Supply Chain Management: A Simulation Application Page 108 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 ABSTRACT From an organizational perspective, innovation leads to the implementation of new processes or the introduction of new products. The impact of innovations on single firm’s activities and how they are managed has been extensively studied. From a supply chain perspective, however, where the focus is on at least two firms or players, the applicabil- ity of the findings on managing innovations in a single firm is open to question. Hence, it appears that there is an ap- parent need to focus our efforts on examining how innova- tions can be successfully implemented in supply chains. We examine a production line system by employing a supply chain management approach. We look at a problem where a manufacturer needs to place sensors in order to supervise the production line. This work has implications for the de- sign of systems in today’s complex environment. INTRODUCTION Various types of Information Systems are widely used in today‟s businesses (e.g., Chang and Bai, 2010; Woo and Hu, 2009; Chong, 2010). One of the more interesting appli- cations of information systems today is in healthcare set- tings (see, for example, Patel and Chang, 2010; Walsh, 2010; Becker, 2010). This paper explores how decision support systems are used for medical reasons and how we can influence the effectiveness of the systems. Statistics show that adults Chinese who are younger than 65 purchased 10.8 prescription drugs per year in the last few years. Those 65 or older purchased more than that – an average of 26.5 prescription drugs in 2001 and the number has increased since then. Each year, doctors write more than four billion prescriptions, of which four percent contain an error, and 1.5 million people are injured due to preventable adverse drug effects and medication errors. According to a much-cited Institute of Medicine report, dozens of thousands of the prescription errors are fatal. It seems that physicians can prevent 28 percent of those er- rors, but they need more information and better systems. Improving physician responsiveness, facilitating learn- ing and clinical experience are important in preventing fatal errors. As the gatekeepers to prescription medication ac- cess, physicians face significant challenges in keeping up with the developments and new findings in the market each year and in matching the best drugs to individual patients. Some researchers find that the most common prescription errors (in the order of importance) are deficiencies related with (i) choosing the right drug class but the wrong drug, (ii) choosing the correct dosage, and (iii) the clarity of or- ders. After surveying prescriptions, Bharati and Chaudhury (2004) report that many pre- scribed medicine tend to exceed the limits approved by the physicians. Recent empirical studies corroborate the impor- tance of each individual physician‟s learning, clinical ex- perience, and patient interaction on the actual prescription behavior (Reinig, 2003). Patient-physician interaction is important due to potentially unexpected drug reactions on different patients, while clinical experience provide critical information to physicians during the prescription process. DeLone and McLean (2003) and Ben-Zvi T. and Rohmeyer (2010) show that even the least effective decision support system may still have effect if used properly, and therefore can yield significant benefits. Sharda et al. (1988) explore decision support systems in various areas. They show that users of those systems are initially reluctant to use new systems and underestimate the quality of innovations. They also find that physicians regularly update their beliefs on the efficacy of new drugs based on their clinical experi- ence. Further, the authors observe that prices of drugs do not have much effect on physicians‟ prescription choices. Levi and Ma (2010) expand this study to different types of information systems. Green and Cohen (2010) show the application of IT to reduce business risks. Other researchers also present the merit of using deci- sion support systems in this field. Srinivasan (1985) de- velop a forward-looking framework to examine the learn- ing behavior of patients who switch between different treat- SUPPLY CHAIN MANAGEMENT: A SIMULATION APPLICATION Ting Ma Michigan State University East Lansing, Michigan Ting.ma@msu.ed Chong Po Michigan State University East Lansing, Michigan Chong.po@msu.ed mailto:Ting.ma@msu.ed mailto:Chong.po@msu.ed Page 109 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 ments. The authors find that (i) patients search for a match among different treatments for their problems, (ii) they learn fairly quickly about drug effects, and (iii) their drug efficacy perceptions vary substantially. Adams et al. (2010), Ben-Zvi (2010) and Bodevin and Suttikul (2010) provide further evidence of tracking emerging technologies and their benefits in the Information Systems field. Other researchers concentrate on prevention and the use of information to assist patients. Seidling et al. (2007) suggest that prevention of prescription dosage errors are possible but require implementation of an appropriate data- base and decision support tools. To help with the learning process, Lei and Chong (2010) suggested using simulation techniques. Furthermore, Bochicchio et al. (2006) report that the use of web-based handheld decision support tech- nology is highly effective in improving antibiotic decision accuracy among physicians. In a recent review of the litera- ture, Ammenwerth et al. (2008) provide evidence that the use of