Forecasting Bus 255 Goals By The End Of This Chapter

Forecastingbus255goalsby The End Of This Chapter You Should Knowimpo

Forecasting BUS255 Goals By the end of this chapter, you should know: Importance of Forecasting Various Forecasting Techniques Choosing a Forecasting Method 2 Forecasting Forecasts are done to predict future events for planning Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan Forecasts are made on many different variables Forecasts are important to managing both processes and managing supply chains 3 Key Decisions in Forecasting Deciding what to forecast Level of aggregation Units of measurement Choosing a forecasting system Choosing a forecasting technique Forecasting Techniques Qualitative (Judgment) Methods Sales force Estimates Time-series Methods Naïve Method Causal Methods Executive Opinion Market Research Delphi Method Moving Averages Exponential Smoothing Regression Analysis Qualitative (Judgment) methods Salesforce estimates: Forecasts derived from estimates provided by salesforce. Executive opinion: Method in which opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast. Market research: A scientific study and analysis of data gathered from consumer surveys intended to learn consumer interest in a product or service. Delphi method: A process of gaining consensus from a group of experts while maintaining their anonymity. 6 Case Study Reference: Krajewski, Ritzman, Malhotra. (2010). Operations Management: Processes and Supply Chains, Ninth Edition. Pearson Prentice Hall. P. 42-43. 7 Case study questions What information system is used by UNILEVER to manage forecasts? What does UNILEVER do when statistical information is not useful for forecasting? What types of qualitative methods are used by UNILEVER? What were some suggestions provided to improve forecasting? 8 Causal methods – Linear Regression A dependent variable is related to one or more independent variables by a linear equation The independent variables are assumed to “cause” the results observed in the past Simple linear regression model assumes a straight line relationship 9 Causal methods – Linear Regression Y = a + bX where Y = dependent variable X = independent variable a = Y-intercept of the line b = slope of the line 10 Causal methods – Linear Regression Fit of the regression model Coefficient of determination Standard error of the estimate Please go to in-class exercise sheet Coefficient of determination: Also called r-squared. Measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Range between 0 and 1. In general, larger values are better. Standard error of the estimate: Measures how closely the data on the dependent variable cluster around the regression line. Smaller values are better. 11 Time Series A time series is the repeated observations of demand for a service or product in their order of occurrence There are five basic time series patterns Horizontal Trend Seasonal Cyclical Random Time series methods use historical information regarding only the dependent variable. 12 Demand Patterns Quantity Time (a) Horizontal: Data cluster about a horizontal line 13 Demand Patterns Quantity Time (b) Trend: Data consistently increase or decrease 14 Demand Patterns Quantity | | | | | | | | | | | | J F M A M J J A S O N D Months (c) Seasonal: Data consistently show peaks and valleys Year 1 Year Demand Patterns Quantity | | | | | | Years (d) Cyclical: Data reveal gradual increases and decreases over extended periods 16 The extended periods of time could be years or even decades. Cyclical patterns could arise from product life cycle and business cycle. Demand Patterns Four of the patterns – horizontal, trend, seasonal, and cyclical – combine in varying degrees to define the underlying time pattern Fifth pattern Random variation: Results from chance causes and cannot be predicted Random variation is what makes every forecast ultimately wrong 17 Time-Series methods Use only historical information rather than independent variables (as used by Regression) Assumption is that past pattern continues in future In a naive forecast the forecast for the next period equals the demand for the current period (Forecast = Dt) Naive forecast works well when horizontal, trend, or seasonal patterns are stable and random variation is small. 