Simulation Validates Design and Scheduling of a Production Line

November 21, 2013

Simulation Validates Design and Scheduling of a Production Line


esmith |


Vidyasagar Murty | Neelesh A. Kale | Rohit Trivedi | Onur M. Ülgen | Edward J. Williams


University of Michigan – Dearborn 4901 Evergreen Road

Dearborn, Michigan 48128 U.S.A.


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.



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.



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:

  1. Raw material is always available
  2. Operators are modeled as resources that are always
  3. The production is fully automated and
  4. Robots have 5% of downtime with one hour as mean time to repair (MTTR)
  5. Travel time between the buffer and furnace conveyor is zero.
  1. There is no blocking of parts upon exiting the furnace
  2. Die setup takes eight hours and startup takes two Both add to a total of ten hours for the changeover of die.
  3. The robot assembles the parts on a FIFO [first-in, first-out]
  4. 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.



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).



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:

  1. 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.
  2. 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
  3. 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.

  1. Batch scheduling is significantly superior to sequential


All five authors take pleasure in commending anonymous referees for their valuable suggestions to improve the organization and clarity of this paper.



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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.


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