In Proceedings of the 21st European Conference on Modelling and Simulation, eds. Ivan Zelinka, Zuzana Oplatková, and Alessandra Orsoni, 45-49.
El Wakil, Sherif D. 1998. Processes and Design for Manufacturing, 2nd edition. Boston, Massachusetts: PWS Publishing Company.
Groover, Mikell P. 2007. Work Systems and the Methods, Measurement, and Management of Work. Upper Saddle River, New Jersey: Pearson Education, Incorporated.
Kelton, W. David, Randall P. Sadowski, and David T. Sturrock. 2007. Simulation with Arena, 4th edition. New York, New York: The McGraw-Hill Companies, Incorporated.
Miller, Scott, and Dennis Pegden. 2000. Introduction to Manufacturing Simulation. In Proceedings of the 2000 Winter Simulation Conference, Volume 1, eds. Jeffrey A. Joines, Russell R. Barton, Keebom Kang, and Paul A. Fishwick, 63-66.
Morath, Eric. 2007. Auto Sales Outlook Gloomier. The Detroit News 134(100) [18 December 2007], pages 1A and 6A.
Mundel, Marvin E., and David L. Danner. 1994. Motion and Time Study: Improving Productivity, 7th edition. Englewood Cliffs, New Jersey: Prentice-Hall, Incorporated.
Otamendi, Javier. 2005. Simulation-Based Decision Support System for an Assembly Line. In Proceedings of the 19th European Conference on Modelling and Simulation, eds. Yuri Merkuryev, Richard Zobel, and Eugène Kerckhoffs, 335-340.
Ryan, Barbara, Brian Joiner, and Jonathon Cryer. 2005. Minitab® Handbook, 5th edition. Belmont, California: Thomson Learning, Incorporated.
Parker, Sybil P., editor. 1994. McGraw-Hill Dictionary of Scientific and Technical Terms, 5th edition. New York, New York: McGraw-Hill, Incorporated.
Sargent, Robert G. 2004. Validation and Verification of Simulation Models. In Proceedings of the 2004 Winter Simulation Conference, Volume 1, eds. Ricki G. Ingalls, Manuel D. Rossetti, Jeffrey S. Smith, and Brett A. Peters, 17-28.
Ståhl, Ingolf. 2007. Teaching Simulation to Business Students: Summary of 30 Years’ Experience. In Proceedings of the 2007 Winter Simulation Conference, eds. S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle,
D. Tew, and R. R. Barton, 2327-2335. CD ISBN #1- 4244-1306-0.
Steringer, Robert, and Johann Prenninger. 2003. Simulation of Large Standard Stillage Placement on a Diesel-Engine Assembly. In Proceedings of the 15th European
Simulation Symposium, eds. Alexander Verbraeck and Vlatka Hlupic, 425-259.
Weiss, Zenobia, and Maria Piłacińska. 2005. Data Collection for Systems of Production Simulation. In Proceedings of the 19th European Conference on Modelling and Simulation, eds. Yuri Merkuryev, Richard Zobel, and Eugène Kerckhoffs, 364-369.
AUTHOR BIOGRAPHIES
TERESA LANG is a student of Industrial and Systems Engineering at the University of Michigan – Dearborn campus. She expects to be graduated with a Bachelors of Science after the winter 2008 semester. She currently holds a 3.12 overall grade-point average and a 3.48 in her engineering discipline (maximum = 4.0). She was drawn to industrial engineering due to her passion for creating efficient systems, satisfaction in creating organization out of chaos, and enjoyment of statistical analysis. She has been an employee in the forging industry for the past seven years, where she has worked as a Product and Process Engineer, Tooling Coordinator, Customer Service Engineer, and Program Manager. Currently she is working as the Quality and Engineering Coordinator / Lean Promotion Officer / TS Management Representative, responsible for development of new business, maintenance of the quality management system, and improvement of plant operations through elimination of waste and reduction of variability. She is a six-sigma black belt, a certified lead TS auditor, and certified lean champion. She specializes in cold forging and die design, statistical analysis, and program management.
