The client is a multinational conglomerate that focuses on industrial engineering and steel production. PMC offered a discrete-event simulation model to evaluate the design of a new production line and validate the throughput capacity envisioned by the client. Our utilization of discrete-event simulation techniques allowed the client to test different layout and process configurations in their design phase of their project.
SYSTEM DESCRIPTION
The facility has four operations – two assembling operations, one press area and one assembling/testing process split into various work stations.
One operator replenishes raw components for the first two operations and there is one robotic arm in each of the operations to transfer parts between the work stations.
The press area contains one press that receives assembled parts from previous operations and compress them so one of the three operators, on the last two operations, can pick and transfer the compressed part to the last operation.
The fourth operation has one welder, one conveyor, three work stations and three operators who will finish assembling the parts and perform various tests accordingly.
OPPORTUNITY
The current line design was not finalized and had not been tested to see if it was able to meet customer demands in terms of volume, cost, and quality. Hence, there was a need to simulate the different operations to identify any design problems, equipment utilization, headcount and overall throughput capacity.
APPROACH
The data and layout provided by the client was imported to SIMUL8®. The four different operations were included in the model. An Excel® interface was created to input data for the simulation model. This unique technique by PMC allowed the client to have the flexibility of changing most of the inputs for the simulation directly from the Excel® interface, reducing the modeling.
SOLUTION
The discrete-event simulation model successfully and accurately determined the overall throughput capacity of the given production line design as well as the utilization of the different operators and equipment. Using the results from the baseline model, process improvements were made to the original production line. These improvements were then tested by running the simulation model for multiple scenarios. The results were used to find the best configuration that would maximize the overall throughput capacity and reduce the headcount.
BENEFIT
In addition to the simulation study, an Excel® interface was provided to the client for making changes to the operation times, which will allow them to run what-if scenarios in case the process specifications change. Additionally, by using the simulation model to test different layout and process configurations, the client reduced the headcount by one and the number of tools used on the last operation by two. Furthermore, the client also found the best way to use its resources and maximize the line production capacity. The ROI on this project was 10 times the amount invested on the simulation study.
PMC helped a major automaker design the layout of a parts warehouse. Using simulation, researchers determined the staffing levels that different proposed layouts needed to achieve the facility’s targeted throughput.
SYSTEM DESCRIPTION
The proposed warehouse was to receive, store and distribute windshields and many parts associated with them. One group of resources, the “pickers,” were to traverse the warehouse picking parts out of inventory to fill purchase orders. Other resources, the “restockers,” were to continually replenish inventory. The physical layout of the plant was not yet determined; one proposal called for a two-tier system, with inventory arranged along seven aisles, while another prescribed a one-tier system and thirteen aisles.
OPPORTUNITY
Pickers and restockers were to work simultaneously, which raised issues of traffic flow, material flow and safety. Also, it was known that the warehouse would have to attain a high level of throughput, but the automaker wished to achieve this aim with a minimum of workers in order to limit labor costs. Given all these complications, the automaker needed to determine optimum warehouse configuration prior to construction, in order to prevent the future expense of high staff levels or overhaul of the physical layout.
APPROACH
The objective was to determine how many workers were required to safely and reliably produce a total throughput of 900,000 pieces per year for the one and two-tier proposed scenarios.
SOLUTION
Researchers began by collecting information on the specific dimensions of the proposed layouts. They also studied representative samples of parts orders, plans for storage of parts within the warehouse, and the decision algorithms and floor-scale motions that workers in the warehouse would need to make. After reviewing these findings with the client, PMC researchers built a series of simulation models.
BENEFIT
Results of simulation runs indicated that the one-tier scenario would yield the best performance, meeting the target of 900,000 pieces per year with only 13 pickers and 6 restockers.
An airframe manufacturer sought to improve their throughput rate. Previously, they had been first-to-market with a particular airframe model, resulting in both a high number of orders and a great deal of production process uncertainty. PMC was tasked with providing a plan that would allow the manufacturer to double production levels. Our utilization of discrete event simulation techniques allowed the company to identify and eliminate non-value-added production steps, evaluate plant layouts, review hiring and training plans, and observe tooling selections in a virtual setting.
SYSTEM DESCRIPTION
Three product groups were produced at each of the facilities: Doors, Curtain Walls and Skylights. The manufacturing systems are set up for both custom products and standard products. Each engineering process is similar but uses differing software and processes to achieve similar goals. The client required a CAD solution, along with a PLM system, that would be common across all Engineering and Manufacturing facilities.