CPOE leads substantial reductions in medication errors and ADEs (13 to 99 percent, as reported by 23 of the 25 studies that have been reviewed). The authors also find that the use of CPOE was associated with a 66 percent re- duction in total prescribing errors in adults. CDSS may also reduce the ongoing dosage- and drug application-related errors once the drug is prescribed. Kirk et al. (2005) assess the rate of medication errors in predominantly ambulatory pediatric patients and the effect of decision support systems on medication error rates of two commonly prescribed drugs. They find a computer- calculated error rate of 12.6 percent compared with the traditional error rate of 28.2 percent, with most errors re- sulting from under-dosage. Berner et al. (2006) conduct a randomized, controlled experiment and find that partici- pants with a personal digital assistant-based CDSS made fewer unsafe treatment decisions than participants without the CDSS. Mirco et al. (2005) find evidence that the use of clinical decision support systems is vital in achieving maxi- mum medication safety and reducing medication error rates. In this paper, we propose a clinical learning model for physicians supported by two important CDSS features. We expand the finding of Po and Deng (2010) as we examine additional features. The first feature is related to the initial drug selection. The second CDSS feature provides an ongo- ing dosage and application support for a focal drug. The proposed framework provides an analytical model to inves- tigate the effects of different CDSS features. Our focus is DSS and the factors that affect their effectiveness. Using the proposed model, we investigate how the two CDSS features relate to the clinical learning of physicians. The analytical results suggest that the decision support on drug selection is critical. Improving the initial drug selection process raises the drug-patient match conviction and posi- tively influences the importance of the patient-level infor- mation for the physician. On the other hand, absent im- provements in successful drug selection, the use of CDSS may in fact negatively influence the clinical learning. The intuition behind this result is the following. CDSS makes physicians more certain on the expected efficacy of a drug without affecting their patient-drug match conviction. Con- sequently, the information gathered from individual pa- tients is weighed relatively less compared to their efficacy expectations while prescribing a drug. We next present a model for the clinical learning mechanism and then analyze the role of CDSS on physicians‟ learning behavior. We conclude the paper concludes with a summary of results and briefly outline the salient aspects of an empirical analy- sis that we aim to conduct in this domain. THE MODEL We follow the approach taken by Hu and Zeng (2010) in their study of decision support systems: Consider a phy- sician who needs to decide whether to prescribe a focal drug representing a treatment plan. Selecting the treatment requires an ongoing decision on dosage and application of the focal drug. For example, a patient may be diagnosed with bi-polar disorder. Then, the treatment plan requires an initial decision on prescribing a treatment in the therapeutic category. Once a specific treatment is prescribed, the physi- cian observes the patient‟s response to the drug and collects additional information on an ongoing basis. Prescription preferences evolve over time. The prefer- ences of a certain Physician i can be represented by a set of vectors Q. Those include his past preferences and his or her present ones. Note that the prescription at hand may differ based on their prescription habits, and the subscript i cap- tures the physician-specific carryover coefficients. A differ- ent value of i implies that physicians carry over their pref- erences into the future periods. For example, when a physi- cian prescribes a mature drug that has been in the market for a sufficiently long time and follows an established treat- ment plan, there may be limited new information during period t. Then, the preference towards the treatment plan would be mainly based on past preferences. When consid- ering the value of I, one needs to characterize the prescrip- tion behavior that is not much influenced by the previous period‟s preferences. When enough new information is available for a treatment plan, the prescription preference becomes a function of the most recent information. The error term captures the errors associated with the drug efficacy which depend on the use, application and dosage. For example, depending on the specific condition of the patient, the optimal prescription dosage, frequency of use and overall application may change. We let the error to follow a normal distribution a certain mean and standard deviation. When a physician is not using a CDSS, the phy- sician relies only on her own memory. This is the case where d equals to one and all the uncertainty is captured by the physician-specific variance i V . On the other hand, availability of a CDSS reduces the uncertainty. The effec- tiveness of the CDSS in reducing the uncertainty is cap- tured by d. As d decreases towards zero, the CDSS be- comes more effective and essential in identifying and mini- Page 110 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 mizing dosage related errors. Note that, according to the model, although CDSS provides a useful tool in reducing uncertainties, physicians still differ given their own work environments and skills, and a physician experiencing a high degree of uncertainty benefits from the CDSS more than a