5 Steps to Creating a Forecast

A sea captain would not try to sail the Atlantic on a cloudy day without a navigational system. Likewise, a healthcare financial manager should always use a reliable forecasting system when evaluating his or her organization's performance. Simple average calculations or untested business targets that provide general direction aren't enough; financial managers need to be able to identify potential challenges, just as a navigation system would identify open-sea hazards. Yet forecasting is not an idea that should be dismissed out of fear of complex spreadsheets and mystical practices of statisticians and PhDs.

Just as ship captains today use powerful navigational systems to plot, monitor, and adjust their voyage, healthcare financial managers can leverage advanced forecasting techniques to better plan, manage, and adjust the decisions that will help them achieve their performance goals. Although an introduction to forecasting warrants a much larger discussion, this article outlines the major steps and considerations that a healthcare manager should address when embarking upon forecasting.

Step 1. Establish the Business Need

Healthcare financial managers need to clearly understand how their forecast will influence business planning and decisions within their organization. Without this important understanding, the resulting effort will very likely produce adverse results. For example, many business managers rely on monthly cash forecasts. These are used by collections managers for setting monthly cash collection goals, by finance to schedule capital expenditures for clinical equipment, and by staffing managers for their budgets.

Those are just a few examples; actually every employee walking through the halls of a hospital is a knowing or unknowing stakeholder in a forecast. Imagine if the cash forecast wasn't in synch with business expenses-the results on reserves could be disastrous. To establish the business need, these key questions should be answered:

* What decisions will the forecast influence?

* Who are the key stakeholders?

* What metrics are needed and at what level of detail?

* How far forward should the forecast project in terms of years, months, weeks, or days?

* How will accuracy be measured, and what is the acceptable level?

* What is the impact of under- and overcasting?

The results of forecasting a metric, whether they include revenue, patient visits, or uninsured bad debt, are always needed to support many organizational decisions. To best answer the above questions, the healthcare financial manager needs to ensure that the forecasting efforts meet organizational needs.

Once these questions have been answered, it is important to identify the potential drivers of a forecast. For example, gross revenue is driven by a number of factors throughout the organization from clinical to financial to strategic. Furthermore, each driver can be grouped into internal factors and external (exogenous) factors.

Healthcare organizations can find internal factors like those shown for key hospital revenue drivers in the sidebar above quantified within their own data. As healthcare organizations adopt more sophisticated techniques for forecasting, they should advance their models to consider key market and strategic business influences like those shown in the sidebar below.

Step 2. Acquire Data

For each business driver and influencing factor, the typical forecasting effort should use at least two years, and ideally up to five years, of historical data. When forecasting efforts have short time horizons in small time periods, fewer data can be used. To collect the most accurate and robust data sets, all available data sources, such as multiple healthcare information systems (HIS), spreadsheets, small departmental databases, and/or an enterprise data warehouse, should be used. By sourcing from multiple areas, differences in organizational behavior can be balanced out to yield the best data set.

All data should be drawn incrementally in their pure form from available data sources to build up the needed accuracy and completeness. To ensure the richest representation of historical events, the data should not be altered and quality issues should be addressed sooner in the process rather than later. A common challenge a hospital may face in forecasting is the practice of purging of aging trial balance data from an HIS after one or two years. This common practice makes accurate forecasting very difficult. Hospital financial managers need to review purging policies or acquire tools to ensure historical data are available for budgeting and planning.

Collecting exogenous data often requires involving third-party data sources. Several potential sources for external data exist in free public sources, such as census data, the Centers for Disease Control and Prevention, and the Centers for Medicare and Medicaid Services (CMS), as well as private information firms. Financial managers need to find the optimal source that can provide reliable, high-quality data that can be incorporated into their data structures. In fact, incorporating third-party data can provide valuable benchmarks later in the analysis.

Once the data are collected, it is important to ensure they are clean. Cleaning data often requires more effort than developing the forecast. One of the most efficient ways to perform this process is to visualize the data in trend, distribution, and scatter graphs to find anomalies. This review should be conducted for each toplevel metric and major subgrouping as well as all driving factors to help identify:

* Missing values and gaps: Missing values can be caused by HIS purging policies, changes to business processes and practices, and switching patient access or billing systems. Gaps in valuable historical data can limit forecasting accuracy.

* Outliers: These are business events that may skew a forecast, such as how Hurricane Katrina affected healthcare facilities in Louisiana and Mississippi. Rather than manually looking through all possible slices of data for outliers, it is best to identify and look for specific events that may be unique to an organization, coupled with tools that automatically search data to find outliers.

