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Forecasting and Trend Analysis Paper presented at The 2014 Training Workshop of Committee of Directors of Academic Planning of Nigerian Universities By Samson Akinyosoye October 2014

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Learning Objectives This lecture will have the following broad objectives: 1.To understand the role of data in planning 2.To highlight the role of data in forecasting 3.To identify different data presentation for forecasting 4.To discuss simple forecasting and trend analysis methods 5.To introduce participants to simple tools for forecasting and trend analysis

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Opening Vignette "It's tough to make predictions, especially about the future.“ Yogi Bera

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The Demand Dynamics for Planning in institutions Its important to use data to travel to the future…why? 1.Government all over the world is increasingly bothered about tasking institutions to deliver qualitative education in accordance to set guidelines and standards. 2.Employers of labour are worried about the quality of entrants into the work force, most especially having sound employees in order to cope with the demands of global competition. 3.Globalisation is changing the structure and design of education across several spheres 4.Advances in technology especially in IT, information spread, and instructional tools have direct implications for changing instructional delivery systems.

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The Place of Forecasting in Planning 1.Academic Planning proceeds from “what was” to “what is” and “what should be” in the overall interest of progress and development. 2.Forecasting helps build the bridge between the past and the picture of the desired future. 3.Forecasting is a prerequisite for planning as the planner must know what is likely to happen if the present trends continue without policy intervention. 4.A Planner typically puts up a programme for action-a blueprint for translating policy into practical needs and utility.

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Forecasting and Environmental Scanning Inputs for forecasting are generally information that are sourced around. This links Forecasting closely with the concept of Environmental Scanning 1.Analysis of environmental change and the development of institutional policies to deal with this change are key to an organization's success. 2.Scanning the general environment for trends that affect the organization's mission is essential to developing an effective strategic plan. 3.Every academic plan becomes real when it takes into account the realities of the space in which it is implemented. Most institutions have a dedicated unit for this e.g. The Office of Institutional Analysis (OIA) of the University of Wisconsin-Madison has the primary institutional responsibility for the collection and analysis of quantitative and qualitative information on the institution, its students, its faculty, its programs, its publics, its practices and its services. The office provides analytic support for the planning, evaluation and policy initiatives of the Provost and senior leadership and acts as the institution's reporting agent.

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Environmental Scanning-An Accelrator for Staying Ahead Environmental Scanning provides a catalyst for staying competitive: 1.a University that establishes an environmental scanning and forecasting system has benefits of an early warning system to identify trends and events that, when forecasted, present both threats and opportunities to the University 2.With early warning, administrators can prepare their response options in anticipation of changes implied by these trends and events. 3.System will increase management efficiency in dealing with uncertainties inherent in the future by anticipating change and influencing the future rather than by simply reacting to it. 4.An environmental scanning system is structured to identify and evaluate trends, events, and emerging issues important to the institution

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Forecasting Defined 1.A forecast is a prediction of what will happen in the future. 2.Forecasting implies the application of a variety of tools to predict future situations, or results. 3.If we move backward, attempting to predict a previous condition, the term backcasting can be used. 4.If we know existing time series data points, but there are some holes, we can estimate the values for those missing points between known points by a process called interpolation.

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The Types of Forecasting Based on numeric data from several different time periods of the past that can be assumed to provide a pattern that allows us to predict the future. Quantitative forecasting Relies more heavily on non- numeric data, the judgments of specialists, and their variety of knowledge. Qualitative forecasting

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Typical Planning Intervention Data Source for Forecasting GoalStrategyAction PlanData Needs Sustain and develop select graduate and professional programs of national and international distinction. Support the graduate and professional programs that are already in the top-tier Moving on a strong upward trajectory toward national and international prominence in faculty research and scholarly productivity, including creative work and artistic performance Number of research work published, number of patents created from research, etc Performing at rates comparable to or above those of peer/aspirant programs at top-tier public research universities on commonly benchmarked metrics (e.g., job placement, time-to-degree, licensure pass rates, preparedness of admittees, extramural fellowships/funding for students) Number of job placement created, % licensure pass rates, number of eligibilities for research grants, etc Offer a resource-rich training environment for graduate and professional students. Increase the recruitment and retention of graduate and professional students from underrepresented groups % of admitted students from EDS, number of indigenes on scholarship, etc (Source: UNIVERSITY OF CONNECTICUT ACADEMIC PLAN 2009 – 2014)

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A Look at Planning Data (Source: UNIVERSITY OF WISCONSIN DATA DIGEST)

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A Further Look at Planning Data

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(Source: UNIVERSITY OF WISCONSIN DATA DIGEST) A Further Look at Planning Data

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(Source: UNIVERSITY OF WISCONSIN DATA DIGEST) A Further Look at Planning Data

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Long Term 5+ years into the future Program planning, R&D, school location, etc Principally judgement-based Medium Term 1 session to 4 years Aggregate planning, capacity planning, enrolment/employment forecasts Mixture of quantitative methods and judgement Short Term 1 day to 1 year, less than 1 session Demand forecasting, staffing levels, applications, inventory requirements Majorly quantitative methods Forecasting Horizons

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There are four basic patterns of trendlines. a. Constant. : those where there is no net increase or decrease. b. Linear : those that show a steady, straight-line increase or decrease. May go up or down, and the angle may be steep or shallow.. c. Exponential :those where the data rises or falls not at a steady rate, but at an increasing rate. d. Polynomial :those best modeled by a polynomial equation. They may be second-order (quadratic) equations of the form y = ax 2 + bx + c resulting in a parabolic shape or 3rd order. a. Moving average :those that uses the average of a number of past events to even out the likelihood of the next event.. Trend Patterns