EDWARD J. WILLIAMS holds bachelor’s and master’s degrees in mathematics (Michigan State University, 1967; University of Wisconsin, 1968). From 1969 to 1971, he did statistical programming and analysis of biomedical data at Walter Reed Army Hospital, Washington, D.C. He joined Ford Motor Company in 1972, where he worked until retirement in December 2001 as a computer software analyst supporting statistical and simulation software. After retirement from Ford, he joined PMC, Dearborn, Michigan, as a senior simulation analyst. Also, since 1980, he has taught evening classes at the University of Michigan, including both undergraduate and graduate simulation classes using GPSS/HÔ, SLAM IIÔ, SIMANÔ, ProModelÒ, SIMUL8Ò, or Arena®. He is a member of the Institute of Industrial Engineers [IIE], the Society for Computer Simulation International [SCS], and the Michigan Simulation Users’ Group [MSUG]. He serves on the editorial board of the International Journal of Industrial Engineering – Applications and Practice. During the last several years, he has given invited plenary addresses on simulation and statistics at conferences or seminars in Monterrey, México; İstanbul, Turkey; Genova, Italy; Rīga, Latvia; Göteborg, Sweden; and Jyväskylä, Finland. He has served as Program Chair of the 2004, 2005, and 2006 Summer Computer Simulation Conferences, and also for the 2005 IIE Simulation Conference.
ONUR M. ÜLGEN is the president and founder of Production Modeling Corporation (PMC), a Dearborn, Michigan, based industrial engineering and software services company as well as a Professor of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn. He received his Ph.D. degree in Industrial Engineering from Texas Tech University in 1979. His present consulting and research interests include simulation and scheduling applications, applications of lean techniques in manufacturing and service industries, supply chain optimization, and product portfolio management. He has published or presented more that 100 papers in his consulting and research areas.
Under his leadership PMC has grown to be the largest independent productivity services company in North America in the use of industrial and operations engineering tools in an integrated fashion. PMC has successfully completed more than 3000 productivity improvement projects for different size companies including General Motors, Ford, DaimlerChrysler, Sara Lee, Johnson Controls, and Whirlpool. The scientific and professional societies of which he is a member include American Production and Inventory Control Society (APICS) and Institute of Industrial Engineers (IIE). He is also a founding member of the MSUG (Michigan Simulation User Group).
SIMULATION OF MEDICAL LABORATORY OPERATIONS TO ACHIEVE OPTIMAL RESOURCE ALLOCATION
Ravindra Lote | Edward J. Williams | Onur M. Ülgen
PMC
15726 Michigan Avenue Dearborn, MI 48126 U.S.A.
ABSTRACT
As competitive pressures increase within the health care sectors of economies worldwide, and especially within the United States, the importance of achieving operational efficiencies to reduce costs and thence to increase profits while keeping and attracting customers steadily increases. Simulation, optimization, time studies, value stream mapping, and process improvement methodologies have long been key allies of the industrial engineer assigned to find and progress along the often difficult and challenging road leading to such efficiencies; experienced industrial engineers know these methodologies work better synergistically than individually. The presentation here, and undertaken collaboratively between the medical laboratory (client) and the industrial engineering service company (consultant), concentrates primarily on the use and achievements of discrete-event process simulation and its allied industrial-engineering techniques in improving the operations of a medical testing laboratory, and hence its services to its clients, both hospitals and clinics.
INTRODUCTION
Historically, the first major application area of discrete-event process simulation was the manufacturing sector of the economy (Miller and Pegden 2000). With the passage of time, simulation has become more closely allied with other industrial engineering techniques such as time and motion studies, value stream mapping, ergonomics studies, and “5S” examinations used concurrently to improve generic operations (Groover 2007), and has also expanded rapidly into the service and health care industries (Lowery 1998). Illustrative examples of simulation applications to the health care sector appearing in the literature are: improvement of appointment scheduling in a dental clinic (Czech, Witkowski, and Williams 2007), the analysis of incentives and scheduling within the operating room of a major metropolitan hospital (Ferrin et al. 2004), the coordinated provision of emergency medical services immediately subsequent to a serious traffic accident (Guimarans et al. 2006), and aggressive efforts to improve health care delivery in hospitals in the United Kingdom (Pidd and Günal 2008). The survey article (McGuire 1998) provides an excellent overview of simulation use in health care.
Improvement of the delivery of health care services is especially pressing in the United States. As Margaret Brandeau bluntly stated in her keynote address to the 2008 Winter Simulation Conference (Miami, Florida, United States, 8 December 2008) “The United States spends more per capita on health than any other nation, yet has worse health outcomes than many other countries. Moreover, expenditures on health in the U.S. are growing rapidly, and are taking up an increasingly larger share of per capita gross domestic product.” (Brandeau 2008). These urgently needed improvements involve the metrics of timeliness, quality, and cost – and these metrics are strongly affected by services the typical patient does not “see” (Galloro, 2008) – such as those provided by medical laboratories. The recent work of (Chinea, Rodríguez, and González 2009) provides an excellent synopsis of simulation used in hospital resource management.