Examples of these potential issues are shown in the graph, below. Once found, there are many well-known methods for addressing missing data and outlying data points, including statistical methods such as nearest neighbor or forecasted value, or by adjusting the data manually to a known true outcome.

Step 3. Build the Model

Once the business needs, drivers, and influencing factors have been established with the associated historical data, a decision needs to be made on the type of forecasting model to use. The forecasting model is the technique or algorithm that determines the projections based on identified business drivers, influencing factors, and business constraints. There are three major categories of forecasting models: cause-and-effect, time series, and judgment.

Many more forecasting models are also available, and there is no overall best choice. In fact, forecasting models are often combined to produce the most accurate results for a given business need, and it may be necessary to consult with business and technical experts for advice when selecting the best model for a given situation.

As an example, let's explore creating a hospital revenue forecast that has seasonal considerations. The graph at the top of page 104 depicts hospital revenue trending up and peaking at each year end. There are 4,8 months of historical data available, and the most recent five months of data have been selected to test and evaluate the model once it is built. This technique is referred to as training and testing the model.

An application of a cause-and-effect model that incorporates a seasonably and judgment factor is shown in the graph at the bottom of page 104. Seasonal variations can be attributable to a number of reasons, such as a hospital's being located near a ski resort during the ski season, or the "snowbird" effect, when a large population migrates south during the winter months. Clinical factors often have seasonal factors, with the most obvious being flu season. Because the healthcare financial manager in this example knew there were seasonal trends, and ample historical data were available, the model was constructed to include these influences.

Judgment variables can also be used to account for infrequent events such as employee strikes, acquisition or construction of a new acute care center, construction of a new wing, or construction of a new hospital in the area. For example, a healthcare financial manager can use the opening of a physical therapy center at an acute care hospital in 2006 to predict the effect of opening a physical therapy center at a different facility in 2008.

Step 4. Evaluate the Results

Once the model has been built and executed, the resulting forecast accuracy should be evaluated using the most recent time period. Overall model accuracy should be measured using tatistical functions such as F statistic, standard error of the estimate, or R^sup 2^. R^sup 2^ is a statistical measure used for regression models describing what percentage of the changes from month to month can be explained by the forecast. By visualizing the results as shown in the graph below, a healthcare manager can easily understand a model's accuracy.

Model accuracy should be tracked and monitored by calculating the difference from month to month. The accuracy rate may vary from month to month, but in any month, a forecast accuracy of more than 85 percent is considered to be very good. To compare forecast accuracy over time, the simple yet powerful mean absolute percentage error (MAPE) test should be used. If the MAPE increases over time, then not all influencing factors have been included in the model. By constantly keeping an eye on this test, a healthcare financial manager can easily understand whether the forecast model needs to be tuned.

Another powerful tool to test how a model will handle future conditions is scenario analysis. By running different scenarios, especially with known outcomes, healthcare managers can gain a comfort level of model behavior and accuracy. For example, a forecast for inpatient volume could test a base, a conservative, and an aggressive scenario for population growth rates, additions of new managed care populations and employer groups, and clinical initiatives to convert inpatients to outpatients. Under each scenario, the forecast should provide reasonable results.

Step 5. Apply the Forecast

Once all the work has been done to create a highquality forecast, it should be deployed to the stakeholders and end users in a manner tailored to their use. The forecast should ideally be made accessible to all appropriate business areas in reports and analyses packaged to unique enduser perspectives. For example, a contract manager is most interested in revenue forecasts by payer contracts. Each healthcare financial manager should have access to a "sandbox" area to perform "what-ifs" to better understand the impact of business decisions.

An often overlooked value of a forecast model is that it allows financial managers to better evaluate how the hospital is performing when controlled for external factors. When the forecast is for an increase in revenues for a particular month, and the actual performance is below both the forecast and the previous month's actuals, there may be a problem that needs to be resolved. For example, in the case of gross revenue, a hospital could control for the fact that it's the holiday season and no one wants to be in the hospital, or perhaps local employment is down, or people are moving out of the county. By letting the forecast adjust for factors beyond their control, financial managers can objectively judge how items they can control (such as quality of care, prices, and payer relationships) are performing.

A consistent challenge healthcare organizations face is identifying a problem after it has already damaged organization performance. To mitigate this problem, healthcare financial managers should put into place tools to check their forecasts on an ongoing basis so they will be aware of downward trends months before those trends affect the performance of the organization.

"Those WKo Fail to Plan, Plan to Fail"

Today's advanced forecasting techniques allow healthcare managers to plan, manage, and continually monitor where their organization is headed in the future. It is often said that "Those who fail to plan, plan to fail." Through advanced forecasting, healthcare financial managers can ensure that their organizations have a successful voyage to better performance.

