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8.3.1 Keys to Good Forecasting



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References

  1. ^ 1. First, forecasters are subject to anchoring bias, where forecasts are perhaps inappropriately ‘anchored’ in recent historical figures,
  2. ^ capacity constraints are sometimes misinterpreted as forecasts
  3. ^ Bonus plans can cause managers to artificially inflate or deflate forecasts
  4. ^ Sandbagging’ – setting the forecast or target at an easily achievable figure in order to earn bonuses when that figure is beaten – is common.
  5. ^ unstated but implicit assumptions can overstate a well-intentioned forecast.




Details


Several sources of potential bias in forecasts should be recognised.

[1] 1. First, forecasters are subject to anchoring bias, where forecasts are perhaps inappropriately ‘anchored’ in recent historical figures, even though market conditions have markedly changed, for better or worse.[16]
Second,[2] capacity constraints are sometimes misinterpreted as forecasts.


Examples :
Car Wash
Someone planing to open a car wash that can process one car every seven minutes would probably be amiss in assuming sufficient demand to actually run at that rate all the time.

Restaurant Chain

A restaurant chain that is able to turn its tables 2.5 times each night, on average, must still do local market research to ascertain how much volume a new restaurant will really produce. Putting similar 80-table restaurants in two trade areas with different population makeup and density, with different levels of competition, will result in varying sales levels.



Another source of bias in forecasting is incentive pay. [3] Bonus plans can cause managers to artificially inflate or deflate forecasts, whether intentionally or otherwise.
[4]Sandbagging’ – setting the forecast or target at an easily achievable figure in order to earn bonuses when that figure is beaten – is common.

Finally, [5] unstated but implicit assumptions can overstate a well-intentioned forecast.



Example : Fresh Pasta

While 34.5 per cent of those surveyed (after adjustments, as shown in Exhibit 8.2) may indicate their willingness to buy a new grocery product, such as fresh pasta, for such a forecast to pan out requires that consumers actually are made aware of the new product when it is introduced, and that the product can actually be found on supermarket shelves.


Assumptions of awareness and distribution coverage at levels less than 100 per cent, depending on the nature of the planned marketing programme for the product, should be applied to such a forecast, using the chain ratio method (see Exhibit 8.3).








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  1. client contact systems
  2. collector bias
  3. competitive advantage
  4. competitive intelligence
  5. computerised reorder system
  6. consumer behaviour
  7. data sources
  8. evidence based forecast
  9. experienced user
  10. internal records
  11. just in time
  12. logistical alliance
  13. market potential
  14. market segmentation
  15. market segments
  16. marketing program
  17. marketing research
  18. mass market
  19. mass market strategy
  20. michelin; us west;
  21. micro segmentation
  22. middleman
  23. modified rebuy
  24. multi-functional sales teams
  25. multilevel selling
  26. multiple buying
  27. multiple level relationships
  28. mutual trust
  29. narrow market segment
  30. narrow niche
  31. nationalisation of producers
  32. nerve center
  33. new task buy
  34. nine west group
  35. observation;direct observation' tanzania mobile;
  36. on-time delivery
  37. opportunity; research
  38. order handling
  39. organisation market
  40. organization marketing behaviour
  41. organizational behaviour
  42. organizational customers
  43. organizational demand
  44. organizational market
  45. organizational purchasing behaviour
  46. organizational purchasing process
  47. paperless exchange
  48. parity pricing
  49. personal selling
  50. personal use
  51. political risk
  52. potential market; penetrated market
  53. pre-delivery inspection
  54. pre-sale service
  55. prestige buyer
  56. pretender
  57. primary data
  58. procurement costs
  59. purchasing criteria
  60. qualitative data
  61. qualitative research
  62. quality assurance
  63. quality standards
  64. quantitative data
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  66. research objectives
  67. retention programme
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  87. technical experts;
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  89. transaction cost
  90. trend forecasting
  91. trusting patron
  92. underlying consumer demand
  93. unethical demands
  94. unstated but implicit assumptions
  95. users
  96. value analysis
  97. value shopper
  98. vertical integration
  99. visceral thing that cannot be trained
  100. wild guess