In the case study discussed here, a medical laboratory in the eastern part of the United States, sought to improve its financial efficiency, operational efficiency, and service to its clients. Accordingly, this laboratory undertook to use, with the collaboration and guidance of an industrial engineering consulting company, the techniques of industrial engineering, including discrete-event process simulation. This certified and accredited laboratory provides, over a multi-county area, pick-up and delivery courier services for supply requests, medical specimens, test result reports, and medical supplies. In this context, the project goals of the client laboratory were to:
Optimize the numerous courier routes to improve transport efficiency, particularly to deliver work (i.e., medical specimens to be analyzed) to the laboratory earlier in the work day. In the specific context of this simulation project, an ongoing, formally stated objective was (and is) “test impact of route optimization initiative on load leveling of resources and provide necessary feedback for further fine tuning of optimization model.” Hence very early in the life of this project, the client recognized the first phase of the simulation study as a “bootstrap” toward continuous improvement (usually this recognition dawns later, after a client – especially one new to simulation – comes to appreciate the analytical power of simulation).
Gain analytical insights into the interrelationships between courier route improvements and operational performance metrics of the laboratory
Achieve leveling of workload, in conjunction with leveling of resource usage, within various departments (e.g., serology, microbiology, and hematology) departments in the laboratory via smoother delivery of
Achieve cost-savings via appropriate redeployment of personnel with no degradation of service metrics to hospitals and clinics (the clients of the laboratory).
OVERVIEW OF PROCEDURES AT THE MEDICAL LABORATORY
The operations studied intensively and comprehensively at this laboratory comprised the delivery of medical specimens to the laboratory, their processing within the laboratory, pick-up and delivery of items entrusted to its courier service, and delivery of test result reports and medical supplies to its client hospitals and health care clinics. At the initiation of the project, the laboratory used a fleet of fifteen courier vehicles, employed fifteen full-time-equivalent headcount, and ran eleven total courier routes daily (only one of these a local run). Originally, courier routing instructions were handwritten on route sheets. Also, the laboratory was acutely aware of chronically high specimen processing costs, due primarily to overtime attributed to unbalanced rates of specimen arrival. The arrival rates predictably spiked about 11:30 each morning, between 14:00 and 17:00 each afternoon, and again at 22:00 each evening. The number of requisitions processed was typically between 1,000 and 1,100 per day. Unbalanced rates of specimen arrival resulted in suboptimal utilization of medical technologists. Overtimes were frequently enforced, in addition to the implementation of a midnight shift, to achieve required turn-around-time of 24 hours. Early observations and discussions with the client attributed this undesirable situation to suboptimal workload leveling. Additionally, unobtrusive workload observation techniques confirmed a long-standing client suspicion that between ½ and ¾ hour of courier time was typically squandered between specimen drop-off and route continuation. As so often happens (Kroemer and Grandjean 1999), stressful working conditions, including compulsory overtime, often resulted in erroneous specimen results. Accordingly, the client and the consultant decided to concentrate early efforts on improvement of the courier routings and assignment of personnel thereto, not operations within the testing laboratory itself.
DATA COLLECTION AND INPUT ANALYSIS
A major portion of the data collection involved the courier routes; actual trip times (which had high variability) were collected and analyzed for each route. This need for extensive, accurate data collection is frequent in simulation studies, especially those pertaining to the health care industry. As (Carter and Blake 2004) remark, “In our experience in health care, no one ever had the right data in the form that we needed it.” In this study, data collection included not only the routing details mentioned above, but the resource availabilities of couriers by shift, the proportions of specimens requiring attention in the chemistry, hematology, microbiology, and serology subdivisions of the laboratory, and staffing levels in each of these divisions. Additionally, all incoming specimens required generic preprocessing before being routed to one or more of the laboratory subdivisions; input data were also collected to assess the workload imposed by this preprocessing.
