Demand Planing
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45 terms
Terms | Definitions |
|---|---|
 Demand Forecasting | Predicting future customer demand |
| Demand Management | Influencing either pattern or consistency of demand |
| Demand Planning | Both forecasting and managing customer demand to reach operational and financial goals |
| Demand Forecasting | Environmental markets Historical demand Expert judgments |
| Demand Management | Pricing Promotion Order scheduling |
| Long term goals | Strategic Operations Planning and Design |
| Intermediate Term | Sales and Operations Planning |
| Short Term | Materials and Resource Requirements Planing |
| Forecasting Process | Step 1: Define users and processes Step 2: Identify data sources Step 3&4 Select and document forecasting tools Step 5 : Monitor and continuously improve the process |
| Forecast Qualitative | Judgmental Market Research |
| Forecast Quantitative | Time Series Causal Simulation |
| Grassroots | Input from those close to products or customers |
| Executive Judgment | Input from experience |
| Historical Analogy | Assume past demand is a good predictor of future demand |
| Delphi Method | Input for panel of experts |
| Marketing research | Examine patterns of current customers (focus groups, customer, surveys) |
| Time Series Analysis | Forecasts are based solely on past historical demand Identifies and measures patterns in data Assumes that patterns will continue in the future (Black Box approach) |
| Causal Modeling | Assumes demand is influenced by one or more factors Identified important factors and measures their effects |
| Simulation models | Sophisticated techniques that allow for the evaluation of multiple scenarios |
| Characteristics of Qualitative Forecasts | Subjective, judgmental Can integrate soft data Can be used for new products Better with one off events |
| Characteristics of Quantitative Forecasts | Objective, statistical Can handle large data sets Consistent Efficient Requires hard data |
| Advantages of Causal Modeling | Best for long range forecasting Best for predicting turning points or shifts in data patterns Can generate a better understanding of the mechanisms influencing sales/demand etc. |
| Disadvantages of Causal Modeling | Requires more data Requires special expertise that many managers do not have |
| Time Series Analysis Characteristics | Multiple identifiable patterns in demand: Random fluctuations, Trends, Seasonality, Cyclical Typically multiple patterns coexist and they can be separated and identified. The goal is to " EXTRAPOLATE" |
| Naive Method | Tomorrow's demand will be the same as today's demand |
| Simple Moving Average | Simple average places the same weight on all past demand periods. This method works well when the demand is fairly stable over time. Decreasing the number of periods in forecast, creates more responsive data. The forecast lags the demand because of averaging effect This method doesn't do a good job forecasting when trends are present in a data |
| Weighted Moving Average | Allows greater emphasis to be placed on more recent data points to reflect changes in the demand. Information used can be based on the forecaster's experience. Decreasing # of periods in forecast or increasing the size of the weights creates a more responsive forecast. The forecast still lags the demand because of averaging effect This method does not do a good job of forecasting when trends are present in the data. |
| Simple & Weighted Moving Average | A simple or weighted moving average does not "hold to the past" will be forgotten by a moving average. Thus, outliers and extreme observations will eventually cease to negatively affect a moving average forecast. |
| Simple Exponential Smoothing | Like other time serious models, it is suitable for data that show no trend patterns. Due to its simplicity, exponential smoothing is a very popular forecasting technique. however the forecast will also lag any trend in the actual data. , Smoothing Methods a) Simple moving averages b) Simple exponential smoothing |
| Evaluating Forecasts | Quality of forecast depends on accuracy and bias. Forecasting errors are absolute or relative. |
| Mean Forecast Error | Average of forecasting errors from period 1 to period current period. MFE is a measure of bias, but not accuracy. Smaller MFE means relatively unbiased forecasts. Larger MFE means biased forecasts which indicates overestimation or underestimation |
| Mean Absolute Deviation | Average of absolute values of forecasting errors. MAD measures the dispersion(variance) of the forecasting errors. positive and negative forecast errors do not cancel each other out. Smaller MAD means relatively more accurate forecasts Larger MAD means relatively less accurate forecasts |
| Mean Squared Error | Average of squared values of forecasting errors. It is dispersion measure of the forecasting errors. Positive and negative errors do not cancel each other Smaller MAD means relatively more accurate forecasts, larger MAD means relatively less accurate forecasts. The difference between MSE and MAD is that MSE penalizes larger forecast errors to a greater degree. And MSE is sensitive to extreme forecast errors. |
| Mean Absolute Percent Error | Standardizes the forecast errors with respect to the actual demand. Measure the average relative magnitude of the forecasts errors. Expressed as a % of the actual demand. |
| Forecast Quality | Short term forecast are more accurate than long term. Aggregate forecast are more accurate than detailed forecasts. Information from more sources yields a more accurate forecast |
| Fluctuating Customer Demand | cause operational inefficiencies. Timing and level of activities determine operational costs. Smoother operations means lower costs and higher profits Fluctuating operations means higher costs and lower profits. |
| Fluctuating Operations | Lead to expansion/contraction of capacity (equipment, labor, materials) Backlog (delayed fulfillment of orders) Customer dissatisfaction System Buffering (safety stock, safety lead time, capacity cushions) |
| CPFR | Collaborative planning, Forecasting, and Replenishment: Focuses on information sharing among trading partners, and eliminates typical order processing. The goal to ensure effecient replenishment |
| Market Planing: | changes to products, locations, pricing and promotions |
| Demand and Resource Planing | Forecasting |
| Execution | order fulfillment |
| Analysis | data on key performance metrics |
| CPFR in process | Step1: Market/item knowledge store planing item planing by individual stores(marketing and promotional input from sales/marketing) Step2: POS Data(production planners validate item -level forecast) Step 3:Create Item level forecast and special even calendar. (forecast drives productions) Step 4: Create purchase order item (product shipped purchase order specification) |
| Value of CPFR | an exchange of forecasting information not from complex algorithms to forecasting accuracy Better information leads to forecasting accuracy Better forecasting accuracy leads to better decisions, better resource allocation, better utilization,better coordination. |
| Benefit of CPFR | Strength partner relationship Provides analysis of sales and order forecasts upstream and downstream Uses POS data, seasonal activity, promotions, new product intros, and store openings or closing to improve forecast accuracy Manages the demand chain by exception and proactively eliminates problems before the appear Allows collaboration on future requirement and plans |
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