OPPORTUNITY
Many of the stations within the production system were experiencing high ‘waiting’ and ‘blocked’ times. This wasted time represented an opportunity for improvement. Throughput could be increased by minimizing these times. Additionally, high variability in cycle times was constraining the plane production rate.
APPROACH
PMC developed a discrete event simulation model using Simul8 software. The model was used first to verify the opportunities related to the station in-state times, and subsequently employed to by the PMC team to conduct what-if analyses and create a plan for an improved production process.
SOLUTION
PMC’s simulation model verified that the manufacturer’s goal of doubling throughput was feasible. An improvement roadmap was compiled detailing what actions were required to meet the goal, and the quantitative gains predicted with the completion of each prescribed action.
BENEFIT
Upon project completion, PMC had supplied an explicit plan for meeting the goal of doubled throughput. Additionally, PMC illustrated that quadrupling production levels was possible while still using the existing plan footprint. Excessive work-in-process (WIP) inventory and less than optimal levels of personnel utilization were eliminated by the identification and removal of waste throughout the system. The manufacturer was able to use the simulation model and improvement roadmap supplied by PMC to create plant layouts, design training and hiring plans, and make informed tooling purchases.
A major defense contractor retained PMC to perform a capacity analysis study of a vehicle processing facility. The goal of the project was to allow for
of the vehicle throughput and processing bay utilization under different operating scenarios. PMC utilized a discrete event simulation model to meet the project requirements.
Client Challenges
Funding Constraints
Undersized Facility
Tight Delivery Schedules
Underutilization of available resources
SYSTEM DESCRIPTION
The vehicle was first produced then transported through a variety of stations and two processing bay areas, before reaching the final process and exiting the facility.
OPPORTUNITY
Throughput at the facility was lower than required for meeting demand. The capacity allotment and utilization rate of the processing bays was suspected to be the constraining factors.
APPROACH
PMC’s plan consisted of four main steps:
1. Identify and collect information relating to facility, vehicle, processes,
and other resources
2. Develop discrete event simulation model using SIMUL8 software
3. Simulate production under four demand scenarios
4. Perform output analysis and system evaluation
SOLUTION
The SIMUL8 model created by PMC’s simulation team allowed the facility to improve the layout and design of the facility. Simulation was performed on the four scenarios:
• Fixed sequence + fixed inter-arrival times (FSFI)
• Random sequence + fixed inter-arrival times (RSFI)
• Fixed sequence + random inter-arrival times (FSRI)
• Random sequence + random inter-arrival times (RSRI)
BENEFIT
This simulated model granted the manufacturer the flexibility to study the effect of demand fluctuations on
throughput, and also to quantify costs due to storage delays and waiting times. The model provided the
ability to plan ahead for future demand, and design the facility in a manner that would permit the desired
throughput.
DEVELOPMENT AND USE OF A GENERIC AS/RS SIZING SIMULATION MODEL
Srinivas Rajanna Edward Williams Onur M. Ülgen
PMC
15726 Michigan Avenue Dearborn, MI 48126 USA
Vaibhav Rothe
PMC India
ABSTRACT
As usage of simulation analyses becomes steadily more important in the design, operation, and continuous improvement of manufacturing systems (and historically, manufacturing was the sector of the economy first eagerly embracing simulation technology), the incentive to construct generic simulation models amenable to repeated application increases. Such generic models not only make individual simulation studies faster, more reliable, and less expensive, but also help extend awareness of simulation and its capabilities to a wider audience of manufacturing personnel such as shift supervisors, production engineers, and in-plant logistics managers.
In the present study, simulation consultants and client manufacturing personnel worked jointly to develop a generic simulation model to assess in- line storage and retrieval requirements just upstream of typical vehicle final assembly operations, such as adding fluids, installing seats, emplacing the instrument panel, and mounting the tires. Such a final assembly line receives vehicles from the paint line. The generic model permits assessment of both in-line vehicle storage [ILVS] requirements and AS/RS [automatic storage/retrieval system] configuration and performance when designing or reconfiguring vehicle paint and/or final assembly lines. The AS/RS is the physical implementation of the ILVS. These assessments, at the user’s option, are based upon current production conditions and anticipated future body and paint complexities.
1. INTRODUCTION
Manufacturing systems represent perhaps both the most frequent and the oldest application areas of simulation, dating to at least the early 1960s (Law and McComas 1998). Questions asked of a simulation model now go far beyond “Will the manufacturing system reach its production quota?” [often expressed as “JPH” = “jobs per hour]. With ever-sharpening competition driving management demand for lean efficient operation ( 2010), simulation analyses are now being called upon to not only achieve production quotas, but also to minimize inventory (both in-line and off-line) and the time and resources to access that inventory whenever necessary.
