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Demand Forecasting

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Demand Forecasting BIZ2121 Production & Operations Management Demand Forecasting Sung Joo Bae, Associate Professor Yonsei University School of Business Unilever Customer Demand Planning (CDP) System Statistical information: shipment history, current order information Demand-planning system with promotional demand increase, and other detailed information (external market research, internal sales projection) Forecast information is relayed to different distribution channel and other units Connecting to POS (point-of-sales) data and comparing it to forecast data is a very valuable ways to update the system Results: reduced inventory, better customer service Forecasting Forecasts are critical inputs to business plans, annual plans, and budgets Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan: ◦ output levels ◦ purchases of services and materials ◦ workforce and output schedules ◦ inventories ◦ long-term capacities Forecasting Forecasts are made on many different variables ◦ Uncertain variables: competitor strategies, regulatory changes, technological changes, processing times, supplier lead times, quality losses ◦ Different methods are used Judgment, opinions of knowledgeable people, average of experience, regression, and time-series techniques ◦ No forecast is perfect Constant updating of plans is important Forecasts are important to managing both processes and supply chains ◦ Demand forecast information can be used for coordinating the supply chain inputs, and design of the internal processes (especially the inventory level) Demand Patterns Forecastable factors: things that can be forecasted (e.g. surge in demand for lawn fertilizers in the spring and summer) Uncontrollable factors (weekly demand change due to the weather) Demand Patterns 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 Demand Patterns Figure 13.1 – Patterns of Demand Quantity Time (a) Horizontal: Data cluster about a horizontal line Demand Patterns Figure 13.1 – Patterns of Demand Quantity Time (b) Trend: Data consistently increase or decrease Demand Patterns Figure 13.1 – Patterns of Demand Year 1 Quantity Year 2 | | | | | | | | | | | | J F M A M J J A S O N D Months (c) Seasonal: Data consistently show peaks and valleys Demand Patterns Figure 13.1 – Patterns of Demand Quantity | | | | | 1 2 3 4 5 Years (d) Cyclical: Data reveal gradual increases and decreases over extended periods Causes: 1. Business Cycle (recession to expansion) – unpredictable 2. Product Life Cycle – somewhat predictable Demand Patterns Figure 13.1 – Patterns of Demand Quantity | | | | | | 1 2 3 4 5 6 Years (d) Random: The unforecastable variation in demand However, these random effects are embedded in any demand time series Key Decisions What to forecast What forecasting system to use What forecasting technique to use Key Decisions Deciding what to forecast ◦ Level of aggregation Most companies are successful in predicting the annual total demand for all services and products (error is less than 5% normally) Aggregation: making forecast on families of services or goods that have similar demand requirements and common processing, labor, and material requirements Aggregation is followed by forecast for individual item (SKUs – Stock Keeping Units: an individual item with an identifying code for inventory and tracking in the supply chain) ◦ Units of measure Product or service units (such as SKUs) are better than the dollar amount Key Decisions Choosing a forecasting system – Wal-Mart’s CPFR (Collaborative Planning, Forecasting, and Replenishment) system o (p.487): Wal-Mart uses CPFR and the internet to improve demand planning performance o What is Wal-Mart’s new approach for forecast? o What are the system’s benefits to Wal-Mart (rtl), Warner-Lambert (mfr)? o Increased sales and reductions in inventory costs (stock-out reduced from 15% to 2%) o Stock-out: Demand that cannot be fulfilled by the current level of inventory Key Decisions Choosing the type of forecasting technique Judgment and qualitative methods - Other methods (casual and time-series) require an adequate history file, which might not be available - Useful when contextual knowledge from experience matters - Can be used for modifying the quantitative analysis - Cause-and-effect relationships, environmental cues, and organizational information - Executive opinion, expert opinions (demand and technological forecasting in semiconductor), market research (consumer surveys), salesforce estimates Causal methods - Use historical data on independent variables (e.g. promotional campaigns, economic