See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/242781666 A CREATIVE APPROACH TO MODELING PRODUCT ORDERS IN AUTOMOD ARTICLE READS 8 2 AUTHORS, INCLUDING: Edward Williams University of Michigan-Dearborn 103 PUBLICATIONS 200 CITATIONS SEE PROFILE All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. Available from: Edward Williams Retrieved on: 08 January 2016
A CREATTVE APPROACH TO MODELING PRODUCT ORDERS IN AUTOMOD
Edward J. Williamslndustrial & Manufacturing Systems Engineering
University of Michigan - Dearborn4901 Evergreen RoadDearborn. MI 48128
ABSTRACT
This paper presents a creative approach to modeling product orders inAutoMod. The technique presentedis ahighly effrcientand accurate methodwhen product orders are complicated but occur with a fairly low volume. Webegin by discussing the two typical means of representing product orders:( I ) using an order history file and (2) using statistical distributions to modelall aspects of product orden. We then discuss the "new" approach that wehave developed that involves using distributions to model order volume, butthen reading in actual product orders into large AutoMod variable arrays andrandomiy sampling orders from the AutoMod "order matrix," thus createdin order to accurately represent characteristics associated with orders. Wethen discuss the issues associated with implementing this technique inAutoMod and the use of this technique in a simulation model formanpowerand a*ssembly cell planning that was developed for a water faucet manufac-turing firm. We conclude by describing the situations where this approachis preferable to other approaches.
I INTRODUCTION
The importance of accurate representation of incoming orders extendsacross a broad spectrum of simulation applications. The work of Chinand Sprecher ( 1992), adapting a manufacturing-based simulation pack-age to model a customer service center, is an example of this breadth ofapplicability. Pritsker and Yancey ( 1991) describe determining capacityrequirements of a manufacturing facility appropriate to order arrivals.Optimization of inventory sysrems (Fu and Healy, lgg}), spare partscontrol, and inventory planning (Bier and Tjelle, 1994) must be based onaccurate representation of order arrivals. Likewise, Takakuwa's model-ing approach (1991) to achieve efficiency of computer-aided cart sys-tems needs accurate models of incoming demands.
There are two traditional means of modeling product orders:
o use an actual order history "as is"
. use distributions to model the number, timing, and characteristics
of incoming orders
Each of these approaches has advantages and disadvantages, which are
discussed in the remainder of this section.
2.1 Traditional Approach #1: Using an order history "as is"
ln this approach, the actual order history over the period of interest is
obtained and product orders are brought into simulation runs exactly as
they were in the past in "real-life." To illustrate, if order #348678 for 25
units of parr #A8456 occurred at 8:35 AM on the 2lst day of the period
from which data was obtained, then in every simulation run this exact order
is read into the model at 8:35 AM on the 21st day of the simulation run'
Although this technique is a generally poor method of analysis, these
advantages are associated with it:
l. Using actual data as the order history is almost certainly the
quickest modeling method, requiring no preparatory analysis'
2. Despite its analytical shortcomings, this approach is very believ-
able to the model user, particularly those having little or no prior
experience with simulation. Hence, this approach readily achieves
credibility for the model.
The analytical problems associated with using this technique are as follows:
l. Replications will be unable to model true system variability, since
this approach in effect says "the only input the model will ever be
able to experience is those data the real-world system has already
experienced."
2. Running the model for a time period different from (particularlygreater than) the period covered by the order history is difficult.
3. This "actual history" approach causes difficulty in experimentingwith changes to order characteristics. Such experimentation is
often a major motivation for the simulation study - for example,
the users may have requested the model to explore the effects ofanticipated increases in the percentage oforders requiring process-
ing on acertain machine orto explore the effects of producing a new
product. Both of these changes to a "base model" are difficult to
incorporate using the "actual history" approach.
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2.2 Traditional Approach #2: Using Statistical Distributions
The second traditional approach entails using statistical distributions tomodel all aspects of order arrival and order characteristics. For example,following the arrival of an order, a statistical distribution mightbe sampledto model the time between the order that just arrived and the next order.Then, upon arrival of the new order, statistical distributions would besampled to determine such things as order type, order size, and variouscharacteristics associated with the order such as order priority, deadlines,machine requirements, manpower requirements, etc.
Using statistical distributions fitted to the order history data is a moreaccurate technique than the "actual history" approach described in Section2.1. Nonetheless, it does have its disadvantages in addition to its advan-tages. The advantages of using statistical distributions are as follows:
l. Using distributions to model order parameters immediately re-moves the restriction that only data actually seen may appearin themodel, thereby enabling the model to reflect system variability.
2. The effects of changes in order frequency or characteristics can readilybe assessed by changing parameters of the fitted disnibutions.
3. The model can readily be run for whatever length of simulatedtime is required to support statistical analyses of its output.
However, using statistical distributions to model all aspects of incomingproduct orders does have some drawbacks, which are indicated below:
1. Fitting statistical distributions to the observed data requires con-srderable analysis time; this time requirement is likely to fall on thecritical path of the simulation project because model verificationand validation cannot begin until the fitted distributions are in-serted into the model.