CONSTRUCTION, VERIFICATION, AND VALIDATION OF THE SIMULATION MODEL
Construction of the simulation model began concurrently with the collection and statistical analysis of its input data. The simulation software tool chosen in consensus by the client and the industrial engineering company was Enterprise Dynamics® (Hullinger 1999). This software tool, a worthy competitor among many, provides comprehensive analysis techniques, a convenient user interface, excellent three-dimensional animation, and provision for modelers to construct customized reusable “atoms” (Swain 2007). For the convenience of both the modelers and the client’s management, the simulation model was constructed to read its input data from Excel® workbooks and to export its numerical results to Excel® workbooks.
The scope of the simulation model included the courier team’s acquisition of specimens, their preprocessing on delivery to the laboratory, their subsequent routing to specialized analytical operations (chemistry, hematology, microbiology, and serology), and the delivery of results and any requisitioned medical supplies to the client hospital or clinic. In the model, all courier routes were “black-boxed,” i.e., treated as an advance of time sampled from the distribution of actual trip times collected for each pertinent route. Additionally, and with client concurrence, weekends were not modeled, and operator walk times and lunch breaks were ignored.
Verification and validation of the simulation model used many techniques well recognized in the literature (Sargent 2004), such as:
Running the model with only one
Running the model with only one courier
Eliminating all randomness and then cross- checking results against “desk ”
Using structured walkthroughs of model logic and
Undertaking “directional testing” (e.g., if a cycle time increases, throughput should decrease or remain the same).
Cross-checking extensively with the client, including step-by-step tracking of model execution and its animation.
Availability of three-dimensional animation proved of ongoing value when the results were presented to client engineers and those engineers in turn presented them to their upper management (Kelton, Sadowski, and Sturrock 2007). An example of a three-dimensional animation appears as Figure 3 in the Appendix. Likewise, the availability of interactive route maps interfaced with the simulation analysis, one of which appears in Figure 4, were a valuable visual aid to understanding the implications of various suggestions for routing improvement.
RESULTS AND OPERATIONAL CONCLUSIONS
The results of this simulation study included several pertinent and valuable recommendations, among them:
Optimizing routes initially in use allowed the re- allocation of two couriers.
One re-allocated courier, redeployed as a runner, retrieved specimens from other couriers returning to the
Workload leveling achieved as a result of optimizing the routes helped the client eliminate the night shift and improve utilizations of medical technologists during the morning One example of workload leveling achieved is illustrated in Figures 1 and 2, which show the percent utilizations of the medical technologists in the chemistry department. Resource leveling improved from “considerably worse than two-to- one” to “uniformity of usage within 10%.” Similar quantitative improvements were achieved in the serology, microbiology, and hematology departments.
A lesser ratio improvement, but one involving a more heavily utilized group of technologists overall (and hence of high importance to the client) was achieved in the preprocessing department, as shown in Figures 5 and 6 in the Appendix. In this department, maximum utilization fell from 100% (and that among half of the technicians) to 97%, and minimum utilization rose from 60.2% to 81.7%.
Route optimization and subsequent workload leveling saved approximately $110,000 annually in payroll
It is important to understand that the progress from optimization of courier routes to achievement of workload leveling was not a quick “step one, step two” process. The actual work involved improving the courier routes, testing the impact of these improved routes on workload leveling, using insights from the newly improved leveling to further improve the routes, etc. – an iterative process. Relative to the simulation experimentation itself, the scenario runs were terminating (due to the daily “restart” nature of the courier runs and laboratory operations), each replication lasted six weeks of simulated time, and sufficiently narrow confidence intervals required four replications of each scenario examined.
Attractively, no personnel represented by the payroll cost savings were laid off; rather, the client company deployed them in expansions of this service and in newly offered services, thereby increasing its profitability. As one example of this reallocation, one courier was newly deployed as a runner assigned to retrieve laboratory specimens, together with handheld computers already in routine use to upload preliminary computations to desktop computers, from other couriers returning to the central laboratory from their runs.
INDICATED FURTHER WORK
As a matter of standard policy, the model was built and documented with the intention that it be available for and adaptable to continued use. Such continued use is indeed already impending: almost inevitably, changes in the number and location of customer pick-up/drop-off points are appearing, as are changes in the “specimen traffic” (number of specimens arriving or departing at each of these points). Therefore, courier routing optimization will be an ongoing process. Via the Excel® input interface, client analysts and managers can and do run the model to incorporate these changes, the routing optimization is successfully keeping apace of the market demand changes experienced by the laboratory.
Additionally, as a result of a first successful foray into simulation by the client company, its management is now considering the use of simulation for an incremental study focusing attention more specifically on the “in-house” laboratory operations.
