مطالعات شبیه سازی تجربی در مدیریت عملیات: فرصت های زمینه، روندها و تحقیق
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|7732||2004||10 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Operations Management, Volume 22, Issue 4, August 2004, Pages 345–354
This study investigates, published simulation studies in operations management (OM) that are empirically based. The results of the study are based on an exhaustive search of twenty leading operations management journals over the period from 1970 to 2000. Approximately, 600 published simulation studies in operations management were identified, but only 85 of these were subsequently identified as being empirical in nature. The 85 articles were next classified into one of 17 categories. Results by journal, topic, time period, and combinations of these factors are reported. Finally, opportunities for future research are discussed.
Computer simulation is one of the most widely used research methodologies employed in the field of operations management (OM). For example, Amoako-Gympah and Meredith (1989) found that of the 363 articles published between 1982 and 1987 included in their survey of OM research, that modeling was the most widely used research approach accounting for 38.0% of the articles followed by simulation, which represented another 30.8% of the articles. The authors also noted that the simulation studies tended to use randomly generated data and real-world data was only used in a few cases. More recently, Pannirselvam et al. (1999) surveyed the OM literature to investigate the issue of an OM research agenda. Based on the OM papers published in seven targeted journals between 1992 and 1997 it was concluded that computer simulation was the second most commonly used methodology, behind optimization. More specifically, computer simulation was the primary research methodology employed in approximately 18% of the published articles surveyed. Based on the results of the study, Pannirselvam et al. (1999) appeared to be in agreement with Meredith et al. (1989) assertion that OM research tends to be artificial in nature due to its reliance on modeling as opposed to empirical research. We comment that while simulation studies are typically associated with modeling research, the flexibility of the simulation methodology readily lends itself to modeling real world scenarios. As opposed to previous studies that have investigated published research in OM across a variety of topics and methodologies, the purpose of this study is to focus on the use of a specific research methodology, computer simulation, in operations management research. Specifically, we examine those simulation studies that are empirical in nature, that is they either are modeled to represent specific real situations/environments or data from real situations are used as a basis for setting the levels of key parameters in the simulation study. Our goal is to identify and evaluate past research trends in this area, determine if any gaps exist in this literature, and then based on this insight uncover opportunities for future research. This paper is organized as follows. In the Section 2, relevant research is reviewed. This is followed by a discussion of the research methodology employed in this study. Next, the results from this study are presented, showing the categorization of empirically-based simulation studies over the 30-year period studied. Finally, the paper is concluded and avenues for future research are discussed.
نتیجه گیری انگلیسی
As Chase (1980) points out, “research on research is often a perilous undertaking, with the list of caveats exceeding the list of results” (p. 13). This study is no exception. In our case, while every effort was made to perform an exhaustive search of published empirical OM simulation studies, it is quite possible one or more contributions were overlooked. Therefore, while we cannot claim that our survey is exhaustive, we are confident that it provides a reasonable sample of empirical OM simulation studies. Another limitation is that categorizing research requires subjective judgment calls. We attempted to minimize this limitation by using previously established classification schemes, having each author independently categorize the papers, and then reconciling any differences. There were a number of unique aspects associated with this study including its focus on a specific research methodology, its scope being bounded to empirically-based research, the comprehensive set of journals included, and the duration of the study. In terms of key results, over a third of the empirical articles were published in Interfaces and the Journal of Operational Research Society, and more than half of the articles appeared in these two journals plus Decision Sciences. One of our objectives for this research was to determine if gaps in the literature existed, based upon our review of the topics addressed by empirically-based simulation research to date. In addition, reviewing trends that occurred during the last three decades should also provide some insights as to which areas are more important today—at least to leading academic journals. Following is a list of observations concerning our perspective on gaps in the current literature and guidelines for filling them. 1. In terms of the popularity of research topics, we found that the topic of scheduling dominates empirical OM simulation studies as it tends to dominate OM research studies in general. Furthermore, not only was scheduling the most frequently published topic, it was also published in the largest number of journals. The predominance of scheduling papers suggests that there is an opportunity and need for future studies in the other areas. While there has been a recent increase in empirically-based simulation studies in the areas of Capacity Planning, Cellular Manufacturing, and Process Design, the ability of simulation to model stochastic phenomena makes it an ideal research methodology for virtually all the topics listed in Table 3, the one notable exception being perhaps strategy. Our data suggest that already we are beginning to see this. To illustrate, observe that in Table 5 the frequencies are more uniformly distributed during the last 5-year period covered in this study (1996–2000) than the other periods. The variation of the 96–00 column is also lower than the variation of the 86–90 and 91–95 columns. This suggests that indeed research is being spread more uniformly across the OM topic areas. Furthermore, given increased practitioner interest in many of these areas, greater availability of data created as a byproduct of our information technology society, and the availability of more powerful and easier to use simulation packages, our expectation is the trend toward spreading research attention more evenly across the OM topics will continue into the foreseeable future. Therefore, given the relative paucity of empirically based simulation studies in most of the areas listed in Table 3, researchers will likely find significant opportunities for empirically based simulation studies in virtually all of these areas. 