conditions, competitor’s actions) Time-series analysis - Statistical approach to predict future demand using the historical demand data Delphi Method Delphi method is a process of gaining consensus from a group of experts while maintaining their anonymity Anonymity is important to avoid “bandwagon effect” or “halo effect” ◦ a cognitive bias whereby the perception of one trait (i.e. a characteristic of a person or object) is influenced by the perception of another trait Initiated as a military project to forecast the impact of technology on war at the beginning of the Cold War Coordinator Sending out aggregates questions the results Keep sending out another round of surveys to narrow down the gaps ◦ Iterate the process with pre-determined criteria or until the consensus is reached Useful when no historical data are available Can be used to develop long-range forecasts and technological forecasting Key factor in choosing the proper forecasting approach is the time horizon for the decision requiring forecasts Causal Method: 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 is a straight line Y = a + bX where Y = dependent variable X = independent variable a = Y-intercept of the line b = slope of the line Linear Regression Deviation, Regression Y or error equation: Y = a + bX Estimate of Y from regression equation Actual value of Y Dependent Dependent variable Value of X used to estimate Y X Independent variable Figure 13.2 – Linear Regression Line Relative to Actual Data Linear Regression Linear Regression Linear Regression The sample correlation coefficient, r ◦ Measures the direction and strength of the relationship between the independent variable and the dependent variable. ◦ The value of r can range from –1.00 ≤ r ≤ 1.00 The sample coefficient of determination, r2 Measures the amount of variation in the dependent variable about its mean that is explained by the regression line The values of r2 range from 0.00 ≤ r2 ≤ 1.00 The standard error of the estimate, syx Measures how closely the data on the dependent variable cluster around the regression line Using Linear Regression EXAMPLE 13.1 The supply chain manager seeks a better way to forecast the demand for door hinges and believes that the demand is related to advertising expenditures. The following are sales and advertising data for the past 5 months: Sales (thousands Advertising Month of units) (thousands of $) 1 264 2.5 2 116 1.3 r = 0.980 2 3 165 1.4 r = 0.960 4 101 1.0 syx = 15.603 5 209 2.0 The company will spend $1,750 next month on advertising for the product. Use linear regression to develop an equation and a forecast for this product. Using Linear Regression SOLUTION We used the previous formula to determine the best values of a, b, the correlation coefficient, and the coefficient of determination, and the standard error of the estimate are already given. a = –8.135 r = b = 109.229X r2 = r = 0.980 syx = r2 = 0.960 syx = 15.603 The regression equation is Y = –8.135 + 109.229X Using Linear Regression The regression line is shown in Figure 13.3. The r of 0.98 suggests an unusually strong positive relationship between sales and advertising expenditures. The coefficient of determination, r2, implies that 96 percent of the variation in sales is explained by advertising expenditures. Brass Door Hinge 250 – X 200 – X 150 – X X Data 100 – X X Sales (000 units) (000 Sales Forecasts 50 – 0 – | | 1.0 2.0 Advertising ($000) Figure 13.3 – Linear Regression Line for the Sales and Advertising Data Application: Using Linear Regression The manager at Tuscani Pizza seeks a better way to forecast the demand for Calzone pizza at a certain region and believes that the demand is related to the promotion expenditures. The following are sales and promotion data for the past 5 months: Sales (thousands Promotion Cost Month of units) (thousands of $) 1 156 3.2 2 132 2.4 r2 = 0.850 3 144 3.1 4 201 3.9 5 194 3.5 The company will spend $2,900 next month on advertising for the product. Use linear regression to develop an equation and a forecast for this product. Time Series Methods In a naive forecast the forecast for the next period equals the demand for the current period (Forecast = Dt) Estimating the average: simple moving averages ◦ Used to estimate the average of a demand time series and thereby remove the effects of random fluctuation ◦ Most useful when demand has no pronounced trend or seasonal influences ◦ The stability of the demand series generally determines how many periods to include Time Series Methods 450 – 430 – 410 – 390 – 370 – Patient Patient arrivals 350 – | | | | | | 0 5 10 15 20 25 30 Week Figure
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