18 Time-Series methods This section considers time-series methods with demand that has no trend, seasonal, or cyclical patterns All variation in time series is due to random variation, so the following techniques are appropriate: Simple moving average Weighted moving average Exponential smoothing Simple Moving Average The forecast for period t + 1 can be calculated at the end of period t (after the actual demand for period t is known) as Ft+1 = Sum of last n demands / n Dt + Dt-1 + Dt-2 + … + Dt-n+1 n where Dt = actual demand in period t n = total number of periods in the average Ft+1 = forecast for period t + 1 Forecast error For any forecasting method, it is important to measure the accuracy of its forecasts. Forecast error is simply the difference found by subtracting the forecast from actual demand for a given period, or where Et = forecast error for period t Dt = actual demand in period t Ft = forecast for period t Et = Dt – Ft Simple Moving Average Please refer to problem in the in-class exercise Using this method, each historical demand in the average can have its own weight, provided that the sum of the weights equals 1.0. Weighted Moving Average Ft+1 = W1D1 + W2D2 + … + WnDt-n+1 A three-period weighted moving average model with the most recent period weight of 0.50, the second most recent weight of 0.30, and the third most recent might be weight of 0.20 Ft+1 = 0.50Dt + 0.30Dt–1 + 0.20Dt– Weighted Moving Average Please refer to problem in the in-class exercise Exponential Smoothing A sophisticated weighted moving average that calculates the average of a time series by giving recent demands more weight than earlier demands Requires only three items of data The last period’s forecast The demand for this period A smoothing parameter, alpha (α), where 0 ≤ α ≤ 1. The equation for the forecast is Ft+1 = α(Dt) + (1 – α)(Ft) = Ft + α(Dt – Ft) or the equivalent 25 Exponential Smoothing Please refer to problem in the in-class exercise Exponential Smoothing The emphasis given to the most recent demand levels can be adjusted by changing the smoothing parameter Larger α values emphasize recent levels of demand and result in forecasts more responsive to changes in the underlying average Smaller α values treat past demand more uniformly and result in more stable forecasts Exponential smoothing is simple and requires minimal data When the underlying average is showing some trend, different model is needed 27 Choosing a Time-Series Method Forecast performance is determined by forecast errors Forecast errors detect when something is going wrong with the forecasting system Forecast errors can be classified as either bias errors or random errors Bias errors (or systematic errors) are the result of consistent mistakes Random error results from unpredictable factors that cause the forecast to deviate from the actual demand 28 So, what do we mean by systematic error? Operations Management Introduction to Forecasting 29 Note that for almost all of the periods, the forecasted value is below the actual data value. This is a systematic error. Measures of Forecast Error Forecast Error = Demand value – Forecast Value Mean absolute deviation (MAD) Mean signed deviation (MSD) Tracking signal (TS) Mean squared error (MSE) Mean absolute percentage error (MAPE) Et = Dt – Ft Mean Absolute Deviation (MAD) MAD is the average of the absolute values of the errors. Stated in the same units as the forecast Captures the magnitude of the forecasting error Compute MAD for the example problem in Excel sheet (tab 2) and interpret the results “|Et|” Mean Sign Deviation (MSD) MSD is the average of the errors. Stated in the same units as the forecast. Signs (+/-) of the error terms tend to cancel each other out. A large value (+/-) indicates systematic forecast error. Compute MSD for the example problem in Excel sheet (tab 2) and interpret the results “Et” Tracking Signal (TS) measures systematic error. TS is unitless and is between -1 and 1. Think of it as percentage of forecast error that is systematic. MSD MAD TS = Tracking Signal (TS) Interpreting Tracking Signal (TS):