7. Distribution fitting requires the use of software specialized to thattask; t)'picai statistical-analysis software packages do not includethis capability'.
3. Accurate re presentation of interrelationships (correlations) amongorde r parameters is also difficult. For example, orders that arrivefrequently (short interarrival times) may tend to be for smallerquantities of product than orders arriving less frequently. Thisdistribution-fitting approach, first fitting a distribution to rheobserved interarriva.l times and then to the observed order sizes,would produce two distributions and their accompanying param-eters. However, neither distribution. inserted into the simulationmodel, w'ould acknowledge the existence or influence of the other.There are methods for attempting to take into account interrelation-
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ships among input parameters, but they can be difficult to imple-ment, particularly when dealing with categorical data, non-linearrelationships, and/or interrelationships involving several charac-teristics. A discussion of modeling correlated input variables is
discussed in some simulation textbooks, including Law and Kelton(1991) and Banks and Carson (1983).
Accurate representation of temporal variations among orders islikewise difficult. A single fitted distribution is unable to modelcommon situations such as more frequent orders at the beginningof each month or larger order sizes in spring and summer than in falland winter.
Finally, since this approach is less intuitive than the "use data as is"approach, it is less credible, particularly to the less experienceduser.
3 AN ALTERNATTVE COMPOSITE APPROACH
3.1 Description of Composite Approach
The approach that we have developed is a composite of the two traditionalapproaches and is rather simple to comprehend and implement. In ourapproach, we use statistical distributions to model the interarrival timesbetween orders, just as one would do when using Traditional Approach #2.Then, instead of using distributions to model order characteristics, weincorporate an alternative strategy. First, at the start of a simulation run, allproduct orders are read into variable arrays to create a "product ordermatrix." Then, each time an order is brought into the model, one of theactual product orders is randomly selected from the "product order matrix"and the characteristics from that order are used as the characteristics for theorder entering the model. ln other words, it randomly selects actual ordersfrom the observed data to set pertinent characteristics of each orderbeginning its flow through the model.
3.2 Advantages of Composite Approach
The advantages of this approach are as follows:
I . This approach reduces the analysis time required to fit distributionsto observed data.
2. Order characteristics are set such that each random,ly generatedorder is a replica of an actual order, greatly strengthening modelcredibility with the user.
3. Interrelationships among order characteristics can be represented
easily and accurately.
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3.3 Disadvantages of Composite Approach
However, there are some drawbacks to this technique:
When the quantity of orders obtained from the historical periodbeing used for analysis is very large, the representation ofthis orderhistory within model variables will place heavy computationaldemands on the simulation model. In the event that all orderscannotbe read intothe "orderhistory matrix" and only apercentageof the orders can be read in, then variation among orders may besuch that an accurate representation of true system variation will bedifficult.
If an order must be represented by multiple records, the design ofthe "order history matrix" can become a complicated databasedesign problem.
4 IMPLEIVIENTING THIS APPROACHINAIITOMOD
This approach to modeling the arrival of orders hasbeenusedin amanpowerand assembly planning model developed in AutoMod for a water faucetmanufacturing firm. ln this application, a key component of the model wasthe accurate modeling of the arrival of faucet orders at the company.calendar year 1993 was the period of interest for which historical faucetorder data were to be used for analysis in the modeling of incoming orders.
The faucet orders that the company receives are quite complex (from amanufacturing standpoint) in that specific primary and secondary process-ing times and locations are associated with each part, orders have differentpriority classifications, and some parts utilizing automated equipmentrequire specific quantities of people to man the equipment while they arebe i n g ru n. There are al so interrel ationships between these order character-istics. Finallv, it was also desired to model the production of subassem-blies needed to produce final assemblies and each parent part has specificsubcomponents associated with it. It was found that AutoMod's "orderlists" and its ability for loads to modify the attributes associated with otherIoads using "load pointer attributes" served as an excellent means ofhaving loads representing orders for final assemblies await productionuntil all necessary subassemblies had been produced.
The ability to model complex orders easily and accurately is an advantageof this method over the two traditional techniques and consequently thistechnique was chosen for use. unfortunately, the volume of orders for the1993 calendar was too great for all orders to be read into AutoMod.Consequently, it was necessary to produce a separate order history file foreach true model replication, which was a time-consuming task. Theseorder data files were created in Microsoft Access, exported in a tab-
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delimited format, and easily read into AutoMod. (A "snippet" from one ofthese datafiles is shown in Figure I for illustration purposes.)
Due to large memory requirements associated with thrs techruque, it was
necessary to increase the swap space on the workstation on which the modelwas being built in order to be able to read a reasonable quantity of orders into
the model. (Incidentally, for this project we were not able to read in areasonable quantity of orders into the PC version of AutoMod being run on a
32 MB PC.) After increasing the swap space on our SGI Indigo-2 Extreme
workstation from 40 MB to 80 MB, it was found that I 600 orden could be used
for order datafiles and still be capable of being read into AutoMod. Since it was
necessary to represent many orders by more than one record (due to orders for
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multiple products and the modeling of subassembly production), these l60Gorder order datafiles typically contained around 25,000 records and we werequite impressed with AutoMod's ability to read such a massive amount of datainto variable arrays and still perform simulation runs.