Furthermore, the accelerating pace of change, often driven by both fickle marketplace demands and by competitive pressures, have increased interest, on the part of both managers and production engineers, in the availability of generic adaptable simulation models. These models, when feasible, represent attractive improvement relative to “We wish the world would stop evolving while we await the building, verification, and validation of a custom-built simulation model for answering our pressing questions.” This interest is hardly new – the software tool “GENTLE” [GENeralized Transfer Line Emulation], which allowed quick study of a common type of automotive manufacturing line via a model built in GPSS (Schriber 1974), dates back nearly two decades (Ülgen 1983). As the attractions of such generic models become more widely known, their development is becoming more frequent. For example, (Legato et al. 2008) describes the development and use of a generic model for the study of maritime container terminals. Still more recently, (Zelenka and Hájková 2009) describes the development and use of a generic model for the study of road traffic.
The generic model described here permits examination of the ILVS capacity requirements interposed between the paint line and the final assembly line within vehicle assembly plants. Such examination demands high flexibility relative to volatile production conditions, future market demand, and particularly variations in vehicle resequencing. Vehicles typically exit the paint line in a sequence very different from that anticipated by the final assembly line operators. Perhaps shockingly, occasionally fewer than 5% of the vehicles arriving at final assembly are in “correct” (i.e. expected) sequence. Therefore, the ILVS must be capable of short-term storage so the operator can shunt one vehicle aside to attend to another arriving later but originally expected earlier. We first provide details of the project objectives and the key performance metrics to be tracked each time the generic model is used. Next, we describe the methods of obtaining and cleansing input data for a typical scenario using the model. Next, we describe the structure of the generic simulation model itself. Last, we show the results from a typical application of this model, and indicate directions for future work and enhancements to the model.
2. PROJECT CONTEXT AND OBJECTIVES
The goal of this simulation study was quantitative assessment of the in-line storage requirements between the paint line and the downstream final assembly line in the automotive manufacturing process. Ideally, vehicles exit the paint line in strict accordance with a previously planned production sequence. This ideal sequence is determined by production scheduling engineers using a standard optimization program. This program minimizes (almost always succeeds in setting to zero) the number of violations of long- standing production rules. Examples of these rules are “Avoid scheduling two moonroof-equipped vehicles consecutively” or “Avoid scheduling two vehicles with identical engine-powertrain configurations consecutively.” If this optimized sequence could actually be maintained in production practice (veteran production managers in the industry might cynically grumble “Perhaps on some distant planet.”), these storage requirements would remain at or near zero – the right parts for the exiting vehicle next in line would themselves be next at the assembly line. For example, the seats poised to be installed in the vehicle would be the correct seats for that vehicle type and paint color. In actuality, due to inevitable production plan changes (such as revisions to the proportions of different models demanded by the marketplace) and other transient problems in the paint shop (and indeed in other operations upstream of the paint shop), vehicles never arrive in the originally planned sequence. This simulation study sought to examine, relative to various performance metrics, the extent of ILVS needed to install the right parts in vehicles at assembly, and the amount of labor needed to access those parts from the storage. The client and consultant managers reached consensus that the model would be generic in that it could accept data from typical automotive plants having body, paint, and final assembly in that order, as almost all such plants do. Such plants, when run at fewer than three shifts per day, will inevitably have non-zero storage requirements even in the limiting case, mentioned above, when no sequence changes occur. Therefore, the model developed is also generic in the sense that it can readily be run with no sequence violations but on one or two shifts, thereby allowing client engineers and managers to assess “background” storage requirements.
To introduce and explain these metrics, let us consider the situation in which vehicles originally scheduled in order 1, 2, 3, 4, 5 leave the paint shop in order 1, 4, 5, 2, 3. A vehicle is considered “in sequence” if its sequence number exceeds that of all vehicles which have preceded it. In this example, the first 3 of the 5 vehicles are in sequence, giving a “percent in sequence” of 60%.