physician with a low degree of uncertainty. In addition, patient life styles vary and may influence the initial decision to follow a specific treatment. Some patients working under strenuous conditions or suffering from other pains may not follow certain types of treatments or take drugs that may interfere with their conditions. The number of new prescriptions is a function of preference, where the random term includes the errors related to the drug selection. This randomness follows a normal distribu- tion also with given mean and variance. We allow the vari- ance to vary across physicians due to differences in patient profiles. The parameter g captures another feature of the CDSS. A low value of g indicates that the CDSS is effec- tive in identifying and reducing the potential drug interac- tion with patient profile match related errors. Suppose during period t physician i handles it n new patients. Let Y denote the total number of new prescriptions in period t and follow a Poisson distribution. The probabil- ity of observing y prescriptions equals where the mean of the distribution is is proportional to an exponential one. Note that according to this data, the mean number of pre- scriptions for the focal drug Y depends on the total number of new patients. We see that a change alters the probability of prescribing the focal drug and may take a high value if the drug works all the time for all patients in the therapeutic category. On the other hand, a low value reduces physi- cians‟ probability of prescribing the focal drug. One effective way to measure those changes is by us- ing a simulation. A simulation is by definition a highly complex man-made environment. Its objective is to offer participants the opportunity to learn by doing and to engage them in a simulated experience of the real world (e.g., Gar- ris et al., 2002; Martin, 2000). This makes it possible to come up with conclusions that can later be generalized to reality as the behavior of participants changes along the simulation (e.g., Lainema and Makkonen, 2003). When looking at the literature, many studies looked at simulations or used simulations as a research tool. For ex- ample, Courtney and Paradice, 1993; Dickson et al., 1977; Durget, 2009; Faria, 1987, 1998. However, researchers that explored simulations and decision support systems did not find one tool that may become handy in measuring the ef- fectiveness of systems. See Affisco and Chanin 1989; Chen and Lin, 2009; Goslar et al. 1986, Kasper 1985. The game we use in this study represents a tool that is successfully implemented in our classes and then can be used for other purposes. We use it in the healthcare setting we have created. The simulation develops several skills that we regard as important to explore, definitely in the field of medicine. METHODOLOGY The need for curricula to be up-to-date with the knowl- edge of current practices, business models and applications is well recognized in the current dynamic environment. Responding to the challenge of meeting the ever moving target of „being current‟ and „relevant‟, academic institu- tions are involved in an on-going curriculum development effort. Developing and teaching a current and relevant cur- riculum is challenging and stimulating because of the topic‟s rapid evolution and its interfacing effect on every aspect of business. The dot com crash in 2001 undermined some of the foundational premises on which technology is taught in business schools. For example, the electronic marketplaces and application service providers (ASPs) that were predicted to create multi-billion dollar markets by 2004, rapidly faded out as several firms went out of busi- ness. Also, in China, the number of electronic marketplaces has declined significantly from around 150 in 2001 to less than three in 2006. It is challenging to keep up-to-date and be on top of the changing nature of technology applications, teaching materials and the introduction and occasional disappear- ance of some new and interesting business models, soft- ware applications and environmental conditions. Because of the ever-changing nature of course content and case studies, it is very hard to develop a course that is stable on some theory and applications, and has some longevity. It is possible that a certain course which was considered suc- cessful in 2005 may be viewed as a significant failure by 2010. For example, an established brick-and-mortar retailer in Australia has acquired its strong online competitor, a successful online retailer of green groceries and simply merged it with its existing fledgling online retailing unit. With these dynamic changes occurring regularly, it is diffi- cult to maintain a set of local case studies and examples and present them for analysis in the class. Taking into con- sideration these dynamic changes, simulation courses may simply consist of some interesting overseas case studies of successes and failures, and an explanation of current appli- cations. Such courses simply lack the sufficient depth in content and process, and do not equip students with the conceptual frameworks and critical skills necessary to deal with the changing technology and business models in the workplace. Rapid changes in the field make course development and maintenance extremely resource intensive. In addition to keeping abreast of the evolving and changing content, academic staff teaching these courses must also continu- ously learn constantly evolving software applications, hard- ware and networks. To be effective across the broader cur- riculum, teaching simulations requires bringing together a wide variety of skills from a number