2. A related research issue that emerges from this study as well as earlier studies is the reason for the popularity of scheduling as a research topic. Also related, is it the volume of research that is driving the acceptance of scheduling by so many journals or is it that because this research has so many outlets researchers gravitate toward it? Two observations and suggestions result for these questions. First, it appears that whatever reasons are found for the high percentage of the scheduling research published, it continues to be a popular area for journals to publish. Researchers need to consider that this area is still acceptable by OM journal editors. Second, it would be helpful to better understand industry’s specific needs for this type of research and the availability of data for such empirically-based simulation research. It may simply be that the problems in this area more naturally lend themselves to data-sharing with researchers as opposed to being a higher priority area to practitioners. 3. In terms of trends related to published empirical OM simulation studies, since the mid to late 1980s an increase in the number of articles published is evident. Also, in terms of the quantity of publications, scheduling studies have dominated since the mid 1970s. Regarding journals, Decision Sciences displayed the largest decrease in its market share of published studies in the last 5-year period investigated relative to its long-term average while Interfaces and the Production and Inventory Management Journal experienced the largest increases in their respective market shares in the last 5-year period relative to their long-term average market shares. Decision Sciences is a highly regarded academic journal, as indicated by previous surveys of academicians, and it has had one of the largest proportions of papers published in this area (15.3%) over the past 3 decades. However, with the marked decrease of published empirically-based simulation studies during the last 5-years, it is unclear whether it will continue to be a research outlet for such studies in the future. The Journal of the Operational Research Society has published 17.6% of these studies in the past, and maintained a relatively strong proportion of these published studies since 1986, so could be considered as a likely outlet in the future. For those studies that would be interesting to both academicians and practitioners, it should be noted that Interfaces and Production and Inventory Management have published 17.6% and 9.4% of these studies during the timeframe studied and are likely outlets for this niche. 4. It is worth highlighting the areas where no empirical simulation studies were identified. These areas were project management, quality, maintenance, and aggregate planning. In Pannirselvam et al. (1999), simulation studies were identified for each of these areas. Therefore, it appears that simulation is applicable to each of these topics. Furthermore, in our view, the types of data needed to conduct a simulation study would seem to be readily available or obtainable for each of these topic areas. Thus, while it is somewhat surprising to us that no empirical simulation studies were identified in these four areas, a few possible reasons are plausible. Regarding project management, we note that each project is unique and that projects are typically undertaken on an ad hoc basis. Both of these factors may limit the availability and quantity of key data. In the quality area, we note that data is likely to be of a sensitive nature and therefore, held proprietary by firms. The maintenance and aggregate planning topic areas have not been popular area of research recently, thus the small sample size of research here may have precluded the introduction of empirically based simulation studies. 5. Finally, we note the increase in the volume of empirically based OM simulation studies, starting in the period 1986–1990. Does this increase reflect a general increase in the use of simulation or a general increase in empirical research, or perhaps both? And to what extent is simulation becoming more accepted by practitioners and what impact is this having? Our review of the simulation studies by 5-year periods since 1970 indicated that the number of simulation-based research papers peaked in the 91–95 timeframe with a reduction in this number in later years. However, we note that empirically-based simulation research papers are still on the rise, with a clear trend upwards through the year 2000. Based upon our extensive review of the literature, it appears that empirically based studies, in general, are becoming more accepted in the OM literature. Additionally, providing a basis in real environments certainly provides more credibility of the research from the practitioner’s point of view. The difficulty of appeasing both parties (journal editors and practitioners) is that it is often difficult and time-consuming to find sufficient company data that is usable in a research study in order to provide the generalizability required for good academic research. We found that a number of the published studies were more typical of consulting project reports, with results that were specific to one situation. Future researchers in this area must be careful to design experiments that use empirical data in a way that can also provide meaningful results to a variety of industries and environments. We believe that if increased generalizability is provided, future research studies will be increasingly accepted for publication in the more research-oriented journals. Indeed, there is already a clear trend indicating that such journals are publishing a greater number of such articles. In conclusion, the goal of this study was to identify and evaluate past research trends in the area of empirical simulation studies in OM and, then, based on this insight uncover opportunities for future research. It would be of value for additional studies to evaluate other research methodologies with a similar focus on empirical research. Such research would do much to identify worthwhile research projects that move the field in the desirable direction of addressing issues of managerial and practical significance. Indeed, combining the power and flexibility of simulation with empirical data can be one of the most effective ways to help bridge the often present gap between academic rigor and managerial applicability. To bridge this gap, however, researchers will need to gather data from sources that ensure the generalizability of the research results. Of course, such an approach often entails a marked increase in the research effort due to the difficulty of gathering and filtering data from real environments. In the end, empirically-based simulation research can offer a quantum step forward in providing better managerial guidance to operations managers, and, perhaps, acceptance of the research results themselves.