supply chain chapter 12

the sum of all at (in weighted moving average) should equal

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Steps for designing a forecasting process

1) Identify the users and decision making processes that the forecast will support 2)Identify likely sources of the best data inputs 3) select forecasting techniques that will most effectively transform data into timely accurate forecasts over the appropriate planning horizon 4) document and apply the proposed technique to the data gathered for the appropriate business process 5) monitor the performance of the forecasting process for continuous improvement

fluctuations in demand cause operational inefficiencies such as

1) extra resources to expand and contract capacity to meet varying demand 2)backlogging (delivering later than originally promised) 3) customer dissatisfaction with the system's inability to meet all demands 4) buffering the system through the use of safety stocks (excess inventories), safety lead time (lead times with a cushion), or safety capacity (excess resources)

tactics to manage demand

1) influence the timing or quantity of demand through pricing changes, promotions or sales incentives 2) manage the timing of order fulfillment-- disney placing signs telling you what the wait time is or negotiating with customers regarding time of delivery 3) substitute by encouraging to shift their orders from one product to another or from one provider to another

situational drivers of forecast accuracy

1) short term forecasts are usually more accurate than long term forecasts 2) forecasts of aggregate demand are usually more accurate than forecasts of demand at detailed levels 3) forecasts developed using multiple information sources are usually more accurate than forecasts developed from a single source

Demand forecasting

A decision process in which managers predict demand patterns

marketing research

A forecasting technique that bases forecasts on the purchasing patterns and attitudes of current or potential customers -customer surveys, interviews and focus groups

Demand Management

A proactive approach in which managers attempt to influence the pattern of demand

seasonality and cycles

Regular demand patterns of repeating highs and lows. Seasonality may be daily, weekly, monthly or longer. ex: restaurants experience seasonal patterns during the day with peaks for breakfast, lunch and dinner

Demand Planning

The combined process of forecasting and managing customer demands to create a planned pattern of demand that meets the firm's operational and financial goals

stable pattern

a consistent horizontal stream of demands. ex: mature consumer products like shampoo or milk

forecasting models that are based only on a series of past demands assume that

a demand pattern of the past will continue in the future

weighted moving average

a forecasting model that assigns a different weight to each periods's demand according to its importance

moving average models

a forecasting model that computes a forecast as the average of demands over a number of immediate past periods

executive judgement

a forecasting technique that use input from high level experienced managers . useful for long term sales or business patterns

historical analogy

a forecasting technique that uses data and experience from similar products to forecast the demand for a new product ex: when color television sets were introduced managers used sales patterns for black and white television sets to predict the life cycle stages for the new TVs

Regression

a mathematical approach for fitting an equation to a set of data

Mean squared error (MSE)

a more sensitive measure of forecast errors that approximates the error variance

exponential smoothing

a moving average approach that applies exponentially decreasing weights to each demand that occurred farther back in time

shift or step change

a one time change in demand, usually due to some external influence on demand. ex: a major product promotional campaign

smoothing coefficient

a parameter indicating the weight given to the most recent demand

postponable product

a product designed so that it can be configured to its final form quickly and inexpensively once actual customer demand is known

naive model

a simple forecasting approach that assumes that recent history is a good predictor of the near future ex: if a restaurant serves 55 people on a given dat, managers will expect to serve 55 people the following day---ignores trend, seasonal or other components

grassroots forecasting

a technique that seeks inputs from people who are in close contact with customers and products ex: asking a sales representative on their sales estimates and comments on current market conditions

seasonal index

an adjustment factor applied to forecasts to account for seasonal changes or cycles in demand

A forecasting process

attempts to understand the various components of demand so that it can convert data inputs into reliable predictions of future events

mean percentage error (MPE)

average error represented as a percentage of demand

forecast bias

average error. indicates the tendency of a forecasting technique to continually over predict or under predict demand. the average forecast error over a number of periods

judgement based forecasting

built upon the estimates and opinions of people, most often experts who have related sales or operational experience useful when there is a lack of quantitative historical information ex: when new product is about to be launched

moving average models are used when

demand pattern is relatively stable without trend or seasonality

Forecasting error

dt-F

time series analysis models

forecasting models that compute forecasts using historical data arranged in the order of occurrence

delphi method

forecasts developed by asking a panel of experts to individually and repeatedly respond to a series of questions -forecaster compiles and analyzes the respondents inputs and shares data with group -once everyone has seen the collective responses they are given the chance to revise their responses or to ask new questions -this process is repeated until a consensus is achieved that reflects input from all experts

a negative bias indicates that

forecasts tend to be too high

a positive forecast bias indicates that over time

forecasts tend to be too low

The higher the value of the smoothing coefficient, the

greater the weight placed on most recent actual demand value

forecasting activities integrate

information gathered from the market, from internal operations, and from the larger business environment to make predictions about future demand

demand management purpose

is to adjust product characteristics including price, promotion and availability to influence product demand to achieve sales objectives and to accomodate the supply chain resources and capacities that the firm has in place.

costs of making forecasts too low include

lost sales and lowered product availability for customers

forecast accuracy

measures how closely the forecast aligns with the observations over time (every error, whether forecast was too high or too low reduces accuracy)

Costs of making forecasts too high include

money lost in holding inventory that is never sold, lost capacity that is spent making products that no one wants to buy, paying workers that are not needed, etc

Demand planning helps

operations managers know what customers they should serve and at what levels of service

measures like MAD and MAPE are sometimes inadequate as measures of forecast accuracy in that they do not

recognize that forecasts that are really far off the mark may be more harmful to the use than forecasts that miss the actual demand by a small amount

Increasing the number of periods (n)

reduces the number of random or atypical demands in isolated time periods but it also reduces the sensitivity of the moving average to actual shifts in demand

reducing lead time improves forecast accuracy because

shorter lead times require shorter term forecasts

the square root of MSE provides a good approximation of the

standard deviation

Mean absolute percentage error (MAPE)

the MAD represented as a percentage of demand. How large errors are relative to the actual demand quantitites

forecast error

the actual demand value minus the forecasted demand value for a given time period

Linear regression seeks to minimize the sum of the squared errors between

the actual values of demand and the values of demand predicted by the straight line

mean absolute deviation (MAD)

the average size of forecast errors, irrespective of their directions. also called mean absolute error.

autocorrelation

the correlation of current demand values with past demand values

forecast error

the difference between the forecast and the actual demand

trend

the general sloping tendency of demand, either upward or downward in a linear or non linear fashion ex: new products in the growth phase of the life cycle

Tracking signal

the ratio of a running total of forecasts error to MAD that indicates when the pattern of forecast error is changing significantly. a comparison of forecast bias to forecast accuracy over n periods

Usually demand management involves

the use of pricing and promotional activities

in exponential smoothing demand, each weight is a certain percentage smaller than

the weight assigned to demand data for the previous period

typically more weight is given to more recent demand becayse

this is thought to capture market effects that are more relevant to the current situation