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22 Cards in this Set
- Front
- Back
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Marketing Managment process relies on... |
The Marketing Mix AKA the 4 P's: 1. Product- design, quality, cost, etc 2. Price- commodity or premium etc 3. Promotion- advertising, etc 4. Place- sales channels etc. Objective: leverage each P to... (page 2-7) |
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Three business processes that relate to demand management: |
1. marketing Management 2. CRM 3. demand planning (forcasting and orders) |
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Order qualifying |
characteristics that must be in product or service in order to be on the market. they do not differentiate product (like speed of service, dependability, flexibility, cost). this is component of marketing mix/4P's |
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Order Winning |
This causes customers to choose one product over another. it is component of marketing mix. |
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CRM (again/ was in Session 1 also) |
the collection and analysis of info for S&M to understand and support existing and potential customer needs. |
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Recognition of demand comes in two forms: |
1. demand forecasts 2. management of actual customer orders from internal and external customer. |
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Independent demand |
demand for a product that is not related to the demand for any other product. this type of demand is forecasted. |
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Dependent demand |
demand for a product that is related to the demand of another product. like parts of a bike. this demand is calculated based on the forcast for the independent item. like parts for a bike during the Materials Requirement planning (MRP) and are calculated based on bike forecast. |
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5 Sources of Independent Demand |
1. Forecast- based on quantitative and/or qualitative 2. customer orders- actual demand. 3. replenishment orders from distribution centers 4. Interplant transfers- orders placed from other divisions in the company 5. other sources of demand- ex: products needed for manufacturing. |
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4 Basic Demand Patterns |
1. trend- increasing decreasing or level. 2. seasonal- weather, school etc 3. random- special events, inclement weather, etc 4. Cyclical- over long time spans. tied to external influences like business cycle. |
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Demand forecasts support planning on these 3 levels: |
1. business planning- sales volume; new market and SC initiatives; 2 to 10 years. 2. sales and operations planning (S&OP) - physcial units of production at the product family level. 1 to 3 years 3. master scheduling- physical unites of production at the end item level. 3 to 18 months. |
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Principles of Forecasting: |
1. forecasts are rarely 100 percent accurate. as must be expected. 2. *important- every forecast must include an estimate of error. this is used to determining the level of safety stock. 3. forecasts are more accurate for families of products 4. forecasts are more accurate in the short term. |
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Three major principles of data collection and preparation for use in forecasting |
1. record data in the terms needed for the forecast (like demand instead of sales or shipments; use necessary unit of time; etc) 2. record the circumstances relating to the data. (like promotions, weather, etc) 3. record demand separately for different customer groups. |
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Forecasting Techniques (AKA methods) Categories |
1. Qualitative- based on informed opinion. used where business conditions, op[s and trends are likely to change 2. Quantitative (extrinsic and intrinsic- see other cards for definition) |
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Extrinsic Quantitative Forecasting Technique |
based on correlation and cause and effect. they rely on external indicators. usually look beyond short term and are based on families of products. two types of leading indicators: 1. economic- Ex= housing starts, construction contract awards etc. 2. demographic- ex= change in population, age distribution, etc. |
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Intrinsic Quantitative Forecasting Technique |
use time-sequenced historical data or time series data. this uses a number of assumptions: what happened in the past helps understand the future, patterns are revealed with time, availability of data. two types= moving average and exponential smoothing (see other cards |
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Moving Average Forecasting |
-Method: forcast the next time period based on the average of the previous range of time periods. (length of time varies) -Uses: Best when demand is stable, there is little trend or seasonality, and demand variations are random. best to use to filter out random variation. -Issues: does not quikcly respond to increase or decrease in demand. further back=greater lag. but shorter might over react. Sample calculation on 2-27 and practice on page 2-31 |
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Exponential Smoothing Forecasting |
Method- use old forecast and new demand for current period, assign weighting average/smoothing constant (alpha), and calculate weighted average. New Forecast= (Alpha)(latest demand)+(1-Alpha)(Previous Forecast) - must analyze demand patterns to determine best alpha/smoothing constant. Trend: Lower Alpha= (like 0.2) gives more weight on the old forecast. use when not upward or downward trend. Higher alpha= use for upward or downward trend. practice on pg 2-37 |
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Seasonal Forecast |
Note: Assume seasonal periods are quarters of the year. Steps: 1. calculate a seasonal index of demand for each period 2. develop deseasonablized demand by getting the average demand for each period in previous [year]. 3. develop seasonal forecast for each quarter but multiplying each deseasonanlized demand by each quarter's seasonal index. Practice on pg 2-39 |
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Basic concepts of Tracking the Forecast |
1. forecast error- 1a. bias= when cumulative variation is not zero (+ or -). 1b. random variation/error= when the cumulative variation is zero but the forecast was incorrect from month to month. 2. measuring forecast error- use MAD or standard deviation (see other cards). this helps determine safety stock. |
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Methods to measure Forecast error |
1. Mean Absolute Deviation (MAD)= (Sum of absolute ERRORS)/ (number of periods).... * remember: error = actual-forecast 2. Standard Deviation. |
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SCM Implications from forecasting |
1. rely on forecasting as less as possible 2. reduce lead times as much as possible. 3. increase collaboration with customer and suppliers 4. increase manufacturing flexibility and operational intergration externally |