OVERALL CONCLUSIONS AND IMPLICATIONS
This case study illustrates the value of simulation in a setting fundamentally logistical, in the context of providing health care. Use of simulation in conjunction with allied analytical techniques such as route optimization, value stream mapping, work sampling, and resource leveling provides synergistic value to all these industrial engineering techniques. Whereas many studies documented in the literature are directed to the “front of the house” delivery of care directly visible to patients, this analysis devoted attention to a “back of the house” function, much less conspicuous from a patient’s viewpoint, but nonetheless vital to the delivery of timely and high-quality health care at manageable cost.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the contributions of Mike Ricard, Kevin Kohls, and Eric Lammers, industrial engineers and colleagues, to this project. Additionally, various anonymous referees have contributed valuable suggestions to improve the presentation and organization of this paper.
REFERENCES
Braudeau, Margaret. 2008. Modeling and Simulation in Public Health: A Little Help Can Go a Long Way. In Proceedings of the 2008 Winter Simulation Conference [ISBN 978-1-4244-2708-6, IEEE catalog number CFP08WSC-CDR, Library of Congress 87-654182], eds. Scott Mason, Ray Hill, Lars Mönch, Oliver Rose, Tom Jefferson, and J. W. Fowler.
Carson II, John S. 2004. Introduction to Modeling and Simulation. In Proceedings of the 2004 Winter Simulation Conference, Volume 1, eds. Ricki G. Ingalls, Manuel D. Rossetti, Jeffrey S. Smith, and Brett A. Peters, 9-16.
Carter, Michael W., and John T. Blake. 2004. Using Simulation in an Acute-Care Hospital: Easier Said than Done. In Operations Research and Health Care: A Handbook of Methods and Applications, eds. Margaret L. Brandeau, François Sainfort, and William P. Pierskalla,
191-215. Dordrecht, The Netherlands: Kluwer Academic Publishers.
Chinea, R. M. Aguilar, I. Castilla Rodríguez, and R. C. Muñoz González. 2009. Hospital Resource Management. In Simulation-Based Case Studies in Logistics: Education and Applied Research, eds. Yuri Merkuryev, Galina Merkuryeva, Miquel Àngel Piera, and Antoni Guasch. London, UK: Springer-Verlag London Limited, 65-84.
Czech, Matthew, Michael Witkowski, and Edward J. Williams. 2007. Simulation Improves Patient Flow and Productivity at a Dental Clinic. In Proceedings of the 21st European Conference on Modelling and Simulation, eds. Ivan Zelinka, Zuzana Oplatková, and Alessandra Orsoni, 25-29.
Ferrin, David M., Martin J. Miller, Sherry Winiger, and Michael S. Neuendorf. 2004. Analyzing Incentives and Scheduling in a Major Metropolitan Hospital Operataing Room through Simulation. In Proceedings of the 2004 Winter Simulation Conference, Volume 2, eds. Ricki G. Ingalls, Manuel D. Rossetti, Jeffrey S. Smith, and Brett A. Peters, 1975-1980.
Galloro, Vince. 2008. Healthcare Holds On Tight. Modern Healthcare 38(38):6-11 [22 September 2008].
Groover, Mikell P. 2007. Work Systems and the Methods, Measurement, and Management of Work. Upper Saddle River, New Jersey: Pearson Education, Incorporated.
Guimarans, D., J. J. Ramos, M. A. Piera, and A. Guasch. 2006. A Simulation Based Decision Tool to Coordinate Emergency Services in a Road Accident. In Proceedings of the 2006 Summer Computer Simulation Conference, eds. Agostino G. Bruzzone and Edward Williams, 110- 115.
Hullinger, D. Roger. 1999. Taylor Enterprise Dynamics. In Proceedings of the 1999 Winter Simulation Conference, Volume 1, eds. Phillip A. Farrington, Harriet Black Nembhard, David T. Sturrock, and Gerald W. Evans, 227-229.
Kelton, W. David, Randall P. Sadowski, and David T. Sturrock. 2007. Simulation with Arena, 4th edition. Boston, Massachusetts: The McGraw-Hill Companies, Incorporated.
Kroemer, K. H. E., and E. Grandjean. 1999. Fitting the Task to the Human, 5th edition. Philadelphia, Pennsylvania: Taylor and Francis Incorporated.
Lowery, Julie C. 1998. Getting Started in Simulation in Healthcare. In Proceedings of the 1998 Winter Simulation Conference, Volume 1, eds. D. J. Medeiros, Edward F. Watson, John S. Carson, and Mani S. Manivannan, 31-35.
