HP Applies Management Science Modeling

Read the end-of-chapter application case “HP Applies Management Science Modeling to Optimize Its Supply Chain and Wins a Major Award” at the end of Chapter 10 in the textbook, and answer the following questions.

1. Describe the problem that a large company, such as HP, might face in offering many product lines and options.

2. Why is there a possible conflict between marketing and operations?

3. Summarize your understanding of the models and the algorithms used in this case.

4. What benefits did HP derive from implementation of these models?

System Dynamics Modeling

System dynamics  was introduced in the opening vignette as a powerful method of analysis. System dynamics models are macro-level simulation models in which aggregate values and trends are considered. The objective is to study the overall behavior of a system over time, rather than the behavior of each individual participant or player in the system. The other major key dimension is the evolution of the various components of the system over time and as a result of interplay between the components over time. System dynamics (SD) was first introduced by Forrester (1958) to address problems in industrial systems. He later expanded his work and used system dynamics to model and simulate a classic supply chain (1961). Since then, system dynamics has contributed to theory building, problem solving, and research methodology. SD has been used with operations research and management science approaches ( Angerhofer & Angelides, 2000 ) where SD and operations research are considered complementary techniques in which SD can provide a more qualitative analysis for understanding a system, while operations research techniques build analytical models of the problem. System dynamics has been used extensively in the area of information technology, which usually changes an organization’s business processes and behavior. Using system dynamics, possible changes in organizations are projected and analyzed through conceptual models and simulations. The SD technique also has been used in evaluating IT investments:  Marquez and Blanchar (2006)  developed a system dynamics model to analyze a variety of investment strategies in a high-tech company. Their simulation allows them to analyze strategies and trade-offs that are hard to investigate in real cases. A system dynamics model can capture IT benefits that are sometimes nonlinear and achieved over years.

To create an SD model, we need to draw causal loop diagrams for all processes that lead to some benefits. This is a qualitative step in which the processes, variables, and relationships within the conceptual model are identified. These causal loop diagrams are then transformed into mathematical equations that represent the relations among variables. The equations and stock and flow diagrams are then used to simulate different practical and theoretical scenarios.

Causal loop diagrams show the relationships between variables in a system. A link between two elements shows that changes in one element lead to changes in the other one. The direction of the link shows the direction of influence between two elements. The sign of each arrow shows the direction of change between each pair of elements. A positive sign means both elements change in the same direction while a negative sign means the elements change in opposite directions. Feedback processes in the causal loops are the key components by which a variable re-affects itself over time through a chain of causal relationships.

We illustrate a basic application of system dynamics modeling through a partial model of the impact of electronic health record (EHR) systems. This is based on Kasiri, Sharda, and Asamoah (2010). Implementing electronic health record (EHR) systems is on the agenda for many healthcare organizations in the next few years. Before investing in an EHR system, however, decision makers need to identify and measure the benefits of such systems. Using a system dynamics approach, it is possible to map complex relationships among healthcare processes into a model by which one can dynamically measure the effect of any changes in the parameters over time. Simulation of EHR implementations using a system dynamics model produces useful data on the benefits of EHRs that are hard to obtain through empirical data collection methods. The results of an SD model can then be transformed into economic values to estimate financial performance.

Let us consider some of the factors that impact healthcare delivery in the hospital as a result of the implementation of an electronic health records system. The causal loop diagram in  Figure 10.9  shows how different processes and variables interrelate in an electronic health records system to offer significant benefits to healthcare delivery. The sign on each arrow indicates the direction of change between each pair of elements. A positive relationship means both elements change in the same direction while a negative relationship means the elements change in opposite directions.

Figure 10.9 Causal Loop Diagram for Effects of EHR.

Source: From Kasiri et al., 2010.

Electronic notes (e-notes) and electronic prescribing (e-Rx) are shown as two common processes in EHRs that contribute to an increase in the amount of staff time saved ( McGowan et al., 2008 ). They also contribute to a decrease in patient treatment time, which is the time it takes for a patient to receive medical assistance starting from initial contact with the receptionist to the time he or she leaves the hospital after receiving medical care from the physician and other hospital staff. The average increase in patient treatment time as a result of adverse drug events (ADEs) is 1.74 per occurrence ( Classen et al., 1997 ). According to  Anderson (2002) , entering records directly entered into computer-based medical information systems contributes to increased quality of care and reduces costs related to ADEs. Hence, instead of paper notes and paper prescriptions, doctors can reduce costs when notes on patients and prescriptions are entered directly into the EHR system. Quality of care is directly affected by the amount of time a patient spends at the hospital. Based on the diagram in  Figure 10.9 , there is a positive link between e-note and staff time saved as well as e-Rx and staff time saved. This indicates that the more physicians use the EHR system, the less time nurses and other staff need to manually retrieve records and files on patients in order to offer medical support to them; in fact, there is no need to transfer files and paper documents from one department to another physically. Staff can therefore transfer the time saved on dealing with documentation to having direct contact with the patients and, hence, improve the quality of healthcare given to patients and decrease ADEs.

