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MKT303 Unit Two

MGT303 Unit Two

QuestionAnswer
Why is forecasting important? budget, new product launch, headcount decisions, capacity planning, facility design, scheduling, inventory management
Components of demand trend, seasonality, noise
Independent Demand The demand for a finished product such as a tricycle.
Dependent Demand The demand for component parts or sub assemblies such as as tires.
Strategic - medium term (1-2 years) to long term (2-5 years) - decisions related to overall strategy, capacity, process design, location, distribution design, sales and operations planning, equipment purchases, staff planning
Tactical - Short term forecasts. Estimate demand in near future (next day, week, month, quarter) - Used for day-to-day operations, job scheduling, inventory purchasing, weekly labor requirements, etc.
Qualitative forecasting - take advantage of expert knowledge - Flexible, Utilizes Intuition Cons: Errors in Judgment, Unexpected Changes, Bias - Examples: Executive / Sales Force opinion, Market research, Historical analogy, Delphi method / Panel consensus
Quantitative forecasting - concrete inputs regarding sales, inventory and labor based on the company's historical data - Pros: Addresses Historic Data, Exposes Patterns, Attracts Stakeholders - Cons: Lacks Detail, Cost • Examples: Time Series, Casual Relationship. Regress
Time Series forecasting - series of data points ordered in time. - time is often the independent variable and the goal is usually to make a forecast for the future. - Factors to consider •Data availability •Time horizon •Accuracy required
Time Series example - moving average, exponential smoothing, linear regression analysis
Casual Relationship linear and multiple regression analsis
Simple Moving Average - calculated as the average of a fixed number of past periods •Useful when demand is not growing or declining rapidly and no seasonality is present •Removes some of the random fluctuation from the data •Longer periods provide more smoothing and Short
weighted moving average - allows unequal weighting of prior time periods •The sum of the weights must be equal to one •Often, more recent periods are given higher weights than periods farther in the past
Sources of Forecasting errors •Bias – when a consistent mistake is made •Random – errors that are not explained by the model being used
Use for forecasts with low volume/sporadic pattern: MAD or MAPE? MAD
Use for forecast with high volume / regular demand MAPE
Use to compare different products MAPE
3 data required for exponential smoothing • Most recent forecast • Actual demand for the forecast period • Smoothing constant alpha () •Determines the level of smoothing and speed of reaction - The value depends on how much random variation in demand (0.1 – 0.3) or (10 – 30%)
EXPONENTIAL FORECASTS VERSUS ACTUAL DEMAND FOR PRODUCT OVER TIME SHOWING FORECAST LAG The presence of a trend in the data causes the exponential smoothing forecast to always lag behind the actual occurrence •corrected by adding a trend adjustment (delta = δ) •Both α and δ reduce the impact of the variance between the actual &forecast
EXPONENTIAL SMOOTHING MODEL Adjusts forecast for random variation using smoothing constant α.
Supply Chain Disruptors • Increased consumer demand • Shortage of truck drivers • Shortage of containers and congestion at ports • Lack of raw materials
Contributing factors behind the bullwhip • Demand forecast updating • Order batching practice • Price fluctuations and trade promotions • Rationing and short gaming
Tactical forecasts are _________ term, while strategic forecasts are _____________ term short, medium/long
Created by: cqfe222
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