McGuire, Frank. 1998. Simulation of Healthcare. In Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, ed. Jerry Banks. New York, New York: John Wiley & Sons, Incorporated, 605-627.
Miller, Scott, and Dennis Pegden. 2000. Introduction to Manufacturing Simulation. In Proceedings of the 2000 Winter Simulation Conference, Volume 1, eds. Jeffrey A. Joines, Russell R. Barton, Keebom Kang, and Paul A. Fishwick, 63-66.
Mundel, Marvin E., and David L. Danner. 1994. Motion and Time Study: Improving Productivity, 7th edition. Englewood Cliffs, New Jersey: Prentice-Hall, Incorporated.
Pidd, Michael, and Murat Günal. 2008. Better British Hospitals: Improving Health Care with Whole Systems Thinking and Simulation. Industrial Engineer 40:12(24- 29) [December].
Sargent, Robert G. 2004. Validation and Verification of Simulation Models. In Proceedings of the 2004 Winter Simulation Conference, Volume 1, eds. Ricki G. Ingalls, Manuel D. Rossetti, Jeffrey S. Smith, and Brett A. Peters, 17-28.
Swain, James J. 2007. New Frontiers in Simulation: Biennial Survey of Discrete-Event Simulation Software Tools. OR/MS Today 34:5(32-43) [October].
AUTHOR BIOGRAPHIES
RAVI LOTE holds a bachelor’s degree in Mechanical Engineering (Shivaji University, India) and a master’s degree in Industrial Engineering (University of Massachusetts, Amherst). Currently, he is studying to earn a Masters in Business Administration from the Ross School of Business, University of Michigan in Ann Arbor, Michigan. He is a certified six sigma black belt (ASQ), a certified supply chain professional (APICS), and a certified MODAPTS practitioner. He is a Project Manager at PMC, and highly familiar with simulation and facilities-layout optimization systems including AutoMod®, WITNESS®, Simul8®, QUEST®, and Flow Planner®. He is a member of the APICS and American Society for Quality [ASQ].
EDWARD J. WILLIAMS holds bachelor’s and master’s degrees in mathematics (Michigan State University, 1967; University of Wisconsin, 1968). From 1969 to 1971, he did statistical programming and analysis of biomedical data at Walter Reed Army Hospital, Washington, D.C. He joined Ford Motor Company in 1972, where he worked until retirement in December 2001 as a computer software analyst supporting statistical and simulation software. After retirement from Ford, he joined PMC, Dearborn, Michigan, as a senior simulation analyst. Also, since 1980, he has taught evening classes at the University of Michigan, including both undergraduate and graduate simulation classes using GPSS/HÔ, SLAM IIÔ, SIMANÔ, ProModelÒ, SIMUL8Ò, or Arena®. He is a member of the Institute of Industrial Engineers [IIE], the Society for Computer Simulation International [SCS], and the Michigan Simulation Users’ Group [MSUG]. He serves on the editorial board of the International Journal of Industrial Engineering – Applications and Practice. During the last several years, he has given invited plenary addresses on simulation and statistics at conferences or seminars in Monterrey, México; İstanbul, Turkey; Genova, Italy; Rīga, Latvia; Göteborg, Sweden; and Jyväskylä, Finland. He has served as Program Chair of the 2004, 2005, and 2006 Summer Computer Simulation Conferences, and also for the 2005 IIE Simulation Conference.
ONUR M. ÜLGEN is the president and founder of Production Modeling Corporation (PMC), a Dearborn, Michigan, based industrial engineering and software services company as well as a Professor of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn. He received his Ph.D. degree in Industrial Engineering from Texas Tech University in 1979. His present consulting and research interests include simulation and scheduling applications, applications of lean techniques in manufacturing and service industries, supply chain optimization, and product portfolio management. He has published or presented more that 100 papers in his consulting and research areas.
Under his leadership PMC has grown to be the largest independent productivity services company in North America in the use of industrial and operations engineering tools in an integrated fashion. PMC has successfully completed more than 3000 productivity improvement projects for different size companies including General Motors, Ford, DaimlerChrysler, Sara Lee, Johnson Controls, and Whirlpool. The scientific and professional societies of which he is a member include American Production and Inventory Control Society (APICS) and Institute of Industrial Engineers (IIE). He is also a founding member of the MSUG (Michigan Simulation User Group).