- Absolute Magnitude

- Sign

- Low (0.0 - 0.2): Forecast OK

- Medium (0.2 - 0.5): Forecast lag below actual

- High (0.5 and above): Serious lag below actual

Paper For Above instruction

Forecasting plays a pivotal role in the strategic planning and operational management of organizations. Accurate forecasts enable businesses to allocate resources effectively, optimize supply chains, and meet consumer demands efficiently. This paper explores the significance of forecasting, examines various forecasting techniques, and discusses how to select the most appropriate method based on specific circumstances. It draws on academic research and case studies, notably from operations management literature, to illustrate key concepts and practical applications.

Introduction to forecasting underlines its importance as a tool for predicting future demand based on historical data and current trends. Effective forecasting supports decision-making across functions such as finance, human resources, marketing, and operations. These forecasts guide inventory management, production schedules, staffing levels, and financial planning. As forecast accuracy directly impacts organizational performance, selecting suitable techniques is crucial.

Types of Forecasting Techniques

The literature categorizes forecasting methods into qualitative and quantitative techniques. Qualitative methods rely on expert judgments, intuition, and market research, making them suitable in situations where historical data is limited or unavailable. Techniques such as executive opinion, market surveys, and the Delphi method fall under this category.

Quantitative techniques are data-driven and involve statistical models. Time-series methods, such as moving averages, exponential smoothing, and trend analysis, leverage historical data patterns to generate forecasts. Causal models, notably linear regression, analyze relationships between dependent and independent variables, allowing predictions based on causal factors.

Qualitative Forecasting Methods

Qualitative approaches involve gathering expert insights and consumer perceptions. Executive opinion synthesizes managerial insights to produce forecasts, while market research collects and analyzes consumer data. The Delphi method seeks consensus among experts anonymously, reducing bias. Salesforce estimates involve forecasts based on sales personnel input, often incorporating experiential knowledge.

Quantitative Forecasting Techniques

Quantitative methods include time-series analysis and causal modeling. Time-series techniques—such as naive forecasts, moving averages, and exponential smoothing—assume past demand patterns will persist into the future. Causal models establish relationships between the forecasted variable and causal factors, with linear regression being a common example.

Linear Regression Analysis

Linear regression models the relationship between a dependent variable and one or more independent variables through a straight-line equation, typically expressed as Y = a + bX. Here, Y is the dependent variable, while X represents the independent variable. The model estimates coefficients to explain variations in Y based on X. The fit of the model is evaluated using metrics like the coefficient of determination (R-squared) and the standard error of the estimate, indicating the model's predictive accuracy.

Time Series Demand Patterns

Understanding demand patterns is vital for selecting suitable forecasting methods. Common patterns include horizontal (stable demand), trend (gradual increase or decrease), seasonal (regular fluctuations within a year), and cyclical (long-term fluctuations over years). Recognizing these patterns helps determine whether methods like moving averages or exponential smoothing are appropriate or if more sophisticated models are needed.

Time-Series Methods

Time-series methods focus solely on the historical demand data, assuming the same patterns will continue. The naive method assumes the forecast equals the demand of the current period, effective under stable conditions. Moving averages smooth out fluctuations by averaging past data points, while exponential smoothing assigns exponentially decreasing weights to older data, making it responsive to recent changes.

Forecast Accuracy and Error Measurement

Assessing forecast accuracy involves metrics such as Mean Absolute Deviation (MAD), Mean Sign Deviation (MSD), Tracking Signal (TS), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). MAD captures the average magnitude of errors, while MSD indicates bias in the forecast. Tracking signal monitors systematic errors over time, guiding adjustments to improve accuracy. Smaller error measures reflect better forecasting performance.

Forecasting as a Process

Forecasting is an iterative process involving data collection, model development, review, and revision. Cross-functional collaboration and consensus are essential for refining forecasts. Proper communication of forecast assumptions and results ensures organizational alignment and better decision-making.

Conclusion

In conclusion, effective forecasting requires understanding the nature of demand patterns, selecting appropriate methods, and continuously monitoring performance through error analysis. The integration of qualitative insights and quantitative models enhances forecast reliability, ultimately supporting organizations' strategic and operational objectives. As forecasting inherently involves uncertainty, ongoing refinement and adaptation are vital.

References

• Krajewski, Ritzman, Malhotra. (2010). Operations Management: Processes and Supply Chains (9th ed.). Pearson Prentice Hall.
• Makridakis, Wheelwright, Hyndman. (1998). Forecasting: Methods and Applications. Wiley.
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• Chatfield, C. (2000). The Analysis of Time Series: An Introduction. Chapman & Hall/CRC.
• Makridakis, Spiliotis, Assimakopoulos. (2018). The M4 Competition: Results, Findings, and Implications. International Journal of Forecasting.
• Wang, W., & Riser, C. (2007). Forecasting Techniques: A Review. Journal of Business & Economic Statistics.
• Fildes, R. & Goodwin, P. (2007). Against the Fundamentals: The Case for Judgmental Forecasting. International Journal of Forecasting.
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• Syntetos, A. A., Babai, M. Z., & Gaukler, G. (2020). Forecasting in Supply Chain Management: An Overview. International Journal of Production Economics.