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Constant TrendLinear Trend Exponential Trend2 nd Order Polynomial Trend Trend Patterns Illustrated

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3 rd Order Polynomial Trend Moving Average Trends Trend Patterns Illustrated

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Deciding what to forecast Level of aggregation. Units of measure. Choosing the type of forecasting method: Qualitative methods Judgment Quantitative methods Causal Time-series Designing The Forecast System

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Level of Aggregation: The act of clustering several similar services or programs so that institutions can obtain more accurate forecasts. Units of measurement: Determining the standard calibration for the items in analysis. Deciding What to Forecast

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Judgment methods: A type of qualitative method that translates the opinions of managers, expert opinions, and user surveys into quantitative estimates. Causal methods: A type of quantitative method that uses historical data on independent variables, such as demographic composition, economic conditions, and competitors’ actions, to predict future expectations. Time-series analysis: A statistical approach that relies heavily on historical demand data to project the future expectations and recognizes trends and seasonal patterns. Choosing the Type of Forecasting Techniques

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Causal methods are used when historical data are available and the relationship between the factor to be forecasted and other external or internal factors can be identified. Linear regression: A causal method in which one variable (the dependent variable) is related to one or more independent variables by a linear equation. Dependent variable: The variable that one wants to forecast. Independent variables: Variables that are assumed to affect the dependent variable and thereby “cause” the results observed in the past. Causal Method: Linear Regression

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Dependent variable Independent variable X Y Estimate of Y from regression equation Actual value of Y Value of X used to estimate Y Deviation, or error { Causal Methods : Linear Regression Regression equation: Y = a + bX Y = dependent variable X = independent variable a = Y-intercept of the line b = slope of the line

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A time series is a set of observations measured at successive points in, or over successive periods of time. The objective of time series methods is to discover a pattern in the historical data and then extrapolate this pattern into the future. The forecast is based solely on past values of the variable that we are trying to forecast and/or on past forecast errors. Three time series methods are: naive forecast smoothing trend projection Time Series Method

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Assumptions of Time Series Models There is information about the past; This information can be quantified in the form of data; The pattern of the past will continue into the future. Assumptions

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Naive forecast: A time-series method whereby the forecast for the next period equals the demand for the current period, or Forecast = D t Naive Trend: A time-series method whereby the forecast for the next period is based on the most recent change between the last two data points Time Series Methods: Naïve Forecasts

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In cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects, one can use smoothing methods to average out the irregular components of the time series. Three common smoothing methods are: Moving averages Weighted moving averages Exponential smoothing Smoothing Methods

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Simple moving average method: A time-series method used to estimate the average of a demand time series by averaging the demand for the n most recent time periods. It removes the effects of random fluctuation and is most useful when demand has no pronounced trend or seasonal influences. The term moving indicates that, as a new observation becomes available for the time series, it replaces the oldest observation in the equation, and a new average is computed. … Simple Moving Average

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Weighted moving average method: A time-series method in which each historical demand in the average can have its own weight; the more recent observations are typically given more weight than older observations. The sum of the weights equals 1.0. F t+1 = W 1 D t + W 2 D t-1 + …+ W n D t-n+1 Weighted Moving Average

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Using exponential smoothing, the forecast for the next period is equal to the forecast for the current period plus a proportion ( ) of the forecast error in the current period. The forecast is calculated by: [the actual value for the current period] + (1- )[the forecasted value for the current period], where the smoothing constant, , is a number between 0 and 1. Exponential smoothing is the most frequently used formal forecasting method because of its simplicity and the small amount of data needed to support it. Exponential Smoothing

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Trend-adjusted exponential smoothing method: The method for incorporating a trend in an exponentially smoothed forecast. With this approach, the estimates for both the average and the trend are smoothed, requiring two smoothing constants. For each period, we calculate the average and the trend. Trend Adjusted Smoothing

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Delphi Approach Scenario Writing Subjective or Interactive Approaches Qualitative Approaches to Forecasting

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Delphi Approach A panel of experts, each of whom is physically separated from the others and is anonymous, is asked to respond to a sequential series of questionnaires. After each questionnaire, the responses are tabulated and the information and opinions of the entire group are made known to each of the other panel members so that they may revise their previous forecast response. The process continues until some degree of consensus is achieved. Qualitative Approaches to Forecasting

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Scenario Writing Scenario writing consists of developing a conceptual scenario of the future based on a well defined set of assumptions. After several different scenarios have been developed, the decision maker determines which is most likely to occur in the future and makes decisions accordingly. Qualitative Approaches to Forecasting

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Subjective or Interactive Approaches These techniques are often used by committees or panels seeking to develop new ideas or solve complex problems. They often involve "brainstorming sessions". It is important in such sessions that any ideas or opinions be permitted to be presented without regard to its relevancy and without fear of criticism. Qualitative Approaches to Forecasting

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Tools for Trend Analysis 1.Formulas 2.Microsoft Excel Charts 3.JavaScripts 4.Custom Apps [SuperSMITH Visual, Question Pro]

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Hands on Exercise on Forecasting and Trend Analysis Simple Forecasting with 1.Excel Chart Tools 2.Simple Moving Averages

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Questions and Answers

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Thank you Further questions could be directed to samsonakinyosoye@yahoo.com

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