of academic disci- plines. Because of its multi-disciplinary nature, simulations also include some traditional content from other disciplines such as finance, accounting and logistics. This requirement Page 111 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 creates a need to integrate the offerings and content across different courses taught in the business schools. The difficulties of delivering an effective and relevant course may be exacerbated if the classes are small. With increasing number of electives to choose from, this is often the case in many business schools. This together with the recent down turn of the demand for information technol- ogy/system based courses in general in many universities; the class sizes have typically become smaller. While small classes facilitate critical analysis of case studies and critical appraisal of the latest frameworks and technology, and learning by sharing and interacting, lecture-based teaching typical in large classes is considered inappropriate for such a subject. The following table (Table 1) presents some data about our two test groups. Table 1 Demographic Statistics for the Two Investigated Groups HYPOTHESIS TESTS RESULTS When measured the levels of effectiveness of the cre- ated decision support systems, we used 4 indexes: use, de- sign, satisfaction and contribution. We asked the partici- pants what was their level of use; whether the design of the system was a burden, what was the level of satisfaction and whether the system contributed to making the right deci- sions. Those questions represent the different measures of effectiveness of the systems. We present the main results in Table 2. Performing statistical tests, our findings show that the levels of satisfaction were the highest, although the contribution was lower than expected. Next, we examined how the participants felt when us- ing the simulation: whether they had greater control over the experience. We ran tests in both groups. Our findings show that although the participants experienced moral di- lemmas, the two test groups did not show a higher level of dilemmas than other studies in the past. Therefore, we could not confirm that decision support system helps re- solve moral dilemmas. However, on average, the partici- pants came to realize that when making ethical decisions using a system, one should pay attention to his or her con- science. Also, they understood that solutions to ethical problems are usually not easily definable. Therefore, when examining the use of decision support systems, one should pay close attention to other issue than effectiveness. The results from both groups along with the statistical tests are presented in Table 3. Our final investigation dealt with the degree the addi- tional technical burden placed on the participants, due to the fact that they had to interface via the internet and the system is not just a static system that they operate. We also studied how technical factors affect their behavior and what is the nature of communications conducted between players and the administrators. Based on the information presented in Table 3, it can be concluded that timeliness was not achieved and internet-use problems, rather than learning coaching, dominated participant communications for both groups. The results, however, present a significant differ- ence between the two groups. DISCUSSION AND CONCLUSIONS Business value of information technology in general, and decision support systems in particular are a major con- cern to any company today. The adoption of information technology with its variety of components, such as infor- mation systems, expert systems and decision support within the health care sector is extremely important as data is be- ing accumulated in a faster pace than ever. Implementing information technology in healthcare settings has been the Variable Test Group 1 Test Group 2 Male 358 725 Female 293 525 % of Fe- male 45 42 Variable Group 1 Group 2 Z p-value Mean S.D. Mean S.D. Use 5.68 0.58 5.57 0.74 1.36 0.1854 Design 5.14 0.46 4.82 0.67 1.64 0.1168 Satisfaction 4.89 0.26 4.54 0.52 1.84 0.0791 Contribution 5.25 0.18 5.25 0.32 1.13 0.2335 Table 2 Means, Standard Deviations (S.D.), Z values and p-values of System Effectiveness Page 112 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 focus of many information system researchers in the last few years. Our modest contribution to this literature is by investi- gating how clinical decision support systems may support physician practice, learning and their prescription behavior over time. We investigate the conditions under which adop- tion of these types of systems improves clinical learning and contributes to the reduction of drug-related errors. Im- proved patient-drug match facilitates a more responsive physician behavior and, therefore, positively contributes to the improvements in the prescription behavior. Our results show that the participants came to realize that when making ethical decisions using a system, one should pay attention to his or her conscience. Also, they understood that solu- tions to ethical problems are usually not easily definable. Therefore, when examining the use of decision support systems, one should pay close attention to other issue than effectiveness. Our next step would be to conduct an empirical analy- sis that incorporates some of the physician-level character- istics that may affect clinical behavior and decision support systems use. We have obtained a dataset from a large phar- maceutical company in the United States that includes indi- vidual physician prescription records in a therapeutic cate- gory. We have the