Source: Doug Stark, David Mould, Alec Schweikert. Healthcare Financial Management, apr 2008

Inventory control with product returns: The impact of imperfect information

Product returns are characterized by considerable uncertainty on time and quantity. In the literature on inventory management for product return environments best forecasts of future returns are associated with methods that use the most information regarding product return history. In practice, however, data is often scarce and unreliable, while forecasts based on historical data, reliable or not, are never perfect. In this paper we therefore investigate the impact of imperfect information with respect to the return process on inventory management performance. We show that in the case of imperfect information the most informed method does not necessarily lead to best performance. The results have relevant implications regarding investments in product return information systems.

Source: Marisa P de Brito, European Journal of Operational Research 2009

FUZZY PRESENT VALUE ANALYSIS MODEL FOR EVALUATING INFORMATION SYSTEM PROJECTS

In this article, the economic evaluation of information system projects using present value is analyzed based on triangular fuzzy numbers. Information system projects usually have numerous uncertainties and several conditions of risk that make their economic evaluation a challenging task. Each year, several information system projects are cancelled before completion as a result of budget overruns at a cost of several billions of dollars to industry. Although engineering economic analysis offers tools and techniques for evaluating risky projects, the tools are not enough to place information system projects on a safe budget / selection track. There is a need for an integrative economic analysis model that will account for the uncertainties in estimating project costs, benefits, and useful lives of uncertain and risky projects. In this study, we propose an approximate method of computing project present value using the concept of fuzzy modeling with special reference to information system projects. This proposed model has the potential of enhancing the project selection process by capturing a better economic picture of the project alternatives. The proposed methodology can also be used for other real-life projects with high degree of uncertainty and risk.

Source: Olufemi A Omitaomu, The Engineering Economist 2007

IT Management Models

Models as I like to call it took three years to develop. It is a unique book containing 72 models to help managers learn about, discuss, and remember management concepts and issues easily.

Models is a truly unique book. The approach taken in this book is unlike any other you have read. It is 200 pages long but the reading will be quick and enjoyable. The models themselves make the book different and interesting to learn. They will also be extremely easy to remember and to use in your day to day activities. In fact, one of my reviewers of the book said he, "used three models in one day while reviewing the book."

Using models in your IT management or project management activities is a natural thing to do. "A picture says a thousand words," they say. I've used models my entire career and now there is an entire set designed specifically for the technology manager or project manager to use in helping you achieve more success.

Models is a fun book. Managing technology is hard work and full of stress. We all need to view our problems from a "lighter side" at times. Some of the models like Bite the head of a frog provide a meaningful management principle and insight while taking a more fun approach to the situation.

Believe me, when you read about this model, you won't forget it for a long time and you will be amazed at how easy it will be to use the model in daily situations.

Models is a practical book. All of my work approaches IT management and project management from a very practical perspective. The job of managing technology resources is complex to be sure but managing doesn't have to be hard and complex when you know what to do, how to go about it, and have the tools to be successful. Models is the fourteenth book of my IT Manager Series publications and an excellent tool to help simplify managing technology resources to allow you to achieve more success.

Who is Models intended for

Models is primarily intended for IT managers of all levels, project managers, team leaders, and supervisors. However, it is a tool that can be understood and used by virtually anyone.

What is a model Good question. A model starts with a picture or simple graphic used to depict a specific management principle or issue. There are 72 models in the book. Each model has a unique graphic, a brief description, and bullet points that highlight key points of the model. In addition, there is a full page discussion of the model and each of the key points.

Models are grouped into six management discipline categories. The book is organized so you may learn the models one by one or focus in on a specific management discipline to learn about that area of management. The management discipline categories are:

- IT Assessment

- Strategy and Planning

- Project Management and Process

- Organization and Staff

- Financial Management

- Measurements and Communication

The models cover a complete overview of the requirements of an IT manager, delve into specifics of project management, and focus specifically on issues pertinent to information technology.

Source : Business Wire. New York: Nov 3, 2008

Multi-modal visual experience of brand-specific automobile design

This article presents a questionnaire study of brand-specific perceptions of automotive design using subjective rating methods. The purpose of the paper is to explore the multiple modalities of the visual product experience of automobile design as perceived by the general public. Furthermore, the experiences were analysed using a framework for visual product experience (VPE). Design/methodology/approach - Respondents were asked to assess the design of two car models at an international car show in relation to brand perceptions and visually perceived attributes using, among other tools, visual analogue scales. Analysis was done using a qualitative technique. Findings - Results from the study indicate that there is a correlation/relation between experiential modes, in that respondents tended to rate attributes consistently high or low across modes. This implies that if the aesthetics are not perceived as favourable, neither is the expression of the car. Furthermore, respondents' assessments of aesthetic appeal and expression are on an average strikingly similar, suggesting that the level of aesthetic appeal correlates with the level of semantic understanding of the design. The general rating of emotional response follows a similar consistent pattern for the two studied cars. Originality/value - Study approach as a way to gain insights into subjective perceptions of products based on appreciation and interpretation of visual product form. VPE framework recognising, mapping and clarifying the multiple modes of the visual experience.