Now, let us consider the actions of the worker, at a specific workstation, responsible for installing the front passenger seat (one of the four seats per vehicle) relative to the stored parts when vehicle 4 arrives. Each front passenger seat comes from a separate storage rack – and these seats arrived from a supplier in a specified sequence. For example, the supplier received advisory “A white seat must be first, then a gray one, then a dark blue one, in accordance with our planned production schedule.” When vehicle 4 arrives, the operator must remove the front passenger seat intended for vehicle 2 and the front passenger seat intended for vehicle 3 from the appropriate racks , and set them aside (in the “set-aside rack”). The “set aside” metric is then 2. This incremental work for the operator (an occasion of moving parts around, which is muda [non-value-added activity]) represents one “dig.” By contrast, installing the seats in the recently painted vehicle is a value-added activity. Relative to this dig, the operator removed 2 seats from each storage rack (for example, he or she removed front passenger seats for vehicles 2 and 3 to access (“get at”) the front passenger seat for vehicle 4. Hence, this dig has a “dig depth” of 2. The set-aside metric and the dig depth metric are closely correlated with the “spread” of the sequence – the maximum difference between sequence numbers of adjacently arriving vehicles. In this arrival sequence 1, 4, 5, 2, 3; the spread is 3 (between vehicles 1 and 4).
In this context, this simulation study sought to specify the proper ILVS size (vehicle capacity) relative to current and anticipated production conditions, particularly the amount of “complexity”– the product of the number of vehicle varieties and the number of paint color choices. Additionally, the study investigated two key in-transit production times:
Time-in-system vehicles spend between match-point (the milestone in body-&- assembly (upstream of painting) where a vehicle receives its vehicle identification number [VIN] and all its features are defined, and hang-to-paint (where a vehicle leaving body-&-assembly is suspended from a conveyor-carried hook and carried into the paint shop)
Time-in-system vehicles spend between hang- to-paint and entry to the AS/RS constituting the ILVS, at which time they are painted and await final assembly.
3. INPUT DATA – SOURCE AND CLEANSING
One of the most vital, though often unheralded, phases of any analytical simulation project is obtaining (and equally important, checking and cleansing) the input data required (Williams 1996). In this project, the existing process already had equipment installed for extensive data collection. Accordingly, the data necessary to build and validate (after verification) this model came from a database which automatically recorded more than a dozen date/time stamps on each vehicle passing through the process. These data, pertaining to approximately 11,000 vehicles, each identified by its VIN [vehicle identification number], were obtained from the database. The data were uploaded first to a large Microsoft Excel® workbook. There, the data were cleansed by visual inspection, by using Excel®’s data validation techniques, and by inspecting a variety of quickly and easily generated plots. Once ensconced in Excel®, the data could readily be input into the simulation model to control arrival times and/or for use in validating the simulation model against actual production.
As an example of important information obtained from these data, Figure 1 (Appendix) shows the empirical distribution of transit times between the body-&-assembly match point and entry into the ILVS AS/RS system between the painting and final assembly operations. These data are strongly positively skewed (right-skewed): although fewer than one-sixth of the observations are greater than 20 hours (performance goal), the mean time is 17.2 hours and the maximum time 41.1 hours. The 20- hour threshold (elapsed time from match point to completion of painting should not exceed this value), chosen by high-level production management of the client company, represents an attempt to keep the AS/RS inline storage requirements small. When this transit time exceeds 20 hours, excessive AS/RS capacity represents a palliative for inefficiencies in the body and/or the paint operations.
4. SIMULATION MODEL CONSTRUC- TION, VERIFICATION, AND VALIDATION
After discussion of alternatives, client personnel and the simulation analysts agreed on the use of the SIMUL8® simulation software tool (Hauge and Paige 2001) to build the model. Like its numerous competitors, SIMUL8® provides built-in constructs for the modeling of buffers and conveyors, both of significant importance to this model. Figure 4 (Appendix) is a screen shot of this model. Furthermore, this tool affords convenient importation of large blocks of data from Excel® workbooks. After examination of sample data, appropriate distributions (usually exponential or Erlang) were chosen for process times using a distribution fitter – a specialized software tool which examines an empirical data set and chooses a suitable statistical distribution for its characterization (Law and McComas 2002).
Verification and validation of this model used traditional techniques. These techniques included informal inspections and walkthroughs among the model developers, step-by-step execution while watching the animation, removing all randomness from the model temporarily, allowing only one entity into the model, and directional testing (Sargent 2004). After errors (e.g., mismatched time units at various points of the model) were corrected, the model achieved agreement within 5% of typical plant experience, and specifically with reference to the key performance metric of “elapsed vehicle time between match point in body & assembly to entry into the AS/RS.” Hence, the model achieved credibility among client management.
5. RESULTS
The major usage first made of the model was relative to achievement of the AS/RS performance goals established by plant management. These goals specified that the AS/RS must be of sufficient size (but not unnecessarily large) to achieve the performance metrics summarized in Table 1.