number of new prescriptions written by each physician in the sample during each month in the past 7 years. The data also include the number of details (visits by sales representatives) and the number of samples re- ceived by each physician per month for the drug. We also have data on each physician‟s specialty and location by zip code. We will augment the data made available by the pharmaceutical firm with secondary data about per capita income and other demographic indexes of each zip code in which the physicians in our sample are located. We are planning to use this data to estimate physicians‟ response to detailing (by physician type and location) and the persis- tence in their preferences toward the drug‟s efficacy over time. We will also analyze the estimation results by the type (general practice vs. specialty) and location (high vs. low income zip code) of the physicians. Such an analysis would provide insights on which types of decision support offer more potential for which categories of physicians, and correspondingly, which decision support systems imple- mentations are more likely to fail. We expect to obtain the empirical results in the near future. While in this paper our main focus is on the clinical learning aspect of decision support systems, we acknowl- edge that physicians in general have access to and can benefit from other information sources as well. Those sources can very well be training and detailing by pharma- ceutical companies and others. A decision support system may be used for training activities as well. Those activities can serve as tools for medical students, still obtaining their degree. In addition, while our model can incorporate such additional information sources, the relative importance of these sources (e.g., detailing) diminishes once physicians start prescribing a focal drug. 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Table of Contents Volume 38, 2011 Simulated Tabletop Exercise for Risk Management - Anti Bio-terrorism Scenario Simulated Tabletop Exercise From Business Games to Simulations - Simuworlds & Microworlds Demand Equation Redux: The Design and Functionality of the Gold/Pray Model in Computerized Business Simulations Managing Client-Based Learning: Insights from Successful Teaching Project Courses in Marketing Tracking Forecast Error Type, Frequency and Magnitude with the Forecast Error Package Responding to Facilitate Collaboration Simulating Sudden Change and the Value of Timely Information Managing Human Resources Simulation A Study on Collectivism and Group Decision-Making: An International Comparison of Japan, China, and Russia Using a Gaming Simulation The Use of Management Games in the Management Research Agenda Gaming On-Line: A Simulation Application Positioning and Performance in Simulated Networks Supply Chain Management: A Simulation Application Simulation as a Teaching Method in Strategic Management Distance Studies Entrepreneurship: A Game of Risk and Reward Phase II: The Start-Up Return to the Paradise Islands: From Confrontation to Cooperation Effect on Market Performance of Displaying Supply and Demand Curves in a Business Simulation Appreciating Complexity: The Chief of Staff of the Army Game Managing Organizations: Experiential MBA Course Teaches Alternatives to the Machine Model A Simulation Model for Analyzing the Night-Time Emergency Health Care System in Japan The Continuing Evoluation of Assessing Project Management as an Academic Learning Outcome (ALO) Should College Instructors Change Their Teaching Styles to Meet the Millenial Student? The Mouse Game and its Effects on Team Interdependence Learning from the Gulf Oil Spill to Prepare for a Brighter Future: A New Game Engaging Stake Holders in Triple Bottom Line Accounting & Strategic Planning Video Killed the Biblio Star: The Impact of Digital Media on Student Learning Outcomes Exploring Motivation: Using Emoticons to Map Student Motivation in a Business Game Exercise An Alternative to PC and Internet Based Simulations: The Internet Integrated Mode MiddleState University -- A Crisis in Education Complexity Avoidance, Narcissism and Experiential Learning Examining the Cognitive, Affective, and Psychomotor Dimensions in Management Skill Development Through Experiential Learning: Developing a Framework A Situational Leadership Exercise Based on the Biology of a Starfish JOGAI CEFET -- The Industrial Administration Undergraduate Game Would You Take a Marketing Man to a Quick Service Restaurant? Modeling Corporate Social Responsibility In A Food Service Menu-Management Simulation Use of a Simulation in a Large Class Environment for a Marketing Principles Class: A Qualitative Analysis of Whether Learning Objectives were Met A Team Based Information Literacy Exercise ABSEL Marketing Communications Plan An Interdisciplinary Study of the Impact of Playing a Marketing Simulation Game on Student Knowledge of Management Accounting/Finance Principles Analyzing Construction Planning of Interiro Finish Work of Apartment Building by Simulation Doing Murder One Again The Simple Business Game and Simulation Transfering the Knowledge of Middle Management to Novices Infectious Disease Simulation Model for Estimation of Spreading Understanding the Relative Influence of Several Factors in ERP Simulation Performance: An Exploration of Ecological Validity Tragedy of the Commons: An Exercise Using Clickers to Illustrate and Teach a Key Concept in Negotiations The Meaning of Firm Demand in Business Simulations If the Games Work, Why Aren't More Faculty Willing to Play? Those Who Do and Those That Don't: A Study of Engaged and Disengaged Business Game Players