Module 02 Flashcards
A statement about the future value of a variable of interest.
Forecast
T/F
The primary goal of operations management is to match supply to demand.
True
T/F
There are three important aspects of forecast: Expected Level of Demand, Accuracy, and Reliability.
False
Answer: There are only two important aspects of forecast.
The level of demand may be a function of some structural variations such as trend or seasonal variation.
Expected Level of Demand
Related to the potential size of forecast error.
Accuracy
Pertain to ongoing operations. Serves as a basis to predict requirements for labor and other resources needed to respond to changes in demand.
Short-range Forecasts
Needed for strategic changes such as developing new products or services, new equipment, new facilities, or new markets.
Long-range Forecasts
Cost/profit estimate, cash management
Accounting
Cash flow and funding, equipment/equipment reading
Finance
Hiring/recruiting/training, Layoff planning
Human Resources
Pricing, promotion, strategy
Marketing
IT/IS Systems, Internet Services
MIS
Schedules, material requirements planning (MRP), workloads
Operations
New products and services, revision of current features
Product/Service Design
Relates to the percentage of capacity being used. Accurate forecasts can help managers plan tactics to match capacity with demand, thereby achieving high yield levels.
Yield Management
Generally involves long-range plans related to: types of products and services to offer; facility and equipment levels; and facility location.
Plan the System
Generally involves short- and medium-range plans related to: inventory management, workforce levels, purchasing, budgeting, production, and scheduling.
Plan the Use of the System
T/F
Forecasts are perfect, that is, actual results usually differ from predicted values.
False
Answer: Forecasts are not perfect, that is, actual results usually differ from predicted values. Allowances should be made for forecast errors.
Those that can respond quickly to changes in demand - require shorter forecasting horizons.
Flexible Business Organizations
T/F
Elements of a Good Forecast should be timely, accurate, reliable, be expressed in meaningful units, in writing, be simple to understand and use, and be cost-effective.
True
Inaccurate forecasts can lead to _
Shortages and excesses throughout the supply chain
The first step in the forecasting process.
Determine the purpose of the forecast
The second step in the forecasting process
Establish a time horizon
The third step in the forecasting process
Obtain, clean, and analyze appropriate data
The fourth step in the forecasting process
Select a forecasting technique
The fifth step in the forecasting process
Make the forecast
The sixth and final step in the forecasting process
Monitor the forecast errors
T/F
Error = Actual - Forecast
True
Relies on the analysis of subjective inputs obtained from various sources (e.g., consumer surveys and sales staff) that provide insights that are not otherwise available.
Judgmental Forecasting
Simply attempts to project past experiences into the future.
Time-series Forecasts
A small group of upper-level managers may meet and collectively develop a forecast.
Executive Opinions
Members of the sales or customer service staff can be good sources of information due to their direct contact with customers and may be aware of plans customers may be considering for the future.
Sales Force Opinions
Since consumers ultimately determine demand, it makes sense to solicit input from them.
Consumer Surveys
Managers may solicit opinions from other managers or staff people or outside experts to help with developing a forecast.
The Delphi Method is an iterative process intended to achieve a consensus.
Other Approaches
It is an iterative process in which managers and staff complete a series of questionnaires, each developed from the previous one, to achieve a consensus forecast.
Delphi Method
These techniques rely on hard data. This technique involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast.
Quantitative Forecasting
Forecasts that project patterns identified in recent time-series observations.
Time-Series Forecasts
A time-ordered sequence of observations taken at regular time intervals.
Time-Series
A long-term upward or downward movement in data.
Trend
Short-term, fairly regular variations related to the calendar or time of day.
Seasonality
Wavelike variations of more than one year duration.
Cycles
Due to unexpected or unusual circumstances that do not reflect typical behavior.
Irregular Variations
Residual variations that remain after all other behaviors have been accounted for.
Random Variables
Uses a single previous value of a time series as the basis for a forecast.
The forecast for a time period is equal to the previous time period’s value.
Naive Forecast
These techniques work best when a series tends to vary about an average.
They can handle step changes or gradual changes in the level of a series.
Averaging
What are the techniques under averaging?
Moving Average, Weighted Moving Average, Exponential Smoothing
Technique that averages a number of the most recent actual values in generating a forecast.
Moving Average
The most recent values in a time series are given more weight in computing a forecast.
Weighted Moving Average
A sophisticated weighted averaging method that is still relatively easy to use and understand.
Exponential Smoothing
Analysis of trend involves developing an equation that will suitably describe the trend (assuming that trend is present in the data). Trends may be linear or nonlinear.
Techniques for Trends
Regularly repeating movements in series that can be tied to recurring events.
Seasonal Variations
Seasonality is expressed as a quantity that gets added to or subtracted from the time-series average in order to incorporate seasonality.
Additive
Seasonality is expressed as a percentage of the average (or trend) amount, which is then used to multiply the value of a series in order to incorporate seasonality
Multiplicative
The seasonal percentage used in the multiplicative seasonally adjusted forecasting model; seasonal indices.
Seasonal Relatives
The most widely used method for computing seasonal relatives since it effectively accounts for any trend (linear or curvilinear) that might be present in the data. The use of the software is recommended since the manual computation is a bit cumbersome.
Centered Moving Average
It is an alternative way. Each seasonal relative is the average for that season divided by the average for all seasons. It can be used to obtain fairly good values of seasonal relatives as long as the ratio of the intercept to the slope is large.
Simple Average (SA) Method
Similar to seasonal variations but are of longer duration.
Cycles
Tracking forecast errors and analyzing them can provide useful insight into whether forecasts are performing satisfactorily.
Monitoring the Forecast
A very useful tool for detecting non-randomness in errors.
Control Charts
Relates the cumulative forecast to the average absolute error (MAE) in order to detect any bias in errors over time.
Tracking Signal
The persistent tendency for forecasts to be greater or less than the actual values of a time series.
Bias
T/F
The factors to consider in choosing a forecasting technique are cost, accuracy, availability of historical data, availability of forecasting software, time needed to gather and analyze data and prepare a forecast, and forecast horizon.
True
The better forecasts are, the more able organizations will be to take advantage of future opportunities and reduce potential risks.
Operations Strategy
T/F
If non-randomness is found, corrective action is not needed.
False
Answer: If non-randomness is found, corrective action is needed.
T/F
The larger the ratio, the smaller the error.
True