Source : TQM Journal, Author :Anders Warell

Production Systems Control

In a manufacturing plant, inventories consist of basic materials, good in process, and finished stock. Production planning is concerned with determining what resources (materials,supplies,space,people,and equipment) must be available on-site over time to ensure that manufacturing goals are accomplished. Production control is concerned with timely issuance of available on-site materials to the manufacturing process in such a way that materials are made available in a cost-effective manner where and when needed. The fundamental purpose of inventory is to provide an essential decoupling between unequal flow rates.EOQ and EPQ were early mathematical formulations that considered some of the important costs associated with inventory, but less mathematically rudicable aspects, such as immediacy of quality feedback effects, were not represented in these early formulations. The use of Just In Time (JIT) in recent times has demonstrated that these earlier formulations were therefore significantly incomplete and misleading.
Most manufacturing firms learned to incorporate MRP (Material Requirements planning)systems to keep track of lead times and assembly builds. With the introduction of JIT systems, so much less inventory is work-in-process and is on the production floor for such a short time that, as a component of overall inventories, work-in-process inventory has typically been greatly reduced or even no longer exists as a material category.
The development of the kanban system by Toyota and the concentration of effort on the reduction of setup time by such innovative thinkers as Shigeo Shingo have greatly enhanced the banafits of adopting a JIT philosophy. Reducing the setup time is now seen as productivity opportunity, whereas in the past it was typically ignored-something we wanted to do but never got around to doing.
Quality control and quality assurance promised but often failed to deliver in indentifying quality problem at source. It was never a production responsibility, and so, as a staff function, it rarely got the resources it needed or the attention it deserved for doing the job right. Making quality a production responsibility has focused increased attention on quality needs, and making the production team take responsibility for identifying and resolving quality issues has greatly enhanced the team's effectiveness. The quality control department, a technical staff resource, can now be effectively employed as the technical means of assisting production productivity and of helping quality teams employ the technology needed to resolve problems.
Taguchi techniques, a technical area that for years in the United States has been called design-of-experiments, is a fairly technically demanding area that needs to be applied to production problems by those with sufficient statistical training. A partnership is needed between those in production who have the problems and those skilled in statistical analysis to effectively capitalize on these opportunities.

Source: Industrial Engineering and Management book's

Frederick W. Taylor

Member of the American of the American Society of Mechanical Engineers (ASME), much impressed by the concepts offered by Towne (Henry Towne of the Yale and Towne Company published a paper Transactions f the American Society of Mechanical Engineers, stressed the need for engineers o be concerned with the econoimic effects of their decisions), was Frederick W. Taylor. Taylor have ofetn been referred to as the "father of industrial Engineering". Taylor offered the concept that it was an engineering responsibility to design, measure, plan, and schedule work.
Taylor, influenced by both Towne and Wentworth, developed the concept that work design, work measurement, production scheduling, and other staff functions were engineering responsibilities. His attempts to implement his concepts revolutionized industrial productivity.
Even more important than the improvement in shoveling productivity was the concept of applying engineering analysis to human work situations. Taylor initiated the practice of performing an engineering analysis of work requirements specifying the exact method, tools, and equipment to be employed, and then training the worker to perform the opearations as specified.
He eventually determined an optimum method of selected workers to perform this task and carefully trained them to perform the task, exactly as he specified. As a result of his analysis, dramatic changes in pig iron handling productivity resulted.
Analysis of the work requirements and specifications for a method to perform an operation is now called work design or methods study. However, Taylor also pioneered the activity we now generally call work measurement. This activity is concerned with determining the amount of time an operator should be allowed for performing an operation.
Taylor invented stopwatch timestudy, which is still used extensively to determine the time to perform an operation, commonly referred to today as a time standard. Taylor development of timestudy led to time standards, which are the underlying basis for control of labor costs and are a necessary input to scheduling and pricing activities in industry.
Industrial Engineering, which represents the science of operations, has not been embraced by many responsible for operations today; they choose to deny its existence a century after its initiation. they choose to believe that operation simply require common sense. the history of engineering is replete with such attitudes. There are firms just today discovering that industrial engineering effectively deals with problems they have been unable to solve effectively throughout their past. In a sense, even today, in some firms, doing industrial engineering work ia a bit like doing missionary work.