session 1: the forecasting process demand forecasting and planning in crisis 30-31 july, shanghai...
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Session 1: The forecasting process
Demand Forecasting and
Planning in Crisis
30-31 July, Shanghai
Joseph Ogrodowczyk, Ph.D.
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 2
The forecasting process
Session agenda Practical guidelines to creating a forecasting process Forecasting and revenue management: Are they the
same? Tips for the successful forecasting function Disaggregation: Top-down or bottom-up?
Activity: Group discussion of forecasting process
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 3
The forecasting process
Practical Guidelines for Forecasting What is forecasting?
Numerical estimates achieved through a systematic process Systematic process using statistical and judgmental (expert)
analysis Estimates targeted for specific dates or times in the future Estimates used for business planning purposes including
strategic acquisitions, operational decisions, process improvement and other business goals
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 4
The forecasting process
Practical Guidelines for Forecasting Why should businesses seek to forecast?
Forecasting cannot be avoided Everyone guesses about future events/information
Forecasting (and data analysis) is a critical component to decision making Better (more accurate) forecasts lead to better decisions
Questions to guide the forecasting process How will the forecasts be used? What requires prediction? What is the value of increased accuracy? What resources/data are available for forecasting? How can those resources/data be allocated?
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 5
The forecasting process
Practical Guidelines for Forecasting Importance of measurement
Overforecasting leads to excess inventory and overcapacity Underforecasting leads to product shortages and lines down What should be measured and how should it be measured?
Forecast accuracy & measurement metrics are covered on Day 2
Role of the forecaster Impartial data analyst, not biased implementer
Difficult role because we do not work in a vacuum, but unbiased forecasts require objectiveness
Forecaster needs to be fully integrated into the decision making process
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 6
The forecasting process
Forecasting and Revenue Management What is revenue management?
Revenue management: selling the right product at the right time and price to maximize revenue (profit) Revenue and profit are not the same
Forecasting: Unbiased prediction of future demand usually unconstrained by capacity or other supply chain issues
How forecasting aids in revenue management Forecasting should be based on units, not dollars
Forecasting in dollars does not account for changes in prices against changes in quantities
RM brings in pricing and costs
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 7
The forecasting process
Forecasting and Revenue Management Temptations and pitfalls
Forecasting in dollars Forecasting in dollars does not account for changes in prices
against changes in quantities Calculating forecasts based on revenue goals
This is backwards logic Revenue goals originate in the finance department while forecasts
originate with planning/capacity/supply chain Best practice forecasts are separate from any production
constraints (capacity, retail space, etc.)
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 8
The forecasting process
Tips for a successful forecasting function Important elements in describing the forecasting process
Purpose of forecast, including a timeframe May include multiple forecasts for different objectives (e.g.
strategic planning) Model type and any underlying model assumptions Historical data used Forecasts produced
Include units, hierarchy level (product, geography and time intersection)
Graphical display of data and results Tracking and reporting accuracy of prior forecast performance
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 9
The forecasting process Tips for a successful forecasting function
Knowing the limitations of forecasting Believing too much in the reliability of models can lead to
underestimating future uncertainty Believing too little in the reliability of models can lead to
omitting valuable information in the decision-making process Management support
Improvement in the forecasting process is dependent upon accountability of the forecasters and measurement of forecast
Needed for a consensus forecast and company-wide use of forecast
Expert knowledge Can be used to guide statistical models Disagreements can illuminate important variables
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 10
The forecasting process
Disaggregation: Top-down or bottom-up What is disaggregation and when is it used?