Once the swap-space issues were resolved on the workstation (a difficultresolution with the particular version of UNIX being employed), noproblems were encountered with the reading of order datafiles intoAutoMod, except for a small, but difficult-to-diagnose problem thatoccurred with reading in a part number containing an embedded space.
In retrospect, the volume of orders involved with this particular project wassuch that "Traditional Approach #2", using statistical distributions torepresent all aspects ofproduct orders, would probably have been a befterapproach for this particular application. Nonetheless, there are certainsituations (albeit somewhat limited) in which we feel that the techniquepresented in this paper is better suited for modeling incoming product ordersthan either of the two traditional approaches used for this task.
5 RECOMMENDATIONS
Our hybrid approach for modeling product orders is recommended whenthe system being modeled has the following properties:
l. The system has low-to-moderate order volume and few records perorder, such that all orders from the period of interest can be readinto AutoMod.
2. There are many (e.g., more than ten) characteristics associated witheach order.
3. There are complex interrelationships among order characteristicsthat cannot be easily represented by linear correlations.
6 INDICATIONS FOR FURTHER WORK
The ease and efficiency with which our hybrid approach can be employed(when appropriate) can be increased via the following:
. increased knou,ledge of relational database design and implemen-tatlon concepts and methods among simulation practitioners andmodel builders
' incrcased attention to interfaces between simulation software toolsand relational database management systems among the vendorsof model-building software languages and packages.
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ACKNOWLEDGMENTS
Dr. Onur Ulgen of Production Modeling Corporation (PMC) and professor
of Industrial and Manufacturing Engineering at the University of Michigan- Dearbom made valuable criticisms toward improving the clarity of this
paper. Dr. Ali Gunal and Dr. Dev Sathyadev (both of PMC) provided
valuable assistance with the inco5poration of our technique in AutoMod.Jennifer Rusinowski (also from PMC) provided valuable assistance in the
formatting of this paper.
APPENDIX: TRADEMARK
Microsoft Access is a registered trademark of Microsoft Corporation.
REFBRENCES
Bier, Isai ah J. and James P. lelle. I 994. The Importance of Interoperabilityin a Simulation Prototype for Spares Inventory Planning. Proceed-ings of the 1994 Winter Simulation Conference, eds. Jeffrey D.Tew, S. Manivannan, Deborah A. Sadowski, and Andrew F. Seila,913-919. Seattle, WA: The Boeing Company.
Chin, Victor, and Steven C. Sprecher. 1990. Using a Manufacturing Based
Simulation Package to Model a Customer Service Center. Proceed-
ings of the 1990 Winter Simulation Conference, ed. Osman Balci,Randall P. Sadowski, and Richard E. Nance,9M-907. Florence,KY: Square D Company.
Fu, Michael C. and Kevin J. Healy. I 992. Simulation Optimization of (s,S )Inventory Systems. Proceedings of the 1992 Winter SimulationConference, ed. James J. Swain, David Goldsman, Robert C. Crain,and James R. Wilson,506-514. College of Business and Manage-ment, University of Maryland; School of Industrial Engineering,Purdue University.
Pritsker, A. Alan B. and David P. Yancey. 1991. Total CapacityManagement Using Simulation. Proceedings of the 1991 WinterSimulation Conference, ed. Barry L. Nelson, W. David Kelton, and
Gordon M. Clark, 348-355. West Lafayette,IN: Pritsker Corporation.
Taliakuwa, Soemon. 1991. Design and Analysis of Computer-AidedCartsystems for Picking Discrete Items. Proceedings of the 1991
Winter Simulation Conference, ed. Barry L. Nelson, W. DavidKelton, and Gorton M. Clark, 365-314. Tokyo, Japan: School ofBusiness Administration, Too University.
Law, Averill M. and W.D. Kelton. 1991. Simulation Modeling and
Analysis. New York: McGraw-Hill.
Banks, Jerry and Carson, John S. 1983. Discrete-Event System Simula-tion. Enslewood Cliffs. NJ: Prentice Hall.
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AUTHOR BIOGRAPHIES
EDWARD J. WILLIAMS holds bachelor's and masrer's degrees inmathematics (Michigan State University, 1967; university of wisconsin,1968). From 1969 to 1971, he did statistical programming and analysis ofbiomedical data at Walter Reed Army Hospital, Washingron, D.C. Hejoined Ford in 1972, where he works as a computer analyst supportingstatistical and simulation software. since 1980, he has taught eveningclasses at the University of Michigan, including both undergraduate andgraduate simulation classes.
ERrc R. HAAN holds a B.S. in Industrial and operations Engineeringfrom The University of Michigan (Ann Arbor). He worked at GM -Lansing Automotive Division as a co-op engineering student from 1986 to1992 and was hired as a Quality Engineer in May,1992. since November,1992, he has been an Applications Engineer with Production Modelingcorporation in Dearborn, MI, working primarily with discrete-eventsimulation chiefl y involvin g automotive-industry manufacturing.
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