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Supply Chain Management (SCM) Forecasting 3. Dr. Husam Arman . Today’s Outline . Qualitative methods Economic indicators Market research Historical analogy Delphi method Sales force composites Scenario writing and analysis Contemplations and conclusions .

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Presentation Transcript
today s outline
Today’s Outline
  • Qualitative methods
    • Economic indicators
    • Market research
    • Historical analogy
    • Delphi method
    • Sales force composites
    • Scenario writing and analysis
  • Contemplations and conclusions
qualitative forecasting techniques
Qualitative forecasting techniques
  • Often use data and models but with human interpretation/ judgment to form a view on the future

Qualitative forecasting techniques


Economic indicators

Scenario writing

Sales force composites

Market research

More human judgment

More models and data

Delphi Methods

Historical analogy

economic indicators 1
Economic indicators 1
  • Originated in the US following the depression
    • Monthly, quarterly and annual series on prices, employment, production etc
  • Closely relates to observed economic activity and business cycles
  • Useful for interpretative, judgmental forecasting by many organizations
economic indicators 2
Economic indicators 2
  • Economic indicator: an economic series from which a forecast is based
    • Leading indicators: advance warning of probable change in economic activity
    • Coincident indicators: reflect current performance of economy
    • Lagging indicators: confirm changes previously signaled
    • Interpretation/impact depends on nature of the forecast, sector, type of organization, location etc
market research 1
Market research 1
  • Extracts information form a sample of a target market and infers something about the population
  • Useful for information on product preferences
    • e.g. opinions on existing products, opinions on new products, opinions on competitors products and more general preferences
  • May provide sophisticated accurate forecasts on market potential
market research 2
Market research 2
  • Needs to be designed, executed and analyzed with care
    • Decisions on sample size and sample type
    • Decisions on medium and method for information gathering
    • Prior selection methods for statistical inference
  • Many sources of expertise
  • May be costly and time-consuming
  • How do we do it? 
historical analogy 1
Historical analogy 1
  • Forecasting relation to new products, take up of new technologies where little or no previous market experience
  • Link the new products with an assumed analogous occurrence in the past
  • Forecast for the demand for a product in a new market might be made by analogy with the known demand for the same product in a mature market
historical analogy 2
Historical analogy 2
  • Forecast demand for a new product by analogy with known demand for a related product
  • Analogy of mail order as a basis for predicting the development of e-shopping
  • If Ad-hoc method, many potential dangers
  • May aid understanding with qualitative information on the shape of the demand curve
delphi methods
Delphi methods
  • DELPHI method attempts to systematically evaluate expert judgment on the likelihood of future events without expert or analyst interaction
delphi steps
Delphi steps
  • Establish panel of expert
  • Establish a questionnaire
  • Evaluate responses by producing numerical summary
      • - Modal values and extreme values are highlighted
  • Controlled feedback
  • - Make the extremists justify their position and decide whether to include or exclude extreme values.
  • Repeat (3) and (4) until a clear, not necessarily unanimous, forecast emerges. Extremes may persist
  • Summaries the result
  • Difficulties
    • How many experts to use, how many rounds are appropriate, when should extremes be eliminated?
    • Time consuming and may be costly
  • Successful in broad studies of issues that affect demand in many businesses in the longer term. e.g.
    • future of the Common Agricultural
    • growth in different tourist destinations
sales force composites
Sales force composites
  • Utilizes knowledge and experience of sales-force to produce a forecast
  • Useful when
    • complex product mix, few customers
    • where sales force have close contact with customers, technical expertise, closely involved in negotiation, pricing and specification
  • but there are many problems / sources of error,
  • like what ?
scenario writing and analysis 1
Scenario writing and analysis 1
  • A scenario is a narrative description of future conditions and how a business and its competitors may react to those conditions
    • Identifies the principal factors that affect the future and explores a number of different future scenarios with some indications of the likelihood of each scenario occurring
    • Closely linked with corporate strategy and planning
scenario writing and analysis 2
Scenario writing and analysis 2
  • Attempts to understand and plan for the future rather than producing ’blind’ forecasts
  • Acknowledges that different scenarios may be plausible from a given starting point
  • No generally accepted way of constructing scenarios
  • Simulation approaches may be useful particularly System Dynamics
contemplation and conclusions
Contemplation and Conclusions
  • Many ‘advanced’ time series extrapolation methods – little evidence that complex methods significantly outperform simpler approaches
  • Errors made consistently in one direction imply bias, important to track errors and bias over time
  • Automation of forecasting techniques for large scale inventory systems is difficult - challenging in ERP
how much should we invest in forecasting
How much should we invest in forecasting?

Naive models

Sophisticated models

Increasing costs

Cost of operating a forecasting process

Cost of forecasting error

Decreasing forecast errors

forecasting in scm
Forecasting in SCM
  • Whatever techniques are employed, forecasts need to be embedded in the decision making processes
  • Failure to forecast or act on forecasts may
    • lead to implicit acceptance of a previous outdated forecasts
    • may be an assumption that present conditions will persist in the future
    • result in lack of preparation for change
longer term higher level forecasting
Longer term/higher level forecasting
  • In operations we typically need longer term forecasts for:
    • Strategy – decide if demand is sufficient to entre a market
      • e.g. 3-10 years
    • Longer term capacity needs for facility design
      • e.g. exceeding 2 years
    • Medium term capacity and resource ‘flexing’
      • recruiting/shedding labor, balancing production across multiple sites
      • supply chain ‘ramp’ up and down
      • e.g. 6 months to 2 years
selecting the appropriate forecasting techniques
Selecting the appropriate forecasting techniques
  • What is the purpose of the forecast? How is it to be used?
  • What are the dynamics of the system for which the forecast will be made?
  • How important is the past in estimating the future?
  • What about the different stages of the product life cycle?
  • Can we use more than one technique?