OPMA (forecasting)

• Forecasting

  • is the process of making predictions of the future based on the past and present data.
  • by analysis of trend
  • estimation of some variable of interest at some specified future date.
• Forecast and forecasting

  • are sometimes served for estimates of value at certain specific future times
• Prediction-used for more general estimates, such as the number of times floods will occur over a long period.
• Sales forecast

  • estimate of how much the company will actually sell
• 3 Forecasting Horizons

  1. Long term forecasting
  2. Medium term forecasting
  3. Short term forecasting
• Long term forecasting

  • tends to be completed at high levels in the organization.
  • longer than 2 years in the future
  • qualitative forecasting
• Medium term forecasting

  • tends to be several months up to 2 years into the future and is referred to as intermediate term.
  • both quantitative and qualitative forecasting may be used in this time frame
• Short term forecasting

  • daily up to months in the future
  • used for operational decision making such as inventory planning, ordering and scheduling of the workforce.
  • quantitative forecasting
• 2 categories of Forecasting Methods

  1. Qualitative forecasting
  2. Quantitative forecasting
• Qualitative forecasting

  • techniques are subjective, based on the opinion and judgment of consumers and experts
  • usually applied to intermediate-or long-range decisions
• Quantitative Forecasting

  • used to forecast future data as a function of past data.
  • suitable for analyzing past numerical data and assuming future patterns in the data are likely to persist.
  • usually applied to short-or intermediate range decisions.
• 4 Categories of Qualitative Forecasting

  1. Executive Judgment (Top Down)
  2. Sales Force Opinions (Bottom Up)
  3. Delphi Method
  4. Market Surveys
• Executive Judgment (Top Down)

  • Collaborate to examine market data and look at future trends for the business.
  • comes from management of the company
• Sales Force Opinions (Bottom Up)

  • Asked to give their future projections for their area or territory
  • first level management sets the forecast
  • sales employees set the forecast
• Delphi Method

  • Was created by the Rand Corporation in the 1950s.
  • Key aspect of the Delphi method is that the responses are anonymous, and respondents do not have any knowledge about what information has come from which sources.
  • time-consuming
  • experts/group of experts/ executive level sets the forecast
• Market Surveys

  • Solicit information from consumers regarding opinions on products and future purchasing plans.
  • consumers set the forecast
• Regression analysis

  • is a set of statistical processes for estimating the relationships among variables.
  • includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or ‘predictors’)
  • helps one understand how the typical value of a dependent variable (criterion variable) changes when one of the independent variables is varied.
• 5 Demand Patterns

  1. Trend
  2. Cycle
  3. Seasonal
  4. Irregular variations
  5. Random variations
• Trend

  • is consistent upward or downward movement of the demand.related to the product’s life cycle
• Cycle

  • is a pattern in the data that tends to last more than one year in duration.
  • related to events such as interest rates, the political climate, consumer confidence or other market factors.
• Seasonal

  • generally predictable changes in demand are recurring
• Irregular variations

  • often demand can be influenced by an event or series of events that are not expected to be repeated in the future.
• Random variations

  • are the unexplained variations in demand that remain after all other factors are considered.
  • often referred to as noise
• 5 factors that would affect Productivity

  1. Methods
  2. Capital
  3. Quality
  4. Technology
  5. Management
• Time Series Methods

  • use historical data as the basis of estimating future outcomes.
  • is a series of data points indexed (or listed or graphed) in time order
  • a sequence taken at successive equally spaced points in time.
  • used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.
• 5 Time Series Forecasting Methods

  1. Naive Method
  2. Simple Moving Average
  3. Weighted Moving Average
  4. Exponential Smoothing
  5. Seasonal Index
• Naïve Method

  • simplest forecasting method
  • can be used as a benchmark in order to evaluate and compare other forecast methods.
• Simple Moving Average

  • take the average of the last “n” periods and use that as the forecast for the next period.
  • value of “n” can be defined by the management in order to achieve a more accurate forecast.
• Weighted Moving Average

  • same as the simple moving average with the addition of a weight for each one of the last “n” periods.
  • needs to be determined in a way to produce the most accurate forecast.
• Exponential Smoothing

  • uses a combination of the last actual demand and the last forecast to produce the forecast for the next period
  • an easy method that enables forecasts to quickly react to new trends or changes
  • requires the use of a smoothing coefficient called Alpha (a)
• Seasonal Index

  • may be used to assist in the calculation of a forecast
• Forecast Accuracy Measures

  1. Mean Absolute Deviation (MAD)
  2. Mean Squared Error (MSE)
  3. Mean Absolute Percentage Error (MAPE)
• Regression analysis is a statistical tool used to analyze the relationship between two variables.
• How does Value Analysis aide in design/redesign?

  1. reduce the cost

  2. improve performance of a product
• Design should focus on what factors?

  -Customer satisfaction

  -Function

  -Cost and potential profit

  -Quality

  -Appearance

  -Forecasted volume

  -Ease of production, assembly and maintenance or service
• Designing for Operations:

  Taking into account the capabilities of the

  organization in designing goods and services
• Designing for Operations

  Reduced productivity

  Reduced quality

  Increased costs
• How do product features and its potential hazards

  contribute to improvements of the design?

  1. Usable for its intended purposes

  2. Product safety
• Product Liability

  -Responsibility of a manufacturer for any

  -injuries or damages caused by a faulty product

  Legal, ethical and environmental considerations
• other issues that companies must

  take into account in product & service design:

  1. Product Life Cycle (PLC)

  2. Standardization and Customization

  3. Product Reliability

  4. Range of operating conditions of the product’s function