E-note and e-Rx also impact the occurrence of adverse drug events ( Garrido, 2005; McGowan et al., 2008 ). The more the system is utilized to record notes on patients and to write prescriptions, the fewer the mistakes in the administration of drugs that stem directly from inefficiencies in manual drug administration processes. Hence, patients spend less time at the hospital as a result of not having to deal with delays related to complications that could occur with paper notes and paper prescriptions. Also, “staff time saved” is increased because the time needed to correct the mistakes related to ADEs is eliminated. The occurrence of ADEs in hospitals is estimated to be an average of 6.5 events per 100 hospitals ( Bates et al., 1995  Leape et al., 1995 ). Subsequently, when the ADE rate decreases through the use of e-note and e-Rx, ADE correction costs also decrease.

The electronic records storage (e-storage) variable refers to the capability to store records in the hospital that otherwise would have been stored in paper format. E-storage is important because it helps in easy retrieval of medical records of patients even after many years. For instance, EHR enables the use of e-note and e-Rx, electronic forms of paper notes and paper prescriptions, which are easier to store and retrieve than are data in hard-copy formats. Hence, EHR helps facilitate the storage and retrieval of health records. Access to the patient’s electronic health records helps physicians easily make decisions and diagnoses based on past records. The delay link from e-storage to patient treatment time indicates that patients can be taken care of much faster if electronic data that offer quicker retrieval are available. Uncertainty in clinical decision making on the part of physicians is greatly reduced as a result of e-storage capability (Garrido, 2005). Of course, electronic storage of this data is enhanced by greater use of e-Rx and e-note.

Hospitals are required to comply with certain standards regarding the administration of medication and other related healthcare administration processes ( Sidorov, 2006 ). Certain drugs may be restricted, and the amount given to a particular patient must be closely watched at any period in time by staff. With EHR, physicians can easily track patients’ records to know how much has been given and what amount is yet to be given. If an attempt is made to prescribe an amount that is more than the requisite amount for that particular patient, a “red flag message” can be generated to warn the physician of the imminent breach in compliance. In this way, it is easier to comply with regulations regarding the dispensing of a particular medicine and ensure that the maximum amount that is supposed to be given to the patient is not exceeded. Also, rules can be set in the EHR system to prevent physicians from prescribing certain combinations of drugs because of negative reactions such combinations may cause. If a particular rule is violated during e-prescribing, a warning message can be immediately generated to warn the physician of the imminent danger. ADEs that may occur as a result of incorrect amounts and combinations of drugs given can hence be minimized.

The likelihood that any information system in an organization will be used is closely related to how well the users are trained in using the system. Hence, when staff, including nurses, physicians, and lab assistants, are given adequate periodic training, the use and acceptance of e-Rx, e-note, and the EHR system in general increases. Training also leads to greater compliance with standards.

In addition, when EHR is integrated with other healthcare delivery departments such as the radiology and laboratory departments, their performance level is increased. Greater efficiency in the radiology and laboratory departments leads to fewer ADEs and shorter patient treatment times. Also, using EHR reduces the rate of duplication in radiology work and provides quicker access to radiology records and, hence, directly increases the savings in staff time. With the EHR system, a functional department like the radiology department can directly access the patient’s x-ray order through the e-note functionality. Hence, mistakes related to incorrect interpretation of physicians’ handwritten orders can be avoided, leading to a decrease in patient treatment time at the hospital.

The causal loop diagram shows various benefits of EHRs such as lower rate of ADEs, higher amounts of staff time saved, and lower patient treatment times. In the next section, we develop a stock and flow diagram with loops that reflect some of the most important factors that impact the flows. These relationships and effects can be translated into mathematical equations for simulation purposes. Based on estimated parameters and initial values, we simulate the model and discuss the results.

Because the goal of this section is only to introduce some concepts of system dynamics simulation, we will not go into all the details of the technique. Once the causal loop diagrams are built, one can build the stock and flow diagrams, which lead to developing the mathematical equations for simulating the behavior of the underlying system under study. Results can provide considerable insight into the growing behavior of the system under consideration. In another project,  Kasiri and Sharda (2012) , for example, studied the effects of introducing radio-frequency identification (RFID) tags in retail stores on each item. They built system dynamics models to identify impacts of such technology in a retail store—increased visibility of information about what is on the shelves leading to a decrease in inventory inaccuracy, better pricing management, etc. Industry participants were able to provide inputs on such effects to be able to build models for investment decisions.

Many software tools are now available for building system dynamics models. Such listings are usually updated on Wikipedia and other sites. Some of the popular tools that include academic and commercial pricings include VenSim, Vissim, and many others. One free software, Insightmaker, appears to offer both system dynamics and agent-based modeling capabilities in its Web version.




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