Table 1. AS/RS Performance Metric Goals
% Vehicles in
Sequence
No less than
98%
Vehicle set-asides
No more than
10
Dig depth
No more than
5
Digs/100
No more than
2
The model was repeatedly run with the current complexity level (220) and the hypothesized AS/RS capacity increased by one unit at a time, beginning at 350. Runs were made on a steady- state basis with warm-up time 2880 minutes (48 hours, equivalent to one calendar week at the plant) and simulation time 20,000 minutes (about seven weeks calendar production time). Graphical results of particular importance are shown in the Appendix (Figures 2 and 3). These runs demonstrated that the minimum acceptable capacity for the AS/RS, at current complexity levels, was 365 units. Since the client specified most production parameters (e.g., cycle time, BIW complexity), sensitivity analyses were not performed.
Table 2 below, shows detailed results of 15 distinct replications, with a different random number stream generator used for each replication.
Min.
Fill Level
% in Seq. (ASRS
Out)
Max.
Set Aside
Max.
Dig Depth
Digs/ 100
386
98.01%
8
2
1.98
393
98.00%
7
2
1.99
391
98.03%
7
2
1.96
391
98.04%
7
2
1.95
390
98.00%
8
2
1.98
389
98.00%
8
2
1.98
392
98.03%
8
2
1.96
393
98.04%
9
2
1.94
393
98.04%
8
3
1.95
391
98.01%
8
3
1.98
391
98.04%
8
2
1.95
392
98.02%
8
2
1.97
392
98.01%
9
2
1.98
393
98.00%
9
3
1.98
391
98.03%
9
2
1.96
Table 2. Detailed Results of Fifteen Replications
6. CONCLUSIONS AND FUTURE WORK
In the future, the complexity level will surely change periodically. Since this level depends heavily on marketing plans, production managers will have reasonable (several weeks or months) notice, during which operational parameters may be adjusted. Using this model, these managers will be able to insert a new complexity level and determine an updated AS/RS capacity requirement. With this comforting capability in reserve, managers in the client company have come to embrace the “simulate earlier” exhortation as enunciated within (Ball and Love 2009).
ACKNOWLEDGMENTS
The authors gratefully express gratitude to colleague and team leader Ravi Lote for his high- quality guidance of this project. Additionally, collaboration from the client’s engineers was most helpful. Comments from anonymous referees have improved the presentation and clarity of this paper.
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AUTHOR BIOGRAPHIES
SRINIVAS RAJANNA, CPIM, is a Senior Manager with over fourteen years of experience in simulation, lean, production, process improvement, six-sigma, theory of constraints, supply chain, and managing projects. He was graduated from Bangalore University with a Bachelor of Engineering in Mechanical Engineering. He holds a Master’s Degree in Industrial Engineering from West Virginia University and an MBA from The Eli Broad Graduate School of Management, Michigan State University.
Srinivas has broad industry experience including automotive, aerospace, semiconductor, consumer, healthcare, and pharmaceutical. He has experience providing solutions that include: developing a throughput improvement roadmap to meet the production target, assessing the operation strategies of a pharmaceutical firm, conducting material flow studies to reduce traffic congestion, optimizing the utilization of staff and equipment, applying lean strategies in manufacturing and service industries, and using analytical techniques such as flow charts, value stream mapping, and process mapping.
VAIBHAV ROTHE is a Technical Lead with interests in the various applications of simulation in the field of Industrial Engineering. Vaibhav has experience in Simul8®, Enterprise Dynamics®, Witness® and Arena®. He received a Master’s degree in Industrial Engineering from the University of South Florida and a Bachelor’s degree in Mechanical Engineering from Regional College of Engineering, Nagpur, India. Vaibhav’s recent projects have spanned a number of industry sectors: aerospace, automotive, steel etc. He has worked as a consultant on projects providing solutions such as capacity planning, scheduling, logistics, six sigma and lean manufacturing. He has had experience in completing several successful simulation-based studies, providing training and customized technical support.
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 classes at the University of Michigan, including both undergraduate and graduate simulation classes using GPSS/H , SLAM II , SIMAN , ProModel , SIMUL8 , 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; İstanbul, Turkey; Genova, Italy; Rīga, Latvia; and Jyväskylä, Finland. He served as a co-editor of Proceedings of the International Workshop on Harbour, Maritime and Multimodal Logistics Modelling & Simulation 2003, a conference held in Rīga, Latvia. Likewise, he served the Summer Computer Simulation Conferences of 2004, 2005, and 2006 as Proceedings co-editor. He is the Simulation Applications track co-ordinator for the 2011 Winter Simulation Conference. His email address is ewilliams@pmcorp.com.
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).