Business needs often require forecasts to be made in groups such as similar customers, regional retail stores, family of products, or market segment
Disaggregation is the distribution method for allocating quantities of a group forecast to the individual members
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 11
The forecasting process Disaggregation: Top-down or bottom-up
Top-down forecasting: Producing forecasts at an aggregate level (by grouping products, customers, etc.) and allocating the forecasted quantities to its members
Reasons for top-down forecasting Data availability: Lack of historical data at lower level Resource accessibility: Inadequate resources for producing
large sets of forecasts Forecasting implementation: Judgment forecasts and
managerial adjustments are more easily accomplished at higher levels
Note: Forecasting at the lower level may be unwise Analyze the returns to and the costs associated with
forecasting at lower-levels
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 12
The forecasting process
Disaggregation: Top-down or bottom-up Bottom-up forecasting: Producing forecasts at customer,
item, or store level (most detailed level of data available) and adding forecasts to produce aggregated information
Reasons for bottom-up forecasting Data behavior: Individual components have very different
demand patterns Resource planning: Inventory, transportation logistics or
product manufacturing requires specific information Disaggregation methods: No reliable method exists for
disaggregation of forecasts. Benefits of chosen methods are subjective
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 13
The forecasting process
Disaggregation: Top-down or bottom-up?Top-down Examples
Time Entity 1 Entity 2 Entity 3 Total Time Forecast 1 Forecast 2 Forecast 3 TotalMonth 1 64 74 83 221 January 77 77 76 230Month 2 75 53 80 207 February 63 63 62 188Month 3 35 23 7 65 March 23 23 22 70Month 4 21 91 19 131 April 32 32 31 95Month 5 27 22 86 135 May 45 45 44 134Month 6 87 56 68 211 June 68 68 67 205Month 7 21 51 77 149 July 51 51 50 152Month 8 69 69 31 169 August 52 52 51 155Month 9 73 99 55 227 September 73 73 72 220Month 10 41 22 12 76 October 24 24 23 71Month 11 6 60 52 119 November 40 40 39 120Month 12 18 53 22 93 December 31 31 30 93
Note: Each member received exactly 1/3 of the totalhigher-level forecast
Historical data Forecasted data
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 14
The forecasting process
Disaggregation: Top-down or bottom-up?Top-down Examples
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Entity 1 Entity 2 Entity 3 Forecast 1
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 15
The forecasting process
Disaggregation: Top-down or bottom-up?Bottom-up Examples
Time Entity 1 Entity 2 Entity 3 Total Time Forecast 1 Forecast 2 Forecast 3 TotalMonth 1 64 74 83 221 January 67 77 86 230 230Month 2 75 53 80 207 February 68 55 65 188 188Month 3 35 23 7 65 March 40 20 10 70 70Month 4 21 91 19 131 April 21 70 4 95 95Month 5 27 22 86 135 May 30 25 79 134 134Month 6 87 56 68 211 June 85 60 60 205 205Month 7 21 51 77 149 July 15 65 72 152 152Month 8 69 69 31 169 August 65 75 15 155 155Month 9 73 99 55 227 September 60 105 55 220 220Month 10 41 22 12 76 October 42 15 14 71 71Month 11 6 60 52 119 November 10 70 40 120 120Month 12 18 53 22 93 December 20 55 18 93 93
Note: Each member was forecasted independentlyThe total is the sum of the lower level forecasts
Historical data Forecasted data
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 16
The forecasting process
Disaggregation: Top-down or bottom-up?Bottom-up Examples
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Entity 1 Forecast 1
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Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 17
The forecasting process
Disaggregation: Top-down or bottom-up? Methods to disaggregation
Disaggregate before forecasting (Called decomposition) Bottom-up forecasting Forecasting parts of the whole
Forecast trend and seasonal components separately Disaggregate after forecasting (done with Top-down)
Many methods for this based on: Number of products within a grouping Percentages of historical demand Combining with bottom-up forecasts
Session 1 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 18
The forecasting process
References Jain, Chaman L. and Jack Malehorn. 2005. Practical Guide
to Business Forecasting (2nd Ed.). Flushing, New York: Graceway Publishing Inc.
Lapide, Larry. 2006. Top-down and bottom-up forecasting in S&OP. Journal of Business Forecasting. Summer: 14-16.
Newbold, Paul and Theodore Bos. 1994. Introductory Business & Economic Forecasting (2nd Ed.). Cincinnati, Ohio: South-Western Publishing Co.